Public report of apache/datafusion-comet, published 26 October 2026. It has the same score, grades and number of
findings as the full report. The details of security findings (where they are in the code, which known
vulnerabilities and which passwords or keys were found) are left out, so nobody can use them against the project.
Public
Executive reportMeasured under the Code Assurance Index, an open standard for code quality, using its published scoring rules (rubric version 2026.10.5). Watchdog signed this survey and filed it in the standard's public register, where anyone can look it up by its survey ID. Check this survey →
Signed and filed in the standard's public register, 26 October 2026, 08:16 UTC · Survey ID cd_d5eb24b03c174fc6ba4df4f2b572ddcb
apache/datafusion-comet
Measured 26 October 2026, 08:10 UTC, at code version c4a0b1f
No regression since the last survey
A regression is a drop of 0.5 points or more in the score, or of 2.5 or more out of 10 in a dimension, since the last survey.
CAI scoreAt riskUp 1 since the last survey
The score is built from these lenses. Each lens grades one area of the code, such as how easy it is to change or how well it is protected. The weakest lens counts most, so one weak area pulls the score down instead of being averaged away.
A large codebase: 272,142 lines of product code in 16 projects, tests not counted · Estimated rebuild effort: about 3.8 person-years · Weakest lens: Code Health, 60
Findings by how serious they are
173 critical840 serious52 minor
16 dimensions are left out of the score. For each one, Watchdog either couldn't measure it in this code, or found no evidence of the safeguard it looks for, such as a tested backup restore. They count neither for nor against the score, and Limitations lists each one with its reason.
This survey was produced by
Watchdog
Producer
Canine Development
Analyzer
Watchdog engine 1.0.0
Measured
26 October 2026, 08:10 UTC
A measurement, not a certificate. The Code Assurance Index records a number
together with the evidence it was worked out from, so anyone can recalculate it, but it doesn't certify or guarantee
the code. The standard was written by Canine Development, which also builds Watchdog. It is open to any tool that
implements it, and today Watchdog is the only one that does.
A critical security finding is open, such as a library with a known critical vulnerability or a password or key found in the code. The score itself is unchanged, but its band shows At risk until the finding is fixed. See the Security and Compliance lens.
The score is deterministic, which means the same code always gets the same score. Watchdog works it out with tools that follow published rules, and an AI model never decides it, so anyone who applies the same rules to the same code gets the same number. How it is done is explained under Methodology ▸
68of 68 dimensions measured by tools.
1048of 1065 findings point to an exact file and line, so you can open each one in the code. The other 17 concern the repository as a whole, such as missing documentation.
68dimensions across 6 lenses, read from 272,142 lines of product code in 16 projects.
apache/datafusion-comet is rated At risk because critical security findings are open, most of them in the build pipeline. It scores 68 out of 100, which the At risk rating leaves unchanged.
Code Health, at 60, is the weakest lens and counts most in the score. The best first step is declaring which values may be missing, worth about 7 points. Handling the errors the code catches and ignores comes next, worth about 2 points. Raising Code Health to 70 would lift the score to about 73.
130 pipeline tools are referenced by a changeable name, so the build pipeline can be tampered with. Twenty-four pipeline steps put outside input straight into a shell command, so an attacker can run commands in the build pipeline, and seven sets of files in separate parts keep changing together, which points to hidden dependencies. Pinning each tool to an exact version, passing outside input through environment variables and making the hidden dependencies explicit would close these risks. Updating the vulnerable packages would fix the two packages with a known security hole, and the three unsafe settings in infrastructure or container files need changing to safe values.
The product code is 272,142 lines, about €550,000 to rebuild.
A lens is capped at Adequate while one of its dimensions scores below 4 out of 10, however well the rest of the lens does. That way a serious problem in one place always shows in the lens's rating.
How the score is built: what each lens counts forThe lenses don't count equally. The weakest lens counts most, the next weakest a little less, and so on, so a weak area pulls the score down instead of being averaged away. In the bar below, each part's width shows how much that lens counts and its colour shows its rating. Improving the widest part moves the score most, and as that lens improves, it counts for less, because another lens becomes the weakest.
If Code Health rose from 60 to 70, the lowest score rated Strong, the score would rise from 68 to about 73.
Code composition: what the lines of code are
Product code 62%Tests 38%
New since the last survey: 126 findings
These 126 critical or serious findings weren't in the previous survey, on 12 October 2026. The first 100 are listed here, and the findings file in the download package lists them all.
Watchdog compares every finding with the previous survey's. A finding that has only moved to another line in the same file counts as the same finding, so only new findings appear here. Advisory notes from the AI model are left out, because the model words them afresh in every survey.
Fixed since the last survey: 69 findings
These 69 critical or serious findings were in the previous survey, on 12 October 2026, and are gone from this one.
Exception handling · Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala
Exception handling · Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala
Rebuild cost: about €550,000 · Estimate
Cost to rebuild
€180,000 to €920,000, for 3,049 to 9,672 hours of work, about 3.8 person-years for 2 to 8 engineers
Difficulty of the problem
Standard, judged from the kind of software and how it is built. Code that solves a harder problem, such as payments or medical bookings, costs more per line to write.
Quality factor
×1.0 at a score of 68. Better code takes more work to write, so the factor is ×1.0 at a score of 70, lower below it and higher above it.
Size
Large: 272,142 lines of product code
What it is not
Not a quotation: nobody has offered to build it for that. Not a valuation: it says nothing about what the business is worth. Not a certified figure: no professional has signed it as one.
How the estimate is made: Watchdog prices the hours a team would need to write this code again, at €60 to €95 an hour. Declarations and boilerplate are priced at a fast rate and logic at a slower one, and the logic is then multiplied by the difficulty of the problem and by the quality factor. The estimate can be about 30% off either way, and it moves most with the hourly rate and the difficulty. For this code, every line is priced as straight logic, which keeps the estimate on the low side.
Top priorities
The three actions that raise the score most for the work they take. The full ranked list is in the Roadmap.
1
On Scala 3, add `-Yexplicit-nulls` to `scalacOptions` so `String` and `String | Null` mean different things, and type each Java API's result as nullable where it can return null; on Scala 2 (or as a first step), enable WartRemover's `Null` wart (`wartremoverErrors += Wart.Null`) or Scalafix's `DisableSyntax.noNulls = true` so the code holds absence in `Option`.
Then pay the assertions down rather than adding to them: each `.nn` is a null the compiler could not rule out — wrap the Java value in `Option(…)` at the boundary, pattern-match on `null` once there, or change the API so the null cannot arrive; an `unsafeNulls` import switches the checking off for everything after it.
Resolve the 1 No ADRs found finding(s) in ADR Quality.
Raises the score by about 1 point · Low effort · ADR Quality
Diagnosis: what the measurements show together
Each conclusion below combines several dimensions into one point that no single dimension shows, and lists the measurements it rests on.
Where the investment is most exposed · Value at risk
This code would cost about €550,000 to build again, about 3.8 person-years of work. Its weakest lens is Code Health, at 60 (Adequate). Code Health is about how easy the code is to read and change safely, and code that is hard to read is slow and risky to change, so every future change costs more than it should. Because the weakest lens counts most in the score, work on Code Health also moves the score most.
Based on: the rebuild estimate · the Code Health grade · how much each lens counts
What to do: Start with the Code Health actions in Top priorities.
If you do one thing first · First action
On Scala 3, add `-Yexplicit-nulls` to `scalacOptions` so `String` and `String | Null` mean different things, and type each Java API's result as nullable where it can return null; on Scala 2 (or as a first step), enable WartRemover's `Null` wart (`wartremoverErrors += Wart.Null`) or Scalafix's `DisableSyntax.noNulls = true` so the code holds absence in `Option`. Of all the actions in this report, it raises the score most for the work it takes: about 7 points for medium effort. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort
Architecture — module dependency graph
Project dependencies, layered top-to-bottom; arrows show direction. Any dashed red edge points upward or sideways, which only happens where the projects form a dependency cycle. A graph without cycles has none.
Architecture: which parts of the code depend on which
The code is divided into modules, groups of code that each handle one part of the product, and a module depends on another when it uses code from it. The table lists the modules both down the side and across the top, in an order where each module should only use modules listed above it. A number in a cell means the module on that row uses the module in that column, and it counts the pairs of building blocks (types) that connect them. The one thing to look for is a mark above the diagonal line. It means two or more modules use each other in a loop (a dependency cycle), so none of them can be changed, tested or released on its own.
392 modules, with 288 dependencies between them. 2 loops run through 15 of the modules, marked above the diagonal.
The table shows the 40 modules with the most connections; 352 more are left out to keep it readable.
Which modules use which. The module on each row uses the module in each column, and the number counts the pairs of types that connect them. A cell above the diagonal is part of a loop.
3 pairs of types, where a type in datafusion_comet.execution.operators.iceberg_write uses a type in datafusion_comet.execution.operators.iceberg_dictionary.
datafusion_comet.execution.operators.iceberg_write uses datafusion_comet.execution.operators.iceberg_dictionary, so a change to datafusion_comet.execution.operators.iceberg_dictionary can break datafusion_comet.execution.operators.iceberg_write, while a change to datafusion_comet.execution.operators.iceberg_write can't break datafusion_comet.execution.operators.iceberg_dictionary.
Position
Below the diagonal: datafusion_comet.execution.operators.iceberg_write uses a module listed above it, which is the healthy direction.
datafusion_comet_common.error uses datafusion_comet_common.query_context, so a change to datafusion_comet_common.query_context can break datafusion_comet_common.error, while a change to datafusion_comet_common.error can't break datafusion_comet_common.query_context.
Position
Below the diagonal: datafusion_comet_common.error uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.partitioners.multi_partition uses datafusion_comet_shuffle.comet_partitioning, so a change to datafusion_comet_shuffle.comet_partitioning can break datafusion_comet_shuffle.partitioners.multi_partition, while a change to datafusion_comet_shuffle.partitioners.multi_partition can't break datafusion_comet_shuffle.comet_partitioning.
Position
Below the diagonal: datafusion_comet_shuffle.partitioners.multi_partition uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.partitioners.multi_partition uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.partitioners.multi_partition, while a change to datafusion_comet_shuffle.partitioners.multi_partition can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.partitioners.multi_partition uses a module listed above it, which is the healthy direction.
12→19 datafusion_comet_shuffle.spark_unsafe.unsafe_object uses datafusion_comet_shuffle.spark_unsafe.rowpart of a loop✕
Connections
1 pair of types, where a type in datafusion_comet_shuffle.spark_unsafe.unsafe_object uses a type in datafusion_comet_shuffle.spark_unsafe.row.
datafusion_comet_shuffle.spark_unsafe.unsafe_object uses datafusion_comet_shuffle.spark_unsafe.row, so a change to datafusion_comet_shuffle.spark_unsafe.row can break datafusion_comet_shuffle.spark_unsafe.unsafe_object, while a change to datafusion_comet_shuffle.spark_unsafe.unsafe_object can't break datafusion_comet_shuffle.spark_unsafe.row.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
datafusion_comet_shuffle.writers.partition_writer uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.writers.partition_writer, while a change to datafusion_comet_shuffle.writers.partition_writer can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.writers.partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.shuffle_block_writer uses datafusion_comet_shuffle.codec_context, so a change to datafusion_comet_shuffle.codec_context can break datafusion_comet_shuffle.writers.shuffle_block_writer, while a change to datafusion_comet_shuffle.writers.shuffle_block_writer can't break datafusion_comet_shuffle.codec_context.
Position
Below the diagonal: datafusion_comet_shuffle.writers.shuffle_block_writer uses a module listed above it, which is the healthy direction.
15→22 org.apache.comet.serde.operator uses org.apache.spark.sql.cometpart of a loop✕
Connections
10 pairs of types, where a type in org.apache.comet.serde.operator uses a type in org.apache.spark.sql.comet.
org.apache.comet.serde.operator uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.comet.serde.operator, while a change to org.apache.comet.serde.operator can't break org.apache.spark.sql.comet.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
15→31 org.apache.comet.serde.operator uses org.apache.comet.shimspart of a loop✕
Connections
1 pair of types, where a type in org.apache.comet.serde.operator uses a type in org.apache.comet.shims.
org.apache.comet.serde.operator uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.comet.serde.operator, while a change to org.apache.comet.serde.operator can't break org.apache.comet.shims.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
15→37 org.apache.comet.serde.operator uses org.apache.comet.serdepart of a loop✕
Connections
12 pairs of types, where a type in org.apache.comet.serde.operator uses a type in org.apache.comet.serde.
org.apache.comet.serde.operator uses org.apache.comet.serde, so a change to org.apache.comet.serde can break org.apache.comet.serde.operator, while a change to org.apache.comet.serde.operator can't break org.apache.comet.serde.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
15→38 org.apache.comet.serde.operator uses org.apache.cometpart of a loop✕
Connections
7 pairs of types, where a type in org.apache.comet.serde.operator uses a type in org.apache.comet.
org.apache.comet.serde.operator uses org.apache.comet, so a change to org.apache.comet can break org.apache.comet.serde.operator, while a change to org.apache.comet.serde.operator can't break org.apache.comet.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
datafusion_comet.execution.jni_api uses datafusion_comet.execution.spark_plan, so a change to datafusion_comet.execution.spark_plan can break datafusion_comet.execution.jni_api, while a change to datafusion_comet.execution.jni_api can't break datafusion_comet.execution.spark_plan.
Position
Below the diagonal: datafusion_comet.execution.jni_api uses a module listed above it, which is the healthy direction.
datafusion_comet.execution.jni_api uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet.execution.jni_api, while a change to datafusion_comet.execution.jni_api can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet.execution.jni_api uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bench_support uses datafusion_comet_shuffle.comet_partitioning, so a change to datafusion_comet_shuffle.comet_partitioning can break datafusion_comet_shuffle.bench_support, while a change to datafusion_comet_shuffle.bench_support can't break datafusion_comet_shuffle.comet_partitioning.
Position
Below the diagonal: datafusion_comet_shuffle.bench_support uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bench_support uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.bench_support, while a change to datafusion_comet_shuffle.bench_support can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.bench_support uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bench_support uses datafusion_comet_shuffle.partitioners.multi_partition, so a change to datafusion_comet_shuffle.partitioners.multi_partition can break datafusion_comet_shuffle.bench_support, while a change to datafusion_comet_shuffle.bench_support can't break datafusion_comet_shuffle.partitioners.multi_partition.
Position
Below the diagonal: datafusion_comet_shuffle.bench_support uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bench_support uses datafusion_comet_shuffle.writers.partition_writer, so a change to datafusion_comet_shuffle.writers.partition_writer can break datafusion_comet_shuffle.bench_support, while a change to datafusion_comet_shuffle.bench_support can't break datafusion_comet_shuffle.writers.partition_writer.
Position
Below the diagonal: datafusion_comet_shuffle.bench_support uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.shuffle_writer uses datafusion_comet_shuffle.comet_partitioning, so a change to datafusion_comet_shuffle.comet_partitioning can break datafusion_comet_shuffle.shuffle_writer, while a change to datafusion_comet_shuffle.shuffle_writer can't break datafusion_comet_shuffle.comet_partitioning.
Position
Below the diagonal: datafusion_comet_shuffle.shuffle_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.shuffle_writer uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.shuffle_writer, while a change to datafusion_comet_shuffle.shuffle_writer can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.shuffle_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.shuffle_writer uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet_shuffle.shuffle_writer, while a change to datafusion_comet_shuffle.shuffle_writer can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet_shuffle.shuffle_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.spark_unsafe.row uses datafusion_comet_shuffle.codec_context, so a change to datafusion_comet_shuffle.codec_context can break datafusion_comet_shuffle.spark_unsafe.row, while a change to datafusion_comet_shuffle.spark_unsafe.row can't break datafusion_comet_shuffle.codec_context.
Position
Below the diagonal: datafusion_comet_shuffle.spark_unsafe.row uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.spark_unsafe.row uses datafusion_comet_shuffle.spark_unsafe.unsafe_object, so a change to datafusion_comet_shuffle.spark_unsafe.unsafe_object can break datafusion_comet_shuffle.spark_unsafe.row, while a change to datafusion_comet_shuffle.spark_unsafe.row can't break datafusion_comet_shuffle.spark_unsafe.unsafe_object.
Position
Below the diagonal: datafusion_comet_shuffle.spark_unsafe.row uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.spark_unsafe.row uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet_shuffle.spark_unsafe.row, while a change to datafusion_comet_shuffle.spark_unsafe.row can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet_shuffle.spark_unsafe.row uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.rss.rss_partition_writer uses datafusion_comet_shuffle.codec_context, so a change to datafusion_comet_shuffle.codec_context can break datafusion_comet_shuffle.writers.rss.rss_partition_writer, while a change to datafusion_comet_shuffle.writers.rss.rss_partition_writer can't break datafusion_comet_shuffle.codec_context.
Position
Below the diagonal: datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.rss.rss_partition_writer uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.writers.rss.rss_partition_writer, while a change to datafusion_comet_shuffle.writers.rss.rss_partition_writer can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a type in datafusion_comet_shuffle.writers.partition_writer.
datafusion_comet_shuffle.writers.rss.rss_partition_writer uses datafusion_comet_shuffle.writers.partition_writer, so a change to datafusion_comet_shuffle.writers.partition_writer can break datafusion_comet_shuffle.writers.rss.rss_partition_writer, while a change to datafusion_comet_shuffle.writers.rss.rss_partition_writer can't break datafusion_comet_shuffle.writers.partition_writer.
Position
Below the diagonal: datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a type in datafusion_comet_shuffle.writers.shuffle_block_writer.
datafusion_comet_shuffle.writers.rss.rss_partition_writer uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet_shuffle.writers.rss.rss_partition_writer, while a change to datafusion_comet_shuffle.writers.rss.rss_partition_writer can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet_shuffle.writers.rss.rss_partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr uses datafusion_comet_common.error, so a change to datafusion_comet_common.error can break datafusion_comet_spark_expr, while a change to datafusion_comet_spark_expr can't break datafusion_comet_common.error.
Position
Below the diagonal: datafusion_comet_spark_expr uses a module listed above it, which is the healthy direction.
org.apache.spark.sql.comet uses org.apache.spark.sql.comet.shims, so a change to org.apache.spark.sql.comet.shims can break org.apache.spark.sql.comet, while a change to org.apache.spark.sql.comet can't break org.apache.spark.sql.comet.shims.
Position
Below the diagonal: org.apache.spark.sql.comet uses a module listed above it, which is the healthy direction.
org.apache.spark.sql.comet uses org.apache.comet.serde.operator, so a change to org.apache.comet.serde.operator can break org.apache.spark.sql.comet, while a change to org.apache.spark.sql.comet can't break org.apache.comet.serde.operator.
Position
Below the diagonal: org.apache.spark.sql.comet uses a module listed above it, which is the healthy direction.
22→31 org.apache.spark.sql.comet uses org.apache.comet.shimspart of a loop✕
Connections
2 pairs of types, where a type in org.apache.spark.sql.comet uses a type in org.apache.comet.shims.
org.apache.spark.sql.comet uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.spark.sql.comet, while a change to org.apache.spark.sql.comet can't break org.apache.comet.shims.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
22→37 org.apache.spark.sql.comet uses org.apache.comet.serdepart of a loop✕
Connections
24 pairs of types, where a type in org.apache.spark.sql.comet uses a type in org.apache.comet.serde.
org.apache.spark.sql.comet uses org.apache.comet.serde, so a change to org.apache.comet.serde can break org.apache.spark.sql.comet, while a change to org.apache.spark.sql.comet can't break org.apache.comet.serde.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
22→38 org.apache.spark.sql.comet uses org.apache.cometpart of a loop✕
Connections
19 pairs of types, where a type in org.apache.spark.sql.comet uses a type in org.apache.comet.
org.apache.spark.sql.comet uses org.apache.comet, so a change to org.apache.comet can break org.apache.spark.sql.comet, while a change to org.apache.spark.sql.comet can't break org.apache.comet.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
datafusion_comet.parquet.parquet_support uses datafusion_comet_spark_expr, so a change to datafusion_comet_spark_expr can break datafusion_comet.parquet.parquet_support, while a change to datafusion_comet.parquet.parquet_support can't break datafusion_comet_spark_expr.
Position
Below the diagonal: datafusion_comet.parquet.parquet_support uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bin.shuffle_bench uses datafusion_comet.execution.operators.iceberg_dictionary, so a change to datafusion_comet.execution.operators.iceberg_dictionary can break datafusion_comet_shuffle.bin.shuffle_bench, while a change to datafusion_comet_shuffle.bin.shuffle_bench can't break datafusion_comet.execution.operators.iceberg_dictionary.
Position
Below the diagonal: datafusion_comet_shuffle.bin.shuffle_bench uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bin.shuffle_bench uses datafusion_comet_shuffle.comet_partitioning, so a change to datafusion_comet_shuffle.comet_partitioning can break datafusion_comet_shuffle.bin.shuffle_bench, while a change to datafusion_comet_shuffle.bin.shuffle_bench can't break datafusion_comet_shuffle.comet_partitioning.
Position
Below the diagonal: datafusion_comet_shuffle.bin.shuffle_bench uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bin.shuffle_bench uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet_shuffle.bin.shuffle_bench, while a change to datafusion_comet_shuffle.bin.shuffle_bench can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet_shuffle.bin.shuffle_bench uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.bin.shuffle_bench uses datafusion_comet_shuffle.shuffle_writer, so a change to datafusion_comet_shuffle.shuffle_writer can break datafusion_comet_shuffle.bin.shuffle_bench, while a change to datafusion_comet_shuffle.bin.shuffle_bench can't break datafusion_comet_shuffle.shuffle_writer.
Position
Below the diagonal: datafusion_comet_shuffle.bin.shuffle_bench uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.local.local_partition_writer uses datafusion_comet_shuffle.codec_context, so a change to datafusion_comet_shuffle.codec_context can break datafusion_comet_shuffle.writers.local.local_partition_writer, while a change to datafusion_comet_shuffle.writers.local.local_partition_writer can't break datafusion_comet_shuffle.codec_context.
Position
Below the diagonal: datafusion_comet_shuffle.writers.local.local_partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.local.local_partition_writer uses datafusion_comet_shuffle.metrics, so a change to datafusion_comet_shuffle.metrics can break datafusion_comet_shuffle.writers.local.local_partition_writer, while a change to datafusion_comet_shuffle.writers.local.local_partition_writer can't break datafusion_comet_shuffle.metrics.
Position
Below the diagonal: datafusion_comet_shuffle.writers.local.local_partition_writer uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in datafusion_comet_shuffle.writers.local.local_partition_writer uses a type in datafusion_comet_shuffle.writers.partition_writer.
datafusion_comet_shuffle.writers.local.local_partition_writer uses datafusion_comet_shuffle.writers.partition_writer, so a change to datafusion_comet_shuffle.writers.partition_writer can break datafusion_comet_shuffle.writers.local.local_partition_writer, while a change to datafusion_comet_shuffle.writers.local.local_partition_writer can't break datafusion_comet_shuffle.writers.partition_writer.
Position
Below the diagonal: datafusion_comet_shuffle.writers.local.local_partition_writer uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in datafusion_comet_shuffle.writers.local.local_partition_writer uses a type in datafusion_comet_shuffle.writers.shuffle_block_writer.
datafusion_comet_shuffle.writers.local.local_partition_writer uses datafusion_comet_shuffle.writers.shuffle_block_writer, so a change to datafusion_comet_shuffle.writers.shuffle_block_writer can break datafusion_comet_shuffle.writers.local.local_partition_writer, while a change to datafusion_comet_shuffle.writers.local.local_partition_writer can't break datafusion_comet_shuffle.writers.shuffle_block_writer.
Position
Below the diagonal: datafusion_comet_shuffle.writers.local.local_partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_shuffle.writers.local.local_partition_writer uses datafusion_comet_shuffle.shuffle_writer, so a change to datafusion_comet_shuffle.shuffle_writer can break datafusion_comet_shuffle.writers.local.local_partition_writer, while a change to datafusion_comet_shuffle.writers.local.local_partition_writer can't break datafusion_comet_shuffle.shuffle_writer.
Position
Below the diagonal: datafusion_comet_shuffle.writers.local.local_partition_writer uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.welford uses datafusion_comet_common.query_context, so a change to datafusion_comet_common.query_context can break datafusion_comet_spark_expr.agg_funcs.welford, while a change to datafusion_comet_spark_expr.agg_funcs.welford can't break datafusion_comet_common.query_context.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.welford uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.welford uses datafusion_comet_spark_expr, so a change to datafusion_comet_spark_expr can break datafusion_comet_spark_expr.agg_funcs.welford, while a change to datafusion_comet_spark_expr.agg_funcs.welford can't break datafusion_comet_spark_expr.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.welford uses a module listed above it, which is the healthy direction.
org.apache.spark.sql.comet.execution.shuffle uses org.apache.spark.sql.comet.shims, so a change to org.apache.spark.sql.comet.shims can break org.apache.spark.sql.comet.execution.shuffle, while a change to org.apache.spark.sql.comet.execution.shuffle can't break org.apache.spark.sql.comet.shims.
Position
Below the diagonal: org.apache.spark.sql.comet.execution.shuffle uses a module listed above it, which is the healthy direction.
org.apache.spark.sql.comet.execution.shuffle uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.spark.sql.comet.execution.shuffle, while a change to org.apache.spark.sql.comet.execution.shuffle can't break org.apache.spark.sql.comet.
Position
Below the diagonal: org.apache.spark.sql.comet.execution.shuffle uses a module listed above it, which is the healthy direction.
27→31 org.apache.spark.sql.comet.execution.shuffle uses org.apache.comet.shimspart of a loop✕
Connections
1 pair of types, where a type in org.apache.spark.sql.comet.execution.shuffle uses a type in org.apache.comet.shims.
org.apache.spark.sql.comet.execution.shuffle uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.spark.sql.comet.execution.shuffle, while a change to org.apache.spark.sql.comet.execution.shuffle can't break org.apache.comet.shims.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
27→38 org.apache.spark.sql.comet.execution.shuffle uses org.apache.cometpart of a loop✕
Connections
3 pairs of types, where a type in org.apache.spark.sql.comet.execution.shuffle uses a type in org.apache.comet.
org.apache.spark.sql.comet.execution.shuffle uses org.apache.comet, so a change to org.apache.comet can break org.apache.spark.sql.comet.execution.shuffle, while a change to org.apache.spark.sql.comet.execution.shuffle can't break org.apache.comet.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
datafusion_comet.execution.planner uses datafusion_comet.execution.spark_plan, so a change to datafusion_comet.execution.spark_plan can break datafusion_comet.execution.planner, while a change to datafusion_comet.execution.planner can't break datafusion_comet.execution.spark_plan.
Position
Below the diagonal: datafusion_comet.execution.planner uses a module listed above it, which is the healthy direction.
datafusion_comet.execution.planner uses datafusion_comet_common.query_context, so a change to datafusion_comet_common.query_context can break datafusion_comet.execution.planner, while a change to datafusion_comet.execution.planner can't break datafusion_comet_common.query_context.
Position
Below the diagonal: datafusion_comet.execution.planner uses a module listed above it, which is the healthy direction.
datafusion_comet.execution.planner uses datafusion_comet_spark_expr, so a change to datafusion_comet_spark_expr can break datafusion_comet.execution.planner, while a change to datafusion_comet.execution.planner can't break datafusion_comet_spark_expr.
Position
Below the diagonal: datafusion_comet.execution.planner uses a module listed above it, which is the healthy direction.
datafusion_comet.execution.planner uses datafusion_comet.parquet.parquet_support, so a change to datafusion_comet.parquet.parquet_support can break datafusion_comet.execution.planner, while a change to datafusion_comet.execution.planner can't break datafusion_comet.parquet.parquet_support.
Position
Below the diagonal: datafusion_comet.execution.planner uses a module listed above it, which is the healthy direction.
datafusion_comet.parquet.schema_adapter uses datafusion_comet_common.error, so a change to datafusion_comet_common.error can break datafusion_comet.parquet.schema_adapter, while a change to datafusion_comet.parquet.schema_adapter can't break datafusion_comet_common.error.
Position
Below the diagonal: datafusion_comet.parquet.schema_adapter uses a module listed above it, which is the healthy direction.
datafusion_comet.parquet.schema_adapter uses datafusion_comet.parquet.parquet_support, so a change to datafusion_comet.parquet.parquet_support can break datafusion_comet.parquet.schema_adapter, while a change to datafusion_comet.parquet.schema_adapter can't break datafusion_comet.parquet.parquet_support.
Position
Below the diagonal: datafusion_comet.parquet.schema_adapter uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.variance uses datafusion_comet_spark_expr.agg_funcs.welford, so a change to datafusion_comet_spark_expr.agg_funcs.welford can break datafusion_comet_spark_expr.agg_funcs.variance, while a change to datafusion_comet_spark_expr.agg_funcs.variance can't break datafusion_comet_spark_expr.agg_funcs.welford.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.variance uses a module listed above it, which is the healthy direction.
org.apache.comet.shims uses org.apache.spark.sql.comet.execution.shuffle, so a change to org.apache.spark.sql.comet.execution.shuffle can break org.apache.comet.shims, while a change to org.apache.comet.shims can't break org.apache.spark.sql.comet.execution.shuffle.
Position
Below the diagonal: org.apache.comet.shims uses a module listed above it, which is the healthy direction.
31→37 org.apache.comet.shims uses org.apache.comet.serdepart of a loop✕
Connections
5 pairs of types, where a type in org.apache.comet.shims uses a type in org.apache.comet.serde.
org.apache.comet.shims uses org.apache.comet.serde, so a change to org.apache.comet.serde can break org.apache.comet.shims, while a change to org.apache.comet.shims can't break org.apache.comet.serde.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
datafusion_comet.execution.planner.expression_registry uses datafusion_comet.execution.planner, so a change to datafusion_comet.execution.planner can break datafusion_comet.execution.planner.expression_registry, while a change to datafusion_comet.execution.planner.expression_registry can't break datafusion_comet.execution.planner.
Position
Below the diagonal: datafusion_comet.execution.planner.expression_registry uses a module listed above it, which is the healthy direction.
datafusion_comet.execution.planner.operator_registry uses datafusion_comet.execution.planner, so a change to datafusion_comet.execution.planner can break datafusion_comet.execution.planner.operator_registry, while a change to datafusion_comet.execution.planner.operator_registry can't break datafusion_comet.execution.planner.
Position
Below the diagonal: datafusion_comet.execution.planner.operator_registry uses a module listed above it, which is the healthy direction.
2 pairs of types, where a type in datafusion_comet.parquet.eager_page_index_reader_factory uses a type in datafusion_comet.execution.operators.iceberg_dictionary.
datafusion_comet.parquet.eager_page_index_reader_factory uses datafusion_comet.execution.operators.iceberg_dictionary, so a change to datafusion_comet.execution.operators.iceberg_dictionary can break datafusion_comet.parquet.eager_page_index_reader_factory, while a change to datafusion_comet.parquet.eager_page_index_reader_factory can't break datafusion_comet.execution.operators.iceberg_dictionary.
Position
Below the diagonal: datafusion_comet.parquet.eager_page_index_reader_factory uses a module listed above it, which is the healthy direction.
datafusion_comet.parquet.eager_page_index_reader_factory uses datafusion_comet.parquet.parquet_support, so a change to datafusion_comet.parquet.parquet_support can break datafusion_comet.parquet.eager_page_index_reader_factory, while a change to datafusion_comet.parquet.eager_page_index_reader_factory can't break datafusion_comet.parquet.parquet_support.
Position
Below the diagonal: datafusion_comet.parquet.eager_page_index_reader_factory uses a module listed above it, which is the healthy direction.
datafusion_comet.parquet.eager_page_index_reader_factory uses datafusion_comet.parquet.schema_adapter, so a change to datafusion_comet.parquet.schema_adapter can break datafusion_comet.parquet.eager_page_index_reader_factory, while a change to datafusion_comet.parquet.eager_page_index_reader_factory can't break datafusion_comet.parquet.schema_adapter.
Position
Below the diagonal: datafusion_comet.parquet.eager_page_index_reader_factory uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.correlation uses datafusion_comet_common.query_context, so a change to datafusion_comet_common.query_context can break datafusion_comet_spark_expr.agg_funcs.correlation, while a change to datafusion_comet_spark_expr.agg_funcs.correlation can't break datafusion_comet_common.query_context.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.correlation uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.correlation uses datafusion_comet_spark_expr, so a change to datafusion_comet_spark_expr can break datafusion_comet_spark_expr.agg_funcs.correlation, while a change to datafusion_comet_spark_expr.agg_funcs.correlation can't break datafusion_comet_spark_expr.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.correlation uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.correlation uses datafusion_comet_spark_expr.agg_funcs.welford, so a change to datafusion_comet_spark_expr.agg_funcs.welford can break datafusion_comet_spark_expr.agg_funcs.correlation, while a change to datafusion_comet_spark_expr.agg_funcs.correlation can't break datafusion_comet_spark_expr.agg_funcs.welford.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.correlation uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.correlation uses datafusion_comet_spark_expr.agg_funcs.variance, so a change to datafusion_comet_spark_expr.agg_funcs.variance can break datafusion_comet_spark_expr.agg_funcs.correlation, while a change to datafusion_comet_spark_expr.agg_funcs.correlation can't break datafusion_comet_spark_expr.agg_funcs.variance.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.correlation uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.regr uses datafusion_comet_common.query_context, so a change to datafusion_comet_common.query_context can break datafusion_comet_spark_expr.agg_funcs.regr, while a change to datafusion_comet_spark_expr.agg_funcs.regr can't break datafusion_comet_common.query_context.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.regr uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.regr uses datafusion_comet_spark_expr, so a change to datafusion_comet_spark_expr can break datafusion_comet_spark_expr.agg_funcs.regr, while a change to datafusion_comet_spark_expr.agg_funcs.regr can't break datafusion_comet_spark_expr.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.regr uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.regr uses datafusion_comet_spark_expr.agg_funcs.welford, so a change to datafusion_comet_spark_expr.agg_funcs.welford can break datafusion_comet_spark_expr.agg_funcs.regr, while a change to datafusion_comet_spark_expr.agg_funcs.regr can't break datafusion_comet_spark_expr.agg_funcs.welford.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.regr uses a module listed above it, which is the healthy direction.
datafusion_comet_spark_expr.agg_funcs.regr uses datafusion_comet_spark_expr.agg_funcs.variance, so a change to datafusion_comet_spark_expr.agg_funcs.variance can break datafusion_comet_spark_expr.agg_funcs.regr, while a change to datafusion_comet_spark_expr.agg_funcs.regr can't break datafusion_comet_spark_expr.agg_funcs.variance.
Position
Below the diagonal: datafusion_comet_spark_expr.agg_funcs.regr uses a module listed above it, which is the healthy direction.
org.apache.comet.serde uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.comet.serde, while a change to org.apache.comet.serde can't break org.apache.spark.sql.comet.
Position
Below the diagonal: org.apache.comet.serde uses a module listed above it, which is the healthy direction.
org.apache.comet.serde uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.comet.serde, while a change to org.apache.comet.serde can't break org.apache.comet.shims.
Position
Below the diagonal: org.apache.comet.serde uses a module listed above it, which is the healthy direction.
37→38 org.apache.comet.serde uses org.apache.cometpart of a loop✕
Connections
1 pair of types, where a type in org.apache.comet.serde uses a type in org.apache.comet.
org.apache.comet.serde uses org.apache.comet, so a change to org.apache.comet can break org.apache.comet.serde, while a change to org.apache.comet.serde can't break org.apache.comet.
Position
Above the diagonal, so it is part of a loop. Neither module can be changed, tested or released on its own until one of these dependencies is removed.
org.apache.comet uses org.apache.spark.sql.comet.shims, so a change to org.apache.spark.sql.comet.shims can break org.apache.comet, while a change to org.apache.comet can't break org.apache.spark.sql.comet.shims.
Position
Below the diagonal: org.apache.comet uses a module listed above it, which is the healthy direction.
org.apache.comet uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.comet, while a change to org.apache.comet can't break org.apache.spark.sql.comet.
Position
Below the diagonal: org.apache.comet uses a module listed above it, which is the healthy direction.
org.apache.comet uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.comet, while a change to org.apache.comet can't break org.apache.comet.shims.
Position
Below the diagonal: org.apache.comet uses a module listed above it, which is the healthy direction.
org.apache.comet uses org.apache.comet.serde, so a change to org.apache.comet.serde can break org.apache.comet, while a change to org.apache.comet can't break org.apache.comet.serde.
Position
Below the diagonal: org.apache.comet uses a module listed above it, which is the healthy direction.
org.apache.comet.rules uses org.apache.spark.sql.comet.shims, so a change to org.apache.spark.sql.comet.shims can break org.apache.comet.rules, while a change to org.apache.comet.rules can't break org.apache.spark.sql.comet.shims.
Position
Below the diagonal: org.apache.comet.rules uses a module listed above it, which is the healthy direction.
org.apache.comet.rules uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.comet.rules, while a change to org.apache.comet.rules can't break org.apache.spark.sql.comet.
Position
Below the diagonal: org.apache.comet.rules uses a module listed above it, which is the healthy direction.
org.apache.comet.rules uses org.apache.comet.shims, so a change to org.apache.comet.shims can break org.apache.comet.rules, while a change to org.apache.comet.rules can't break org.apache.comet.shims.
Position
Below the diagonal: org.apache.comet.rules uses a module listed above it, which is the healthy direction.
org.apache.comet.rules uses org.apache.comet.serde, so a change to org.apache.comet.serde can break org.apache.comet.rules, while a change to org.apache.comet.rules can't break org.apache.comet.serde.
Position
Below the diagonal: org.apache.comet.rules uses a module listed above it, which is the healthy direction.
org.apache.comet.rules uses org.apache.comet, so a change to org.apache.comet can break org.apache.comet.rules, while a change to org.apache.comet.rules can't break org.apache.comet.
Position
Below the diagonal: org.apache.comet.rules uses a module listed above it, which is the healthy direction.
org.apache.comet.contrib.delta uses org.apache.spark.sql.comet, so a change to org.apache.spark.sql.comet can break org.apache.comet.contrib.delta, while a change to org.apache.comet.contrib.delta can't break org.apache.spark.sql.comet.
Position
Below the diagonal: org.apache.comet.contrib.delta uses a module listed above it, which is the healthy direction.
org.apache.comet.contrib.delta uses org.apache.comet, so a change to org.apache.comet can break org.apache.comet.contrib.delta, while a change to org.apache.comet.contrib.delta can't break org.apache.comet.
Position
Below the diagonal: org.apache.comet.contrib.delta uses a module listed above it, which is the healthy direction.
org.apache.comet.contrib.delta uses org.apache.comet.rules, so a change to org.apache.comet.rules can break org.apache.comet.contrib.delta, while a change to org.apache.comet.contrib.delta can't break org.apache.comet.rules.
Position
Below the diagonal: org.apache.comet.contrib.delta uses a module listed above it, which is the healthy direction.
At a glance — Code Health · 60% · Adequate · gated by X5 ·
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A05:2025 — Injection
170
High / Critical
A03:2025 — Software Supply Chain Failures
15
High / Critical
A02:2025 — Security Misconfiguration
14
High / Critical
Roadmap
First, enforce strict null safety by enabling explicit null checks in the compiler and eliminating unsafe null assertions at API boundaries. Second, address the seventeen instances where exceptions are silently swallowed and correct the single structured logging issue to ensure errors are visible and logs are parseable. Third, harden the web security posture by configuring essential response headers, such as Content-Security-Policy, directly within the build container or server configuration. Finally, resolve the missing architectural decision record to complete the documentation baseline.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
On Scala 3, add `-Yexplicit-nulls` to `scalacOptions` so `String` and `String | Null` mean different things, and type each Java API's result as nullable where it can return null; on Scala 2 (or as a first step), enable WartRemover's `Null` wart (`wartremoverErrors += Wart.Null`) or Scalafix's `DisableSyntax.noNulls = true` so the code holds absence in `Option`. Then pay the assertions down rather than adding to them: each `.nn` is a null the compiler could not rule out — wrap the Java value in `Option(…)` at the boundary, pattern-match on `null` once there, or change the API so the null cannot arrive; an `unsafeNulls` import switches the checking off for everything after it.
Set security response headers on the surface this repository serves: an `add_header` directive per header in the nginx/Caddy/Apache config, a `_headers` / `vercel.json` / `netlify.toml` entry for a static host, or `helmet()` in the HTTP server. `Content-Security-Policy` is the one that pays for itself first — it is what contains an injected script once one reaches the page — followed by `X-Content-Type-Options: nosniff` and a frame policy (`X-Frame-Options: DENY`, or CSP `frame-ancestors`). Where the app is served from a build container, the header configuration belongs in the image beside the built assets, so it ships with them rather than depending on where it lands.
Every finding carries one of four grades. Three say how serious it is. The fourth says this
survey could not settle it — and it is a grade, not a gap.
Critical — 173
A definite problem that already costs you something and drags the score down: a
missing authorisation check, a dependency with a known exploit, a build that does not reproduce. Failure here
tends to cause failures elsewhere.
Serious — 840
Likely wrong, but not failing yet. It degrades
the codebase over a longer horizon and can cause failures elsewhere — not urgent this week, not something to
carry for two years either.
Minor — 52
Recorded, with no effect on how the codebase functions.
Present so the survey is complete, not because it needs doing.
Could not be resolved — 16
Something this survey could not settle
from the outside, and which could be critical or serious. Either a control was required and no
positive evidence of it exists in the repository — a backup job that nothing shows was ever restored from proves
nothing about restores — or our own analysis could not run over that part of the tree. This is not a clean
result. These are excluded from the score rather than awarded a pass, so the number on the cover neither
rewards nor penalises them: if you act on this survey without resolving them, you carry that risk yourself. Each
one is named under Limitations.
Methodology & how to trust this report
Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 68 of 68 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); none rely on LLM judgement. Overall confidence is 0.9 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.
Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.
What we checked — 68 dimensions across the health lenses
Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.
How to trust any code-health report — three questions
Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 1048 of 1065 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D8 Code Coverage — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Coverage NOT MEASURED: test source is present (.scala, .java, .rs, .mjs) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`addSbtPlugin("org.scoverage" % "sbt-scoverage" % "<version>")` in `project/plugins.sbt`, then `sbt clean coverage test coverageReport`, or JaCoCo XML — `mvn jacoco:report`, or lcov — `cargo install cargo-llvm-cov`, then `cargo llvm-cov --lcov --output-path lcov.info`, or lcov — `vitest --coverage`, `jest --coverage`, `bun test --coverage --coverage-reporter=lcov`, or `nyc`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures. You can widen what we reach: optional: produce a coverage report in a standard format (`addSbtPlugin("org.scoverage" % "sbt-scoverage" % "<version>")` in `project/plugins.sbt`, then `sbt clean coverage test coverageReport`, or JaCoCo XML — `mvn jacoco:report`, or lcov — `cargo install cargo-llvm-cov`, then `cargo llvm-cov --lcov --output-path lcov.info`, or lcov — `vitest --coverage`, `jest --coverage`, `bun test --coverage --coverage-reporter=lcov`, or `nyc`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures — then the real number is read on the next scan.
D10 Test Quality — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. The 4 test(s) behind this row are the ones the JavaScript/TypeScript census could read, and this repository also carries at least 488 test source file(s) (.scala, .java, .rs) that it cannot: it reads JavaScript/TypeScript test declarations off disk, so a JUnit/pytest-style suite is invisible to it. Skipped tests, zero-assertion tests and the other quality signals on this row are UNMEASURED in that suite — their absence from the counts above is a gap in this analyzer's language coverage, not a finding that those tests are sound.
D11 Test Reliability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Test source is present (.scala, .java, .rs, .mjs) but the built-in reliability runner does not support this repository's ecosystem, so flakiness couldn't be assessed. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.
D12 Dependency Hygiene — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Not scored — 5 Maven/Gradle manifest(s) were read, stating a version for 2 dependency(ies) and leaving 52 to a parent or an imported BOM, but no build resolved the dependency graph, so the outdated signal was not established. NOT a finding that these dependencies are current or healthy.
D14 License Compliance — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository declares a Maven POM, but the licence verdict published here was taken over its crate dependencies. Nothing was read about its Maven dependencies' licensing in either direction, and a clean score on this card must not be read as covering them.
D14 License Compliance — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository declares a Python pyproject.toml/requirements.txt (pip/uv/Poetry), but the licence verdict published here was taken over its crate dependencies. Nothing was read about its Python dependencies' licensing in either direction, and a clean score on this card must not be read as covering them.
D19 Documentation Quality — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. Each ecosystem's manifests (what makes a folder runnable or a published package) need their own proof before D19 judges its units. Until then those folders' READMEs are judged only as documents (boilerplate, structure), never counted as complete.
D19 Documentation Quality — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This kind did not reach the 95 % precision gate in three adjudicated runs (docs/plans/deterministic-engine/adjudication/D19-sample3-v3.md), so by the pre-registered fallback it publishes no row until a re-measurement shows it does. The candidates are counted here, not reported as findings, and the score does not include them.
D21 Naming Consistency — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. D21's deterministic naming rules are gated on C# first (docs/plans/deterministic-engine.md, P5): each other language is switched on only after its own concept suite and A/B gate pass. Until then its declarations carry no naming verdict this run — this says nothing about whether its names are consistent.
D22 Internal API Consistency — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. D22's deterministic call-shape rules are gated on C# first (docs/plans/deterministic-engine.md, P5): each other language is switched on only after its own concept suite and A/B gate pass. This says nothing about whether that surface is consistent.
D24 Comment Value — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check's reader does not cover the language this repository's product is written in, so it had nothing of the product to read. That is a gap in this analyzer's language reach — not a finding about this repository.
D27 Navigability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Navigability is taken over call sites whose callee resolved. For C# that population includes calls into referenced assemblies; for this language only calls declared inside the repository count, so the abstention threshold reads the repository's use of its standard library as a resolution failure. That is a limit of this analyzer, not a build problem in this repository.
D32 Data Compliance (PII/GDPR) — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. `spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala` produced a parse error, so every rule in this engine's `gdpr.yml` was absent there. That absence is NOT a clean result: these rules detect personal data crossing a boundary into a log sink, a URL or browser storage, and a file that was never parsed cannot report any of the three. The rest of the tree analysed normally and its rows above stand; only these files are unaccounted for. You can widen what we reach: fix the syntax error (or exclude the file deliberately) and re-scan to cover it.
AXR1 Runtime accessibility — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Render strategy: boot via DockerCompose (benchmarks/tpc/infra/docker/docker-compose.yml). compose up failed (exit 18 — an image could not be pulled) — spark-worker-1 Pulling bench Pulling spark-master Pulling spark-worker-2 Pulling bench Error Get "https://registry-1.docker.io/v2/": Forbidden spark-master Error context canceled spark-worker-2 Error context canceled spark-worker-1 Error context canceled Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the earlier `compose pull` step reported: bench Skipped - Image is already being pulled by spark-worker-2 spark-master Skipped - Image is already being pulled by spark-worker-2 spark-worker-1 Skipped - Image is already being pulled by spark-worker-2 spark-worker-2 Pulling spark-worker-2 Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the runtime sandbox reaches registries only through the in-fence pull-through mirror, so an image the mirror does not carry cannot be fetched — this is a limit of our sandbox, not of your stack; runtime evidence skipped.
C1 Data Protection — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These personal data controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks personal data controls.
C2 Access Controls — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These authorization controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks authorization controls.
C3 Audit Trail — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These audit controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks audit controls.
C4 Data Retention — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These retention controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks retention controls.
C5 Data-Subject Rights — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These data-subject rights controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks data-subject rights controls.
GD1 Unfinished & placeholder code — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, F#, JavaScript/TypeScript, Dart, Ruby and Scala source only, and this repository's Java, Python, Rust is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
IC1 Incompleteness & stubs — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, JavaScript/TypeScript, Java, Dart, Ruby and Scala source only, and this repository's Python, Rust is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
M4 Documentation accuracy — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. M4 checks only the claim kinds adjudicated at or above 95 % precision: links and versions (PREREGISTRATION version 9). Paths, command arguments, installs, environment variables, settings, projects, packages, routes, cd targets, infrastructure products and removed modules are read but not checked, and none of them is reported or counted.
P5 DR & Backup — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. No backup/snapshot/replication config, RTO/RPO or restore-procedure documentation was found — and no production persistence was detected either (no data-access packages, no data-store services, no database resources), so there is nothing in this repository whose loss a DR control would recover. If this system's data lives in a platform or ops repo we can't see, that's where the DR evidence belongs.
Repo exclusion declarations: 4 pattern(s) declared (.gitattributes linguist-generated/vendored, .editorconfig generated_code) excluded 0 source file(s) from code-quality scoring. Declarations are the repo's own visible statement that a tree is machine-written or vendored — auditable in any diff, honored by GitHub the same way.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity — an in-process token-stream comparison over sliding windows, with type-aware normalization — so it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D5 Coupling: Coupling is measured between projects/assemblies — runtime coupling through DI, reflection, messaging or shared databases is invisible to a static reference graph.
D6 Cohesion (LCOM4): LCOM4 cohesion is syntactic — it infers connectivity from which methods touch which fields/methods by name, not from real runtime behaviour or intent.
D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
D10 Test Quality: Assertion density is structural — it cannot tell a meaningful behavioural assertion from a trivial one, only that an assertion is present.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D14 License Compliance: License compatibility is checked against declared package metadata and a policy — mislabelled or missing license metadata, and obligations that depend on how you distribute, are not resolved here.
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
D19 Documentation Quality: Documentation quality publishes two things only: a runnable or packaged unit with no example, and a multi-project root README that maps none of its parts. It does not judge clarity, and its other checks (overview, licence, boilerplate, structure, empty sections, XML-doc coverage) are withheld after adjudication: their candidates are counted in the card's gaps, not shown as rows. A false statement in a document is M4's; units are .NET projects only.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D30 Dependency Vulnerabilities: CVE matching depends on accurate package/version metadata and on the advisory databases — a vulnerability with no published advisory, or in code not declared as a dependency, is not seen. Coverage needs a RESOLVED graph: an unpinned requirements.txt, or a pom without a resolved build, yields partial coverage rather than a clean verdict. An ecosystem the analyzer cannot scan is reported as unmeasured, never as clean.
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
D43 Malicious Dependencies: Only packages some vulnerability database has already NAMED as malicious are seen — a compromise published in the last hours, or never reported at all, is invisible here, and this dimension reading 10 is not evidence that a dependency is trustworthy. There is no typosquat or dependency-confusion analysis: a package nobody has reported is simply absent from the feeds. Coverage is the dependency scan's: an ecosystem that could not be scanned is disclosed as unmeasured, never as clean.
D44 Platform End-of-Life: The support table is FROZEN, so it goes out of date by losing RECALL: a release that ended support after the table was written is missed until the table is refreshed, and this dimension reading 10 is not evidence that a platform is current. Only platforms the repository DECLARES in a place this pass reads are seen — a runtime named only in a REDACTED (D31's subject), in a CI workflow (D29's), or in a file this pass does not parse (go.mod, a Gemfile ruby directive) is invisible here, which is why a repository declaring none of them abstains rather than scoring. Only frameworks with a PUBLISHED support policy are tracked: React, Flask and Express publish none, so their age cannot be judged and their absence from a report is not a statement that they are supported.
AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
AX9 CQS / query purity: Handlers are found by interface/name convention — a query handler using neither is not seen. Mutation is a resolved write/publish invocation (SaveChanges/repository/bus), so a write hidden behind a hand-rolled wrapper, reflection, or a string-keyed service locator resolves to a non-persistence type and isn't flagged; it detects that a query writes state, not whether the write is a legitimate read-side cache update. Clean means "no resolved write/publish in a query body", not a proof of CQS purity.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
110 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was CometScanRule.transformV2Scan at 92. A further 27 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being ExpressionRegistry::get_expression_type at 63 — they are counted neither in the figure above nor in this dimension's score. 8 files carry no cyclomatic complexity row at all for this reason — every one of their over-threshold methods was excluded, so the exclusion is disclosed nowhere in the file itself: native/core/src/execution/planner/expression_registry.rs (ExpressionRegistry::get_expression_type at 63), native/common/src/error.rs (SparkError::error_type_name at 53), native/spark-expr/src/comet_scalar_funcs.rs (datafusion_comet_spark_expr::comet_scalar_funcs::create_comet_physical_fun_with_eval_mode at 51), spark/src/main/scala/org/apache/spark/sql/comet/util/Utils.scala (Utils.fromArrowType at 29), native/core/src/execution/jni_api.rs (datafusion_comet::execution::jni_api::op_name at 25), and 3 more not listed here. They are named here because the per-file figures other dimensions report are taken BEFORE this exclusion, so such a file can show a high maximum complexity elsewhere in this report and nothing here, with nothing to reconcile the two.
+ 61 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 9 bodies over 60 down to 60 or less in Cyclomatic Complexity — start with CometScanRule.transformV2Scan (cyclomatic 92), PhysicalPlanner::create_expr (cyclomatic 85), CometWindowExec.windowExprToProto (cyclomatic 70). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 9 together lifts Cyclomatic Complexity from 6.1 to about 6.5/10, projected with the scoring formula itself and assuming each lands exactly at 60; a cleaner split scores higher.
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: How hard the code is for a person to follow, beyond raw branching.
Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.
200 method(s) exceeded the cognitive complexity threshold of 15; the worst was datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major at 222.
+ 118 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 4 bodies over 120 down to 120 or less in Cognitive Complexity — start with datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major (cognitive 222), datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major (cognitive 218), CometScanRule.transformV2Scan (cognitive 173). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 4 together lifts Cognitive Complexity from 4.9 to about 5.6/10, projected with the scoring formula itself and assuming each lands exactly at 120; a cleaner split scores higher.
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes8.9 / 10Strong✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
Resolve the 48 FileTooLong finding(s) in God Classes — start with operators.scala, planner.rs, CometConf.scala. — One of this dimension's main actionable groups (48 warning-level).
Resolve the 22 MethodTooLong finding(s) in God Classes — start with planner.rs (4), rss_partition_writer.rs (3), aggregate.rs (2). — One of this dimension's main actionable groups (22 warning-level).
Resolve the 17 FunctionTooLong finding(s) in God Classes — start with row.rs (3), planner.rs (2), levenshtein.rs (2). — One of this dimension's main actionable groups (17 warning-level).
Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Copy-pasted code that should be shared instead.
Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.
213 duplicated block group(s) detected. A further 14 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.
+ 86 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 16 Duplicated block (8 lines × 2) finding(s) in Code Duplication — start with log.rs (2), aggregate.rs (2), list.rs. — One of this dimension's main actionable groups (16 warning-level).
Resolve the 16 Duplicated block (7 lines × 2) finding(s) in Code Duplication — start with columnar_to_row.rs (3), string.rs (2), credential_bridge.rs. — One of this dimension's main actionable groups (16 warning-level).
Resolve the 13 Duplicated block (9 lines × 2) finding(s) in Code Duplication — start with row.rs (2), rss_partition_writer.rs, collect.rs. — One of this dimension's main actionable groups (13 warning-level).
Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
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D5 · Coupling9.4 / 10Stronggated by 1 serious finding✓ Tool-verified
What it measures: Whether volatile projects sit underneath others that depend on them (so their churn ripples upward), and whether project dependencies form cycles. A widely-depended-on but stable shared/kernel project is healthy, not penalised.
Method: Dependency cycles via elementary-DFS over real .csproj references, plus Martin instability (afferent/efferent) per project. Exhaustive over the reference graph, deterministic.
Coverage: Exhaustive · type-level: afferent/efferent coupling + cycles computed over every production type — the population is all types, not a name convention.
13 production modules (Maven+Cargo), 0 dependency cycle(s), 0 unstable depended-on module(s). Read from the build's own module declarations; 3 module(s) off the main sequence, with abstractness counted on 10 of the 13 (the rest declare no modelled class or interface, export only macros, declare types in source no model reads, or have no source directory of their own).
Off the main sequence: comet-udf-sdk
Off the main sequence: comet-common-spark${spark.version.short}_${scala.binary.version} · ×2
What to do
Resolve the 1 Off the main sequence finding(s) in Coupling. — One of this dimension's main actionable groups (1 warning-level).
Enforce Coupling in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d5_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether a class's methods are focused on a single responsibility.
Method: LCOM4 cohesion per production class with at least two methods: connected components of methods sharing state or calls, computed syntactically. Deterministic, not a proxy.
Coverage: Exhaustive · type-level: LCOM4 cohesion computed over every production class — the population is all types, not a name convention.
D9 · Test Distribution10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the test suite has a healthy mix of unit / integration / end-to-end tests.
Method: Test projects classified (Unit/Integration/BDD/E2E) from compiled metadata; test methods counted exhaustively across projects with placement-agnostic disk fallback. Deterministic.
5864 test methods: 5856 unit, 8 integration, 0 BDD, 0 e2e. The JavaScript/TypeScript suite contributes 4 `it`/`test` case(s) across 1 test file(s) declaring at least one beside 40 .NET test method(s); its tier split is read from package names and paths only. The Rust suite contributes 2694 `#[test]` function(s) across 249 file(s) declaring at least one; its unit/integration split is Cargo's own — 2 of those file(s) are integration-test targets under a crate's tests/ directory, and the rest are #[test] functions compiled into the crate they test. The Python suite contributes 65 test function(s) across 6 file(s) declaring at least one — every `def test…` in a file pytest or unittest would collect, which is those frameworks' own definition of a case; a parametrize table counts once, so this is a floor. Its tier split is read from file names and paths only. The Scala suite contributes 3061 test case(s) across 165 file(s) in a test source set declaring at least one — every `test("…")`, `it("…")`, `property("…")` and `"…" in {…}` registration and every JUnit `@Test`, which is how munit, ScalaTest, utest, ZIO Test and specs2 declare a case; a case registered under a computed name is not counted, so this is a floor. Its tier split is INFERRED from paths and file names, except sbt's src/it source set, which the build declares.
✓ On the Gold path — maintain.
Detailed fixes: d9_recommendation.md.
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D10 · Test Quality10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the tests truly assert behaviour rather than just running the code.
Method: Per-test assertions, skips, and mock references analyzed via Roslyn; structured skip-reason tags (BUG:/ENV:) separate documented deferrals from debt. Deterministic.
0 skipped, 0 zero-assertion, no mocking-framework packages referenced (hand-written doubles or no mocking) across 4 tests. Measured on the JavaScript/TypeScript suite only — at least 488 test source file(s) (.scala, .java, .rs) went unread, so its test quality is unmeasured and is not in these counts.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: Whether the licenses of third-party packages are compatible with your policy.
Method: Third-party package licenses resolved from declared package metadata and checked against the configured policy (allow/deny/copyleft). Deterministic; clean = no incompatible license found at metadata depth.
0 of 618 shipped crate(s) use a banned license. Licences were resolved from crates.io over the crates a consumer compiles — this repository's REDACTED closed over its manifests' `[dependencies]` and `[build-dependencies]`. Crates it asks for ONLY under `[dev-dependencies]` are excluded: they are not compiled by anything that depends on this repository. A further 3 crate(s) in the closure resolve from git, a path or a private registry and publish no licence this pass can read; they are outside this verdict. This repository publishes itself under Apache-2.0, which is its own choice and is not judged here. ★ COVERAGE OF THIS VERDICT: it grades this repository's crate dependencies and nothing else. The repository also declares a Maven POM and a Python pyproject.toml/requirements.txt (pip/uv/Poetry), and the licences of those dependency graphs were NOT read by this pass — a gap in this engine's coverage, not a statement about them. So this result says the graded closure carries no banned licence; it does NOT say this repository's licensing is clear.
What it measures: Files that change often and are also complex — the riskiest hotspots.
Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.
Top hotspots: native/core/src/execution/planner.rs (101×85=8585); spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala (30×92=2760); spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala (53×52=2756) Repeated repair below the complexity floor: spark/src/main/scala/org/apache/comet/CometExecIterator.scala (14 of 27 changes were fixes); native/core/src/parquet/parquet_exec.rs (12 of 21 changes were fixes); spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeWrite.scala (11 of 18 changes were fixes)
Resolve the 42 Hotspot finding(s) in Churn × Complexity Hotspots — start with ShimSparkErrorConverter.scala (3), planner.rs, CometScanRule.scala. — One of this dimension's main actionable groups (42 warning-level).
Resolve the 38 Repeated repair finding(s) in Churn × Complexity Hotspots — start with mod.rs (4), CometExecIterator.scala, parquet_exec.rs. — One of this dimension's main actionable groups (38 warning-level).
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
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D16 · Bus Factor9.0 / 10Exemplary✓ Tool-verified
What it measures: Whether knowledge is concentrated in too few people (the "bus factor").
Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.
57 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is native/core/src/execution/operators/iceberg_write.rs. Counted over 572 of the 812 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
Off-boarding risk: anonymized user #1 · ×3
Further sole-owners (lower concentration)
✓ On the Gold path — maintain.
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.
Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.
80 deducted task-comment markers across 272142 LoC (0.0/KLoC) → score 9.9. Task comments only: this repository's language is read without a compiler, so D17's suppression, dead-code and commented-out-code arms did not run and this score counts fewer marker kinds than a .NET repository's would.
Resolve the 77 TodoComment finding(s) in Explicit Debt — start with ParquetReadSuite.scala (6), CometExpressionSuite.scala (5), operators.scala (4). — One of this dimension's main actionable groups (77 warning-level).
Resolve the 2 FixmeComment finding(s) in Explicit Debt — start with CometExpressionSuite.scala, CometScanExec.scala. — One of this dimension's main actionable groups (2 warning-level).
Resolve the 1 HackComment finding(s) in Explicit Debt — start with CometColumnarToRowExec.scala. — One of this dimension's main actionable groups (1 warning-level).
Enforce Explicit Debt in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d17_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the project's documentation is clear, complete, and useful.
Method: Every README, docs/ document and root guide parsed by its markup and measured in Unicode letters. Published: a runnable or packaged .NET project whose README (or a document it links, or a root guide) shows no example, and a root README naming none of three or more projects. Overview, licence, boilerplate, wall-of-text, empty-section and XML-doc rows are measured and withheld (counted in gaps) after adjudication. Deterministic, exhaustive, advisory.
1 of 1 documentation facets pass in full. 134 document(s) read across 1 unit(s).
✓ On the Gold path — maintain.
Detailed fixes: d19_recommendation.md.
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D20 · ADR Quality0.0 / 10Critical✓ Tool-verified
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
3 of 16 build units (Cargo, Maven, Python) flagged as possibly oversized/incoherent.
Projects may be oversized for their cohesion
What to do
Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d26_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Secret scan via TWO gitleaks detect passes in an isolated checkout — the full git history, then a second --no-git pass over the working tree as it stands — merged and de-duplicated by (rule, file, line); each match flagged High. Both invocations are recorded in the audit trail. Exhaustive; when the tool is absent, or when its output cannot be parsed into the expected shape, the dimension is WITHHELD as an explicit measurement gap on our side — unscored and excluded from the lens, never a hedged middling score.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.
Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).
170 finding(s): 0 critical, 161 high, 4 medium, 5 low. 93 unpinned-GitHub-Actions row(s) are reported here but scored by D36 (supply-chain provenance), which measures that posture as `pinned_actions` — one pinning decision is charged once, not once per lens. semgrep hit a parse error in 7 file(s) — `contrib/delta-spark/dev/run-delta-regression.sh` (line 36), `dev/local-ci.sh` (line 177, line 203, line 427), `dev/verify-contrib-delta-gate.sh` (line 177, line 182, line 187, …), `docs/source/_templates/layout.html` (lines 20–28), `mvnw` (line 86, line 124, line 134, …), … (+2 more) — so no absence of findings in the named regions is evidence of anything; rows reported elsewhere in those files are real. Fix the syntax error (or exclude the file deliberately) and re-scan to cover them. Separately, one or more rules could not re-parse an embedded snippet in 15 file(s) (e.g. a workflow `run:` block read as shell). Those files WERE scanned and their other rows are unaffected; only those rules' view of those snippets is missing.
REDACTED
REDACTED
REDACTED
REDACTED
REDACTED
+ 9 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 24 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (11), REDACTED (7), REDACTED (2). — One of this dimension's main actionable groups (24 issue-level).
Resolve the 23 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (9), REDACTED (4), REDACTED (2). — One of this dimension's main actionable groups (23 issue-level).
Resolve the 14 REDACTED finding(s) charged to Static Analysis (SAST) — the other 93 are reported here at file:line but scored by D36 (supply-chain provenance), which charges them once. — One of this dimension's main actionable groups (107 issue-level, 14 of them charged here).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any dependency has a known published vulnerability (CVE), direct or transitive, in ANY ecosystem the repository declares — Dart pub, Elixir and Erlang via Hex, Go modules, Java and Kotlin via Maven/Gradle, JavaScript/npm, .NET/NuGet, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift.
Method: Dependency-CVE scan across every ecosystem the repository declares, scored ONCE. Three sources are unioned and deduplicated by advisory identity (rule id + alias closure, CVE<->GHSA) scoped to package+version, keeping the worst severity: `osv-scanner --recursive` over osv.dev for Dart pub, Elixir/Hex (and Erlang, whose `rebar.lock` syft first converts to a CycloneDX SBOM the scanner reads, with rows attributed back to the lock), Go, Java and Kotlin via Maven/Gradle (and Scala, whose sbt build's pinned direct declarations are written into a CycloneDX SBOM the scanner reads, with rows attributed back to the build file), npm, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift; `trivy fs --scanners vuln` for npm lockfiles; and `dotnet list package --vulnerable --include-transitive` for NuGet (with per-advisory collapse of the project x target-framework fan-out), plus a DECLARED-dependency arm that resolves a published gem's gemspec against rubygems.org where no Gemfile.lock is committed. `SeverityScore(c,h,m,l, normalizer 8.0)`. NotApplicable only when NO ecosystem is readable; if any applicable ecosystem could not be scanned the findings are REPORTED and the score is withheld. Supersedes the npm and OSV arms, retired 2026-09-05.
Resolve the 1 Critical CVE finding(s) in Dependency Vulnerabilities — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 1 High vulnerability finding(s) in Dependency Vulnerabilities — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 3 Medium advisory (unmaintained) finding(s) in Dependency Vulnerabilities — start with REDACTED (3). — One of this dimension's main actionable groups (3 warning-level).
Detailed fixes: d30_recommendation.md · top locations in Appendix A, every location in findings.md.
Resolve the 3 High IaC finding(s) in IaC & Container Security — start with REDACTED (3). — One of this dimension's main actionable groups (3 issue-level).
Resolve the 11 Medium IaC finding(s) in IaC & Container Security — start with REDACTED (11). — One of this dimension's main actionable groups (11 warning-level).
Detailed fixes: d31_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
4 of 572 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is native/spark-expr/src/nondetermenistic_funcs/randn.rs. Counted over 572 of the 812 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling. A non-source file is never a coupling PARTICIPANT either: documentation, schemas, config and data files are dropped with the rest, so a code↔docs pair — a command and the reference page that restates it — is not reported however strongly the two co-change; nor is coupling that runs THROUGH a build step or config file.
Resolve the 7 Boundary-crossing change coupling finding(s) in Change Coupling — start with error.rs (3), spark_config.rs, lib.rs. — One of this dimension's main actionable groups (7 issue-level).
Resolve the 1 Change coupling finding(s) in Change Coupling — start with mod.rs. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
+ 1 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any dependency the repository declares is published as MALICIOUS rather than merely vulnerable — a package that is an attacker's work, in any ecosystem osv-scanner reads. Scored apart from D30 because the answer is binary: there is no safe version to upgrade to, and the fix is to remove the package and rotate every credential it could have read.
Method: The same dependency scan D30 reads, partitioned on the scanner's own classification rather than rescanned: a row is MALICIOUS when its id is in the `MAL-` space (the ossf/malicious-packages feed) OR its `database_specific.cwe_ids` carries `CWE-506` ("Embedded Malicious Code"). Both channels are structural; the summary text is deliberately NOT read, because a malicious-package record whose summary says only "Critical severity vulnerability" is a real shape ([GHSA redacted]) and a text matcher misses it. Scored BINARY: any surviving row is 0, whatever its severity and however many CVEs sit beside it — a hostile dependency is not a quantity. Applicability and degradation are D30's: NotApplicable only when no ecosystem is readable, and an unscannable ecosystem degrades rather than reading clean. SCORED, not informational.
What it measures: Whether anyone still ships security patches for the platform this repository RUNS ON — the runtime it pins and the framework majors its own constraints hold it to. Separate from D12 because the question differs: a current Django on an end-of-life Python is perfectly up to date and completely unsupported, and the fix is a migration rather than a version bump. What the repository says it merely SUPPORTS is never charged.
Method: End-of-life PLATFORM read from the repository's own declarations and graded against a FROZEN, dated table of vendor support dates — no network, no feed, no API, so this dimension answers identically inside a closed scan fence. Two subjects: a RUNTIME the project pins (a single or all-end-of-life TargetFramework, a .nvmrc or .python-version, a requires-python CAP) and a FRAMEWORK major a dependency constraint cannot move off (a caret, tilde or exact version; `vue@^2.7.16` pins Vue 2). A FLOOR is deliberately never charged — `requires-python = ">=3.8"` states what a package SUPPORTS, not what it runs on — and a multi-target project is charged only when EVERY target is out of support. Runtime 4.0/product capped 8.0, framework 1.5 capped 4.5. The table is safe to freeze because a statement about support that ended in the past cannot become false: it loses recall as it ages, never precision, and a test asserts every entry predates the freeze date. Disjoint from D31 (a container image's OS layer) and D29 (the toolchain a CI workflow installs). Abstains when the repository declares no platform this pass reads — never scores it clean.
0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 2 platform declaration(s) and 7 dependency declaration(s). This dimension reads what the repository says about ITSELF — a pinned target framework, a version file, a capped requires-python, a Rust toolchain pin, a framework major a constraint cannot move off. A FLOOR is deliberately never charged: `requires-python = ">=3.8"` states what the package SUPPORTS, not what it runs on, and a well-maintained library declares exactly that while running its own CI on a current release. The end-of-life facts are FROZEN and dated, so this dimension needs no network and answers identically inside a closed scan fence; as the table ages it loses recall and never precision, because a statement about support that ended in the past cannot become false. The OS layer of a container image is D31's question and the toolchain a CI workflow installs is D29's; this row is neither.
Other · Architecture — How the codebase splits by code ROLE — domain, application, infrastructure, test, generated. The significance map behind the knowledge/coupling weighting, and a DDD signal in its own right: a thin domain core under fat infrastructure is the anemic-domain smell, quantified. How each file's role is decided, because the split is only as good as that: a generated name or a build-output tree makes it Generated, a test project makes it Test, and otherwise the file's NAMESPACE and PATH words are matched against fixed vocabularies in a fixed ORDER — domain, then infrastructure, then application — so a file whose words hit two layers is counted under the earlier one. A production file matching none of them counts as application, so that share reads 'application or unclassified' rather than a measured application layer. Roles come from naming convention, never from what the code does. On this repository the split was taken from the source tree on disk rather than from a loaded .NET workspace, so a file's role is decided by its PATH segments alone — no declared namespace was available to add to the evidence — and generated output is excluded from the census entirely rather than counted as a generated share.
Method: Roslyn line-count by code ROLE: every source file classified Domain/Application/Infrastructure/Test/Generated by namespace + path convention (the shared CodeRoleClassifier), then significant lines summed per role. Deterministic; the advisory score is the business-logic (domain+application) share of production code.
Coverage: Population: ALL source files, each bucketed into ONE of five roles (Domain/Application/Infrastructure/Test/Generated) by namespace + path convention — a file whose layer isn't named in the convention falls to Application (the neutral default), and the split is line-count, not semantic depth or business value.
What to do
The domain core is a small share of production code, but most of the rest matched no layer vocabulary at all — so this is not yet an anemic-domain finding. The namespace/path convention could not place that code, which makes the composition above a statement about the naming, not about the design. Name the layers (or check that the repository's conventions differ from the ones this check knows) before reading a thin domain into it.
Other · Architecture — Whether the project-reference graph is acyclic (cycles block independent build/deploy and signal eroding boundaries).
Method: Project reference cycles via elementary-DFS over real .csproj references, using the engine shared with D5/D7; cyclic versus acyclic. Exhaustive, deterministic.
Other · Architecture — Whether dependencies point inward (Domain ← Application ← Infrastructure/Web) — the clean-architecture dependency rule, checked across the project graph.
Method: Layer violations by name-segment inference (Domain/Core to Application to Infrastructure/Web) over the project-reference graph. Exhaustive over all projects, deterministic.
Other · Architecture — Whether interfaces stay focused rather than fat — the Interface-Segregation principle (SOLID 'I').
Method: Roslyn scan: public interface declared-member counts (accessors fold into their property/event); fat-interface threshold (over 15 declared members) flagged per type. Each finding also reports the distinct-OPERATION count — members counted by name, so an overload group counts once — which decides whether it states the caller-side ISP harm or the implementer-side burden of an overload set. TypeScript: exported interfaces that declare behaviour, counted over their REQUIRED operations (method signatures and function-typed properties with no `?`, once per signature) — data-shape interfaces and ambient .d.ts declarations are outside the population; JavaScript declares no interfaces and is not applicable. Deterministic, type-level.
Do you agree with this assessment?
AX8 · Test isolation10.0 / 10Exemplary✓ Tool-verified
Other · Architecture — Whether production projects stay free of references to test projects — tests may depend on production, never the reverse.
Method: Csproj graph: each production project checked for references to test projects (identified by test-framework presence, not name). Zero violations is clean. Deterministic.
Other · Architecture — Whether read (query) handlers stay side-effect-free — a query that writes persistent state or raises events breaks CQS and makes reads unsafe to retry, cache, or route to a read replica.
Method: Roslyn scan: CQRS handlers classified query-vs-command by interface (IQueryHandler/ICommandHandler/IRequestHandler<TQuery,TResult>) and name convention (*Query/Get*/Find* vs *Command); each query handler's body checked for persistent-state writes (SaveChanges/repository Add-Update) or event publishes by resolved invocation. Deterministic, type-level, exhaustive over the detected handlers.
Coverage: Population: CQRS handlers identified by IQueryHandler/ICommandHandler/IRequestHandler interface + *Query/Get*/Find*/*Command NAME convention; query purity then checked exhaustively within that set — a query handler using neither convention is invisible, and mutation is a resolved persistence/publish CALL, not full dataflow.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Explain in the README how to run the tests. Add a section called Testing to the README in the repository's top folder, saying how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
Add a README to the 8 of 16 project(s) that lack one — worth up to 1 pts.
Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.
Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.
No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
What to do
Record your significant design decisions. Write one short document per decision, giving its date, the situation, the decision and its consequences, and keep them together with your other design documents. A docs/adr folder with each file named by number and title is the easiest place to find them.
Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
Readiness · Readiness — Whether an automated pipeline builds and tests every change.
Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.
Do you agree with this assessment?
P10 · Library API & versioning10.0 / 10Exemplary○ Nothing flagged
Readiness · Readiness — For a library: a deliberate (small) public API surface and explicit semantic versioning so consumers can depend on it safely.
Method: Roslyn scan: public API surface area and semantic-versioning markers (SemVer attributes, changelog entries) for libraries; off .NET, a library is the ecosystem's publication act (an npm package that is not private and names an entry point, a PyPI distribution with a build system, a Rust library crate, a Maven/Gradle module that publishes, a Go module with no package main, a gemspec, a Composer library, a SwiftPM library product, a pub.dev or Hex package), its surface is the share of types the language model records as public (Rust, Swift, Java, Kotlin, Go, Dart; not measured where the model records no type visibility or, as in TypeScript, only module-level export), and its version is read from the manifest, a semver CHANGELOG, release tooling or semver git tags. Exhaustive, deterministic.
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P2 · Observability5.9 / 10Adequate✓ Tool-verified
Readiness · Readiness — Whether the code is diagnosable in production — structured logging, tracing/metrics, health checks.
Only 5/8 runnable modules use logging (modules with no entry point or server are excluded — they are libraries a runnable module hosts). Silent: `.`, `dev/release`, `docs`.
What to do
Extend structured logging to the remaining runnable modules so everything you run is diagnosable in production.
Consider OpenTelemetry tracing/metrics (opentelemetry with tracing-opentelemetry) and a health-check endpoint (a /health route on your axum/actix router) for operability.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
What to do
Dependabot is configured but does not watch `pip`, `maven` — add those `package-ecosystem` entries to REDACTED so those dependencies get the same automatic update and advisory pressure as the ones it already covers.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
What to do
Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.
Readiness · Performance — Whether the code protects its performance with benchmarks — a benchmark suite, allocation/memory measurement, and (ideally) a CI gate. Presence is credited as a bonus, never a deduction.
Method: Repo + source scan: BenchmarkDotNet referenced (csproj/source), [Benchmark]/[MemoryDiagnoser] attribute counts, and a benchmark step in CI; off .NET, the same ladder over Go testing.B, Rust criterion/#[bench]/divan, JMH/kotlinx-benchmark, pytest-benchmark/asv/pyperf, tinybench/mitata/vitest bench/benchmark.js and Swift package-benchmark — scored as a bonus ladder (absence is neutral, never a deduction). Deterministic, presence detection.
Readiness · Performance — Whether the code is written to minimise allocations so it doesn't pressure its host's memory manager — buffer/slice views over copies, object pooling, stack or value-type allocation, and buffer writers. Reward-only: credited where present, never penalised where a simpler style is fine.
Method: Production-source scan: density (per 1k LoC) of allocation-aware APIs — in .NET Span/Memory, ArrayPool/ObjectPool, stackalloc, ValueTask, value-type structs, IBufferWriter, string.Create, SkipLocalsInit; off .NET, with comments and strings blanked, Go's sync.Pool, preallocated slices/maps, Grow, strconv.Append* and buf[:0] reuse; the JVM's NIO buffer views, MemorySegment, primitive collections, pools and literal presizes (plus Kotlin primitive arrays and value classes, Scala AnyVal and @specialized); Swift's reserveCapacity, ContiguousArray, withUnsafe* access and ~Copyable. Activated off .NET on the same floor (400 lines, and benchmarks or 8 uses); Rust and garbage-collected scripting languages are not applicable. Reward-only. Deterministic, syntax/text detection.
What to do
Raise allocation-aware density on the hot paths — currently 50 use(s) across 88,610 production line(s) (~0.6/1k). More of the idioms below on the allocation-heavy paths climbs this toward 10.
Java: on hot paths, work through buffer views (ByteBuffer/LongBuffer slices, MemorySegment) instead of copies, avoid boxing with primitive collections (fastutil, Eclipse Collections, HPPC, Trove), presize collections and StringBuilders, and pool expensive buffers (Netty's pooled allocator, commons-pool).
Scala: on hot paths, make small wrappers value classes (extends AnyVal), @specialized generic code over primitives, use primitive collections, and presize builders.
Readiness · Performance — Whether asynchronous code stays responsive — it avoids sync-over-async blocking (a .NET .Wait()/.GetAwaiter().GetResult(), a time.sleep or blocking HTTP call inside a Python coroutine, a *Sync call inside an async JavaScript function, block_on inside a Rust async fn, runBlocking inside a Kotlin suspend function, block() inside a Reactor publisher) that stalls a thread or event loop and risks deadlock, and, where the code is a reusable library on .NET, awaits with ConfigureAwait(false) so it never captures and stalls its caller's context.
Method: Production-source scan: sync-over-async blocking counted everywhere — .Wait()/.GetAwaiter().GetResult() in .NET; off .NET, read from the language model, a blocking call inside an async function (Python, TS/JS, Rust, Kotlin, Swift, Dart) or inside a Java method returning a Reactor Mono/Flux or a Scala method returning a Future or effect — and, for a .NET library with ≥5 awaits, the share of awaits using ConfigureAwait(false). Deterministic, syntax/text detection.
Other · Security — Only what this repository's own non-C# files could be read for was assessed — and because this repository commits the configuration that serves its own HTTP surface, that configuration could be read in full for the security response headers it sets. Nothing else in this dimension was assessed: the transport, cookie, input-validation and crypto controls are read from a source model that was not loaded for this repository’s language, so their absence here is not a finding about this repository.
Method: Roslyn plus filesystem scan: HSTS/security headers, secure cookies, input validation, middleware order, weak crypto (MD5/SHA1/DES); HTTPS-metadata context-aware. On a repository with no .NET source it reads JavaScript/TypeScript, Dart, Ruby, Scala, Rust or Elixir source itself (test, vendored, generated and minified paths not) for the arms that hold whether or not the code serves HTTP: a weak hash or cipher, key material fixed in the source, a TLS client that accepts any certificate, and (JavaScript/TypeScript, Ruby, Scala, Rust and Elixir) an obsolete TLS version; the server controls are not assessed there, and a Dart, Ruby, Scala, Rust or Elixir repository that serves no HTTP of its own is N/A on them. Deterministic.
None of the 11 security response headers the OWASP Secure Headers Project recommends (Content-Security-Policy, Strict-Transport-Security, X-Content-Type-Options, X-Frame-Options, …) is configured — defense in depth, even when a reverse proxy could set them. This is reported because `REDACTED` builds the web-server image that serves this application, so the configuration that would carry these headers is in this repository and was read in full. (−2.0 on this card.) — REDACTED:18
What to do
Set security response headers on the surface this repository serves: an `add_header` directive per header in the nginx/Caddy/Apache config, a `_headers` / `vercel.json` / `netlify.toml` entry for a static host, or `helmet()` in the HTTP server. `Content-Security-Policy` is the one that pays for itself first — it is what contains an injected script once one reaches the page — followed by `X-Content-Type-Options: nosniff` and a frame policy (`X-Frame-Options: DENY`, or CSP `frame-ancestors`). Where the app is served from a build container, the header configuration belongs in the image beside the built assets, so it ships with them rather than depending on where it lands.
Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.
Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.
Other · Code Health — Whether any branch is dead by construction — a switch arm whose label can never equal a case-normalised subject, or an `else if` whose predicate the arm above has already swallowed.
Method: Roslyn syntax + semantics: switch labels compared against the subject's own case normaliser, and if/else-if chains checked for a literal an earlier arm's containment test already swallows. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X13 · Undrained process stream10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a child process that has BOTH standard streams redirected drains both — reading one to the end while the other is never read deadlocks once the child fills the unread pipe.
Method: Roslyn syntax + semantics: ProcessStartInfo launches with both streams redirected, checked for a drain of each stream across the enclosing type. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `spawn` imported from `child_process` whose stdout and stderr are both pipes (no options, no `stdio`, or `stdio` of `'pipe'`), bound to a local that never leaves its scope, where exactly one of the two streams is read, the other never, and the child’s `close`/`exit` (or the read stream’s end) is awaited; a shell redirect in the call’s arguments or a `kill` of the child suppresses it. The same rule reads Python’s `subprocess.Popen`, Dart’s `Process.start`, Ruby’s `Open3.popen3`, and Scala’s JDK process starts (`new ProcessBuilder(…).start()` with no `redirectErrorStream`/`redirectError`/`redirectOutput`/`inheritIO`, `Runtime.getRuntime.exec`) where one of `getInputStream`/`getErrorStream` is read and the other never while `waitFor()` is called or the read stream is read to its end. Deterministic, provable per finding. Advisory.
Other · Security — Whether a hand-rolled public/private IP check can be walked past — a method that unwraps IPv4-mapped IPv6 but returns the opposite verdict for the same host written as IPv4-compatible, 6to4 or NAT64.
Method: Roslyn syntax + semantics: methods that unwrap IPv4-mapped IPv6 and hand-roll IPv4 range carve-outs, checked for whether the IPv6 branch also accounts for the IPv4-compatible, 6to4 and NAT64 embeddings. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X15 · Unvalidated length from an untrusted reader3.5 / 10Weak✓ Tool-verified
Other · Security — Whether a length read out of the stream being parsed is bounded before it is allocated or read — an unchecked count taken from the input lets the input choose the allocation.
Method: Roslyn syntax + semantics: integer lengths read from a BinaryReader and spent on a bulk read or an array allocation, checked for any comparison or bounding call on the value anywhere in the method. Deterministic, provable per finding. Advisory.
`n` is read from the bytes being parsed at line 425 (`u32::from_le_bytes`), then used to size a `Vec::with_capacity` at line 426 — and nowhere in its scope is `n` compared to anything, clamped, or bounded against the input that remains. Whoever wrote those bytes therefore chooses the allocation directly: it reserves the whole length before a single byte of the payload is known to exist, so a 32-bit length asks for up to 4 GiB, and an allocation the allocator refuses aborts the process with `memory allocation of … bytes failed` — an abort, not a panic, so no `catch_unwind` intercepts it and the whole process dies; a 64-bit length past `isize::MAX` panics with `capacity overflow` instead, out of a parser whose failure path was written for malformed input, not for hostile input. Bound `n` against the bytes actually remaining and a maximum the format allows before allocating. — native/spark-expr/src/agg_funcs/quantile_summaries.rs:426
What to do
Each finding names the read that takes the length from the input and the site that spends it, and states that the value is never compared in between. Confirm from the two, then bound the length against the remaining stream before using it.
Other · Code Health — Whether a loop that shortens a string until it fits a length budget has a floor — one with none grinds the value down to the empty string, or past it into a negative-length `Substring`.
Method: Roslyn syntax + semantics: while/do loops whose body's only effect on a string is to drop its last character, checked for whether anything — a direct comparison on the length, a body guard, a break — bounds that length below. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `while`/`do` loop whose body’s one assignment to a value is `x = x.slice(0, -1)` or `x = x.slice|substring|substr(0, x.length - 1)`, driven by a condition that reads `x.length` only as a term of a larger expression — never compared directly, never tested for truthiness, and with no other read of `x` — and whose body has no `break`, `return`, `throw` or `if` naming `x`. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X17 · Uncapped recursion over a caller-supplied document10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether a walk that recurses through a JSON/XML tree handed in by its caller bounds how deep it will go — an uncapped walk lets the document's nesting choose the stack depth, and the resulting StackOverflowException cannot be caught.
Method: Roslyn syntax + semantics: methods that take a JSON/XML document node and call themselves with a child of it, reachable from an externally-callable member of the same type that accepts a document, checked for any depth parameter, descent counter or threaded arithmetic anywhere in the walk. On a repository with no .NET source it reads TypeScript off the engine’s own token stream (test, vendored, generated and minified paths and `.d.ts` not) with the same rule: a function or method with a parameter typed as a document value — `unknown`, `any`, `object`, `Record<string, unknown|any>`, a JSON alias (`JsonValue`, `JSONObject`, …) or a DOM node global the file does not rebind — that calls itself (bare, or through `this` for a method, or hands itself to a call over the value as in `value.map(walk)`) with something it took out of that value and never through an ancestor accessor such as `closest()` or `parentElement`, that is exported or reached from an exported function (a public method of the same exported class) taking such a value, and that names no depth, level, nesting, recursion, remaining or budget anywhere and threads no `+`/`-` arithmetic through a self-call. Plain JavaScript is not read: with no annotation nothing tells a parsed document from a tree the code built itself. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a type's disposal matches what it OWNS — releasing what it created, leaving alone what it was handed, and not declaring a finalizer for state that has nothing unmanaged to finalize.
Method: Roslyn syntax + semantics: every assignment to a disposable field is read to decide whether the type CREATED the value or was handed it, and the type's disposal is checked against that answer — an injected interface it disposes, a value it constructed and never releases, a finalizer on a type holding nothing unmanaged, and a disposable local whose every reference is a plain member read. A value handed to a container that disposes its contents (a parent control's `Controls` collection, a component `IContainer`) is released by that container and is not reported; generated code is out of population. On a repository with no .NET source the same ownership questions are read in JavaScript/TypeScript off the engine’s own token stream (test, vendored, generated and minified paths not): a class declaring `dispose()`, `[Symbol.dispose]()` or `[Symbol.asyncDispose]()` that disposes a field it was handed through a constructor parameter typed as a repository interface or resolved by a dependency-injection container; that assigns a field only ever from `new X(…)` of a disposable class and neither releases it anywhere in the class nor names it in its disposal member or a method that member calls, nor hands it to anything else; and a `const`/`let` local built from literals only whose every reference opens a statement operating on a non-release member of it. A class is disposable when every repository declaration of its name declares or inherits a disposal member, or when it is a documented library disposable (`vscode` EventEmitter, CancellationTokenSource and Disposable; three.js geometries, materials, textures, render targets, renderers, controls and composers). The finalizer arm has no JavaScript counterpart: a class cannot declare one. Dart is read for the owned-and-never-released arm: a Flutter `State` (or a class declaring `dispose()`/`close()`) whose library-private field is written only with direct creations of a framework controller, `FocusNode`, `StreamController` or periodic `Timer` (or a `.listen(…)` into a field declared `StreamSubscription`), which nothing in the class or its library releases (`dispose`/`close`/`cancel`) and which is never handed on except as a named argument. Ruby is read for the owned-and-never-released arm and the abandoned-local arm: a class declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose instance variable is written only with a blockless core descriptor creation (`File.open`/`new`, `Tempfile`, `Dir.open`, `IO.popen`, the `socket` and `openssl` socket classes) that no method of the class closes and that is never handed on (passed, returned, stored, exposed by `attr_reader`, delegated) — a class whose superclass or mixin lies outside the repository is not judged — and a blockless creation read through and dropped (`File.open(path).read`) or held in a local that is never closed, returned, passed or stored (a `&:read` block-pass, an `autoclose: false` borrow and a `fileno` hand-over are not abandonment). PHP is read for the same two arms: a class (not a trait, and not one using a trait) declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose `private` property (or a `final` class's `protected` one) is written only with a direct core `fopen`, `tmpfile`, `popen`, `proc_open`, `fsockopen`, `stream_socket_client`/`server` or `opendir` that nothing in the class closes (`fclose`, `pclose`, `closedir`, `unset`, a reset to `null`) and that is never handed on, and a handle held in a function's local, or read through as the argument of a reader, that is never closed, returned, passed, captured or stored; a `php://` stream, `curl_init`, `SplFileObject`, `PDO` and a function the repository declares under the core name are not read. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a method that temporarily changes state belonging to the whole process — the working directory, an environment variable — puts it back on EVERY path: a restore reached only when nothing throws leaks the change to the rest of the process.
Method: Roslyn syntax + semantics: method bodies that write the process working directory or an environment variable and write it back in the same body, checked for whether that restore sits in a `finally`/`catch` or only on the straight-line path. On a repository with no .NET source the same rule reads production JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not): `process.chdir`, `process.env.NAME =`/`["NAME"] =` and `delete process.env.NAME`, and Deno’s `Deno.chdir`/`Deno.env.set`/`Deno.env.delete`, paired per function body (a nested function or arrow is its own body, and module top-level code is none), where the last write puts back a local the body captured from the same global or deletes a variable the first write set, with at least one statement between them; a write in a `catch`/`finally` of that body silences it. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether an argument guard throws the exception its own condition describes — a guard that rejects a value for being EMPTY and reports it as `NullPointerException` (Java, Kotlin, Scala), `ArgumentError.notNull` (Dart) or `ArgumentNullException` (F#'s `nullArg`) tells the caller a parameter was null when it provably was not.
Method: Roslyn syntax: `throw new ArgumentNullException(nameof(p))` statements controlled by an `if`, whose condition is read for a test that is true of a NON-null `p` — an emptiness test that dereferences it (`p.Count == 0`, `!p.Any()`) or a BCL predicate documented true of the empty value (`string.IsNullOrEmpty(p)`). Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a `when` guard is free of side effects — a guard that increments a counter or assigns while deciding whether its arm matches applies that change during PATTERN MATCHING, on an arm that may not be selected, and skips it entirely when a short-circuit to its left answers first.
Method: Roslyn syntax: `when` guards on case labels and switch-expression arms, read for a mutation (`++`/`--`/assignment) sitting in a position the guard's own `&&`/`||`/`??`/`?:`/`?.` can skip. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a method that TAKES a lock or semaphore and gives it back from a flag-guarded `finally` returns the value that flag implies — reporting success while the guard hands the primitive back admits a second caller the exclusion was there to keep out, and reporting failure while the guard keeps it leaves nothing to ever give it back.
Method: Roslyn syntax: `try` statements whose `finally` releases a synchronisation primitive under a bare local-bool guard, where the method also TOOK that same primitive before the `try`, checked for a `return` of a bool literal whose value disagrees with the flag state the method's own straight-line assignments put it in. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether the work a diagnostic log line costs is paid only when that line is wanted — C# evaluates a call's arguments BEFORE the call, so a trace/debug message joined or projected out of a collection is built in full on every pass, and then discarded by a sink the shipped configuration leaves switched off.
Method: Roslyn syntax: log calls at a diagnostic level (a `Log`-prefixed method naming Trace/Debug/Verbose, or a bare `Debug`/`Trace`/`Verbose` on a receiver named for a logger), whose argument list is read for a call whose cost scales with a sequence — a LINQ operator, a materialisation, `string.Join`, a serializer — with no enclosing level check or conditional-compilation region. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `debug`/`trace`/`verbose` call on a receiver named for a logger, or a `log`-prefixed method naming the level, whose argument calls an array operator (`map`/`filter`/`reduce`/`sort`/…), `Array.from`, `Object.keys/values/entries`, `JSON.stringify`/`util.inspect` over anything but a literal, or an array `join` — outside any arrow or function passed as an argument, which the logger calls only when the level is on — with no enclosing `if`, `&&` or `?:` whose condition names a level, a level string, or the `NODE_ENV`/`__DEV__`/`DEV` build switch. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X24 · Document value interpolated into markup unescaped10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether text read out of the document being converted is escaped before it is written into generated markup — a value the document's author chose, interpolated into an attribute the surrounding literal delimits, can close that attribute and open another.
Method: Roslyn semantic model over the whole compilation: a string-typed `Value`/`InnerText`/`InnerXml`/`Text` member declared inside `DocumentFormat.OpenXml` or `System.Xml` is a taint SOURCE, propagated through assignments, returns, arguments, tuple elements and string composition to its transitive closure, then read at interpolated-string holes that sit in a markup position the surrounding literal itself delimits. Escaper/encoder calls and enclosing validator conditions cut the flow. Flow- and container-insensitive by construction. A second arm needs no provenance at all and reports a type that CONTRADICTS ITSELF — the same expression escaped at one delimited markup hole and interpolated raw at another hole in the same markup position of the same type, which the type's own escaping proves is a defect without knowing where the value came from. On a repository with no .NET source it reads JavaScript/TypeScript off the token stream with the same rule: a DOM read of raw document text (`getAttribute`, `textContent`, `innerText`, `nodeValue`) is the source, propagated through local bindings and string composition, and judged at template-literal and concatenation holes in the same two delimited markup positions; escapers and validating conditions cut it, and documentation-site, test, vendored and minified scripts are not read. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value the caller is invited to supply is the value the type actually uses — a constructor parameter stored in a private field that nothing ever reads while the default it was given is spelled out a second time at the site that should have read it, a keyed lookup that falls back to a different setting than the one its key names while the same type falls back to the matching one for that same key, or a culture-sensitive parse given no format provider by a type that feeds its own settable culture to the same kind of parse elsewhere, or a number an entry point writes to standard output under the host's culture while the same entry point formats with the invariant culture elsewhere. Either way, every caller who supplies a value silently gets something else.
Method: Roslyn syntax: private instance fields of a non-partial type assigned in a constructor from one of its own parameters with a `??` fallback, checked for whether anything in the type body reads the field and whether that same fallback expression is spelled out again outside the constructor; and `??` fallbacks onto a member access from a lookup call carrying exactly one string literal, grouped by that key across the type and checked for a fallback member whose folded name disagrees with the key while a sibling site for the same key agrees with it. On a repository with no .NET source the first two arms read JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `#x`, `private` or `private` parameter-property instance field filled in the constructor from a parameter (or one member of one) through `??`/`||` or a parameter default, never read anywhere in the file by name, whose constructed default is spelled again in the class body; and `lookup("key") ?? s.member` grouped by key per class, or per module outside every class. The culture arm has no JavaScript counterpart: its parses take no locale. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value handed from a callback to the body that waits on it crosses on something built to be crossed — a `Queue<T>`/`List<T>`/`Dictionary<K,V>` written inside an event handler and read back outside it is mutated by two flows at once, and the semaphore or completion source beside it orders how MANY items exist while leaving the collection's own head, tail and backing array unprotected.
Method: Roslyn syntax: method, accessor, local-function and lambda bodies that declare BOTH a non-thread-safe generic collection (`Queue`/`Stack`/`List`/`Dictionary`/`HashSet`/`Sorted*`/`LinkedList`) and a synchronisation primitive (`SemaphoreSlim`/`TaskCompletionSource`/`ManualResetEvent(Slim)`/`AutoResetEvent`/`CountdownEvent`) as locals, then read for a `+=`-registered lambda that raises that primitive while the body outside every lambda waits on it — and, in that scope, a mutating call on the collection inside the lambda paired with a mention of it outside. Any `lock` in the scope abstains it. On a repository with no .NET source the same handoff is read in Java off the engine’s own token stream (test source sets, vendored and demonstration paths not): an `ArrayList`/`LinkedList`/`ArrayDeque`/`PriorityQueue`/`Hash*`/`LinkedHash*`/`Tree*` local and a `CountDownLatch`/`Semaphore`/`CompletableFuture` local, a lambda or anonymous class that raises the primitive and mutates the collection, and a wait outside it; any `synchronized` or `lock()` abstains the body. Because each of those primitives orders what the callback wrote before raising it, only a touch that provably overlaps the callback is convicted: one after the registration and before the next wait, or one in a loop registered-before, waiting on every pass and not declaring the collection. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X27 · Collection changed while being enumerated10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether an enhanced `for` leaves the collection it is walking alone — a body that adds to or removes from the very collection the loop is iterating invalidates the fail-fast iterator it is holding, so the next `next()` throws `ConcurrentModificationException` and the remaining elements are never seen. Whether a `for` loop leaves the `RefCell` it is walking alone — safe Rust rejects a body that changes the collection its loop borrows at compile time, except through interior mutability: a `RefCell` borrowed in the loop's header stays borrowed until the loop ends, so a `borrow_mut()` of it inside the body panics with `already borrowed: BorrowMutError` and the remaining elements are never seen. Whether a `for` loop leaves the `dict`, `set` or `deque` it is walking alone — CPython's iterators over those check that the collection has not changed size since the loop began, so a body that removes from (or empties) the very collection the loop is walking makes the next step raise `RuntimeError: dictionary changed size during iteration` and the remaining items are never seen. Whether a `for` comprehension or `foreach` leaves the mutable collection it is walking alone — an `ArrayBuffer` or `ListBuffer` counts its changes and its iterator checks the count, an `ArrayDeque` (and `mutable.Queue`/`mutable.Stack`) is walked by index up to its starting length, and a `java.util` collection walked through `asScala` is fail-fast, so a body that adds to or removes from the very collection the loop is walking makes a later step throw `ConcurrentModificationException` (`IndexOutOfBoundsException` for a deque) and the remaining elements are never seen.
Method: Roslyn syntax + semantics: `foreach` statements whose body calls a structural mutator (`Add`/`Remove`/`Clear`/`Insert`/…) on the very expression the loop is enumerating. Two arms. ARM A — the source is a concrete fragile BCL collection, or a live `Keys`/`Values` view over one, and the mutator resolves to that same collection's own member; concurrent and immutable collections and arrays are outside the population by construction, since their enumerators survive a structural change. ARM B — the source is an argument-less accessor CALL on a receiver whose body is in source: the accessor must return a stored field VERBATIM and a sibling member must structurally change that same field, both read off the implementations rather than from the members' names. A mutation the loop provably exits immediately after (`break`/`return`/`throw`/`goto`), or one written inside a nested loop or a lambda, is counted and never reported. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X28 · Index access outside its own emptiness guard10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a condition that tests a value for emptiness indexes that same value only where the test holds — an `||` written one parenthesis too far to the left leaves an index access outside the guard beside it, so the empty case the guard exists to anticipate reaches the index and throws.
Method: Roslyn syntax only, no semantic model: the OUTERMOST `&&`/`||` of every boolean condition, read for a symbol the condition tests for emptiness (`string.IsNullOrEmpty`/`IsNullOrWhiteSpace`, a `Length`/`Count` comparison against a literal, `Any()`, a `Length`/`Count` pattern, or a comparison against `""`) and ALSO indexes. Each `symbol[...]` access is placed by a boolean-reachability walk from the access up to the outermost connective: an access is COVERED when some enclosing step has it in the right operand and the left operand, under the truth value that step forces, proves the symbol non-empty — a recursion over `&&`/`||` whose true- and false-directions are asymmetric. A finding needs BOTH an uncovered access and a covered one on the same symbol in the same condition, which is the agreeing twin that separates a misplaced parenthesis from an unrelated length test. Bare index accesses with no emptiness test in the condition are neither counted nor reported; a non-identifier receiver and a lambda nested inside the condition are outside the population. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X29 · Per-element action decided by a fixed element10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a decision taken once per element is taken ABOUT that element — a test inside a counted loop that reads a fixed subscript of the very collection its guarded statement indexes by the loop variable applies element zero's answer to all of them, so the elements that differ from it are all handled wrongly, and in the same direction.
Method: Roslyn syntax only, no semantic model: every `for` statement declaring exactly ONE loop variable, and every `if` inside its body that is not under a nested loop or a lambda. A site enters the population when the `if`’s condition never mentions the loop variable while the statement it guards indexes some collection by that variable ALONE (`c[i]`; `c[i + 1]` and `c[i, j]` are outside it). A finding additionally needs the AGREEING TWIN at the same-collection grain: the condition must read THAT SAME collection at a subscript that does not move — written into the condition, or reached through a local declared BEFORE the loop, so an alias bound inside the body is not followed. Both collection expressions must be simple identifiers. On a repository with no .NET source the same rule reads JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `for (let|var|const x = …; …; …)` with one declarator and a braced body, an alias followed only when it is declared before the loop in a block that encloses it and never assigned inside the loop. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.
Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.
`case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. (×6) — spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:466, spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:443, spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:444, …
An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it. (×6) — spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:252, spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CometNativeArrowSource.scala:335, spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala:168, …
`case NonFatal(_)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. (×2) — spark/src/main/scala/org/apache/comet/CometExecIterator.scala:608, spark/src/main/scala/org/apache/comet/CometExecIterator.scala:639
`case _: Throwable` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. (×3) — spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:1343, spark/src/main/scala/org/apache/comet/codegen/CometArrayData.scala:93, spark/src/main/scala/org/apache/comet/codegen/CometMapData.scala:48
What to do
Swallowed exception (caught, then discarded)
Do you agree with this assessment?
X30 · Support guard that admits what it rejects10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a guard written as a NEGATED `||` says what its author meant — `!(a || b || x != k)` is `!a && !b && x == k` by De Morgan, so a bail-out that mixes capabilities the code needs with a fault it refuses turns inside out: it fires only where the capabilities are ABSENT, and lets every value the fault term names walk straight into the body that cannot handle it.
Method: Roslyn syntax only, no semantic model: every logical-not whose operand is a parenthesised `||` chain of two or more disjuncts, flattened (a left-nested `a || b || c` read once would see `(a || b)` as one disjunct). A site enters the population on that shape alone. A finding additionally needs the disjuncts to DISAGREE in polarity: at least one bare boolean read — an identifier or member access, never an invocation, which is a predicate rather than a capability flag — and at least one `x != <constant>`, the only form that negates into an exact-value pin (`== null` negates into a looser requirement and is outside the fault set). Consistently-polarised disjunctions, all-fault or all-capability, are counted and never reported; a negated `&&` is outside the population entirely. No same-receiver gate: it was measured to cost a real defect and remove no false positive. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.
Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `trace`/`debug`/`info`/`warn`/`error`/`fatal`/`verbose`/`silly`/`http`/`log` call on a receiver named for a logger, in a package whose own or an enclosing `package.json` declares a logger that carries values as fields (pino, pino-http, nestjs-pino, Fastify, winston, bunyan, tslog, LogTape, roarr), whose first deciding argument is a template literal with substitutions; an object-literal fields argument is stepped over, a plain string message clears the call. Python is read the same way: a `debug`/`info`/`warning`/`error`/`exception`/`critical`/`log` call on a logger in a file that imports the standard library’s `logging` or structlog (loguru, which formats at the call, is not judged), whose message is a whole f-string with a non-constant field; a `%`-formatted, `.format()`-ed or concatenated message is not convicted, as in C#. PHP is read the same way: a PSR-3 level call (`emergency`…`debug`, `log`) on a logger (`$logger`, `$this->logger`, Laravel’s `Log::`, `logger()`, `\Drupal::logger()`) in a package whose `composer.json` requires a PSR-3 logger or a framework that hands one out (psr/log, Monolog, Laravel, Symfony, Drupal, CakePHP), or a file that imports one, whose message is a double-quoted string interpolating a variable; a single-quoted message clears the call, and `sprintf` or `.` concatenation is not convicted, as in C#. Dart is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on Sentry’s structured logger (`Sentry.logger`) whose message is a whole string literal interpolating a value (`$name`, `${…}`); the `Sentry.logger.fmt` template API clears the call. Dart that logs only through loggers with one message and no fields (package:logging, package:logger, talker, dart:developer) is not applicable rather than a reach gap. Ruby is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on the console gem’s `Console` (declared by a Gemfile, gemspec or Gemfile.lock, or required) or, where SemanticLogger, Ougai or dry-logger is declared or required, on a receiver named for a logger (`logger`, `@logger`, `Rails.logger`, `log`), whose first deciding argument is, as the whole argument, a double-quoted string interpolating a non-constant value (`#{…}`, `#@ivar`); a plain literal clears the call, keyword arguments are the fields, and a block, `%`-format or `+` concatenation is not convicted. Ruby that logs only through the standard Logger (Rails.logger) is not applicable rather than a reach gap. Deterministic.
`info` at line 306 is handed a message assembled at the call — a string concatenated with a value, or `String.format` — through SLF4J or log4j 2, so the logger receives one finished string: the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the level is filtered out. Pass a constant pattern and the values beside it instead: `log.info("Order {} moved to {}", orderId, status)`, which keeps the pattern and its arguments apart on the event for a structured encoder (logstash-logback-encoder, log4j 2's JsonTemplateLayout, OpenTelemetry), or `log.atInfo().addKeyValue("order_id", orderId).log("order moved")` with SLF4J 2's fluent API. An exception goes last, as the throwable argument, not into the text. — spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:306
Other · Code Health — Whether Scala's null is held to the type system and not undermined by assertions — a file compiled with `-Yexplicit-nulls` is one where `String` excludes null and `String | Null` admits it, a file under WartRemover's `Null` wart or Scalafix's `DisableSyntax.noNulls` is one whose code cannot write `null` and must say `Option`, and each `.nn`, `import scala.language.unsafeNulls` or null-lint suppression is the author overruling that.
Method: Roslyn compiler-options scan: NullableContextOptions per project; null-forgiving (!) suppression density per 1k syntax nodes. Deterministic, adoption plus suppression penalty.
No build over this repository's 362 Scala product file(s) passes `-Yexplicit-nulls`, enables WartRemover's `Null` wart or runs Scalafix's `DisableSyntax.noNulls`, so every reference type admits null and nothing reports a `null` written into the code or returned from a Java API — the first one to reach a method call is a `NullPointerException`. `Option` by convention is a habit no tool here holds the code to.
What to do
On Scala 3, add `-Yexplicit-nulls` to `scalacOptions` so `String` and `String | Null` mean different things, and type each Java API's result as nullable where it can return null; on Scala 2 (or as a first step), enable WartRemover's `Null` wart (`wartremoverErrors += Wart.Null`) or Scalafix's `DisableSyntax.noNulls = true` so the code holds absence in `Option`. Then pay the assertions down rather than adding to them: each `.nn` is a null the compiler could not rule out — wrap the Java value in `Option(…)` at the boundary, pattern-match on `null` once there, or change the API so the null cannot arrive; an `unsafeNulls` import switches the checking off for everything after it.
Do you agree with this assessment?
Reference — by lens
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Unscored — 2 check(s) recorded observations but carry no score
These checks ran and found something, but they do not carry a score — either by design (an advisory check reports evidence rather than grading it) or because they could not be scored here. They are excluded from the score for that reason, not because there was nothing to see.
X10 Duplicated predicate — 24 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
X7 Silent fallback defaults — 3 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
Not evidenced — 4 control(s) we could not find positive evidence for
These checks grade a working control, and the repository shows no evidence of one. That is deliberately not scored as a zero: a repository cannot show an ops runbook, a database TTL or an infrastructure-side audit log, so absence of evidence here is not evidence the control is missing. It is also not a statement that the check is irrelevant to this codebase — the thing it grades applies; we just could not see it. Excluded from the score either way.
C3 Audit Trail — Not assessed: these audit controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks audit controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C4 Data Retention — Not assessed: these retention controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks retention controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C5 Data-Subject Rights — Not assessed: these data-subject rights controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks data-subject rights controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
Not included — 46 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC2 Forms & labels — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC3 Page structure — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC4 Keyboard semantics — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC5 ARIA correctness — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC6 Visual & motion safety — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AC7 A11y enforcement — Frontend below the scale floor (1 DOM element(s) < 25) — too little surface to assess accessibility.
AX1 Captive dependencies — Not applicable: this repository's Java imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's; this repository's Python imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's; Rust with no dependency-injection crate has no container to hand one lifetime's instance to another — every value is owned by the code that builds it, and the borrow checker rejects a longer-lived value keeping a borrow of a shorter-lived one; this repository's Scala imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's.
AX2 Stateful singletons — No container singleton was found, so there is no shared instance for concurrent requests to race on. No class in this Java code is a Spring, JSR-330 or CDI singleton bean. No function in this Python code is served by a threaded web framework (a Flask, Bottle or FastAPI route, a Django or Pyramid view), so no module is shared between request threads. No class in this Scala code is a Play controller, a Cask routes object, a Scalatra servlet, a Finatra controller, an Akka/Pekko HTTP routes holder or a JSR-330/Spring singleton, so no instance is shared between request threads. Rust's compiler refuses unsynchronised shared mutation — a value shared across threads must be Sync — so the race this check looks for cannot be written. TypeScript/JavaScript runs each process's requests on one event loop, so no two requests write a shared object at the same instant (interleaving across an await is a different defect).
AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
AXR1 Runtime accessibility — compose up failed (exit 18 — an image could not be pulled) — spark-worker-1 Pulling bench Pulling spark-master Pulling spark-worker-2 Pulling bench Error Get "https://registry-1.docker.io/v2/": Forbidden spark-master Error context canceled spark-worker-2 Error context canceled spark-worker-1 Error context canceled Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the earlier `compose pull` step reported: bench Skipped - Image is already being pulled by spark-worker-2 spark-master Skipped - Image is already being pulled by spark-worker-2 spark-worker-1 Skipped - Image is already being pulled by spark-worker-2 spark-worker-2 Pulling spark-worker-2 Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the runtime sandbox reaches registries only through the in-fence pull-through mirror, so an image the mirror does not carry cannot be fetched — this is a limit of our sandbox, not of your stack; runtime evidence skipped This is a statement about this run, not a statement about your application: nothing here says the surface is inaccessible, only that it was never rendered.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C2 Access Controls — Not assessed: these authorization controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks authorization controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
D11 Test Reliability — Test reliability not included — no .scala test runner
D12 Dependency Hygiene — Not scored — 5 Maven/Gradle manifest(s) were read, stating a version for 2 dependency(ies) and leaving 52 to a parent or an imported BOM, but no build resolved the dependency graph, so the outdated signal was not established. NOT a finding that these dependencies are current or healthy.
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D21 Naming Consistency — Naming consistency is measured on C# only; Java, JavaScript, Python, Rust, Scala not measured.
D22 Internal API Consistency — API consistency is measured on C# only; the exposed Rust surface was not measured.
D23 Boundary Type-Coupling — No bounded-context organisation was detected either — neither a context-shaped layout, nor 2+ sibling source directories each declaring an aggregate root, nor 2+ projects each declaring an entity of their own. Declaring this codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — D24 reads C# and Visual Basic comments only — this repository's production source is .java, .py, .rs, .scala, which was left unread. Not scored: this is a gap in the analyzer, not a verdict about this repository.
D25 ADR Conformance — no ADRs to check
D27 Navigability — D27 counts a call into the standard library or a dependency as unresolved for this repository's language (.rs), so too few call sites counted as resolved to assess navigability. Not scored: this is a gap in the analyzer, not a verdict about this repository.
D32 Data Compliance (PII/GDPR) — 1 file(s) were not parsed by semgrep — the PII/GDPR ruleset never ran over them
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
DM1 Domain Modelling — applicable but not scored (2 signals for this style, 2 needed — the count is met but a required primary signal is absent): 262 value object(s); 2 domain event(s); its domain events are published by services or handlers — no domain entity raises one
ED2 Event/command shape — not scored — deciding whether a command has more than one competing handler requires resolving the call graph, and a call made through an inferred or generic receiver has no resolvable owner in the source. Reported as guidance rather than measured
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this lens looks for
GD1 Unfinished & placeholder code — no source files were read — this check reads C#, F#, JavaScript/TypeScript, Dart, Ruby and Scala, and none was read for this repository's product. That is a limit of the analyzer, not a finding about your code.
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, and the automatic-re-run check to any GitHub-Actions workflow, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P7 Outbound HTTP resilience — not applicable — no HTTP server, API framework or worker entry point was found in the JVM, JavaScript/TypeScript, Python, Rust source, so there is no service whose uptime a failing dependency could take down
P8 Schema migrations — no ORM, schema-migration tool or schema auto-create was found in this repository's dependency manifests or source, so there is no database schema for this check to judge
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (lcov — `cargo install cargo-llvm-cov`, then `cargo llvm-cov --lcov --output-path lcov.info`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures, or wire coverage collection into CI, to enable this cross-layer check
X2 Cancellation propagation — no C# was loaded; this repository's Scala, Java, Python and Rust were read, and no request in it can be cancelled by a caller — a Scala request takes no token: a Future cannot be cancelled, and cats-effect, ZIO and Monix interrupt the fiber that made the request from outside the call, not through a parameter; a Java request off Android takes no token: it is cancelled through the handle it returns (Future.cancel, Call.cancel, a Disposable), ambiently (io.grpc.Context) or by interrupting its thread, not through a parameter; a Python request takes no token: asyncio cancels the task and trio or anyio the scope that made it, from outside the call, and requests, httpx, aiohttp and gRPC take a timeout, not a cancellation; a Rust request takes no token: a future is cancelled by dropping it, from outside the call (select!, a timeout, an aborted task), and reqwest, hyper and tonic take none
X32 Type resolved by simple name across every loaded assembly — This check is about how a .NET program searches the assemblies loaded into its process for a type, and this repository contains no .NET source, so there is nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
X6 Hand-rolled structured-format parsing — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X9 Subsumed condition operand — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
Appendix A — Findings (grouped)
The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.
Boundary-crossing change coupling: spark_config.rs ↔ CometExecIterator.scala native/core/src/execution/spark_config.rs:1— `native/core/src/execution/spark_config.rs` (context native) and `spark/src/main/scala/org/apache/comet/CometExecIterator.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 90% of the time (9 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 9 shared commits counted here, the most recent 3 are `9a3d2a29` perf: load the next Parquet file's footer and page index while the cu…; `cae599ac` feat: add config to disable or restrict native UDF library loading (#…; `66e0c700` test: fail Comet tests when a native plan mislabels a timestamp's tim… — run `git show` on any of them.
Boundary-crossing change coupling: error.rs ↔ ShimSparkErrorConverter.scala native/common/src/error.rs:1— `native/common/src/error.rs` (context native) and `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 76% of the time (13 of the 17 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 13 shared commits counted here, the most recent 3 are `72c48471` fix: preserve Spark errors for Parquet timestamp overflow (#5891); `b8432a00` feat: Surface native scalar Arrow UDF errors as Spark's PythonExcepti…; `070951d2` feat: convert OneRowRelation to Arrow by default (#6666) — run `git show` on any of them.
Boundary-crossing change coupling: error.rs ↔ ShimSparkErrorConverter.scala native/common/src/error.rs:1— `native/common/src/error.rs` (context native) and `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 69% of the time (11 of the 16 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 11 shared commits counted here, the most recent 3 are `72c48471` fix: preserve Spark errors for Parquet timestamp overflow (#5891); `070951d2` feat: convert OneRowRelation to Arrow by default (#6666); `83a7cfb1` fix: match Spark ANSI integral overflow errors (#6217) (#6249) — run `git show` on any of them.
Boundary-crossing change coupling: error.rs ↔ ShimSparkErrorConverter.scala native/common/src/error.rs:1— `native/common/src/error.rs` (context native) and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 65% of the time (11 of the 17 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 11 shared commits counted here, the most recent 3 are `72c48471` fix: preserve Spark errors for Parquet timestamp overflow (#5891); `070951d2` feat: convert OneRowRelation to Arrow by default (#6666); `83a7cfb1` fix: match Spark ANSI integral overflow errors (#6217) (#6249) — run `git show` on any of them.
Boundary-crossing change coupling: lib.rs ↔ NativeBase.java native/core/src/lib.rs:1— `native/core/src/lib.rs` (context native) and `spark/src/main/java/org/apache/comet/NativeBase.java` (context spark) sit in DIFFERENT parts of the tree yet change together 64% of the time (7 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 7 shared commits counted here, the most recent 3 are `52ea7120` feat: keep native Arrow UDFs on Spark's path when the embedded Python…; `e76c35f1` fix: load the Iceberg storage scheme lists from the native factory (#…; `9c7fcc5a` fix: warn when native and JVM timezone databases differ (#6353) — run `git show` on any of them.
Boundary-crossing change coupling: comet_scalar_funcs.rs ↔ CometExprShim.scala native/spark-expr/src/comet_scalar_funcs.rs:1— `native/spark-expr/src/comet_scalar_funcs.rs` (context native) and `spark/src/main/spark-4.1+/org/apache/comet/shims/CometExprShim.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 60% of the time (6 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 6 shared commits counted here, the most recent 3 are `cb0b584b` fix: `get_json_object` returns first value for duplicate keys to matc…; `36146a87` fix: align time parsing and native second extraction with Spark (#5738); `82d674e3` feat: support Spark expression json_array_length (#4365) (at that commit the file was still `spark/src/main/spark-4.1/org/apache/comet/shims/CometExprShim.scala`) — run `git show` on any of them.
Boundary-crossing change coupling: utils.rs ↔ CometMetricNode.scala native/core/src/execution/metrics/utils.rs:1— `native/core/src/execution/metrics/utils.rs` (context native) and `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala` (context spark) sit in DIFFERENT parts of the tree yet change together 50% of the time (5 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see. You can check this without leaving the row: of the 5 shared commits counted here, the most recent 3 are `f90a3aa4` feat: expose native aggregate spill and memory metrics (#5423); `0215f83f` chore: surface DataFusion 54 PruningMetrics and Ratio in CometNativeS…; `62a68acd` feat: Implement metrics for iceberg compat (#2615) — run `git show` on any of them.
TodoComment spark/src/test/scala/org/apache/spark/sql/GenTPCHData.scala:66— // TODO: think a better way to install on each worker node — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/spark/sql/benchmark/CometExecBenchmark.scala:63— // TODO: support dictionary encoding in vectorized execution — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/spark/sql/benchmark/CometShuffleBenchmark.scala:107— // TODO: support dictionary encoding in vectorized execution — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala:1070— // TODO: test ANSI mode — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala:2411— // TODO: why with not in, the plan is only `LocalTableScan`? — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala:2627— // TODO: DataFusion supports only -8334601211038 <= sec <= 8210266876799 — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/main/scala/org/apache/comet/serde/unixtime.scala:27— // TODO: DataFusion supports only -8334601211038 <= sec <= 8210266876799 — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala:2633— // TODO: DataFusion toChar does not support Spark datetime pattern format — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/main/scala/org/apache/comet/serde/unixtime.scala:72— // TODO: DataFusion toChar does not support Spark datetime pattern format — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometNativeCastSuite.scala:685— // TODO: try_cast is not compared for this value because the native TRY path goes through — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometNativeCastSuite.scala:835— // TODO: try_cast is not compared for this value because the native TRY path goes through — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometNativeCastSuite.scala:2288— // TODO add _16 when https://github.com/apache/datafusion-comet/issues/1068 is resolved
TodoComment spark/src/test/scala/org/apache/comet/CometTemporalExpressionSuite.scala:172— // TODO test fails with non-UTC timezone — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/CometTemporalExpressionSuite.scala:201— // TODO test fails with non-UTC timezone — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:783— // TODO: consider merging this with the same method above — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:906— // TODO(https://github.com/apache/datafusion-comet/issues/3432): `_metadata.row_index` is
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:1299— // TODO: This produces incorrect results in the native Parquet scan — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:1361— // TODO: This produces incorrect results in the native Parquet scan — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:1312— // TODO: This test fails for the native Parquet scan — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/parquet/ParquetReadSuite.scala:3064— // TODO: pushed down filters do not used schema adapter in datafusion, will cause empty result — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/exec/CometAggregateSuite.scala:63— // TODO: To be addressed after DF 55 migration — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/exec/CometAggregateSuite.scala:105— // TODO: To be addressed after DF 55 migration — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
TodoComment spark/src/test/scala/org/apache/comet/exec/CometExecSuite.scala:2643— // "DATE": TODO: needs to address issue #1364 first
FileTooLong: comet/operators.scala spark/src/main/scala/org/apache/spark/sql/comet/operators.scala— FileTooLong — 2186 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 1686 over it, 4.37× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: execution/planner.rs native/core/src/execution/planner.rs— FileTooLong — 2054 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), about 69% of them inside a single declaration: PhysicalPlanner (3 blocks, 179-2239). The bar is 500 significant lines; this is 1554 over it, 4.11× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
FileTooLong: comet/CometConf.scala spark/src/main/scala/org/apache/comet/CometConf.scala— FileTooLong — 1615 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 1115 over it, 3.23× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: execution/jni_api.rs native/core/src/execution/jni_api.rs— FileTooLong — 1505 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), declaring 67 free functions. The bar is 500 significant lines; this is 1005 over it, 3.01× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: iceberg/IcebergReflection.scala spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala— FileTooLong — 1369 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 869 over it, 2.74× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: conversion_funcs/string.rs native/spark-expr/src/conversion_funcs/string.rs— FileTooLong — 1359 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), declaring 58 free functions. The bar is 500 significant lines; this is 859 over it, 2.72× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: operators/iceberg_write.rs native/core/src/execution/operators/iceberg_write.rs— FileTooLong — 1329 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 829 over it, 2.66× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: arrow/ArrowWriters.scala spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala— FileTooLong — 1263 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 763 over it, 2.53× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: execution/columnar_to_row.rs native/core/src/execution/columnar_to_row.rs— FileTooLong — 1255 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 755 over it, 2.51× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: spark_unsafe/row.rs native/shuffle/src/spark_unsafe/row.rs— FileTooLong — 1176 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 676 over it, 2.35× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: serde/aggregates.scala spark/src/main/scala/org/apache/comet/serde/aggregates.scala— FileTooLong — 1091 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 591 over it, 2.18× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: rules/CometExecRule.scala spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala— FileTooLong — 1020 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 520 over it, 2.04× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: parquet/schema_adapter.rs native/core/src/parquet/schema_adapter.rs— FileTooLong — 977 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 477 over it, 1.95× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: serde/QueryPlanSerde.scala spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala— FileTooLong — 945 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 445 over it, 1.89× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: ci/check-ci-config.py dev/ci/check-ci-config.py— FileTooLong — 931 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 431 over it, 1.86× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: shuffle/CelebornTransportCallbackTracker.java spark/src/main/java/org/apache/comet/shuffle/CelebornTransportCallbackTracker.java— FileTooLong — 927 significant lines (blank, comment-only and punctuation-only lines excluded), about 97% of them inside a single declaration: CelebornTransportCallbackTracker (55-1409). The bar is 500 significant lines; this is 427 over it, 1.85× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
FileTooLong: delta/DeltaScanSupport.scala contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala— FileTooLong — 900 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 400 over it, 1.80× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: rules/CometScanRule.scala spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala— FileTooLong — 896 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 396 over it, 1.79× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: shuffle/CelebornShufflePartitionPusher.java spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java— FileTooLong — 889 significant lines (blank, comment-only and punctuation-only lines excluded), about 98% of them inside a single declaration: CelebornShufflePartitionPusher (43-1266). The bar is 500 significant lines; this is 389 over it, 1.78× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
FileTooLong: shuffle/CometCelebornShuffleManager.scala spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleManager.scala— FileTooLong — 867 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 367 over it, 1.73× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: rss/rss_partition_writer.rs native/shuffle/src/writers/rss/rss_partition_writer.rs— FileTooLong — 867 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), about 93% of them inside a single declaration: RssPartitionWriter (3 blocks, 48-1133). The bar is 500 significant lines; this is 367 over it, 1.73× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
FileTooLong: kernels/temporal.rs native/spark-expr/src/kernels/temporal.rs— FileTooLong — 855 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), declaring 52 free functions. The bar is 500 significant lines; this is 355 over it, 1.71× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: cast_column/variant.rs native/core/src/parquet/cast_column/variant.rs— FileTooLong — 839 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 339 over it, 1.68× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: serde/arrays.scala spark/src/main/scala/org/apache/comet/serde/arrays.scala— FileTooLong — 837 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 337 over it, 1.67× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: hash_funcs/utils.rs native/spark-expr/src/hash_funcs/utils.rs— FileTooLong — 835 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted). The bar is 500 significant lines; this is 335 over it, 1.67× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Hotspot: native/core/src/execution/planner.rs native/core/src/execution/planner.rs:626— native/core/src/execution/planner.rs changed 101 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 85 in PhysicalPlanner::create_expr at line 626. 35 of those changes were fix/bug commits, and the other 66 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/execution/planner.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:387— spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala changed 30 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 92 in CometScanRule.transformV2Scan at line 387. 17 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:439— spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala changed 53 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 52 in CometExecRule.transform at line 439. 20 of those changes were fix/bug commits, and the other 33 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/spark/sql/comet/operators.scala spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:974— spark/src/main/scala/org/apache/spark/sql/comet/operators.scala changed 49 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 41 in CometNativeExec.buildNativeContext at line 974. 22 of those changes were fix/bug commits, and the other 27 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/operators.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:646— spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala changed 42 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 35 in QueryPlanSerde.serializeDataType at line 646. 15 of those changes were fix/bug commits, and the other 27 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/spark-expr/src/comet_scalar_funcs.rs native/spark-expr/src/comet_scalar_funcs.rs:118— native/spark-expr/src/comet_scalar_funcs.rs changed 26 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 51 in datafusion_comet_spark_expr::comet_scalar_funcs::create_comet_physical_fun_with_eval_mode at line 118. 14 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/comet_scalar_funcs.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/core/src/execution/jni_api.rs native/core/src/execution/jni_api.rs:474— native/core/src/execution/jni_api.rs changed 51 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 25 in datafusion_comet::execution::jni_api::op_name at line 474. 21 of those changes were fix/bug commits, and the other 30 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/execution/jni_api.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:599— spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala changed 23 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 31 in CometShuffleExchangeExec.nativeShuffleFailureReasons at line 599. 10 of those changes were fix/bug commits, and the other 13 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:209— spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala changed 10 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 70 in CometWindowExec.windowExprToProto at line 209. 5 of those changes were fix/bug commits, and the other 5 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/common/src/error.rs native/common/src/error.rs:352— native/common/src/error.rs changed 13 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 53 in SparkError::error_type_name at line 352. 7 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/common/src/error.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:80— spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala changed 10 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 65 in ShimSparkErrorConverter.convertErrorType at line 80. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/expressions/CometCast.scala spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:214— spark/src/main/scala/org/apache/comet/expressions/CometCast.scala changed 16 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 39 in CometCast.isSupported at line 214. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/expressions/CometCast.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/shuffle/src/spark_unsafe/row.rs native/shuffle/src/spark_unsafe/row.rs:823— native/shuffle/src/spark_unsafe/row.rs changed 9 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 68 in datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major at line 823. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/shuffle/src/spark_unsafe/row.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala changed 8 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 62 in ShimSparkErrorConverter.convertErrorType at line 67. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/core/src/parquet/parquet_support.rs native/core/src/parquet/parquet_support.rs:215— native/core/src/parquet/parquet_support.rs changed 26 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 19 in datafusion_comet::parquet::parquet_support::parquet_convert_array_impl at line 215. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/parquet/parquet_support.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala changed 7 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 61 in ShimSparkErrorConverter.convertErrorType at line 67. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/spark-expr/src/conversion_funcs/cast.rs native/spark-expr/src/conversion_funcs/cast.rs:241— native/spark-expr/src/conversion_funcs/cast.rs changed 8 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 53 in datafusion_comet_spark_expr::conversion_funcs::cast::cast_array at line 241. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/conversion_funcs/cast.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/core/src/parquet/schema_adapter.rs native/core/src/parquet/schema_adapter.rs:543— native/core/src/parquet/schema_adapter.rs changed 18 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 21 in datafusion_comet::parquet::schema_adapter::spark_has_updater at line 543. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/parquet/schema_adapter.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: dev/ci/check-ci-config.py dev/ci/check-ci-config.py:777— dev/ci/check-ci-config.py changed 22 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 16 in check-ci-config.check_event_policy at line 777. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- dev/ci/check-ci-config.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala:149— spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala changed 17 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 17 in CometNativeScan.isSupported at line 149. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:815— spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala changed 8 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 35 in FixedWidthArrowFieldWriter.copyValues at line 815. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/core/src/execution/planner/operator_registry.rs native/core/src/execution/planner/operator_registry.rs:216— native/core/src/execution/planner/operator_registry.rs changed 10 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 25 in datafusion_comet::execution::planner::operator_registry::get_operator_type at line 216. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/execution/planner/operator_registry.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometNativeShuffleWriter.scala spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometNativeShuffleWriter.scala:114— spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometNativeShuffleWriter.scala changed 16 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 15 in CometNativeShuffleWriter.writeInternal at line 114. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometNativeShuffleWriter.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: native/core/src/parquet/eager_page_index_reader_factory.rs native/core/src/parquet/eager_page_index_reader_factory.rs:661— native/core/src/parquet/eager_page_index_reader_factory.rs changed 11 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 21 in EagerPageIndexReader::get_metadata at line 661. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/parquet/eager_page_index_reader_factory.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegen.scala spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegen.scala:174— spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegen.scala changed 10 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 17 in CometBatchKernelCodegen.canHandle at line 174. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegen.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
datafusion_comet_spark_expr::math_funcs::abs::abs (cognitive 143) native/spark-expr/src/math_funcs/abs.rs:68— datafusion_comet_spark_expr::math_funcs::abs::abs has cognitive complexity 143 (threshold 15). Drivers by points: if/else 31 (75 pts), match/switch 20 (67 pts), boolean chains 1 (nesting depth added 91). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold (cognitive 90) native/spark-expr/src/string_funcs/levenshtein.rs:178— datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold has cognitive complexity 90 (threshold 15). Drivers by points: if/else 46 (78 pts), loops 6 (11 pts), boolean chains 1 (nesting depth added 37). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::string_funcs::get_json_object::parse_json_path (cognitive 70) native/spark-expr/src/string_funcs/get_json_object.rs:211— datafusion_comet_spark_expr::string_funcs::get_json_object::parse_json_path has cognitive complexity 70 (threshold 15). Drivers by points: if/else 17 (49 pts), loops 4 (13 pts), match/switch 2 (7 pts), boolean chains 1 (nesting depth added 46). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::csv_funcs::to_csv::to_csv_inner (cognitive 64) native/spark-expr/src/csv_funcs/to_csv.rs:118— datafusion_comet_spark_expr::csv_funcs::to_csv::to_csv_inner has cognitive complexity 64 (threshold 15). Drivers by points: if/else 14 (55 pts), boolean chains 5, loops 2 (4 pts) (nesting depth added 43). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
datafusion_comet_spark_expr::string_funcs::get_json_object::violates_jackson_limits (cognitive 49) native/spark-expr/src/string_funcs/get_json_object.rs:348— datafusion_comet_spark_expr::string_funcs::get_json_object::violates_jackson_limits has cognitive complexity 49 (threshold 15). Drivers by points: if/else 7 (20 pts), boolean chains 12, loops 4 (12 pts), match/switch 2 (5 pts) (nesting depth added 24). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::array_funcs::arrays_overlap::arrays_overlap_list_generic (cognitive 35) native/spark-expr/src/array_funcs/arrays_overlap.rs:545— datafusion_comet_spark_expr::array_funcs::arrays_overlap::arrays_overlap_list_generic has cognitive complexity 35 (threshold 15). Drivers by points: if/else 15 (29 pts), boolean chains 3, loops 2 (3 pts) (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::datetime_funcs::to_time::string_to_time (cognitive 34) native/spark-expr/src/datetime_funcs/to_time.rs:83— datafusion_comet_spark_expr::datetime_funcs::to_time::string_to_time has cognitive complexity 34 (threshold 15). Drivers by points: if/else 20 (28 pts), boolean chains 6 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::json_funcs::from_json::append_field_value (cognitive 33) native/spark-expr/src/json_funcs/from_json.rs:260— datafusion_comet_spark_expr::json_funcs::from_json::append_field_value has cognitive complexity 33 (threshold 15). Drivers by points: if/else 16 (25 pts), match/switch 3 (4 pts), loops 1 (3 pts), boolean chains 1 (nesting depth added 12). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance (cognitive 33) native/spark-expr/src/string_funcs/levenshtein.rs:72— datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance has cognitive complexity 33 (threshold 15). Drivers by points: if/else 13 (21 pts), loops 6 (11 pts), boolean chains 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::string_funcs::get_json_object::spark_get_json_object_impl (cognitive 32) native/spark-expr/src/string_funcs/get_json_object.rs:66— datafusion_comet_spark_expr::string_funcs::get_json_object::spark_get_json_object_impl has cognitive complexity 32 (threshold 15). Drivers by points: match/switch 9 (18 pts), if/else 5 (9 pts), loops 2 (4 pts), boolean chains 1 (nesting depth added 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet_spark_expr::array_funcs::array_insert::array_insert (cognitive 31) native/spark-expr/src/array_funcs/array_insert.rs:203— datafusion_comet_spark_expr::array_funcs::array_insert::array_insert has cognitive complexity 31 (threshold 15). Drivers by points: if/else 14 (29 pts), loops 1, match/switch 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner (cognitive 31) native/spark-expr/src/array_funcs/arrays_zip.rs:161— datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner has cognitive complexity 31 (threshold 15). Drivers by points: if/else 9 (19 pts), loops 4 (6 pts), match/switch 3 (6 pts) (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::conversion_funcs::string::date_parser (cognitive 29) native/spark-expr/src/conversion_funcs/string.rs:2104— datafusion_comet_spark_expr::conversion_funcs::string::date_parser has cognitive complexity 29 (threshold 15). Drivers by points: if/else 16 (20 pts), boolean chains 8, loops 1 (nesting depth added 4). Of this number, 22 points are the body's own statements and 7 belong to 3 function items inside it that branch. To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
datafusion_comet_spark_expr::json_funcs::to_json::struct_to_json (cognitive 29) native/spark-expr/src/json_funcs/to_json.rs:190— datafusion_comet_spark_expr::json_funcs::to_json::struct_to_json has cognitive complexity 29 (threshold 15). Drivers by points: if/else 8 (22 pts), loops 2 (4 pts), boolean chains 2, match/switch 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::math_funcs::div::spark_decimal_div_internal (cognitive 27) native/spark-expr/src/math_funcs/div.rs:72— datafusion_comet_spark_expr::math_funcs::div::spark_decimal_div_internal has cognitive complexity 27 (threshold 15). Drivers by points: if/else 15 (21 pts), boolean chains 5, match/switch 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::conditional_funcs::case_when::merge_bytes (cognitive 24) native/spark-expr/src/conditional_funcs/case_when.rs:791— datafusion_comet_spark_expr::conditional_funcs::case_when::merge_bytes has cognitive complexity 24 (threshold 15). Drivers by points: loops 5 (11 pts), if/else 4 (7 pts), match/switch 2 (5 pts), boolean chains 1 (nesting depth added 12). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
datafusion_comet_spark_expr::conversion_funcs::string::extract_offset_suffix (cognitive 23) native/spark-expr/src/conversion_funcs/string.rs:1628— datafusion_comet_spark_expr::conversion_funcs::string::extract_offset_suffix has cognitive complexity 23 (threshold 15). Drivers by points: if/else 10 (20 pts), loops 2, match/switch 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::kernels::temporal::timestamp_trunc_upstream (cognitive 22) native/spark-expr/src/kernels/temporal.rs:1109— datafusion_comet_spark_expr::kernels::temporal::timestamp_trunc_upstream has cognitive complexity 22 (threshold 15). Drivers by points: if/else 10 (13 pts), match/switch 3 (6 pts), boolean chains 2, loops 1 (nesting depth added 6). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
datafusion_comet_spark_expr::string_funcs::split::push_split_parts (cognitive 22) native/spark-expr/src/string_funcs/split.rs:419— datafusion_comet_spark_expr::string_funcs::split::push_split_parts has cognitive complexity 22 (threshold 15). Drivers by points: if/else 7 (12 pts), loops 4 (10 pts) (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::string_funcs::unbase64::decode (cognitive 22) native/spark-expr/src/string_funcs/unbase64.rs:158— datafusion_comet_spark_expr::string_funcs::unbase64::decode has cognitive complexity 22 (threshold 15). Drivers by points: if/else 7 (18 pts), loops 2, boolean chains 1, match/switch 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein (cognitive 21) native/spark-expr/src/string_funcs/levenshtein.rs:389— datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein has cognitive complexity 21 (threshold 15). Drivers by points: match/switch 8 (17 pts), if/else 2 (4 pts) (nesting depth added 11). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet_spark_expr::math_funcs::checked_arithmetic::checked_binary (cognitive 19) native/spark-expr/src/math_funcs/checked_arithmetic.rs:229— datafusion_comet_spark_expr::math_funcs::checked_arithmetic::checked_binary has cognitive complexity 19 (threshold 15). Drivers by points: if/else 5 (9 pts), loops 3 (6 pts), match/switch 2 (4 pts) (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::array_funcs::array_extrema::float_extrema (cognitive 18) native/spark-expr/src/array_funcs/array_extrema.rs:126— datafusion_comet_spark_expr::array_funcs::array_extrema::float_extrema has cognitive complexity 18 (threshold 15). Drivers by points: if/else 3 (10 pts), loops 2 (4 pts), match/switch 1 (4 pts) (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_spark_expr::array_funcs::array_extrema::take_extrema_values (cognitive 18) native/spark-expr/src/array_funcs/array_extrema.rs:187— datafusion_comet_spark_expr::array_funcs::array_extrema::take_extrema_values has cognitive complexity 18 (threshold 15). Drivers by points: match/switch 5 (10 pts), loops 3 (8 pts) (nesting depth added 10). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet_spark_expr::array_funcs::sort_array::sort_floats (cognitive 18) native/spark-expr/src/array_funcs/sort_array.rs:199— datafusion_comet_spark_expr::array_funcs::sort_array::sort_floats has cognitive complexity 18 (threshold 15). Drivers by points: if/else 5 (9 pts), loops 4 (9 pts) (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Repeated repair: spark/src/main/scala/org/apache/comet/CometExecIterator.scala spark/src/main/scala/org/apache/comet/CometExecIterator.scala:984— spark/src/main/scala/org/apache/comet/CometExecIterator.scala changed 27 times in last 90 days and 14 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 9 (its worst body is CometExecIterator.numDriverOrExecutorCores at line 984), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: make fair_unified's per-consumer limit bind when tasks run concurrently (#7096)”; “fix: size the native runtime for standalone executors without spark.executor.cores (#6368)”; “fix: initialize dispatched kernels with the partition the native plan computes (#6700)”; “fix: rethrow the exception a JVM input throws instead of a CometNativeException (#6243)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/CometExecIterator.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/core/src/parquet/parquet_exec.rs native/core/src/parquet/parquet_exec.rs:87— native/core/src/parquet/parquet_exec.rs changed 21 times in last 90 days and 12 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 14 (its worst body is datafusion_comet::parquet::parquet_exec::init_datasource_exec at line 87), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: preserve Spark errors for Parquet timestamp overflow (#5891)”; “fix: normalize float operands in the native comparison builder (#6447)”; “fix: initialize dispatched kernels with the partition the native plan computes (#6700)”; “fix: defer Parquet conversion errors until a row group is decoded, as Spark does (#6515)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/parquet/parquet_exec.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeWrite.scala spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeWrite.scala:353— spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeWrite.scala changed 18 times in last 90 days and 11 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is CometIcebergNativeWrite.effectiveHadoopConf at line 353), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: don't let S3A committer, delete and read defaults block native Iceberg writes (#6808)”; “fix: load the Iceberg storage scheme lists from the native factory (#6065)”; “fix: restore Spark write execs when reverting transition-heavy stages (#5957)”; “fix: gate unsupported Iceberg S3 write settings (#6441)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeWrite.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/core/src/execution/operators/iceberg_write.rs native/core/src/execution/operators/iceberg_write.rs:2070— native/core/src/execution/operators/iceberg_write.rs changed 17 times in last 90 days and 11 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 9 (its worst body is datafusion_comet::execution::operators::iceberg_write::drop_unwritten at line 2070), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: make fair_unified's per-consumer limit bind when tasks run concurrently (#7096)”; “fix: drop values under NULL parents before the native Iceberg writer counts NaNs (#6658)”; “fix: compact already-sliced batches before the native Iceberg writer counts NaNs (#6238)”; “fix Iceberg cleanup handoff (#6582)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/execution/operators/iceberg_write.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/serde/arrays.scala spark/src/main/scala/org/apache/comet/serde/arrays.scala:582— spark/src/main/scala/org/apache/comet/serde/arrays.scala changed 17 times in last 90 days and 9 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is CometArrayJoin.convert at line 582), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: evaluate the array_append item on rows whose array is NULL (#7025)”; “fix: preserve Spark nested null ordering in native sort (#6968)”; “fix: treat -0.0 and 0.0 as one value in arrays_overlap on Spark 4.0.5, 4.1.4 and 4.2.1 (#6989)”; “fix: skip an array or map function's later arguments where the array or map is NULL (#6716)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/serde/arrays.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/array_funcs/mod.rs native/spark-expr/src/array_funcs/mod.rs:61— native/spark-expr/src/array_funcs/mod.rs changed 13 times in last 90 days and 7 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 2 (its worst body is datafusion_comet_spark_expr::array_funcs::set_bits_before at line 61), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: preserve Spark nested null ordering in native sort (#6968)”; “fix: normalize float operands in the native comparison builder (#6447)”; “fix: match Spark's float ordering in min, max, greatest and least (#6457)”; “fix: match Spark's float semantics in array_remove and sort_array (#6518)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/array_funcs/mod.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/conversion_funcs/numeric.rs native/spark-expr/src/conversion_funcs/numeric.rs:1179— native/spark-expr/src/conversion_funcs/numeric.rs changed 11 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 13 (its worst body is datafusion_comet_spark_expr::conversion_funcs::numeric::spark_cast_nonintegral_numeric_to_integral at line 1179), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: build with Rust 1.99, which deprecates the legacy f64 constants and fetch_update (#6507)”; “fix: native ansi-errors raised as spark error (#5169)”; “fix: render float and double Iceberg partition values like iceberg-java (#5840)”; “fix: correctly rounded decimal to double/float cast matching BigDecimal.doubleValue/floatValue (#5684)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/conversion_funcs/numeric.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala:93— spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala changed 10 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 7 (its worst body is CometNativeScanExec.verboseStringWithOperatorId at line 93), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: keep unconverted DPP filters in a scan's canonical form (#6270)”; “fix: do not resolve DPP subqueries when AQE reads a native scan's partitioning (#6268)”; “fix: restore Spark write execs when reverting transition-heavy stages (#5957)”; “fix: key each scan's planning data by its plan node in a native block (#6297)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/jni-bridge/src/errors.rs native/jni-bridge/src/errors.rs:341— native/jni-bridge/src/errors.rs changed 9 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 13 (its worst body is CometError::to_exception at line 341), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: point the spill limit error at spark.comet.maxTempDirectorySize (#7098)”; “fix: log native panic backtraces instead of attaching them to the exception (#7042)”; “fix: convert native ReadAncientDatetime errors to SparkUpgradeException (#6559)”; “fix: reject a file without field ids at any depth whether or not id matching is on (#6116)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/jni-bridge/src/errors.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergNativeScanExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergNativeScanExec.scala:357— spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergNativeScanExec.scala changed 9 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 5 (its worst body is CometIcebergNativeScanExec.stringArgs at line 357), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: do not resolve DPP subqueries when AQE reads a native scan's partitioning (#6268)”; “fix: restore Spark write execs when reverting transition-heavy stages (#5957)”; “fix: key each scan's planning data by its plan node in a native block (#6297)”; “fix: report task input metrics after the native iterator closes and add to Spark's counters (#5880)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergNativeScanExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/core/src/execution/memory_pools/mod.rs native/core/src/execution/memory_pools/mod.rs:75— native/core/src/execution/memory_pools/mod.rs changed 8 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 3 (its worst body is datafusion_comet::execution::memory_pools::create_pool at line 75), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: make fair_unified's per-consumer limit bind when tasks run concurrently (#7096)”; “fix: let a spilled final aggregate read its spill files back past its memory share (#6544)”; “fix: count pool overcommit as untracked memory in the native memory usage log (#6271)”; “fix: return a native plan's memory before its Spark task ends (#6261)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/core/src/execution/memory_pools/mod.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/java/org/apache/spark/CometTaskMemoryManager.java spark/src/main/java/org/apache/spark/CometTaskMemoryManager.java:75— spark/src/main/java/org/apache/spark/CometTaskMemoryManager.java changed 7 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 4 (its worst body is CometTaskMemoryManager.acquireMemory at line 75), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: share the missing task entry retry and stop retrying it after the task has ended (#6813)”; “fix: retry a native acquire that Spark failed after dropping the task's entry (#6310)”; “fix: warn about unreleased native memory only after a task's last native plan closes (#6374)”; “fix: log partial memory grants at DEBUG and drop the memory usage dump (#6269)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/java/org/apache/spark/CometTaskMemoryManager.java`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/CometInMemoryTableScanExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometInMemoryTableScanExec.scala:138— spark/src/main/scala/org/apache/spark/sql/comet/CometInMemoryTableScanExec.scala changed 7 times in last 90 days and 6 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 6 (its worst body is CometInMemoryTableScanExec.doExecuteColumnar at line 138), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: draw the cached plan below CometInMemoryTableScan in the SQL tab and event log (#6577)”; “fix: draw the cached plan below CometInMemoryTableScan in EXPLAIN (#6574)”; “fix: keep Spark's cache scan for a relation whose cached plan records observed metrics (#6421)”; “fix: describe CometInMemoryTableScan by the scan it replaces in EXPLAIN (#6453)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometInMemoryTableScanExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergWriteExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergWriteExec.scala:379— spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergWriteExec.scala changed 8 times in last 90 days and 5 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 3 (its worst body is CometIcebergWriteExec.decodeLocations at line 379), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: restore Spark write execs when reverting transition-heavy stages (#5957)”; “fix Iceberg cleanup handoff (#6582)”; “chore: Fix Scala code warnings (#5876)”; “fix: Delete completed tasks' data files when an Iceberg write job fails (#5663)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometIcebergWriteExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/objectstore/NativeConfig.scala spark/src/main/scala/org/apache/comet/objectstore/NativeConfig.scala:338— spark/src/main/scala/org/apache/comet/objectstore/NativeConfig.scala changed 6 times in last 90 days and 5 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 12 (its worst body is NativeConfig.objectStoreOptionsAndAliasOptions at line 338), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “chore: fix stddev accumulator size and resolve session libhdfs schemes once per scan rule (#7120)”; “fix: read fs.comet.libhdfs.schemes for the scan rule from the Hadoop conf (#6838)”; “fix: read each native scan partition through the object store its files live in (#6753)”; “fix: resolve Azure ABFS authentication through Hadoop and keep ambient credentials out (#6059)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/objectstore/NativeConfig.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/hash_funcs/xxhash64.rs native/spark-expr/src/hash_funcs/xxhash64.rs:131— native/spark-expr/src/hash_funcs/xxhash64.rs changed 7 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 9 (its worst body is datafusion_comet_spark_expr::hash_funcs::xxhash64::type_compatible_with_spark_xxhash64 at line 131), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: hash NullType and ANSI interval values natively (#7084)”; “fix: hash NaN the way Spark does in hash, xxhash64 and native shuffle (#6413)”; “fix: apply the parent struct's null mask before hashing its fields (#5754)”; “fix: keep the dictionary hash fast path off nested and reseeded buffers (#5757)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/hash_funcs/xxhash64.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/java/org/apache/comet/NativeBase.java spark/src/main/java/org/apache/comet/NativeBase.java:282— spark/src/main/java/org/apache/comet/NativeBase.java changed 7 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 7 (its worst body is NativeBase.checkArch at line 282), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: extract bundled native libraries into private directories (#6987)”; “fix: load the Iceberg storage scheme lists from the native factory (#6065)”; “fix: load the bundled native library only once per class loader (#6493)”; “fix: warn when native and JVM timezone databases differ (#6353)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/java/org/apache/comet/NativeBase.java`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/serde/CometSortOrder.scala spark/src/main/scala/org/apache/comet/serde/CometSortOrder.scala:79— spark/src/main/scala/org/apache/comet/serde/CometSortOrder.scala changed 7 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is CometSortOrder.convert at line 79), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: preserve Spark nested null ordering in native sort (#6968)”; “fix: fall back for nested sort keys whose null elements Spark orders differently (#6683)”; “fix: fall back for RANGE frames over nested keys that can hold a null (#6684)”; “fix: match Spark's float ordering for sort and window keys nested in arrays and structs (#6475)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/serde/CometSortOrder.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/hash_funcs/murmur3.rs native/spark-expr/src/hash_funcs/murmur3.rs:28— native/spark-expr/src/hash_funcs/murmur3.rs changed 6 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 5 (its worst body is datafusion_comet_spark_expr::hash_funcs::murmur3::spark_murmur3_hash at line 28), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: hash NullType and ANSI interval values natively (#7084)”; “fix: hash NaN the way Spark does in hash, xxhash64 and native shuffle (#6413)”; “fix: apply the parent struct's null mask before hashing its fields (#5754)”; “fix: keep the dictionary hash fast path off nested and reseeded buffers (#5757)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/hash_funcs/murmur3.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/IcebergCommitExec.scala spark/src/main/scala/org/apache/spark/sql/comet/IcebergCommitExec.scala:75— spark/src/main/scala/org/apache/spark/sql/comet/IcebergCommitExec.scala changed 6 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 4 (its worst body is IcebergCommitExec.collectAndCommit at line 75), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(iceberg): preserve Spark 4.2 write transactions (#6587)”; “chore: Fix Scala code warnings (#5876)”; “fix: wrap Iceberg split-write failures the way Spark does when abort fails (#6153)”; “fix: Delete completed tasks' data files when an Iceberg write job fails (#5663)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/IcebergCommitExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/float_semantics/mod.rs native/spark-expr/src/float_semantics/mod.rs:107— native/spark-expr/src/float_semantics/mod.rs changed 6 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 6 (its worst body is datafusion_comet_spark_expr::float_semantics::compare_floats at line 107), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: normalize float operands in the native comparison builder (#6447)”; “fix: match Spark's float ordering in min, max, greatest and least (#6457)”; “fix: hash NaN the way Spark does in hash, xxhash64 and native shuffle (#6413)”; “fix: match Spark's float semantics in array_remove and sort_array (#6518)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/float_semantics/mod.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala:175— spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala changed 6 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 5 (its worst body is CometScalaUDFCodegen.lookupOrCompile at line 175), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: give each nondeterministic dispatched expression its own kernel (#6712)”; “fix: initialize dispatched kernels with the partition the native plan computes (#6700)”; “chore: Fix Scala code warnings (#5876)”; “fix: codegen dispatcher null short-circuit swallowed ANSI errors, plus two latent TIME-type gaps (#5219)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: native/spark-expr/src/jvm_udf/mod.rs native/spark-expr/src/jvm_udf/mod.rs:153— native/spark-expr/src/jvm_udf/mod.rs changed 6 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 6 (its worst body is JvmScalarUdfExpr::evaluate at line 153), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: initialize dispatched kernels with the partition the native plan computes (#6700)”; “fix: zero sliced boolean offsets at every level before exporting to the JVM (#6339)”; “fix: decode invalid UTF-8 at the JVM to native FFI import boundary (#5310)”; “fix: propagate the Spark task ClassLoader to JVM UDF calls (#5282)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- native/spark-expr/src/jvm_udf/mod.rs`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/comet/DataTypeSupport.scala spark/src/main/scala/org/apache/comet/DataTypeSupport.scala:50— spark/src/main/scala/org/apache/comet/DataTypeSupport.scala changed 5 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is DataTypeSupport.isTypeSupported at line 50), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(cast): fall back for duplicate struct field names (#6671)”; “fix: fall back when struct field names collide case-insensitively (#6326)”; “fix: fall back when a schema repeats a Parquet field id (#6004)”; “fix: decline structs with duplicate field names before they reach Java Arrow (#5866)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/comet/DataTypeSupport.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: spark/src/main/scala/org/apache/spark/sql/comet/CometNativeWriteExec.scala spark/src/main/scala/org/apache/spark/sql/comet/CometNativeWriteExec.scala:268— spark/src/main/scala/org/apache/spark/sql/comet/CometNativeWriteExec.scala changed 5 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is CometNativeWriteExec.prepareOutputPathForMode at line 268), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: run InsertIntoHadoopFsRelationCommand's post-write steps in the Spark 3.x native writer (#6821)”; “fix: honor Spark’s commit protocol in Spark 3.x native writes (#4746)”; “fix: restore Spark write execs when reverting transition-heavy stages (#5957)”; “chore: Fix Scala code warnings (#5876)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-27..2026-10-25, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-27 13:44:06 +00:00' --until='2026-10-25 13:44:06 +00:00' --full-history --no-merges -- spark/src/main/scala/org/apache/spark/sql/comet/CometNativeWriteExec.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
MethodTooLong: PhysicalPlanner.create_expr native/core/src/execution/planner.rs:626— MethodTooLong — create_expr runs 445 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 345 over it, 4.45× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.create_agg_expr native/core/src/execution/planner/aggregate.rs:314— MethodTooLong — create_agg_expr runs 358 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 258 over it, 3.58× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.create_plan native/core/src/execution/planner.rs:1615— MethodTooLong — create_plan runs 321 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 221 over it, 3.21× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: SparkError.params_as_json native/common/src/error.rs:415— MethodTooLong — params_as_json runs 221 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 121 over it, 2.21× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: RssPartitionWriter.estimated_ipc_metadata_scratch native/shuffle/src/writers/rss/rss_partition_writer.rs:390— MethodTooLong — estimated_ipc_metadata_scratch runs 164 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 64 over it, 1.64× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: HashAggBuilder.build native/core/src/execution/planner/aggregate.rs:80— MethodTooLong — build runs 159 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 59 over it, 1.59× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: CelebornShufflePartitionPusher.<init> spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:199— MethodTooLong — <init> runs 156 significant lines (blank, comment-only and punctuation-only lines excluded) in one body. The bar is 100 significant lines; this is 56 over it, 1.56× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.create_window_expr native/core/src/execution/planner/window.rs:305— MethodTooLong — create_window_expr runs 156 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 56 over it, 1.56× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: RssPartitionWriter.push_batch_within_limit native/shuffle/src/writers/rss/rss_partition_writer.rs:154— MethodTooLong — push_batch_within_limit runs 147 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 47 over it, 1.47× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.create_binary_expr_with_options native/core/src/execution/planner.rs:1359— MethodTooLong — create_binary_expr_with_options runs 130 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 30 over it, 1.30× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: RssPartitionWriter.compact_array native/shuffle/src/writers/rss/rss_partition_writer.rs:926— MethodTooLong — compact_array runs 128 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 28 over it, 1.28× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: MultiPartitionShuffleRepartitioner.partitioning_batch native/shuffle/src/partitioners/multi_partition.rs:263— MethodTooLong — partitioning_batch runs 126 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 26 over it, 1.26× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: MergeRowsBuilder.build native/core/src/execution/planner/merge_rows.rs:43— MethodTooLong — build runs 117 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 17 over it, 1.17× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: ColumnarToRowContext.write_column_fixed_width native/core/src/execution/columnar_to_row.rs:1136— MethodTooLong — write_column_fixed_width runs 115 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 15 over it, 1.15× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: CelebornShufflePartitionPusher.pushClaimedPartitionData spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:532— MethodTooLong — pushClaimedPartitionData runs 108 significant lines (blank, comment-only and punctuation-only lines excluded) in one body. The bar is 100 significant lines; this is 8 over it, 1.08× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: TypedElements.write_elements_slow native/core/src/execution/columnar_to_row.rs:685— MethodTooLong — write_elements_slow runs 108 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 8 over it, 1.08× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: ExplodeBuilder.build native/core/src/execution/planner/explode.rs:44— MethodTooLong — build runs 108 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 8 over it, 1.08× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.create_scalar_function_expr native/core/src/execution/planner.rs:2056— MethodTooLong — create_scalar_function_expr runs 107 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 7 over it, 1.07× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: PhysicalPlanner.parse_join_parameters native/core/src/execution/planner/join.rs:518— MethodTooLong — parse_join_parameters runs 105 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 5 over it, 1.05× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: WideDecimalBinaryExpr.evaluate native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:181— MethodTooLong — evaluate runs 105 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 5 over it, 1.05× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: SparkPhysicalExprAdapter.replace_with_spark_cast native/core/src/parquet/schema_adapter.rs:1413— MethodTooLong — replace_with_spark_cast runs 103 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 3 over it, 1.03× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
MethodTooLong: EagerPageIndexReader.get_metadata native/core/src/parquet/eager_page_index_reader_factory.rs:661— MethodTooLong — get_metadata runs 102 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 2 over it, 1.02× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
datafusion_comet_spark_expr::math_funcs::abs::abs (cyclomatic 54) native/spark-expr/src/math_funcs/abs.rs:68— datafusion_comet_spark_expr::math_funcs::abs::abs has cyclomatic complexity 54 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::conversion_funcs::cast::cast_array (cyclomatic 53) native/spark-expr/src/conversion_funcs/cast.rs:241— datafusion_comet_spark_expr::conversion_funcs::cast::cast_array has cyclomatic complexity 53 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold (cyclomatic 38) native/spark-expr/src/string_funcs/levenshtein.rs:178— datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold has cyclomatic complexity 38 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::string_funcs::get_json_object::violates_jackson_limits (cyclomatic 28) native/spark-expr/src/string_funcs/get_json_object.rs:348— datafusion_comet_spark_expr::string_funcs::get_json_object::violates_jackson_limits has cyclomatic complexity 28 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
datafusion_comet_spark_expr::json_funcs::from_json::append_field_value (cyclomatic 25) native/spark-expr/src/json_funcs/from_json.rs:260— datafusion_comet_spark_expr::json_funcs::from_json::append_field_value has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::conversion_funcs::string::date_parser (cyclomatic 24) native/spark-expr/src/conversion_funcs/string.rs:2104— datafusion_comet_spark_expr::conversion_funcs::string::date_parser has cyclomatic complexity 24 (threshold 15). Of this number, 18 points are the body's own statements and 6 belong to 3 function items inside it that branch. To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
datafusion_comet_spark_expr::string_funcs::get_json_object::parse_json_path (cyclomatic 22) native/spark-expr/src/string_funcs/get_json_object.rs:211— datafusion_comet_spark_expr::string_funcs::get_json_object::parse_json_path has cyclomatic complexity 22 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::kernels::temporal::timestamp_trunc_upstream (cyclomatic 21) native/spark-expr/src/kernels/temporal.rs:1109— datafusion_comet_spark_expr::kernels::temporal::timestamp_trunc_upstream has cyclomatic complexity 21 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
datafusion_comet_spark_expr::url_funcs::parse_url::parse_url_component (cyclomatic 21) native/spark-expr/src/url_funcs/parse_url.rs:120— datafusion_comet_spark_expr::url_funcs::parse_url::parse_url_component has cyclomatic complexity 21 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::datetime_funcs::to_time::string_to_time (cyclomatic 20) native/spark-expr/src/datetime_funcs/to_time.rs:83— datafusion_comet_spark_expr::datetime_funcs::to_time::string_to_time has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein (cyclomatic 20) native/spark-expr/src/string_funcs/levenshtein.rs:389— datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein has cyclomatic complexity 20 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::csv_funcs::to_csv::to_csv_inner (cyclomatic 19) native/spark-expr/src/csv_funcs/to_csv.rs:118— datafusion_comet_spark_expr::csv_funcs::to_csv::to_csv_inner has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::math_funcs::div::spark_decimal_div_internal (cyclomatic 19) native/spark-expr/src/math_funcs/div.rs:72— datafusion_comet_spark_expr::math_funcs::div::spark_decimal_div_internal has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner (cyclomatic 18) native/spark-expr/src/array_funcs/arrays_zip.rs:161— datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_spark_expr::math_funcs::checked_arithmetic::checked_arithmetic_internal (cyclomatic 18) native/spark-expr/src/math_funcs/checked_arithmetic.rs:322— datafusion_comet_spark_expr::math_funcs::checked_arithmetic::checked_arithmetic_internal has cyclomatic complexity 18 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::string_funcs::get_json_object::spark_get_json_object_impl (cyclomatic 18) native/spark-expr/src/string_funcs/get_json_object.rs:66— datafusion_comet_spark_expr::string_funcs::get_json_object::spark_get_json_object_impl has cyclomatic complexity 18 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::array_funcs::arrays_overlap::arrays_overlap_list_generic (cyclomatic 17) native/spark-expr/src/array_funcs/arrays_overlap.rs:545— datafusion_comet_spark_expr::array_funcs::arrays_overlap::arrays_overlap_list_generic has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This is NOT this file's highest cyclomatic complexity: datafusion_comet_spark_expr::array_funcs::arrays_overlap::arrays_overlap_rows (cyclomatic 23) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded function is counted neither in this dimension's figures nor in its score.
datafusion_comet_spark_expr::nested_compaction::has_excess_nested_capacity (cyclomatic 17) native/spark-expr/src/nested_compaction.rs:44— datafusion_comet_spark_expr::nested_compaction::has_excess_nested_capacity has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::static_invoke::char_varchar_utils::read_side_padding::spark_read_side_padding2 (cyclomatic 17) native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:45— datafusion_comet_spark_expr::static_invoke::char_varchar_utils::read_side_padding::spark_read_side_padding2 has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance (cyclomatic 17) native/spark-expr/src/string_funcs/levenshtein.rs:72— datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
FunctionTooLong: datafusion_comet::execution::planner::parse_file_scan_tasks_from_common native/core/src/execution/planner.rs:2386— FunctionTooLong — datafusion_comet::execution::planner::parse_file_scan_tasks_from_common runs 227 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 127 over it, 2.27× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::math_funcs::abs::abs native/spark-expr/src/math_funcs/abs.rs:68— FunctionTooLong — datafusion_comet_spark_expr::math_funcs::abs::abs runs 169 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 69 over it, 1.69× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major native/shuffle/src/spark_unsafe/row.rs:823— FunctionTooLong — datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major runs 165 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 65 over it, 1.65× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::conversion_funcs::cast::cast_array native/spark-expr/src/conversion_funcs/cast.rs:241— FunctionTooLong — datafusion_comet_spark_expr::conversion_funcs::cast::cast_array runs 165 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 65 over it, 1.65× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major native/shuffle/src/spark_unsafe/row.rs:373— FunctionTooLong — datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major runs 158 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 58 over it, 1.58× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_shuffle::spark_unsafe::row::append_columns native/shuffle/src/spark_unsafe/row.rs:1079— FunctionTooLong — datafusion_comet_shuffle::spark_unsafe::row::append_columns runs 154 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 54 over it, 1.54× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::comet_scalar_funcs::create_comet_physical_fun_with_eval_mode native/spark-expr/src/comet_scalar_funcs.rs:118— FunctionTooLong — datafusion_comet_spark_expr::comet_scalar_funcs::create_comet_physical_fun_with_eval_mode runs 154 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 54 over it, 1.54× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet::parquet::parquet_exec::init_datasource_exec native/core/src/parquet/parquet_exec.rs:87— FunctionTooLong — datafusion_comet::parquet::parquet_exec::init_datasource_exec runs 147 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 47 over it, 1.47× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_common::bin::analyze_trace::main native/common/src/bin/analyze_trace.rs:116— FunctionTooLong — datafusion_comet_common::bin::analyze_trace::main runs 143 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 43 over it, 1.43× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet::execution::planner::literal_to_array_ref native/core/src/execution/planner.rs:2766— FunctionTooLong — datafusion_comet::execution::planner::literal_to_array_ref runs 122 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 22 over it, 1.22× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner native/spark-expr/src/array_funcs/arrays_zip.rs:161— FunctionTooLong — datafusion_comet_spark_expr::array_funcs::arrays_zip::arrays_zip_inner runs 119 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 19 over it, 1.19× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::conversion_funcs::numeric::spark_cast_nonintegral_numeric_to_integral native/spark-expr/src/conversion_funcs/numeric.rs:1179— FunctionTooLong — datafusion_comet_spark_expr::conversion_funcs::numeric::spark_cast_nonintegral_numeric_to_integral runs 116 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 16 over it, 1.16× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::static_invoke::char_varchar_utils::read_side_padding::spark_read_side_padding2 native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:45— FunctionTooLong — datafusion_comet_spark_expr::static_invoke::char_varchar_utils::read_side_padding::spark_read_side_padding2 runs 113 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 13 over it, 1.13× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold native/spark-expr/src/string_funcs/levenshtein.rs:178— FunctionTooLong — datafusion_comet_spark_expr::string_funcs::levenshtein::levenshtein_distance_with_threshold runs 108 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 8 over it, 1.08× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein native/spark-expr/src/string_funcs/levenshtein.rs:389— FunctionTooLong — datafusion_comet_spark_expr::string_funcs::levenshtein::spark_levenshtein runs 104 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 4 over it, 1.04× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet::execution::delta_dv::attach_access_plan native/core/src/execution/delta_dv.rs:610— FunctionTooLong — datafusion_comet::execution::delta_dv::attach_access_plan runs 103 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 3 over it, 1.03× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
FunctionTooLong: datafusion_comet::execution::operators::iceberg_write::run_write_task native/core/src/execution/operators/iceberg_write.rs:975— FunctionTooLong — datafusion_comet::execution::operators::iceberg_write::run_write_task runs 102 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted) in one body. The bar is 100 significant lines; this is 2 over it, 1.02× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
datafusion_comet::execution::operators::dynamic_filter::early::place (cognitive 50) native/core/src/execution/operators/dynamic_filter/early.rs:60— datafusion_comet::execution::operators::dynamic_filter::early::place has cognitive complexity 50 (threshold 15). Drivers by points: if/else 17 (48 pts), boolean chains 2 (nesting depth added 31). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
datafusion_comet::parquet::cast_column::variant::append_spark_variant (cognitive 36) native/core/src/parquet/cast_column/variant.rs:870— datafusion_comet::parquet::cast_column::variant::append_spark_variant has cognitive complexity 36 (threshold 15). Drivers by points: if/else 9 (19 pts), loops 4 (12 pts), match/switch 3 (5 pts) (nesting depth added 20). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::parquet::cast_column::variant::rewrite_residual_values (cognitive 29) native/core/src/parquet/cast_column/variant.rs:314— datafusion_comet::parquet::cast_column::variant::rewrite_residual_values has cognitive complexity 29 (threshold 15). Drivers by points: if/else 13 (25 pts), boolean chains 3, loops 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::parquet::cast_column::variant::canonicalize_spark_empty_key_metadata (cognitive 26) native/core/src/parquet/cast_column/variant.rs:679— datafusion_comet::parquet::cast_column::variant::canonicalize_spark_empty_key_metadata has cognitive complexity 26 (threshold 15). Drivers by points: if/else 10 (23 pts), boolean chains 2, loops 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::execution::operators::dynamic_filter::parquet_reader::try_attach_parquet_reader_filter (cognitive 25) native/core/src/execution/operators/dynamic_filter/parquet_reader.rs:83— datafusion_comet::execution::operators::dynamic_filter::parquet_reader::try_attach_parquet_reader_filter has cognitive complexity 25 (threshold 15). Drivers by points: if/else 15 (20 pts), match/switch 2 (3 pts), boolean chains 1, loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::parquet::schema_adapter::spark_has_updater (cognitive 25) native/core/src/parquet/schema_adapter.rs:543— datafusion_comet::parquet::schema_adapter::spark_has_updater has cognitive complexity 25 (threshold 15). Drivers by points: if/else 12 (17 pts), boolean chains 5, match/switch 2 (3 pts) (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::execution::delta_dv::build_access_plan (cognitive 23) native/core/src/execution/delta_dv.rs:169— datafusion_comet::execution::delta_dv::build_access_plan has cognitive complexity 23 (threshold 15). Drivers by points: if/else 8 (16 pts), loops 2 (3 pts), match/switch 1 (3 pts), boolean chains 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::execution::planner::literal_to_array_ref (cognitive 23) native/core/src/execution/planner.rs:2766— datafusion_comet::execution::planner::literal_to_array_ref has cognitive complexity 23 (threshold 15). Drivers by points: if/else 8 (15 pts), loops 3 (6 pts), boolean chains 1, match/switch 1 (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet::execution::planner::parse_file_scan_tasks_from_common (cognitive 22) native/core/src/execution/planner.rs:2386— datafusion_comet::execution::planner::parse_file_scan_tasks_from_common has cognitive complexity 22 (threshold 15). Drivers by points: if/else 11 (13 pts), match/switch 4 (6 pts), loops 2 (3 pts) (nesting depth added 5). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
datafusion_comet::parquet::cast_column::variant::extend_shredded_metadata (cognitive 20) native/core/src/parquet/cast_column/variant.rs:604— datafusion_comet::parquet::cast_column::variant::extend_shredded_metadata has cognitive complexity 20 (threshold 15). Drivers by points: if/else 9 (17 pts), loops 2, match/switch 1 (nesting depth added 8). Of this number, 13 points are the body's own statements and 7 belong to one function item inside it that branches. To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::execution::operators::merge_rows::run_group (cognitive 19) native/core/src/execution/operators/merge_rows.rs:481— datafusion_comet::execution::operators::merge_rows::run_group has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7 (13 pts), loops 2 (3 pts), match/switch 1 (2 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::parquet::cast_column::variant::rewrite_shredding_state (cognitive 19) native/core/src/parquet/cast_column/variant.rs:210— datafusion_comet::parquet::cast_column::variant::rewrite_shredding_state has cognitive complexity 19 (threshold 15). Drivers by points: if/else 9 (13 pts), boolean chains 5, loops 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::parquet::datetime_rebase::push_unrequested_leaves (cognitive 19) native/core/src/parquet/datetime_rebase.rs:613— datafusion_comet::parquet::datetime_rebase::push_unrequested_leaves has cognitive complexity 19 (threshold 15). Drivers by points: if/else 3 (6 pts), match/switch 3 (6 pts), loops 2 (5 pts), boolean chains 2 (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet::execution::delta_dv::deserialize_dv_bitmap (cognitive 18) native/core/src/execution/delta_dv.rs:108— datafusion_comet::execution::delta_dv::deserialize_dv_bitmap has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (14 pts), loops 1 (2 pts), boolean chains 1, match/switch 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet::execution::serde::to_arrow_datatype (cognitive 16) native/core/src/execution/serde.rs:71— datafusion_comet::execution::serde::to_arrow_datatype has cognitive complexity 16 (threshold 15). Drivers by points: match/switch 5 (9 pts), if/else 2 (7 pts) (nesting depth added 9). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
datafusion_comet::parquet::parquet_exec::init_datasource_exec (cognitive 16) native/core/src/parquet/parquet_exec.rs:87— datafusion_comet::parquet::parquet_exec::init_datasource_exec has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9 (10 pts), boolean chains 3, match/switch 3 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
Duplicated block (8 lines × 2) native/shuffle/src/spark_unsafe/list.rs:398— native/shuffle/src/spark_unsafe/list.rs:398-405 | native/shuffle/src/spark_unsafe/row.rs:307-314 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (8 lines × 2) native/shuffle/src/spark_unsafe/row.rs:383— native/shuffle/src/spark_unsafe/row.rs:383-390 | native/shuffle/src/spark_unsafe/row.rs:854-861 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/agg_funcs/avg_decimal.rs:258— native/spark-expr/src/agg_funcs/avg_decimal.rs:258-265 | native/spark-expr/src/agg_funcs/avg_decimal.rs:416-423 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/agg_funcs/covariance.rs:421— native/spark-expr/src/agg_funcs/covariance.rs:421-428 | native/spark-expr/src/agg_funcs/variance.rs:362-369 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (8 lines × 2) native/spark-expr/src/agg_funcs/sum_decimal.rs:232— native/spark-expr/src/agg_funcs/sum_decimal.rs:232-239 | native/spark-expr/src/agg_funcs/sum_decimal.rs:402-409 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:522— native/spark-expr/src/agg_funcs/sum_int.rs:522-530 | native/spark-expr/src/agg_funcs/sum_int.rs:1017-1024 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/array_funcs/arrays_overlap.rs:352— native/spark-expr/src/array_funcs/arrays_overlap.rs:352-359 | native/spark-expr/src/array_funcs/arrays_overlap.rs:558-565 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/array_funcs/flatten.rs:118— native/spark-expr/src/array_funcs/flatten.rs:118-125 | native/spark-expr/src/array_funcs/flatten.rs:147-154 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/json_funcs/from_json.rs:240— native/spark-expr/src/json_funcs/from_json.rs:240-247 | native/spark-expr/src/json_funcs/from_json.rs:270-277 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/math_funcs/log.rs:76— native/spark-expr/src/math_funcs/log.rs:76-83 | native/spark-expr/src/math_funcs/log.rs:104-111 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/math_funcs/log.rs:95— native/spark-expr/src/math_funcs/log.rs:95-102 | native/spark-expr/src/math_funcs/log.rs:123-130 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:90— native/spark-expr/src/datetime_funcs/make_date.rs:90-97 | native/spark-expr/src/datetime_funcs/make_time.rs:164-171 — before extracting anything, compare `native/spark-expr/src/datetime_funcs/make_date.rs` and `native/spark-expr/src/datetime_funcs/make_time.rs` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 43 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (8 lines × 2) native/core/src/execution/planner/aggregate.rs:441— native/core/src/execution/planner/aggregate.rs:441-448 | native/core/src/execution/planner/aggregate.rs:453-460 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) native/core/src/execution/planner/aggregate.rs:344— native/core/src/execution/planner/aggregate.rs:344-351 | native/core/src/execution/planner/aggregate.rs:358-365 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:185— spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:185-192 | spark/src/main/scala/org/apache/spark/sql/comet/CometNativeColumnarToRowExec.scala:175-182 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (8 lines × 2) dev/ci/check-ci-config.py:704— dev/ci/check-ci-config.py:704-711 | dev/ci/check-ci-config.py:738-745 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/core/src/cloud/s3/credential_bridge.rs:244— native/core/src/cloud/s3/credential_bridge.rs:244-250 | native/core/src/cloud/s3/credential_bridge.rs:323-329 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/core/src/execution/columnar_to_row.rs:736— native/core/src/execution/columnar_to_row.rs:736-742 | native/core/src/execution/columnar_to_row.rs:751-757 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/core/src/execution/columnar_to_row.rs:766— native/core/src/execution/columnar_to_row.rs:766-772 | native/core/src/execution/columnar_to_row.rs:781-787 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/core/src/execution/columnar_to_row.rs:1062— native/core/src/execution/columnar_to_row.rs:1062-1068 | native/core/src/execution/columnar_to_row.rs:1072-1078 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/spark-expr/src/agg_funcs/avg.rs:286— native/spark-expr/src/agg_funcs/avg.rs:286-292 | native/spark-expr/src/agg_funcs/avg_decimal.rs:498-504 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (7 lines × 2) native/spark-expr/src/agg_funcs/correlation.rs:186— native/spark-expr/src/agg_funcs/correlation.rs:186-192 | native/spark-expr/src/agg_funcs/correlation.rs:206-212 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/spark-expr/src/conversion_funcs/string.rs:365— native/spark-expr/src/conversion_funcs/string.rs:365-371 | native/spark-expr/src/conversion_funcs/string.rs:428-434 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:165— native/spark-expr/src/datetime_funcs/make_date.rs:165-171 | native/spark-expr/src/datetime_funcs/make_time.rs:147-153 — before extracting anything, compare `native/spark-expr/src/datetime_funcs/make_date.rs` and `native/spark-expr/src/datetime_funcs/make_time.rs` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 43 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (7 lines × 2) native/spark-expr/src/hash_funcs/murmur3.rs:45— native/spark-expr/src/hash_funcs/murmur3.rs:45-51 | native/spark-expr/src/hash_funcs/xxhash64.rs:77-83 — before extracting anything, compare `native/spark-expr/src/hash_funcs/murmur3.rs` and `native/spark-expr/src/hash_funcs/xxhash64.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 32 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (7 lines × 2) native/spark-expr/src/conversion_funcs/string.rs:1220— native/spark-expr/src/conversion_funcs/string.rs:1220-1226 | native/spark-expr/src/conversion_funcs/utils.rs:132-138 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (7 lines × 2) native/spark-expr/src/agg_funcs/collect.rs:335— native/spark-expr/src/agg_funcs/collect.rs:335-341 | native/spark-expr/src/agg_funcs/collect.rs:681-687 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) native/shuffle/src/spark_unsafe/list.rs:169— native/shuffle/src/spark_unsafe/list.rs:169-175 | native/shuffle/src/spark_unsafe/map.rs:37-43 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (7 lines × 2) native/spark-expr/src/array_funcs/array_contains.rs:174— native/spark-expr/src/array_funcs/array_contains.rs:174-180 | native/spark-expr/src/array_funcs/array_contains.rs:207-213 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:201— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:201-207 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:201-207 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:201` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it.
Duplicated block (7 lines × 2) spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:194— spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:194-200 | spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:334-340 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) spark/src/main/java/org/apache/comet/vector/CometPlainVector.java:142— spark/src/main/java/org/apache/comet/vector/CometPlainVector.java:142-148 | spark/src/main/java/org/apache/comet/vector/CometPlainVector.java:174-180 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/shuffle/src/spark_unsafe/row.rs:401— native/shuffle/src/spark_unsafe/row.rs:401-409 | native/shuffle/src/spark_unsafe/row.rs:871-879 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/shuffle/src/spark_unsafe/row.rs:1767— native/shuffle/src/spark_unsafe/row.rs:1767-1775 | native/shuffle/src/spark_unsafe/row.rs:1786-1794 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/shuffle/src/writers/rss/rss_partition_writer.rs:670— native/shuffle/src/writers/rss/rss_partition_writer.rs:670-678 | native/shuffle/src/writers/rss/rss_partition_writer.rs:681-689 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/spark-expr/src/agg_funcs/collect.rs:313— native/spark-expr/src/agg_funcs/collect.rs:313-321 | native/spark-expr/src/agg_funcs/collect.rs:659-667 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/spark-expr/src/agg_funcs/sum_decimal.rs:563— native/spark-expr/src/agg_funcs/sum_decimal.rs:563-571 | native/spark-expr/src/agg_funcs/sum_int.rs:1029-1037 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (9 lines × 2) native/spark-expr/src/utils.rs:129— native/spark-expr/src/utils.rs:129-137 | native/spark-expr/src/utils.rs:141-149 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) native/spark-expr/src/string_funcs/get_json_object.rs:70— native/spark-expr/src/string_funcs/get_json_object.rs:70-79 | native/spark-expr/src/string_funcs/split.rs:120-128 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (9 lines × 2) native/spark-expr/src/math_funcs/signum.rs:28— native/spark-expr/src/math_funcs/signum.rs:28-36 | native/spark-expr/src/math_funcs/sqrt.rs:28-36 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (9 lines × 2) native/core/src/execution/operators/arrow_python_udf.rs:157— native/core/src/execution/operators/arrow_python_udf.rs:157-165 | native/core/src/execution/operators/arrow_python_udf.rs:171-179 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) spark/src/main/scala/org/apache/comet/serde/arrays.scala:52— spark/src/main/scala/org/apache/comet/serde/arrays.scala:52-60 | spark/src/main/scala/org/apache/comet/serde/arrays.scala:253-261 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/serde/arrays.scala:52` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 2) spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:733— spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:733-741 | spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:750-759 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:733` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometExecUtils.scala:109— spark/src/main/scala/org/apache/spark/sql/comet/CometExecUtils.scala:109-117 | spark/src/main/scala/org/apache/spark/sql/comet/CometExecUtils.scala:141-149 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometExecUtils.scala:109` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 2) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:46— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:46-54 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:46-54 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (10 lines × 2) native/common/src/utf8.rs:62— native/common/src/utf8.rs:62-71 | native/common/src/utf8.rs:103-112 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) native/shuffle/src/remote_schema.rs:90— native/shuffle/src/remote_schema.rs:90-99 | native/shuffle/src/remote_schema.rs:169-178 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:316— native/spark-expr/src/agg_funcs/sum_int.rs:316-325 | native/spark-expr/src/agg_funcs/sum_int.rs:387-396 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) native/spark-expr/src/array_funcs/array_contains.rs:127— native/spark-expr/src/array_funcs/array_contains.rs:127-136 | native/spark-expr/src/array_funcs/array_remove.rs:124-133 — before extracting anything, compare `native/spark-expr/src/array_funcs/array_contains.rs` and `native/spark-expr/src/array_funcs/array_remove.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 30 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (10 lines × 2) native/spark-expr/src/array_funcs/list_extract.rs:342— native/spark-expr/src/array_funcs/list_extract.rs:342-351 | native/spark-expr/src/array_funcs/list_extract.rs:375-384 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) native/spark-expr/src/conversion_funcs/string.rs:1049— native/spark-expr/src/conversion_funcs/string.rs:1049-1058 | native/spark-expr/src/conversion_funcs/string.rs:1136-1145 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) native/spark-expr/src/hash_funcs/murmur3.rs:149— native/spark-expr/src/hash_funcs/murmur3.rs:149-158 | native/spark-expr/src/hash_funcs/xxhash64.rs:177-186 — before extracting anything, compare `native/spark-expr/src/hash_funcs/murmur3.rs` and `native/spark-expr/src/hash_funcs/xxhash64.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 32 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (10 lines × 2) native/spark-expr/src/math_funcs/ceil.rs:44— native/spark-expr/src/math_funcs/ceil.rs:44-53 | native/spark-expr/src/math_funcs/floor.rs:44-53 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (10 lines × 2) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:619— spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:619-628 | spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenOutput.scala:425-434 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:619` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (10 lines × 2) spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:450— spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:450-459 | spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:468-477 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (10 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3144— spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3144-3153 | spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3365-3374 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3144` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (10 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3337— spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3337-3346 | spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3703-3712 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) native/core/src/execution/operators/iceberg_write.rs:858— native/core/src/execution/operators/iceberg_write.rs:858-863 | native/core/src/parquet/eager_page_index_reader_factory.rs:668-673 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (6 lines × 2) native/core/src/execution/operators/iceberg_write.rs:2004— native/core/src/execution/operators/iceberg_write.rs:2004-2009 | native/core/src/execution/operators/iceberg_write.rs:2022-2027 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) native/spark-expr/src/agg_funcs/covariance.rs:280— native/spark-expr/src/agg_funcs/covariance.rs:280-285 | native/spark-expr/src/agg_funcs/variance.rs:221-226 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (6 lines × 2) native/spark-expr/src/agg_funcs/sum_decimal.rs:419— native/spark-expr/src/agg_funcs/sum_decimal.rs:419-425 | native/spark-expr/src/agg_funcs/sum_decimal.rs:592-597 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) native/spark-expr/src/array_funcs/flatten.rs:130— native/spark-expr/src/array_funcs/flatten.rs:130-135 | native/spark-expr/src/array_funcs/flatten.rs:159-164 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) native/spark-expr/src/math_funcs/pow.rs:108— native/spark-expr/src/math_funcs/pow.rs:108-113 | native/spark-expr/src/math_funcs/pow.rs:120-125 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) native/spark-expr/src/math_funcs/ceil.rs:95— native/spark-expr/src/math_funcs/ceil.rs:95-100 | native/spark-expr/src/math_funcs/floor.rs:95-100 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (6 lines × 2) native/spark-expr/src/math_funcs/negative.rs:179— native/spark-expr/src/math_funcs/negative.rs:179-184 | native/spark-expr/src/nondetermenistic_funcs/shuffle.rs:129-134 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (6 lines × 2) native/core/src/execution/c_udf/mod.rs:109— native/core/src/execution/c_udf/mod.rs:109-114 | native/core/src/execution/c_udf/mod.rs:166-171 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) spark/src/main/scala/org/apache/comet/serde/conditional.scala:73— spark/src/main/scala/org/apache/comet/serde/conditional.scala:73-78 | spark/src/main/scala/org/apache/comet/serde/conditional.scala:113-118 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/serde/conditional.scala:73` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it.
Duplicated block (6 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3362— spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3362-3367 | spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3516-3521 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) spark/src/main/spark-4.0/org/apache/comet/shims/CometExprShim.scala:41— spark/src/main/spark-4.0/org/apache/comet/shims/CometExprShim.scala:41-46 | spark/src/main/spark-4.1+/org/apache/comet/shims/CometExprShim.scala:44-49 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-4.0/org/apache/comet/shims/CometExprShim.scala:41` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major (cognitive 222) native/shuffle/src/spark_unsafe/row.rs:823— datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major has cognitive complexity 222 (threshold 15). Drivers by points: if/else 64 (169 pts), loops 19 (51 pts), match/switch 1 (2 pts) (nesting depth added 138). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major (cognitive 218) native/shuffle/src/spark_unsafe/row.rs:373— datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major has cognitive complexity 218 (threshold 15). Drivers by points: if/else 62 (166 pts), loops 18 (50 pts), match/switch 1 (2 pts) (nesting depth added 137). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::append_field (cognitive 117) native/shuffle/src/spark_unsafe/row.rs:210— datafusion_comet_shuffle::spark_unsafe::row::append_field has cognitive complexity 117 (threshold 15). Drivers by points: if/else 66 (113 pts), loops 1 (2 pts), boolean chains 1, match/switch 1 (nesting depth added 48). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::append_columns (cognitive 111) native/shuffle/src/spark_unsafe/row.rs:1079— datafusion_comet_shuffle::spark_unsafe::row::append_columns has cognitive complexity 111 (threshold 15). Drivers by points: if/else 36 (73 pts), loops 17 (37 pts), match/switch 1 (nesting depth added 57). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::append_list_column_batch (cognitive 71) native/shuffle/src/spark_unsafe/row.rs:623— datafusion_comet_shuffle::spark_unsafe::row::append_list_column_batch has cognitive complexity 71 (threshold 15). Drivers by points: if/else 24 (47 pts), loops 12 (23 pts), match/switch 1 (nesting depth added 34). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::append_map_column_batch (cognitive 35) native/shuffle/src/spark_unsafe/row.rs:714— datafusion_comet_shuffle::spark_unsafe::row::append_map_column_batch has cognitive complexity 35 (threshold 15). Drivers by points: if/else 12 (23 pts), loops 6 (11 pts), match/switch 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::list::append_to_builder (cognitive 32) native/shuffle/src/spark_unsafe/list.rs:302— datafusion_comet_shuffle::spark_unsafe::list::append_to_builder has cognitive complexity 32 (threshold 15). Drivers by points: if/else 8 (15 pts), loops 6 (12 pts), boolean chains 4, match/switch 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::bin::shuffle_bench::main (cognitive 22) native/shuffle/src/bin/shuffle_bench.rs:130— datafusion_comet_shuffle::bin::shuffle_bench::main has cognitive complexity 22 (threshold 15). Drivers by points: if/else 13 (21 pts), loops 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::spark_unsafe::row::process_sorted_partitions (cognitive 17) native/shuffle/src/spark_unsafe/row.rs:1616— datafusion_comet_shuffle::spark_unsafe::row::process_sorted_partitions has cognitive complexity 17 (threshold 15). Drivers by points: if/else 6 (9 pts), loops 3 (6 pts), boolean chains 2 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_shuffle::bin::shuffle_bench::print_shuffle_metrics (cognitive 16) native/shuffle/src/bin/shuffle_bench.rs:263— datafusion_comet_shuffle::bin::shuffle_bench::print_shuffle_metrics has cognitive complexity 16 (threshold 15). Drivers by points: if/else 13 (16 pts) (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
datafusion_comet_shuffle::ipc::read_single_batch (cognitive 16) native/shuffle/src/ipc.rs:259— datafusion_comet_shuffle::ipc::read_single_batch has cognitive complexity 16 (threshold 15). Drivers by points: if/else 5 (10 pts), match/switch 3 (5 pts), loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Swallowed exception (caught, then discarded) spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:466— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:443— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:444— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:663— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:710— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/CometExecIterator.scala:608— `case NonFatal(_)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/CometExecIterator.scala:639— `case NonFatal(_)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:545— `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:1343— `case _: Throwable` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/codegen/CometArrayData.scala:93— `case _: Throwable` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) spark/src/main/scala/org/apache/comet/codegen/CometMapData.scala:48— `case _: Throwable` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
datafusion_comet::execution::serde::to_arrow_datatype (cyclomatic 28) native/core/src/execution/serde.rs:71— datafusion_comet::execution::serde::to_arrow_datatype has cyclomatic complexity 28 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet::execution::planner::literal_to_array_ref (cyclomatic 26) native/core/src/execution/planner.rs:2766— datafusion_comet::execution::planner::literal_to_array_ref has cyclomatic complexity 26 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet::parquet::schema_adapter::spark_has_updater (cyclomatic 21) native/core/src/parquet/schema_adapter.rs:543— datafusion_comet::parquet::schema_adapter::spark_has_updater has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet::execution::operators::dynamic_filter::early::place (cyclomatic 20) native/core/src/execution/operators/dynamic_filter/early.rs:60— datafusion_comet::execution::operators::dynamic_filter::early::place has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet::execution::operators::dynamic_filter::parquet_reader::try_attach_parquet_reader_filter (cyclomatic 20) native/core/src/execution/operators/dynamic_filter/parquet_reader.rs:83— datafusion_comet::execution::operators::dynamic_filter::parquet_reader::try_attach_parquet_reader_filter has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet::parquet::parquet_support::parquet_convert_array_impl (cyclomatic 19) native/core/src/parquet/parquet_support.rs:215— datafusion_comet::parquet::parquet_support::parquet_convert_array_impl has cyclomatic complexity 19 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet::parquet::cast_column::variant::append_spark_variant (cyclomatic 18) native/core/src/parquet/cast_column/variant.rs:870— datafusion_comet::parquet::cast_column::variant::append_spark_variant has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet::parquet::datetime_rebase::push_unrequested_leaves (cyclomatic 17) native/core/src/parquet/datetime_rebase.rs:613— datafusion_comet::parquet::datetime_rebase::push_unrequested_leaves has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
datafusion_comet::parquet::cast_column::variant::rewrite_shredding_state (cyclomatic 16) native/core/src/parquet/cast_column/variant.rs:210— datafusion_comet::parquet::cast_column::variant::rewrite_shredding_state has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet::parquet::cast_column::variant::rewrite_residual_values (cyclomatic 16) native/core/src/parquet/cast_column/variant.rs:314— datafusion_comet::parquet::cast_column::variant::rewrite_residual_values has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
Duplicated block (12 lines × 2) native/core/src/execution/columnar_to_row.rs:1609— native/core/src/execution/columnar_to_row.rs:1609-1620 | native/core/src/execution/columnar_to_row.rs:1659-1670 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/core/src/execution/operators/scan.rs:82— native/core/src/execution/operators/scan.rs:82-95 | native/core/src/execution/operators/shuffle_scan.rs:100-111 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (12 lines × 2) native/core/src/execution/planner.rs:1447— native/core/src/execution/planner.rs:1447-1458 | native/core/src/execution/planner.rs:1519-1530 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/core/src/parquet/cast_column/variant.rs:289— native/core/src/parquet/cast_column/variant.rs:289-301 | native/core/src/parquet/cast_column/variant.rs:500-511 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/shuffle/src/spark_unsafe/row.rs:490— native/shuffle/src/spark_unsafe/row.rs:490-501 | native/shuffle/src/spark_unsafe/row.rs:958-969 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:301— native/spark-expr/src/agg_funcs/sum_int.rs:301-312 | native/spark-expr/src/agg_funcs/sum_int.rs:372-383 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:656— native/spark-expr/src/agg_funcs/sum_int.rs:656-667 | native/spark-expr/src/agg_funcs/sum_int.rs:807-818 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12 lines × 2) native/spark-expr/src/string_funcs/regexp_extract.rs:46— native/spark-expr/src/string_funcs/regexp_extract.rs:46-57 | native/spark-expr/src/string_funcs/regexp_extract_all.rs:49-60 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (12 lines × 2) spark/src/main/spark-4.1/org/apache/spark/sql/comet/shims/ShimCometMapInBatch.scala:39— spark/src/main/spark-4.1/org/apache/spark/sql/comet/shims/ShimCometMapInBatch.scala:39-50 | spark/src/main/spark-4.2/org/apache/spark/sql/comet/shims/ShimCometMapInBatch.scala:39-50 — before extracting anything, compare `spark/src/main/spark-4.1/org/apache/spark/sql/comet/shims/ShimCometMapInBatch.scala` and `spark/src/main/spark-4.2/org/apache/spark/sql/comet/shims/ShimCometMapInBatch.scala` as WHOLE FILES: 100% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (11 lines × 2) native/core/src/parquet/cast_column/variant.rs:266— native/core/src/parquet/cast_column/variant.rs:266-276 | native/core/src/parquet/cast_column/variant.rs:289-299 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11 lines × 2) native/spark-expr/src/agg_funcs/correlation.rs:97— native/spark-expr/src/agg_funcs/correlation.rs:97-107 | native/spark-expr/src/agg_funcs/covariance.rs:103-113 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (11 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:329— native/spark-expr/src/agg_funcs/sum_int.rs:329-339 | native/spark-expr/src/agg_funcs/sum_int.rs:400-410 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11 lines × 2) native/spark-expr/src/conversion_funcs/cast.rs:791— native/spark-expr/src/conversion_funcs/cast.rs:791-801 | native/spark-expr/src/math_funcs/checked_binary_expr.rs:104-114 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (11 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:139— native/spark-expr/src/datetime_funcs/make_date.rs:139-149 | native/spark-expr/src/datetime_funcs/make_time.rs:119-129 — before extracting anything, compare `native/spark-expr/src/datetime_funcs/make_date.rs` and `native/spark-expr/src/datetime_funcs/make_time.rs` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 43 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (11 lines × 2) spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:486— spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:486-496 | spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:505-515 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:542— spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:542-552 | spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:605-615 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:542` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (11 lines × 2) spark/src/main/spark-3.4/org/apache/comet/shims/ShimCometSparkSessionExtensions.scala:30— spark/src/main/spark-3.4/org/apache/comet/shims/ShimCometSparkSessionExtensions.scala:30-40 | spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometSparkSessionExtensions.scala:30-40 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (11 lines × 2) spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java:475— spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java:475-485 | spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java:544-554 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java:475` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (5 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:120— native/spark-expr/src/datetime_funcs/make_date.rs:120-125 | native/spark-expr/src/datetime_funcs/make_time.rs:104-108 — before extracting anything, compare `native/spark-expr/src/datetime_funcs/make_date.rs` and `native/spark-expr/src/datetime_funcs/make_time.rs` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 43 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (5 lines × 2) native/spark-expr/src/iceberg_funcs/temporal.rs:135— native/spark-expr/src/iceberg_funcs/temporal.rs:135-139 | native/spark-expr/src/kernels/temporal.rs:409-413 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (5 lines × 2) native/spark-expr/src/string_funcs/levenshtein.rs:415— native/spark-expr/src/string_funcs/levenshtein.rs:415-419 | native/spark-expr/src/string_funcs/levenshtein.rs:487-491 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (5 lines × 2) native/spark-expr/src/conversion_funcs/cast.rs:90— native/spark-expr/src/conversion_funcs/cast.rs:90-94 | native/spark-expr/src/math_funcs/internal/checkoverflow.rs:57-61 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (5 lines × 2) native/comet-udf-sdk/src/c_abi.rs:505— native/comet-udf-sdk/src/c_abi.rs:505-509 | native/comet-udf-sdk/src/c_abi.rs:562-566 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (5 lines × 2) native/core/src/execution/planner.rs:406— native/core/src/execution/planner.rs:406-410 | native/core/src/parquet/parquet_exec.rs:199-203 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (5 lines × 2) native/shuffle/src/writers/rss/rss_partition_writer.rs:711— native/shuffle/src/writers/rss/rss_partition_writer.rs:711-715 | native/shuffle/src/writers/rss/rss_partition_writer.rs:886-890 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (5 lines × 2) spark/src/main/spark-4.1/org/apache/spark/sql/execution/python/CometArrowPythonRunner.scala:63— spark/src/main/spark-4.1/org/apache/spark/sql/execution/python/CometArrowPythonRunner.scala:63-67 | spark/src/main/spark-4.2/org/apache/spark/sql/execution/python/CometArrowPythonRunner.scala:71-75 — before extracting anything, compare `spark/src/main/spark-4.1/org/apache/spark/sql/execution/python/CometArrowPythonRunner.scala` and `spark/src/main/spark-4.2/org/apache/spark/sql/execution/python/CometArrowPythonRunner.scala` as WHOLE FILES: 88% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (5 lines × 2) spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometBypassMergeSortShuffleWriter.java:46— spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometBypassMergeSortShuffleWriter.java:46-50 | spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java:56-60 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (13 lines × 2) native/core/src/execution/columnar_to_row.rs:1634— native/core/src/execution/columnar_to_row.rs:1634-1646 | native/core/src/execution/columnar_to_row.rs:1718-1730 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 2) native/core/src/execution/columnar_to_row.rs:1744— native/core/src/execution/columnar_to_row.rs:1744-1756 | native/core/src/execution/columnar_to_row.rs:1770-1782 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 2) native/spark-expr/src/conversion_funcs/string.rs:1069— native/spark-expr/src/conversion_funcs/string.rs:1069-1081 | native/spark-expr/src/conversion_funcs/string.rs:1149-1161 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 2) native/spark-expr/src/math_funcs/abs.rs:73— native/spark-expr/src/math_funcs/abs.rs:73-85 | native/spark-expr/src/math_funcs/unhex.rs:151-163 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (13 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:418— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:418-430 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:438-450 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:437` calls `createSizeMetric` and `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:417` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (13 lines × 2) spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala:34— spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala:34-46 | spark/src/main/spark-4.x/org/apache/comet/serde/CometToPrettyString.scala:34-46 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala` and `spark/src/main/spark-4.x/org/apache/comet/serde/CometToPrettyString.scala` as WHOLE FILES: 100% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 2 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (13 lines × 2) spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala:52— spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala:52-64 | spark/src/main/spark-4.x/org/apache/comet/serde/CometToPrettyString.scala:52-64 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/CometToPrettyString.scala` and `spark/src/main/spark-4.x/org/apache/comet/serde/CometToPrettyString.scala` as WHOLE FILES: 100% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 2 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (13 lines × 2) spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometBroadcastExchangeExec.scala:31— spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometBroadcastExchangeExec.scala:31-43 | spark/src/main/spark-4.x/org/apache/comet/shims/ShimCometBroadcastExchangeExec.scala:31-43 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometBroadcastExchangeExec.scala` and `spark/src/main/spark-4.x/org/apache/comet/shims/ShimCometBroadcastExchangeExec.scala` as WHOLE FILES: 100% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major (cyclomatic 68) native/shuffle/src/spark_unsafe/row.rs:823— datafusion_comet_shuffle::spark_unsafe::row::append_struct_fields_field_major has cyclomatic complexity 68 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major (cyclomatic 66) native/shuffle/src/spark_unsafe/row.rs:373— datafusion_comet_shuffle::spark_unsafe::row::append_nested_struct_fields_field_major has cyclomatic complexity 66 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_shuffle::spark_unsafe::row::append_field (cyclomatic 53) native/shuffle/src/spark_unsafe/row.rs:210— datafusion_comet_shuffle::spark_unsafe::row::append_field has cyclomatic complexity 53 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_shuffle::spark_unsafe::row::append_columns (cyclomatic 53) native/shuffle/src/spark_unsafe/row.rs:1079— datafusion_comet_shuffle::spark_unsafe::row::append_columns has cyclomatic complexity 53 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_shuffle::spark_unsafe::row::append_list_column_batch (cyclomatic 35) native/shuffle/src/spark_unsafe/row.rs:623— datafusion_comet_shuffle::spark_unsafe::row::append_list_column_batch has cyclomatic complexity 35 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_shuffle::spark_unsafe::list::append_to_builder (cyclomatic 32) native/shuffle/src/spark_unsafe/list.rs:302— datafusion_comet_shuffle::spark_unsafe::list::append_to_builder has cyclomatic complexity 32 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
datafusion_comet_shuffle::spark_unsafe::row::append_map_column_batch (cyclomatic 17) native/shuffle/src/spark_unsafe/row.rs:714— datafusion_comet_shuffle::spark_unsafe::row::append_map_column_batch has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This is NOT this file's highest cyclomatic complexity: datafusion_comet_shuffle::spark_unsafe::row::make_builders (cyclomatic 20) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded function is counted neither in this dimension's figures nor in its score.
D4 · Code Duplication· Members sharing a duplicated core (4 members, 50+ identical tokens) · ×6
Members sharing a duplicated core (4 members, 50+ identical tokens) native/spark-expr/src/agg_funcs/correlation.rs:220— native/spark-expr/src/agg_funcs/correlation.rs:220-244 | native/spark-expr/src/agg_funcs/correlation.rs:362-389 | native/spark-expr/src/agg_funcs/regr.rs:354-376 | native/spark-expr/src/agg_funcs/regr.rs:477-492 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) native/spark-expr/src/array_funcs/array_contains.rs:65— native/spark-expr/src/array_funcs/array_contains.rs:65-91 | native/spark-expr/src/array_funcs/array_remove.rs:67-90 | native/spark-expr/src/array_funcs/sort_array.rs:75-103 | native/spark-expr/src/array_funcs/sort_array.rs:141-170 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) native/spark-expr/src/math_funcs/checked_arithmetic.rs:327— native/spark-expr/src/math_funcs/checked_arithmetic.rs:327-429 | native/spark-expr/src/math_funcs/div.rs:79-178 | native/spark-expr/src/math_funcs/modulo_expr.rs:90-119 | native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:181-336 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) native/spark-expr/src/string_funcs/split.rs:319— native/spark-expr/src/string_funcs/split.rs:319-341 | native/spark-expr/src/string_funcs/split.rs:346-364 | native/spark-expr/src/string_funcs/split.rs:369-387 | native/spark-expr/src/string_funcs/split.rs:392-414 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:371— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:371-498 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:504-521 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:524-546 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:551-565 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) spark/src/main/spark-3.5/org/apache/comet/iceberg/IcebergDeltaLogicalShim.scala:28— spark/src/main/spark-3.5/org/apache/comet/iceberg/IcebergDeltaLogicalShim.scala:28-46 | spark/src/main/spark-4.0/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 | spark/src/main/spark-4.1/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 | spark/src/main/spark-4.2/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:252— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CometNativeArrowSource.scala:335— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/comet/udf/codegen/CometScalaUDFCodegen.scala:168— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/comet/objectstore/AbfsAuthResolver.scala:328— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala:329— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenOutput.scala:122— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
PhysicalPlanner::create_expr (cyclomatic 85) native/core/src/execution/planner.rs:626— PhysicalPlanner::create_expr has cyclomatic complexity 85 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
PhysicalPlanner::create_agg_expr (cyclomatic 44) native/core/src/execution/planner/aggregate.rs:314— PhysicalPlanner::create_agg_expr has cyclomatic complexity 44 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
PhysicalPlanner::create_plan (cyclomatic 33) native/core/src/execution/planner.rs:1615— PhysicalPlanner::create_plan has cyclomatic complexity 33 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
PhysicalPlanner::create_window_expr (cyclomatic 29) native/core/src/execution/planner/window.rs:305— PhysicalPlanner::create_window_expr has cyclomatic complexity 29 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
PhysicalPlanner::create_binary_expr_with_options (cyclomatic 25) native/core/src/execution/planner.rs:1359— PhysicalPlanner::create_binary_expr_with_options has cyclomatic complexity 25 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
TooManyMethods: IcebergReflection spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala:40— TooManyMethods — 116 methods. The bar is 30 methods; this is 86 over it, 3.87× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: DeltaScanSupport contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:52— TooManyMethods — 63 methods. The bar is 30 methods; this is 33 over it, 2.10× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: CelebornShufflePartitionPusher spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:43— TooManyMethods — 50 methods. The bar is 30 methods; this is 20 over it, 1.67× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: PhysicalPlanner native/core/src/execution/planner.rs:179— TooManyMethods — 41 methods, declared across 5 files: execution/planner.rs (29), planner/join.rs (5), planner/aggregate.rs (3), planner/window.rs (3), +1 more file(s). The bar is 30 methods; this is 11 over it, 1.37× the bar. That list is where to read them, not a suggestion to split the file: the members belong to the type wherever they are declared, so moving them between files leaves the count unchanged. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: QueryPlanSerde spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:56— TooManyMethods — 32 methods. The bar is 30 methods; this is 2 over it, 1.07× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
ClassTooLong: PhysicalPlanner native/core/src/execution/planner.rs:179— ClassTooLong — 1415 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), 41 methods, 3 blocks, lines 179-2239. The bar is 400 significant lines; this is 1015 over it, 3.54× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
ClassTooLong: CelebornTransportCallbackTracker spark/src/main/java/org/apache/comet/shuffle/CelebornTransportCallbackTracker.java:55— ClassTooLong — 896 significant lines (blank, comment-only and punctuation-only lines excluded), 15 methods. The bar is 400 significant lines; this is 496 over it, 2.24× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
ClassTooLong: CelebornShufflePartitionPusher spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:43— ClassTooLong — 869 significant lines (blank, comment-only and punctuation-only lines excluded), 50 methods. The bar is 400 significant lines; this is 469 over it, 2.17× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
ClassTooLong: RssPartitionWriter native/shuffle/src/writers/rss/rss_partition_writer.rs:48— ClassTooLong — 802 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), 17 methods, 3 blocks, lines 48-1133. The bar is 400 significant lines; this is 402 over it, 2.01× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
ClassTooLong: SparkError native/common/src/error.rs:23— ClassTooLong — 586 significant lines (blank, comment-only and punctuation-only lines excluded, and inline test code — #[cfg(test)] modules and bare #[test] functions — not counted), 7 methods, 3 blocks, lines 23-1040. The bar is 400 significant lines; this is 186 over it, 1.47× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
Duplicated block (6 lines × 3) native/spark-expr/src/agg_funcs/covariance.rs:49— native/spark-expr/src/agg_funcs/covariance.rs:49-54 | native/spark-expr/src/agg_funcs/stddev.rs:48-53 | native/spark-expr/src/agg_funcs/variance.rs:48-53 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once.
Duplicated block (6 lines × 3) spark/src/main/scala/org/apache/comet/serde/strings.scala:716— spark/src/main/scala/org/apache/comet/serde/strings.scala:716-721 | spark/src/main/scala/org/apache/comet/serde/structs.scala:137-142 | spark/src/main/scala/org/apache/comet/serde/structs.scala:206-211 — there are 3 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 3 sites; resolving a subset leaves the remainder to drift apart. Note first that the copies are not typed on the same thing: the declarations holding them bind `expr` to `RegExpReplace): SupportLevel = if (!CometConf.isExprAllowIncompat(getExprConfigName(expr)) && nativeSupported(expr)` in one and `StructsToJson): SupportLevel = if (!CometConf.isExprAllowIncompat(getExprConfigName(expr)) && nativeSupported(expr)` in another, and the duplicated lines use it. The extracted unit therefore needs a parameter type that fits BOTH — their common supertype where they have one, or a new abstraction over them where they do not — and settling that is the step that comes BEFORE the extraction above. Where the two types are deliberately unrelated, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (6 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:186— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:186-191 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:186-191 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:203-208 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (6 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:236— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:236-241 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:236-241 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:272-277 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (6 lines × 3) spark/src/main/java/org/apache/comet/vector/CometListVector.java:58— spark/src/main/java/org/apache/comet/vector/CometListVector.java:58-63 | spark/src/main/java/org/apache/comet/vector/CometMapVector.java:79-84 | spark/src/main/java/org/apache/comet/vector/CometStructVector.java:62-67 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
PhysicalPlanner::create_expr (cognitive 73) native/core/src/execution/planner.rs:626— PhysicalPlanner::create_expr has cognitive complexity 73 (threshold 15). Drivers by points: if/else 22 (48 pts), match/switch 8 (23 pts), boolean chains 2 (nesting depth added 41). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
PhysicalPlanner::create_window_expr (cognitive 42) native/core/src/execution/planner/window.rs:305— PhysicalPlanner::create_window_expr has cognitive complexity 42 (threshold 15). Drivers by points: match/switch 15 (27 pts), if/else 9 (15 pts) (nesting depth added 18). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
PhysicalPlanner::create_plan (cognitive 39) native/core/src/execution/planner.rs:1615— PhysicalPlanner::create_plan has cognitive complexity 39 (threshold 15). Drivers by points: if/else 17 (28 pts), match/switch 4 (8 pts), boolean chains 3 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PhysicalPlanner::create_binary_expr_with_options (cognitive 31) native/core/src/execution/planner.rs:1359— PhysicalPlanner::create_binary_expr_with_options has cognitive complexity 31 (threshold 15). Drivers by points: if/else 10 (16 pts), match/switch 4 (8 pts), boolean chains 7 (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
Duplicated block (10 lines × 3) native/shuffle/src/spark_unsafe/row.rs:933— native/shuffle/src/spark_unsafe/row.rs:933-942 | native/shuffle/src/spark_unsafe/row.rs:951-960 | native/shuffle/src/spark_unsafe/row.rs:971-980 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (10 lines × 3) native/spark-expr/src/agg_funcs/avg.rs:257— native/spark-expr/src/agg_funcs/avg.rs:257-266 | native/spark-expr/src/agg_funcs/avg_decimal.rs:470-479 | native/spark-expr/src/agg_funcs/sum_decimal.rs:454-463 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once.
Duplicated block (10 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:110— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:110-119 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:110-119 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:126-135 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:110` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:137` calls `setSemanticMetricsRequired` and `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:121` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (10 lines × 3) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1013— spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1013-1022 | spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:66-75 | spark/src/main/scala/org/apache/comet/rules/EliminateRedundantTransitions.scala:68-77 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1013` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 3) native/spark-expr/src/agg_funcs/hll_plus_plus.rs:389— native/spark-expr/src/agg_funcs/hll_plus_plus.rs:389-397 | native/spark-expr/src/agg_funcs/max_min_by.rs:355-363 | native/spark-expr/src/agg_funcs/percentile.rs:205-213 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once.
Duplicated block (9 lines × 3) native/spark-expr/src/agg_funcs/sum_int.rs:328— native/spark-expr/src/agg_funcs/sum_int.rs:328-336 | native/spark-expr/src/agg_funcs/sum_int.rs:399-407 | native/spark-expr/src/agg_funcs/sum_int.rs:488-496 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (9 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:60— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:60-68 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:60-68 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:70-78 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:60` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:79` calls `mergeActionContext` and `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:69` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (9 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:57— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:57-65 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:57-65 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:53-61 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
datafusion_comet_common::bin::analyze_trace::main (cyclomatic 29) native/common/src/bin/analyze_trace.rs:116— datafusion_comet_common::bin::analyze_trace::main has cyclomatic complexity 29 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_common::utils::decode_long_or_non_ascii_utf8_spark_lossy (cyclomatic 28) native/common/src/utils.rs:89— datafusion_comet_common::utils::decode_long_or_non_ascii_utf8_spark_lossy has cyclomatic complexity 28 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
datafusion_comet_common::utf8::decode_string_arrays (cyclomatic 16) native/common/src/utf8.rs:32— datafusion_comet_common::utf8::decode_string_arrays has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
RssPartitionWriter::compact_array (cyclomatic 23) native/shuffle/src/writers/rss/rss_partition_writer.rs:926— RssPartitionWriter::compact_array has cyclomatic complexity 23 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
RssPartitionWriter::push_batch_within_limit (cyclomatic 19) native/shuffle/src/writers/rss/rss_partition_writer.rs:154— RssPartitionWriter::push_batch_within_limit has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
RssPartitionWriter::estimated_array_compaction_scratch (cyclomatic 17) native/shuffle/src/writers/rss/rss_partition_writer.rs:798— RssPartitionWriter::estimated_array_compaction_scratch has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
ShimSparkErrorConverter.getDataType (cyclomatic 18) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:415— ShimSparkErrorConverter.getDataType has cyclomatic complexity 18 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
ShimSparkErrorConverter.getDataType (cyclomatic 18) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:414— ShimSparkErrorConverter.getDataType has cyclomatic complexity 18 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
ShimSparkErrorConverter.getDataType (cyclomatic 18) spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:437— ShimSparkErrorConverter.getDataType has cyclomatic complexity 18 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
datafusion_comet_common::utils::decode_long_or_non_ascii_utf8_spark_lossy (cognitive 58) native/common/src/utils.rs:89— datafusion_comet_common::utils::decode_long_or_non_ascii_utf8_spark_lossy has cognitive complexity 58 (threshold 15). Drivers by points: if/else 27 (50 pts), boolean chains 7, loops 1 (nesting depth added 23). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_common::bin::analyze_trace::main (cognitive 53) native/common/src/bin/analyze_trace.rs:116— datafusion_comet_common::bin::analyze_trace::main has cognitive complexity 53 (threshold 15). Drivers by points: if/else 25 (42 pts), match/switch 2 (5 pts), loops 3 (4 pts), boolean chains 2 (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_common::utf8::decode_string_arrays (cognitive 16) native/common/src/utf8.rs:32— datafusion_comet_common::utf8::decode_string_arrays has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6 (11 pts), loops 2 (4 pts), match/switch 1 (nesting depth added 7). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
RssPartitionWriter::push_batch_within_limit (cognitive 35) native/shuffle/src/writers/rss/rss_partition_writer.rs:154— RssPartitionWriter::push_batch_within_limit has cognitive complexity 35 (threshold 15). Drivers by points: if/else 13 (26 pts), match/switch 3 (6 pts), boolean chains 2, loops 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
RssPartitionWriter::compact_array (cognitive 32) native/shuffle/src/writers/rss/rss_partition_writer.rs:926— RssPartitionWriter::compact_array has cognitive complexity 32 (threshold 15). Drivers by points: if/else 15 (25 pts), boolean chains 6, match/switch 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
RssPartitionWriter::estimated_array_compaction_scratch (cognitive 16) native/shuffle/src/writers/rss/rss_partition_writer.rs:798— RssPartitionWriter::estimated_array_compaction_scratch has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7 (10 pts), boolean chains 3, match/switch 2 (3 pts) (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ShimSparkErrorConverter.getDataType (cognitive 19) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:415— ShimSparkErrorConverter.getDataType has cognitive complexity 19 (threshold 15). Drivers by points: if/else 5 (10 pts), error handling 2 (7 pts), boolean chains 1, match/switch 1 (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
ShimSparkErrorConverter.getDataType (cognitive 19) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:414— ShimSparkErrorConverter.getDataType has cognitive complexity 19 (threshold 15). Drivers by points: if/else 5 (10 pts), error handling 2 (7 pts), boolean chains 1, match/switch 1 (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
ShimSparkErrorConverter.getDataType (cognitive 19) spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:437— ShimSparkErrorConverter.getDataType has cognitive complexity 19 (threshold 15). Drivers by points: if/else 5 (10 pts), error handling 2 (7 pts), boolean chains 1, match/switch 1 (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D30 · Dependency Vulnerabilities· Medium advisory (unmaintained) · ×3
Duplicated block (20 lines × 2) native/spark-expr/src/agg_funcs/covariance.rs:198— native/spark-expr/src/agg_funcs/covariance.rs:198-217 | native/spark-expr/src/agg_funcs/covariance.rs:240-259 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (20 lines × 2) spark/src/main/spark-3.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala:41— spark/src/main/spark-3.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala:41-60 | spark/src/main/spark-4.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala:41-60 — before extracting anything, compare `spark/src/main/spark-3.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala` and `spark/src/main/spark-4.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala` as WHOLE FILES: 89% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.x/org/apache/comet/iceberg/IcebergDeltaWriterShim.scala:41` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (20 lines × 2) spark/src/main/scala/org/apache/comet/codegen/CometArrayData.scala:43— spark/src/main/scala/org/apache/comet/codegen/CometArrayData.scala:43-98 | spark/src/main/scala/org/apache/comet/codegen/CometMapData.scala:34-53 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (16 lines × 2) native/shuffle/src/writers/rss/rss_partition_writer.rs:835— native/shuffle/src/writers/rss/rss_partition_writer.rs:835-850 | native/shuffle/src/writers/rss/rss_partition_writer.rs:853-868 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (16 lines × 2) spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometWindowGroupLimit.scala:37— spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometWindowGroupLimit.scala:37-52 | spark/src/main/spark-4.x/org/apache/comet/shims/ShimCometWindowGroupLimit.scala:38-53 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/shims/ShimCometWindowGroupLimit.scala` and `spark/src/main/spark-4.x/org/apache/comet/shims/ShimCometWindowGroupLimit.scala` as WHOLE FILES: 95% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (16 lines × 2) spark/src/main/spark-3.4/org/apache/comet/shims/ShimSubqueryBroadcast.scala:27— spark/src/main/spark-3.4/org/apache/comet/shims/ShimSubqueryBroadcast.scala:27-42 | spark/src/main/spark-3.5/org/apache/comet/shims/ShimSubqueryBroadcast.scala:27-42 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/comet/shims/ShimSubqueryBroadcast.scala` and `spark/src/main/spark-3.5/org/apache/comet/shims/ShimSubqueryBroadcast.scala` as WHOLE FILES: 93% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (9–10 lines × 2) native/spark-expr/src/array_funcs/array_contains.rs:66— native/spark-expr/src/array_funcs/array_contains.rs:66-75 | native/spark-expr/src/array_funcs/array_remove.rs:68-76 — before extracting anything, compare `native/spark-expr/src/array_funcs/array_contains.rs` and `native/spark-expr/src/array_funcs/array_remove.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 30 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (9–10 lines × 2) native/spark-expr/src/array_funcs/array_position.rs:44— native/spark-expr/src/array_funcs/array_position.rs:44-52 | native/spark-expr/src/array_funcs/arrays_zip.rs:126-135 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (9–10 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:124— native/spark-expr/src/datetime_funcs/make_date.rs:124-133 | native/spark-expr/src/datetime_funcs/next_day.rs:102-110 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (6 lines × 4) native/shuffle/src/spark_unsafe/row.rs:863— native/shuffle/src/spark_unsafe/row.rs:863-869 | native/shuffle/src/spark_unsafe/row.rs:933-938 | native/shuffle/src/spark_unsafe/row.rs:951-956 | native/shuffle/src/spark_unsafe/row.rs:971-976 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (6 lines × 4) native/spark-expr/src/string_funcs/split.rs:320— native/spark-expr/src/string_funcs/split.rs:320-328 | native/spark-expr/src/string_funcs/split.rs:347-352 | native/spark-expr/src/string_funcs/split.rs:370-375 | native/spark-expr/src/string_funcs/split.rs:393-398 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (6 lines × 4) spark/src/main/scala/org/apache/comet/serde/json.scala:45— spark/src/main/scala/org/apache/comet/serde/json.scala:45-50 | spark/src/main/scala/org/apache/comet/serde/strings.scala:165-170 | spark/src/main/scala/org/apache/comet/serde/strings.scala:764-769 | spark/src/main/scala/org/apache/comet/serde/strings.scala:824-829 — there are 4 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 4 sites; resolving a subset leaves the remainder to drift apart. Note first that the copies are not typed on the same thing: the declarations holding them bind `expr` to `LengthOfJsonArray): SupportLevel = if (!CometConf.isExprAllowIncompat(getExprConfigName(expr))` in one and `InitCap): SupportLevel = if (!CometConf.isExprAllowIncompat(getExprConfigName(expr))` in another, and the duplicated lines use it. The extracted unit therefore needs a parameter type that fits BOTH — their common supertype where they have one, or a new abstraction over them where they do not — and settling that is the step that comes BEFORE the extraction above. Where the two types are deliberately unrelated, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (5 lines × 4) native/spark-expr/src/math_funcs/checked_arithmetic.rs:351— native/spark-expr/src/math_funcs/checked_arithmetic.rs:351-355 | native/spark-expr/src/math_funcs/div.rs:90-94 | native/spark-expr/src/math_funcs/modulo_expr.rs:94-98 | native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:199-203 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 4 call sites, so a change lands once.
Duplicated block (5 lines × 4) native/spark-expr/src/array_funcs/array_contains.rs:70— native/spark-expr/src/array_funcs/array_contains.rs:70-79 | native/spark-expr/src/array_funcs/array_remove.rs:72-78 | native/spark-expr/src/array_funcs/sort_array.rs:89-94 | native/spark-expr/src/array_funcs/sort_array.rs:144-148 — before extracting anything, compare `native/spark-expr/src/array_funcs/array_contains.rs` and `native/spark-expr/src/array_funcs/array_remove.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 30 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (5 lines × 4) spark/src/main/spark-3.4/org/apache/comet/shims/CometExprShim.scala:71— spark/src/main/spark-3.4/org/apache/comet/shims/CometExprShim.scala:71-75 | spark/src/main/spark-3.5/org/apache/comet/shims/CometExprShim.scala:59-63 | spark/src/main/spark-4.0/org/apache/comet/shims/CometExprShim.scala:50-54 | spark/src/main/spark-4.1+/org/apache/comet/shims/CometExprShim.scala:122-126 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/comet/shims/CometExprShim.scala` and `spark/src/main/spark-3.5/org/apache/comet/shims/CometExprShim.scala` as WHOLE FILES: 94% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
FixmeComment spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala:4070— // FIXME: Change checkSparkAnswer to checkSparkAnswerAndOperator after resolving — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
FixmeComment spark/src/main/scala/org/apache/spark/sql/comet/CometScanExec.scala:69— // FIXME: ideally we should reuse wrapped.supportsColumnar, however that fails many tests — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
SumDecimalGroupsAccumulator::merge_batch (cognitive 21) native/spark-expr/src/agg_funcs/sum_decimal.rs:544— SumDecimalGroupsAccumulator::merge_batch has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (16 pts), boolean chains 4, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SumDecimalGroupsAccumulator::update_batch (cognitive 18) native/spark-expr/src/agg_funcs/sum_decimal.rs:436— SumDecimalGroupsAccumulator::update_batch has cognitive complexity 18 (threshold 15). Drivers by points: if/else 5 (12 pts), loops 2 (4 pts), boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ShimSparkErrorConverter.convertErrorType (cognitive 21) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— ShimSparkErrorConverter.convertErrorType has cognitive complexity 21 (threshold 15). Drivers by points: if/else 10 (18 pts), match/switch 2 (3 pts) (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
ShimSparkErrorConverter.convertErrorType (cognitive 21) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— ShimSparkErrorConverter.convertErrorType has cognitive complexity 21 (threshold 15). Drivers by points: if/else 10 (18 pts), match/switch 2 (3 pts) (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D30 · Dependency Vulnerabilities· Medium vulnerability · ×2
REDACTED
REDACTED
D4 · Code Duplication· Members sharing a duplicated core (5 members, 50+ identical tokens) · ×2
Members sharing a duplicated core (5 members, 50+ identical tokens) native/spark-expr/src/array_funcs/array_position.rs:136— native/spark-expr/src/array_funcs/array_position.rs:136-159 | native/spark-expr/src/array_funcs/array_position.rs:170-196 | native/spark-expr/src/array_funcs/array_position.rs:204-233 | native/spark-expr/src/array_funcs/array_position.rs:241-264 | native/spark-expr/src/array_funcs/array_position.rs:272-301 — These 5 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 5 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 5 times.
Members sharing a duplicated core (5 members, 50+ identical tokens) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1134— contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1134-1156 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1239-1261 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1295-1317 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1353-1379 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1651-1676 — These 5 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 5 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 5 times.
Duplicated block (20–22 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:591— native/spark-expr/src/agg_funcs/sum_int.rs:591-610 | native/spark-expr/src/agg_funcs/sum_int.rs:737-758 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (20–22 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:680— native/spark-expr/src/agg_funcs/sum_int.rs:680-699 | native/spark-expr/src/agg_funcs/sum_int.rs:831-852 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (21 lines × 2) native/core/src/execution/c_udf/imported_c.rs:191— native/core/src/execution/c_udf/imported_c.rs:191-211 | native/core/src/execution/c_udf/imported_c.rs:254-274 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (21 lines × 2) native/core/src/execution/operators/dynamic_filter/join.rs:168— native/core/src/execution/operators/dynamic_filter/join.rs:168-188 | native/core/src/execution/operators/dynamic_filter/topk.rs:122-142 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (19 lines × 2) native/jni-bridge/src/shuffle_partition_pusher.rs:113— native/jni-bridge/src/shuffle_partition_pusher.rs:113-131 | native/jni-bridge/src/shuffle_partition_pusher.rs:141-159 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (19 lines × 2) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:287— spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:287-305 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:315-333 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 11 separate duplicated blocks between them, totalling at least 213 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:287` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (18 lines × 3) native/shuffle/src/partitioners/multi_partition.rs:313— native/shuffle/src/partitioners/multi_partition.rs:313-330 | native/shuffle/src/partitioners/multi_partition.rs:366-383 | native/shuffle/src/partitioners/multi_partition.rs:446-463 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (18 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:211— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:211-228 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:211-228 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:247-264 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (18 lines × 2) native/spark-expr/src/agg_funcs/stddev.rs:111— native/spark-expr/src/agg_funcs/stddev.rs:111-128 | native/spark-expr/src/agg_funcs/variance.rs:103-120 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (18 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:166— spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:166-183 | spark/src/main/scala/org/apache/spark/sql/comet/CometNativeColumnarToRowExec.scala:156-173 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (16–17 lines × 3) native/shuffle/src/writers/rss/rss_partition_writer.rs:950— native/shuffle/src/writers/rss/rss_partition_writer.rs:950-966 | native/shuffle/src/writers/rss/rss_partition_writer.rs:976-991 | native/shuffle/src/writers/rss/rss_partition_writer.rs:1044-1059 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (16–17 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:150— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:150-166 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:148-164 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:174-189 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:150` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:190` calls `getOrElse` and `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:167` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (17 lines × 2) native/spark-expr/src/datetime_funcs/unix_timestamp.rs:92— native/spark-expr/src/datetime_funcs/unix_timestamp.rs:92-108 | native/spark-expr/src/datetime_funcs/unix_timestamp.rs:114-130 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (17 lines × 2) spark/src/main/scala/org/apache/comet/serde/strings.scala:642— spark/src/main/scala/org/apache/comet/serde/strings.scala:642-658 | spark/src/main/scala/org/apache/comet/serde/strings.scala:680-696 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/comet/serde/strings.scala:642` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (14 lines × 2) native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:233— native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:233-246 | native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:277-290 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (14 lines × 2) native/spark-expr/src/string_funcs/split.rs:86— native/spark-expr/src/string_funcs/split.rs:86-99 | native/spark-expr/src/string_funcs/split.rs:151-164 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 3) native/spark-expr/src/agg_funcs/sum_int.rs:595— native/spark-expr/src/agg_funcs/sum_int.rs:595-607 | native/spark-expr/src/agg_funcs/sum_int.rs:741-753 | native/spark-expr/src/agg_funcs/sum_int.rs:905-917 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (13 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala:31— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala:31-43 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala:34-46 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala:37-49 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimCometShuffleMaterialization.scala` as WHOLE FILES: 97% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit. Note first that the copies are not typed on the same thing: `SparkSession` names `org.apache.spark.sql.SparkSession` in one and `org.apache.spark.sql.classic.SparkSession` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (12–13 lines × 2) native/core/src/parquet/schema_adapter.rs:1439— native/core/src/parquet/schema_adapter.rs:1439-1450 | native/core/src/parquet/schema_adapter.rs:1554-1566 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (12–13 lines × 2) spark/src/main/scala/org/apache/comet/serde/arrays.scala:503— spark/src/main/scala/org/apache/comet/serde/arrays.scala:503-514 | spark/src/main/scala/org/apache/comet/serde/arrays.scala:1204-1216 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (11 lines × 3) native/shuffle/src/spark_unsafe/row.rs:463— native/shuffle/src/spark_unsafe/row.rs:463-473 | native/shuffle/src/spark_unsafe/row.rs:482-492 | native/shuffle/src/spark_unsafe/row.rs:503-513 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (11 lines × 3) native/spark-expr/src/agg_funcs/sum_int.rs:302— native/spark-expr/src/agg_funcs/sum_int.rs:302-312 | native/spark-expr/src/agg_funcs/sum_int.rs:373-383 | native/spark-expr/src/agg_funcs/sum_int.rs:454-464 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (8 lines × 3) native/spark-expr/src/agg_funcs/sum_int.rs:319— native/spark-expr/src/agg_funcs/sum_int.rs:319-326 | native/spark-expr/src/agg_funcs/sum_int.rs:390-397 | native/spark-expr/src/agg_funcs/sum_int.rs:479-486 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (8 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:48— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:48-55 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:48-55 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:44-51 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:45` calls `sqlCtx`, `getOrElse` and `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:42` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (7 lines × 3) native/spark-expr/src/array_funcs/arrays_zip.rs:180— native/spark-expr/src/array_funcs/arrays_zip.rs:180-186 | native/spark-expr/src/array_funcs/arrays_zip.rs:192-198 | native/spark-expr/src/array_funcs/arrays_zip.rs:204-210 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (7 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:70— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:70-76 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:70-76 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:81-87 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:70` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:79` calls `mergeActionContext` and `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:68` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (6 lines × 5) native/spark-expr/src/array_funcs/array_position.rs:143— native/spark-expr/src/array_funcs/array_position.rs:143-148 | native/spark-expr/src/array_funcs/array_position.rs:177-182 | native/spark-expr/src/array_funcs/array_position.rs:217-222 | native/spark-expr/src/array_funcs/array_position.rs:248-253 | native/spark-expr/src/array_funcs/array_position.rs:286-291 — all 5 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (6 lines × 5) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:452— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:452-462 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:470-478 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:514-519 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:536-544 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:555-560 — all 5 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:513` calls `createSizeMetric` and `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:451` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (95 lines × 2) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:153— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:153-247 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:151-245 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:153` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (95 lines × 2) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:313— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:313-407 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:312-406 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:313` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:310` calls `Some`, `mergeCardinalityViolationError` and `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:311` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
CometScanRule.transformV2Scan (cyclomatic 92) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:387— CometScanRule.transformV2Scan has cyclomatic complexity 92 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometWindowExec.windowExprToProto (cyclomatic 70) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:209— CometWindowExec.windowExprToProto has cyclomatic complexity 70 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
ShimSparkErrorConverter.convertErrorType (cyclomatic 65) spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:80— ShimSparkErrorConverter.convertErrorType has cyclomatic complexity 65 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
FuzzDataGenerator.generateColumn (cyclomatic 62) spark/src/main/scala/org/apache/comet/testing/FuzzDataGenerator.scala:175— FuzzDataGenerator.generateColumn has cyclomatic complexity 62 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
ShimSparkErrorConverter.convertErrorType (cyclomatic 62) spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— ShimSparkErrorConverter.convertErrorType has cyclomatic complexity 62 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
ShimSparkErrorConverter.convertErrorType (cyclomatic 61) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:67— ShimSparkErrorConverter.convertErrorType has cyclomatic complexity 61 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometExecRule.transform (cyclomatic 52) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:439— CometExecRule.transform has cyclomatic complexity 52 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
DeltaScanSupport.declineReason (cyclomatic 50) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:90— DeltaScanSupport.declineReason has cyclomatic complexity 50 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CometNativeExec.buildNativeContext (cyclomatic 41) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:974— CometNativeExec.buildNativeContext has cyclomatic complexity 41 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometCast.isSupported (cyclomatic 39) spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:214— CometCast.isSupported has cyclomatic complexity 39 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
DirectColumnarToRowConverter.writeColumnFixedWidth (cyclomatic 38) spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:383— DirectColumnarToRowConverter.writeColumnFixedWidth has cyclomatic complexity 38 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometIcebergNativeScan.icebergLiteralToProto (cyclomatic 36) spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:127— CometIcebergNativeScan.icebergLiteralToProto has cyclomatic complexity 36 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
QueryPlanSerde.serializeDataType (cyclomatic 35) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:646— QueryPlanSerde.serializeDataType has cyclomatic complexity 35 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
FixedWidthArrowFieldWriter.copyValues (cyclomatic 35) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:815— FixedWidthArrowFieldWriter.copyValues has cyclomatic complexity 35 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometBaseAggregate.doConvert (cyclomatic 32) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2203— CometBaseAggregate.doConvert has cyclomatic complexity 32 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometHashJoin.doConvert (cyclomatic 32) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2834— CometHashJoin.doConvert has cyclomatic complexity 32 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CelebornShufflePartitionPusher.<init> (cyclomatic 32) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:199— CelebornShufflePartitionPusher.<init> has cyclomatic complexity 32 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CometShuffleExchangeExec.nativeShuffleFailureReasons (cyclomatic 31) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:599— CometShuffleExchangeExec.nativeShuffleFailureReasons has cyclomatic complexity 31 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CelebornShufflePartitionPusher.pushClaimedPartitionData (cyclomatic 31) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:532— CelebornShufflePartitionPusher.pushClaimedPartitionData has cyclomatic complexity 31 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometBatchKernelCodegenInput.emitStructScalarGetters (cyclomatic 28) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:868— CometBatchKernelCodegenInput.emitStructScalarGetters has cyclomatic complexity 28 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
TypedElements::write_elements_slow (cyclomatic 27) native/core/src/execution/columnar_to_row.rs:685— TypedElements::write_elements_slow has cyclomatic complexity 27 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
AtLeastNNonNulls::evaluate (cyclomatic 25) native/spark-expr/src/predicate_funcs/at_least_n_non_nulls.rs:62— AtLeastNNonNulls::evaluate has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometArrowEvalPythonExec.getSupportLevel (cyclomatic 24) spark/src/main/spark-4.1+/org/apache/spark/sql/comet/CometArrowEvalPythonExec.scala:89— CometArrowEvalPythonExec.getSupportLevel has cyclomatic complexity 24 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
DirectColumnarToRowConverter.convertRow (cyclomatic 24) spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:189— DirectColumnarToRowConverter.convertRow has cyclomatic complexity 24 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CelebornRawPartitionReader.openPartitions (cyclomatic 23) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleReader.scala:375— CelebornRawPartitionReader.openPartitions has cyclomatic complexity 23 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CelebornShufflePartitionPusher.reconcileAcceptedPushes (cyclomatic 22) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:851— CelebornShufflePartitionPusher.reconcileAcceptedPushes has cyclomatic complexity 22 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
EagerPageIndexReader::get_metadata (cyclomatic 21) native/core/src/parquet/eager_page_index_reader_factory.rs:661— EagerPageIndexReader::get_metadata has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometExecRule._apply (cyclomatic 21) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1024— CometExecRule._apply has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometExecRule.revertUnsafePartialAggregates (cyclomatic 21) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1906— CometExecRule.revertUnsafePartialAggregates has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
check-iceberg-shards.verify_manifests (cyclomatic 21) dev/ci/check-iceberg-shards.py:46— check-iceberg-shards.verify_manifests has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
generate-changelog.generate_changelog (cyclomatic 21) REDACTED:67— generate-changelog.generate_changelog has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometBatchKernelCodegenInput.emitTypedGetters (cyclomatic 19) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:104— CometBatchKernelCodegenInput.emitTypedGetters has cyclomatic complexity 19 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometBatchKernelCodegenOutput.emitWrite (cyclomatic 19) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenOutput.scala:220— CometBatchKernelCodegenOutput.emitWrite has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This is NOT this file's highest cyclomatic complexity: CometBatchKernelCodegenOutput.outputVectorClass (cyclomatic 21) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded method is counted neither in this dimension's figures nor in its score.
EliminateRedundantTransitions._apply (cyclomatic 19) spark/src/main/scala/org/apache/comet/rules/EliminateRedundantTransitions.scala:79— EliminateRedundantTransitions._apply has cyclomatic complexity 19 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometLiteral.makeListLiteral (cyclomatic 19) spark/src/main/scala/org/apache/comet/serde/literals.scala:147— CometLiteral.makeListLiteral has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometWindowExec.rangeBoundLiteral (cyclomatic 19) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:702— CometWindowExec.rangeBoundLiteral has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometShuffleExchangeExec.columnarShuffleFailureReasons (cyclomatic 19) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:748— CometShuffleExchangeExec.columnarShuffleFailureReasons has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
ColumnarToRowContext::write_column_fixed_width (cyclomatic 18) native/core/src/execution/columnar_to_row.rs:1136— ColumnarToRowContext::write_column_fixed_width has cyclomatic complexity 18 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions. This is NOT this file's highest cyclomatic complexity: TypedArray::from_array (cyclomatic 23) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded function is counted neither in this dimension's figures nor in its score.
RankLimitStream::process_batch (cyclomatic 18) native/operators/src/rank_limit.rs:333— RankLimitStream::process_batch has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
NegativeExpr::evaluate (cyclomatic 18) native/spark-expr/src/math_funcs/negative.rs:97— NegativeExpr::evaluate has cyclomatic complexity 18 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
SegmentVisitor::visit_seq (cyclomatic 18) native/spark-expr/src/string_funcs/get_json_object.rs:768— SegmentVisitor::visit_seq has cyclomatic complexity 18 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
CometExecRule.canApplySparkToColumnar (cyclomatic 18) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1520— CometExecRule.canApplySparkToColumnar has cyclomatic complexity 18 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometScanRule.nativeScan (cyclomatic 18) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:247— CometScanRule.nativeScan has cyclomatic complexity 18 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
DecimalWriter.writeColumnSlice (cyclomatic 18) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:1293— DecimalWriter.writeColumnSlice has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This is NOT this file's highest cyclomatic complexity: ArrowWriter.createFieldWriter (cyclomatic 24) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded method is counted neither in this dimension's figures nor in its score.
CometArrowPythonRunnerBase.inputBatchRanges (cyclomatic 18) spark/src/main/spark-4.x/org/apache/spark/sql/execution/python/CometArrowPythonRunnerBase.scala:487— CometArrowPythonRunnerBase.inputBatchRanges has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
WideDecimalBinaryExpr::evaluate (cyclomatic 17) native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:181— WideDecimalBinaryExpr::evaluate has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
DeltaScanSupport.attributesUnusedAbove (cyclomatic 17) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1968— DeltaScanSupport.attributesUnusedAbove has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CometBatchKernelCodegen.canHandle (cyclomatic 17) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegen.scala:174— CometBatchKernelCodegen.canHandle has cyclomatic complexity 17 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing. This is NOT this file's highest cyclomatic complexity: CometBatchKernelCodegen.vectorClassBySimpleName (cyclomatic 18) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded method is counted neither in this dimension's figures nor in its score.
Scanner.parseCountedQuantifier (cyclomatic 17) spark/src/main/scala/org/apache/comet/expressions/CometRegex.scala:260— Scanner.parseCountedQuantifier has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometNativeScan.isSupported (cyclomatic 17) spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala:149— CometNativeScan.isSupported has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
ComparisonUtils.staticFloatingInList (cyclomatic 17) spark/src/main/scala/org/apache/comet/serde/predicates.scala:392— ComparisonUtils.staticFloatingInList has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
Projection.load (cyclomatic 17) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:290— Projection.load has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
bench_delta_comet.main (cyclomatic 17) contrib/delta-spark/dev/bench_delta_comet.py:212— bench_delta_comet.main has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
PivotIndex::try_new (cyclomatic 16) native/spark-expr/src/agg_funcs/pivot_first.rs:225— PivotIndex::try_new has cyclomatic complexity 16 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
CelebornShuffleGenerationCoordinator.claimMapAttempt (cyclomatic 16) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleManager.scala:850— CelebornShuffleGenerationCoordinator.claimMapAttempt has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
CometSortMergeJoinExec.convert (cyclomatic 16) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3557— CometSortMergeJoinExec.convert has cyclomatic complexity 16 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CelebornShufflePartitionPusher.finish (cyclomatic 16) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:1037— CelebornShufflePartitionPusher.finish has cyclomatic complexity 16 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
check-ci-config.check_event_policy (cyclomatic 16) dev/ci/check-ci-config.py:777— check-ci-config.check_event_policy has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
summarize-iceberg-writes.summarize (cyclomatic 16) dev/ci/summarize-iceberg-writes.py:99— summarize-iceberg-writes.summarize has cyclomatic complexity 16 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
HackComment spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:314— // Hack because of type erasure — a workaround marked in source: record what it is compensating for and what would allow its removal (the upstream fix, the API it is waiting on, the invariant it restores), so the next reader can judge whether it is still needed rather than rediscovering why it is there.
CometScanRule.transformV2Scan (cognitive 173) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:387— CometScanRule.transformV2Scan has cognitive complexity 173 (threshold 15). Drivers by points: if/else 59 (104 pts), match/switch 17 (37 pts), error handling 8 (18 pts), boolean chains 12, loops 1 (2 pts) (nesting depth added 76). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometWindowExec.windowExprToProto (cognitive 89) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:209— CometWindowExec.windowExprToProto has cognitive complexity 89 (threshold 15). Drivers by points: match/switch 21 (42 pts), if/else 33 (41 pts), boolean chains 6 (nesting depth added 29). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
DirectColumnarToRowConverter.writeColumnFixedWidth (cognitive 75) spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:383— DirectColumnarToRowConverter.writeColumnFixedWidth has cognitive complexity 75 (threshold 15). Drivers by points: if/else 20 (41 pts), loops 9 (20 pts), boolean chains 9, ternaries 1 (4 pts), match/switch 1 (nesting depth added 35). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
FixedWidthArrowFieldWriter.copyValues (cognitive 72) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:815— FixedWidthArrowFieldWriter.copyValues has cognitive complexity 72 (threshold 15). Drivers by points: if/else 20 (44 pts), loops 6 (18 pts), boolean chains 8, match/switch 2 (nesting depth added 36). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
TypedElements::write_elements_slow (cognitive 59) native/core/src/execution/columnar_to_row.rs:685— TypedElements::write_elements_slow has cognitive complexity 59 (threshold 15). Drivers by points: if/else 20 (42 pts), loops 8 (16 pts), match/switch 1 (nesting depth added 30). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
DeltaScanSupport.declineReason (cognitive 55) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:90— DeltaScanSupport.declineReason has cognitive complexity 55 (threshold 15). Drivers by points: if/else 39 (44 pts), boolean chains 10, match/switch 1 (nesting depth added 5). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
AtLeastNNonNulls::evaluate (cognitive 54) native/spark-expr/src/predicate_funcs/at_least_n_non_nulls.rs:62— AtLeastNNonNulls::evaluate has cognitive complexity 54 (threshold 15). Drivers by points: if/else 19 (37 pts), loops 5 (11 pts), boolean chains 6 (nesting depth added 24). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometIcebergNativeScan.icebergLiteralToProto (cognitive 54) spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:127— CometIcebergNativeScan.icebergLiteralToProto has cognitive complexity 54 (threshold 15). Drivers by points: match/switch 11 (32 pts), if/else 17 (21 pts), boolean chains 1 (nesting depth added 25). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometExecRule.transform (cognitive 51) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:439— CometExecRule.transform has cognitive complexity 51 (threshold 15). Drivers by points: if/else 22 (34 pts), match/switch 5 (10 pts), boolean chains 7 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometBaseAggregate.doConvert (cognitive 50) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2203— CometBaseAggregate.doConvert has cognitive complexity 50 (threshold 15). Drivers by points: if/else 24 (35 pts), match/switch 3 (8 pts), boolean chains 7 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExecRule._apply (cognitive 46) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1024— CometExecRule._apply has cognitive complexity 46 (threshold 15). Drivers by points: if/else 23 (42 pts), match/switch 2 (4 pts) (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
DecimalWriter.writeColumnSlice (cognitive 46) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:1293— DecimalWriter.writeColumnSlice has cognitive complexity 46 (threshold 15). Drivers by points: if/else 11 (27 pts), loops 3 (9 pts), match/switch 2 (6 pts), boolean chains 4 (nesting depth added 26). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
DirectColumnarToRowConverter.convertRow (cognitive 42) spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:189— DirectColumnarToRowConverter.convertRow has cognitive complexity 42 (threshold 15). Drivers by points: if/else 19 (37 pts), loops 2, match/switch 1 (2 pts), boolean chains 1 (nesting depth added 19). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CelebornShufflePartitionPusher.pushClaimedPartitionData (cognitive 41) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:532— CelebornShufflePartitionPusher.pushClaimedPartitionData has cognitive complexity 41 (threshold 15). Drivers by points: if/else 21 (31 pts), error handling 5 (6 pts), boolean chains 3, ternaries 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometShuffleExchangeExec.nativeShuffleFailureReasons (cognitive 39) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:599— CometShuffleExchangeExec.nativeShuffleFailureReasons has cognitive complexity 39 (threshold 15). Drivers by points: if/else 12 (24 pts), loops 5 (9 pts), match/switch 3 (4 pts), boolean chains 2 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CelebornRawPartitionReader.openPartitions (cognitive 37) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleReader.scala:375— CelebornRawPartitionReader.openPartitions has cognitive complexity 37 (threshold 15). Drivers by points: if/else 22 (29 pts), boolean chains 3, error handling 1 (3 pts), loops 2 (nesting depth added 9). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
CelebornShufflePartitionPusher.<init> (cognitive 36) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:199— CelebornShufflePartitionPusher.<init> has cognitive complexity 36 (threshold 15). Drivers by points: if/else 18 (20 pts), error handling 7 (8 pts), boolean chains 5, loops 1 (2 pts), ternaries 1 (nesting depth added 4). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CometNativeExec.buildNativeContext (cognitive 35) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:974— CometNativeExec.buildNativeContext has cognitive complexity 35 (threshold 15). Drivers by points: if/else 16 (18 pts), match/switch 12 (13 pts), boolean chains 4 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
generate-versions.strip_conditional_blocks (cognitive 35) docs/generate-versions.py:56— generate-versions.strip_conditional_blocks has cognitive complexity 35 (threshold 15). Drivers by points: if/else 8 (23 pts), loops 3 (6 pts), ternaries 1 (5 pts), boolean chains 1 (nesting depth added 22). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CollectListGroupsAccumulator::evaluate (cognitive 34) native/spark-expr/src/agg_funcs/collect.rs:359— CollectListGroupsAccumulator::evaluate has cognitive complexity 34 (threshold 15). Drivers by points: loops 7 (18 pts), if/else 6 (14 pts), match/switch 2 (nesting depth added 19). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
CometHashJoin.doConvert (cognitive 34) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2834— CometHashJoin.doConvert has cognitive complexity 34 (threshold 15). Drivers by points: if/else 18 (21 pts), boolean chains 10, match/switch 2 (3 pts) (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
check-iceberg-shards.verify_manifests (cognitive 33) dev/ci/check-iceberg-shards.py:46— check-iceberg-shards.verify_manifests has cognitive complexity 33 (threshold 15). Drivers by points: if/else 12 (23 pts), loops 4 (6 pts), boolean chains 4 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SegmentVisitor::visit_seq (cognitive 32) native/spark-expr/src/string_funcs/get_json_object.rs:768— SegmentVisitor::visit_seq has cognitive complexity 32 (threshold 15). Drivers by points: if/else 5 (12 pts), loops 4 (10 pts), match/switch 5 (10 pts) (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ComparisonUtils.staticFloatingInList (cognitive 32) spark/src/main/scala/org/apache/comet/serde/predicates.scala:392— ComparisonUtils.staticFloatingInList has cognitive complexity 32 (threshold 15). Drivers by points: if/else 15 (19 pts), match/switch 5 (9 pts), boolean chains 4 (nesting depth added 8). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
FuzzDataGenerator.generateColumn (cognitive 32) spark/src/main/scala/org/apache/comet/testing/FuzzDataGenerator.scala:175— FuzzDataGenerator.generateColumn has cognitive complexity 32 (threshold 15). Drivers by points: match/switch 10 (19 pts), if/else 8 (10 pts), loops 1 (2 pts), boolean chains 1 (nesting depth added 12). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
GenerateDocs.generateConfigReference (cognitive 31) spark/src/main/scala/org/apache/comet/GenerateDocs.scala:305— GenerateDocs.generateConfigReference has cognitive complexity 31 (threshold 15). Drivers by points: loops 4 (13 pts), match/switch 3 (10 pts), if/else 4 (8 pts) (nesting depth added 20). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CelebornShufflePartitionPusher.reconcileAcceptedPushes (cognitive 31) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:851— CelebornShufflePartitionPusher.reconcileAcceptedPushes has cognitive complexity 31 (threshold 15). Drivers by points: if/else 9 (17 pts), boolean chains 7, loops 4 (5 pts), ternaries 1 (2 pts) (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
EagerPageIndexReader::get_metadata (cognitive 30) native/core/src/parquet/eager_page_index_reader_factory.rs:661— EagerPageIndexReader::get_metadata has cognitive complexity 30 (threshold 15). Drivers by points: if/else 14 (19 pts), match/switch 4 (6 pts), boolean chains 5 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
GenerateDocs.renderExpressionCompatNotes (cognitive 30) spark/src/main/scala/org/apache/comet/GenerateDocs.scala:398— GenerateDocs.renderExpressionCompatNotes has cognitive complexity 30 (threshold 15). Drivers by points: if/else 10 (19 pts), loops 4 (10 pts), boolean chains 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CheckOverflow::evaluate (cognitive 29) native/spark-expr/src/math_funcs/internal/checkoverflow.rs:105— CheckOverflow::evaluate has cognitive complexity 29 (threshold 15). Drivers by points: if/else 12 (22 pts), match/switch 3 (6 pts), boolean chains 1 (nesting depth added 13). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometShuffleExchangeExec.columnarShuffleFailureReasons (cognitive 29) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:748— CometShuffleExchangeExec.columnarShuffleFailureReasons has cognitive complexity 29 (threshold 15). Drivers by points: if/else 9 (18 pts), loops 5 (9 pts), boolean chains 1, match/switch 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
WideDecimalBinaryExpr::evaluate (cognitive 28) native/spark-expr/src/math_funcs/wide_decimal_binary_expr.rs:181— WideDecimalBinaryExpr::evaluate has cognitive complexity 28 (threshold 15). Drivers by points: if/else 18 (24 pts), match/switch 3 (4 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_required_checks (cognitive 28) dev/ci/check-ci-config.py:1105— check-ci-config.check_required_checks has cognitive complexity 28 (threshold 15). Drivers by points: if/else 12 (25 pts), boolean chains 2, loops 1 (nesting depth added 13). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
RankLimitStream::process_batch (cognitive 27) native/operators/src/rank_limit.rs:333— RankLimitStream::process_batch has cognitive complexity 27 (threshold 15). Drivers by points: if/else 13 (24 pts), match/switch 1 (2 pts), loops 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometCast.isSupported (cognitive 27) spark/src/main/scala/org/apache/comet/expressions/CometCast.scala:214— CometCast.isSupported has cognitive complexity 27 (threshold 15). Drivers by points: if/else 12 (14 pts), match/switch 4 (8 pts), boolean chains 5 (nesting depth added 6). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
CometExecRule.limitCollectProjectionInput (cognitive 27) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:752— CometExecRule.limitCollectProjectionInput has cognitive complexity 27 (threshold 15). Drivers by points: loops 2 (9 pts), match/switch 4 (9 pts), if/else 6 (8 pts), boolean chains 1 (nesting depth added 14). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CelebornShuffleGenerationCoordinator.claimMapAttempt (cognitive 27) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleManager.scala:850— CelebornShuffleGenerationCoordinator.claimMapAttempt has cognitive complexity 27 (threshold 15). Drivers by points: if/else 9 (18 pts), boolean chains 7, loops 1 (2 pts) (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometBatchKernelCodegenOutput.emitWrite (cognitive 26) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenOutput.scala:220— CometBatchKernelCodegenOutput.emitWrite has cognitive complexity 26 (threshold 15). Drivers by points: if/else 17 (25 pts), match/switch 1 (nesting depth added 8). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
Scanner.parseCountedQuantifier (cognitive 26) spark/src/main/scala/org/apache/comet/expressions/CometRegex.scala:260— Scanner.parseCountedQuantifier has cognitive complexity 26 (threshold 15). Drivers by points: if/else 10 (19 pts), match/switch 3 (5 pts), boolean chains 2 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExecRule.tagUnsafePartialAggregates (cognitive 26) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1823— CometExecRule.tagUnsafePartialAggregates has cognitive complexity 26 (threshold 15). Drivers by points: if/else 11 (22 pts), boolean chains 3, match/switch 1 (nesting depth added 11). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometArrowPythonRunnerBase.inputBatchRanges (cognitive 26) spark/src/main/spark-4.x/org/apache/spark/sql/execution/python/CometArrowPythonRunnerBase.scala:487— CometArrowPythonRunnerBase.inputBatchRanges has cognitive complexity 26 (threshold 15). Drivers by points: if/else 11 (16 pts), loops 3 (5 pts), boolean chains 4, match/switch 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
MergeConfig::validate (cognitive 25) native/core/src/execution/operators/merge_rows.rs:156— MergeConfig::validate has cognitive complexity 25 (threshold 15). Drivers by points: if/else 6 (15 pts), loops 3 (6 pts), match/switch 1 (3 pts), boolean chains 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ExtendedExplainInfo.generateTreeString (cognitive 25) spark/src/main/scala/org/apache/comet/ExtendedExplainInfo.scala:145— ExtendedExplainInfo.generateTreeString has cognitive complexity 25 (threshold 15). Drivers by points: if/else 14 (16 pts), match/switch 5 (8 pts), boolean chains 1 (nesting depth added 5). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
Lz4FrameCompressionCodec.doDecompress (cognitive 25) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/Lz4FrameCompressionCodec.scala:136— Lz4FrameCompressionCodec.doDecompress has cognitive complexity 25 (threshold 15). Drivers by points: if/else 9 (18 pts), error handling 2 (5 pts), boolean chains 1, loops 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_event_policy (cognitive 25) dev/ci/check-ci-config.py:777— check-ci-config.check_event_policy has cognitive complexity 25 (threshold 15). Drivers by points: if/else 8 (18 pts), boolean chains 4, loops 3 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_label_runs_separate (cognitive 25) dev/ci/check-ci-config.py:1201— check-ci-config.check_label_runs_separate has cognitive complexity 25 (threshold 15). Drivers by points: if/else 8 (13 pts), loops 5 (11 pts), boolean chains 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
DeltaScanSupport.attributesUnusedAbove (cognitive 24) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1968— DeltaScanSupport.attributesUnusedAbove has cognitive complexity 24 (threshold 15). Drivers by points: match/switch 5 (10 pts), if/else 7 (9 pts), boolean chains 4, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
QueryPlanSerde.aggExprToProto (cognitive 24) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:807— QueryPlanSerde.aggExprToProto has cognitive complexity 24 (threshold 15). Drivers by points: if/else 10 (20 pts), match/switch 2 (3 pts), boolean chains 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometIcebergNativeScan.icebergExprToProto (cognitive 24) spark/src/main/scala/org/apache/comet/serde/operator/CometIcebergNativeScan.scala:683— CometIcebergNativeScan.icebergExprToProto has cognitive complexity 24 (threshold 15). Drivers by points: if/else 12 (15 pts), match/switch 3 (8 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
ShimSparkErrorConverter.convertErrorType (cognitive 24) spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:80— ShimSparkErrorConverter.convertErrorType has cognitive complexity 24 (threshold 15). Drivers by points: if/else 12 (21 pts), match/switch 2 (3 pts) (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
generate-changelog.generate_changelog (cognitive 24) REDACTED:67— generate-changelog.generate_changelog has cognitive complexity 24 (threshold 15). Drivers by points: if/else 5 (10 pts), loops 7 (9 pts), boolean chains 5 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExecRule.revertUnsafePartialAggregates (cognitive 23) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1906— CometExecRule.revertUnsafePartialAggregates has cognitive complexity 23 (threshold 15). Drivers by points: if/else 8 (16 pts), boolean chains 5, match/switch 2 (nesting depth added 8). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
Scanner.parseClass (cognitive 22) spark/src/main/scala/org/apache/comet/expressions/CometRegex.scala:356— Scanner.parseClass has cognitive complexity 22 (threshold 15). Drivers by points: if/else 7 (9 pts), boolean chains 6, match/switch 2 (6 pts), loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometScanRule.validateIcebergFileScanTasks (cognitive 22) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:1430— CometScanRule.validateIcebergFileScanTasks has cognitive complexity 22 (threshold 15). Drivers by points: if/else 11 (16 pts), error handling 3 (5 pts), boolean chains 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ArrowFieldWriter.writeDictionaryVariableWidth (cognitive 22) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:527— ArrowFieldWriter.writeDictionaryVariableWidth has cognitive complexity 22 (threshold 15). Drivers by points: if/else 8 (18 pts), boolean chains 3, loops 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Projection.load (cognitive 22) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala:290— Projection.load has cognitive complexity 22 (threshold 15). Drivers by points: if/else 13 (15 pts), error handling 2 (3 pts), boolean chains 2, loops 2 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
HashAggBuilder::build (cognitive 21) native/core/src/execution/planner/aggregate.rs:80— HashAggBuilder::build has cognitive complexity 21 (threshold 15). Drivers by points: if/else 14 (17 pts), boolean chains 3, match/switch 1 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
CaseWhenExpr::evaluate_eagerly (cognitive 21) native/spark-expr/src/conditional_funcs/case_when.rs:282— CaseWhenExpr::evaluate_eagerly has cognitive complexity 21 (threshold 15). Drivers by points: if/else 8 (14 pts), match/switch 2 (5 pts), boolean chains 1, loops 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
IcebergReflection.resolveFooterReadOptions (cognitive 21) spark/src/main/scala/org/apache/comet/iceberg/IcebergReflection.scala:2258— IcebergReflection.resolveFooterReadOptions has cognitive complexity 21 (threshold 15). Drivers by points: loops 6 (21 pts) (nesting depth added 15). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
QueryPlanSerde.serializeDataType (cognitive 21) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:646— QueryPlanSerde.serializeDataType has cognitive complexity 21 (threshold 15). Drivers by points: if/else 11 (18 pts), match/switch 2, boolean chains 1 (nesting depth added 7). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
ArrowFieldWriter.writeValidity (cognitive 21) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:286— ArrowFieldWriter.writeValidity has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (17 pts), loops 3, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.asf_required_contexts (cognitive 21) dev/ci/check-ci-config.py:1046— check-ci-config.asf_required_contexts has cognitive complexity 21 (threshold 15). Drivers by points: if/else 10 (19 pts), boolean chains 1, loops 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_cache_save_scope (cognitive 21) dev/ci/check-ci-config.py:1497— check-ci-config.check_cache_save_scope has cognitive complexity 21 (threshold 15). Drivers by points: if/else 4 (13 pts), loops 4 (7 pts), boolean chains 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
summarize-iceberg-writes.summarize (cognitive 21) dev/ci/summarize-iceberg-writes.py:99— summarize-iceberg-writes.summarize has cognitive complexity 21 (threshold 15). Drivers by points: if/else 8 (10 pts), loops 6 (10 pts), boolean chains 1 (nesting depth added 6). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
CometBatchKernelCodegenInput.emitTypedGetters (cognitive 20) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:104— CometBatchKernelCodegenInput.emitTypedGetters has cognitive complexity 20 (threshold 15). Drivers by points: match/switch 13 (15 pts), if/else 3, boolean chains 2 (nesting depth added 2). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometBatchKernelCodegenInput.emitStructScalarGetters (cognitive 20) spark/src/main/scala/org/apache/comet/codegen/CometBatchKernelCodegenInput.scala:868— CometBatchKernelCodegenInput.emitStructScalarGetters has cognitive complexity 20 (threshold 15). Drivers by points: match/switch 12, if/else 5 (6 pts), boolean chains 2 (nesting depth added 1). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometNativeShuffleWriter.writeInternal (cognitive 20) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometNativeShuffleWriter.scala:114— CometNativeShuffleWriter.writeInternal has cognitive complexity 20 (threshold 15). Drivers by points: if/else 10 (13 pts), match/switch 3, boolean chains 2, loops 1 (2 pts) (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
NestedPredicate::evaluate (cognitive 19) native/spark-expr/src/array_funcs/nested_comparison.rs:249— NestedPredicate::evaluate has cognitive complexity 19 (threshold 15). Drivers by points: if/else 6 (11 pts), match/switch 3 (5 pts), boolean chains 2, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
QueryPlanSerde.exprToProtoInternal (cognitive 19) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:1034— QueryPlanSerde.exprToProtoInternal has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (14 pts), match/switch 3 (4 pts), boolean chains 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometLiteral.makeListLiteral (cognitive 19) spark/src/main/scala/org/apache/comet/serde/literals.scala:147— CometLiteral.makeListLiteral has cognitive complexity 19 (threshold 15). Drivers by points: if/else 12 (18 pts), match/switch 1 (nesting depth added 6). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometDataWritingCommand.getSupportLevel (cognitive 19) spark/src/main/scala/org/apache/comet/serde/operator/CometDataWritingCommand.scala:57— CometDataWritingCommand.getSupportLevel has cognitive complexity 19 (threshold 15). Drivers by points: if/else 6 (14 pts), match/switch 2 (3 pts), boolean chains 2 (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
NativeFrameInputStream.read (cognitive 19) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleReader.scala:511— NativeFrameInputStream.read has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (16 pts), boolean chains 3 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datafusion_comet_operators::explode::build_batch (cognitive 18) native/operators/src/explode.rs:781— datafusion_comet_operators::explode::build_batch has cognitive complexity 18 (threshold 15). Drivers by points: if/else 3 (7 pts), match/switch 3 (7 pts), loops 2 (4 pts) (nesting depth added 10). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
AvgGroupsAccumulator::update_batch (cognitive 18) native/spark-expr/src/agg_funcs/avg.rs:234— AvgGroupsAccumulator::update_batch has cognitive complexity 18 (threshold 15). Drivers by points: if/else 5 (12 pts), loops 2 (4 pts), boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AvgDecimalGroupsAccumulator::update_batch (cognitive 18) native/spark-expr/src/agg_funcs/avg_decimal.rs:449— AvgDecimalGroupsAccumulator::update_batch has cognitive complexity 18 (threshold 15). Drivers by points: if/else 5 (12 pts), loops 2 (4 pts), boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExecRule.refreshedNativeOp (cognitive 18) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:831— CometExecRule.refreshedNativeOp has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, match/switch 4 (5 pts), boolean chains 2 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CometExecRule.rewriteInSubqueryPlan (cognitive 18) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:927— CometExecRule.rewriteInSubqueryPlan has cognitive complexity 18 (threshold 15). Drivers by points: match/switch 4 (10 pts), if/else 6 (8 pts) (nesting depth added 8). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
QueryContextInterner.walk (cognitive 18) spark/src/main/scala/org/apache/comet/serde/QueryContextInterner.scala:101— QueryContextInterner.walk has cognitive complexity 18 (threshold 15). Drivers by points: if/else 4 (7 pts), loops 2 (5 pts), match/switch 3 (5 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometNativeScan.isSupported (cognitive 18) spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala:149— CometNativeScan.isSupported has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11 (13 pts), boolean chains 5 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
CometWindowExec.rangeBoundLiteral (cognitive 18) spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala:702— CometWindowExec.rangeBoundLiteral has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10 (14 pts), boolean chains 2, error handling 1, match/switch 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ArrowFieldWriter.writeVariableWidth (cognitive 18) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:455— ArrowFieldWriter.writeVariableWidth has cognitive complexity 18 (threshold 15). Drivers by points: if/else 9 (16 pts), boolean chains 1, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Utils.coalesceBroadcastBatches (cognitive 18) spark/src/main/scala/org/apache/spark/sql/comet/util/Utils.scala:379— Utils.coalesceBroadcastBatches has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (12 pts), error handling 1 (3 pts), loops 2 (3 pts) (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
bench_delta_comet.main (cognitive 18) contrib/delta-spark/dev/bench_delta_comet.py:212— bench_delta_comet.main has cognitive complexity 18 (threshold 15). Drivers by points: if/else 8 (10 pts), boolean chains 3, loops 2, ternaries 2, error handling 1 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
ColumnarToRowContext::write_column_fixed_width (cognitive 17) native/core/src/execution/columnar_to_row.rs:1136— ColumnarToRowContext::write_column_fixed_width has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (12 pts), loops 2 (4 pts), match/switch 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
HashJoinBuilder::build (cognitive 17) native/core/src/execution/planner/join.rs:80— HashJoinBuilder::build has cognitive complexity 17 (threshold 15). Drivers by points: if/else 11 (14 pts), boolean chains 3 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
ArrowPythonUdf::evaluate_named (cognitive 17) native/core/src/execution/python_udf.rs:262— ArrowPythonUdf::evaluate_named has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12 (14 pts), loops 3 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
ExplodeStream::poll_next_impl (cognitive 17) native/operators/src/explode.rs:389— ExplodeStream::poll_next_impl has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (14 pts), match/switch 1 (2 pts), loops 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
QuantileSummaries::merge (cognitive 17) native/spark-expr/src/agg_funcs/quantile_summaries.rs:264— QuantileSummaries::merge has cognitive complexity 17 (threshold 15). Drivers by points: if/else 8 (13 pts), loops 3, boolean chains 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SparkArraysOverlap::invoke_with_args (cognitive 17) native/spark-expr/src/array_funcs/arrays_overlap.rs:124— SparkArraysOverlap::invoke_with_args has cognitive complexity 17 (threshold 15). Drivers by points: if/else 8 (12 pts), match/switch 3 (4 pts), boolean chains 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
GenerateDocs.writeCastMatrixForMode (cognitive 17) spark/src/main/scala/org/apache/comet/GenerateDocs.scala:457— GenerateDocs.writeCastMatrixForMode has cognitive complexity 17 (threshold 15). Drivers by points: loops 5 (7 pts), if/else 4 (6 pts), match/switch 1 (4 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExecRule.tryConvertToComet (cognitive 17) spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1203— CometExecRule.tryConvertToComet has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (12 pts), boolean chains 3, match/switch 1 (2 pts) (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometScanRule.nativeScan (cognitive 17) spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:247— CometScanRule.nativeScan has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12, boolean chains 5. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CometElementAt.convert (cognitive 17) spark/src/main/scala/org/apache/comet/serde/arrays.scala:937— CometElementAt.convert has cognitive complexity 17 (threshold 15). Drivers by points: loops 3 (9 pts), if/else 5 (6 pts), boolean chains 1, match/switch 1 (nesting depth added 7). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
ArrowFieldWriter.writeChildRuns (cognitive 17) spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala:383— ArrowFieldWriter.writeChildRuns has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (15 pts), boolean chains 1, loops 1 (nesting depth added 10). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
CometMergeRows.convert (cognitive 17) spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:60— CometMergeRows.convert has cognitive complexity 17 (threshold 15). Drivers by points: if/else 14, boolean chains 2, match/switch 1. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
Spark4xCometExprShim.sparkVersionSpecificExprToProtoInternal (cognitive 17) spark/src/main/spark-4.x/org/apache/comet/shims/Spark4xCometExprShim.scala:60— Spark4xCometExprShim.sparkVersionSpecificExprToProtoInternal has cognitive complexity 17 (threshold 15). Drivers by points: if/else 6 (12 pts), match/switch 3 (5 pts) (nesting depth added 8). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
FactoryHook.invoke (cognitive 17) spark/src/main/java/org/apache/comet/shuffle/CelebornTransportCallbackTracker.java:729— FactoryHook.invoke has cognitive complexity 17 (threshold 15). Drivers by points: if/else 6 (12 pts), error handling 2 (4 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_artifact_names (cognitive 17) dev/ci/check-ci-config.py:937— check-ci-config.check_artifact_names has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (9 pts), loops 5 (8 pts) (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.check_local_actions_have_checkout (cognitive 17) dev/ci/check-ci-config.py:965— check-ci-config.check_local_actions_have_checkout has cognitive complexity 17 (threshold 15). Drivers by points: if/else 4 (12 pts), loops 3 (4 pts), boolean chains 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config.ci_jobs_and_aggregator (cognitive 17) dev/ci/check-ci-config.py:1000— check-ci-config.ci_jobs_and_aggregator has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (14 pts), boolean chains 2, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
check-ci-config._cache_steps (cognitive 17) dev/ci/check-ci-config.py:1473— check-ci-config._cache_steps has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (9 pts), loops 3 (5 pts), boolean chains 3 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
WindowBuilder::build (cognitive 16) native/core/src/execution/planner/window.rs:73— WindowBuilder::build has cognitive complexity 16 (threshold 15). Drivers by points: if/else 8 (12 pts), match/switch 1 (3 pts), loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SparkPhysicalExprAdapter::wrap_all_type_mismatches (cognitive 16) native/core/src/parquet/schema_adapter.rs:1314— SparkPhysicalExprAdapter::wrap_all_type_mismatches has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6 (11 pts), match/switch 1 (4 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SumIntegerAccumulatorTry::merge_batch (cognitive 16) native/spark-expr/src/agg_funcs/sum_int.rs:521— SumIntegerAccumulatorTry::merge_batch has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7 (12 pts), match/switch 1 (2 pts), boolean chains 1, loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SumIntGroupsAccumulatorTry::update_batch::update_groups_sum (cognitive 16) native/spark-expr/src/agg_funcs/sum_int.rs:900— SumIntGroupsAccumulatorTry::update_batch::update_groups_sum has cognitive complexity 16 (threshold 15). Drivers by points: if/else 4 (10 pts), match/switch 1 (3 pts), boolean chains 2, loops 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
QueryPlanSerde.extractQueryContext (cognitive 16) spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:504— QueryPlanSerde.extractQueryContext has cognitive complexity 16 (threshold 15). Drivers by points: if/else 5 (9 pts), boolean chains 3, error handling 1 (2 pts), match/switch 1 (2 pts) (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometIntegralDivide.convert (cognitive 16) spark/src/main/scala/org/apache/comet/serde/arithmetic.scala:316— CometIntegralDivide.convert has cognitive complexity 16 (threshold 15). Drivers by points: if/else 13 (15 pts), match/switch 1 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
CometShuffleExchangeExec.shuffleSupported (cognitive 16) spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometShuffleExchangeExec.scala:488— CometShuffleExchangeExec.shuffleSupported has cognitive complexity 16 (threshold 15). Drivers by points: if/else 12 (13 pts), boolean chains 2, match/switch 1 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
PlanDataInjector.injectPlanData (cognitive 16) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:351— PlanDataInjector.injectPlanData has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6 (9 pts), match/switch 3 (6 pts), loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CometExprShim.sparkVersionSpecificExprToProtoInternal (cognitive 16) spark/src/main/spark-4.1+/org/apache/comet/shims/CometExprShim.scala:56— CometExprShim.sparkVersionSpecificExprToProtoInternal has cognitive complexity 16 (threshold 15). Drivers by points: match/switch 4 (7 pts), boolean chains 5, if/else 4 (nesting depth added 3). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
CometArrowEvalPythonExec.getSupportLevel (cognitive 16) spark/src/main/spark-4.1+/org/apache/spark/sql/comet/CometArrowEvalPythonExec.scala:89— CometArrowEvalPythonExec.getSupportLevel has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9, boolean chains 5, match/switch 2. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CelebornTransportCallbackTracker.installFactoryHook (cognitive 16) spark/src/main/java/org/apache/comet/shuffle/CelebornTransportCallbackTracker.java:137— CelebornTransportCallbackTracker.installFactoryHook has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9 (11 pts), error handling 4, boolean chains 1 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
CelebornShufflePartitionPusher.finish (cognitive 16) spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java:1037— CelebornShufflePartitionPusher.finish has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9, error handling 4, boolean chains 2, loops 1. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
check-ci-config.check_cache_refresh_scope (cognitive 16) dev/ci/check-ci-config.py:1305— check-ci-config.check_cache_refresh_scope has cognitive complexity 16 (threshold 15). Drivers by points: if/else 3 (8 pts), loops 4 (6 pts), boolean chains 2 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Change coupling: mod.rs ↔ comet_scalar_funcs.rs native/spark-expr/src/array_funcs/mod.rs:1— `native/spark-expr/src/array_funcs/mod.rs` and `native/spark-expr/src/comet_scalar_funcs.rs` change together 70% of the time (16 of the 23 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 16 shared commits counted here, the most recent 3 are `348d38f2` fix: preserve Spark nested null ordering in native sort (#6968); `7d8d9b15` feat: normalize floats in native array_distinct and array_union (#6563); `bfacc43a` feat: run array_contains on float elements natively with Spark's equa… — run `git show` on any of them.
D4 · Code Duplication· Near-duplicate member family (3 members, 134 shared lines) · ×1
Near-duplicate member family (3 members, 134 shared lines) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:72— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:72-413 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:72-412 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:85-435 — These 3 members are variants of one another: a block of 134 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 3 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 3 times.
D4 · Code Duplication· Near-duplicate member family (3 members, 67 shared lines) · ×1
Near-duplicate member family (3 members, 67 shared lines) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:59— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:59-136 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:59-136 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:63-153 — These 3 members are variants of one another: a block of 67 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 3 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 3 times.
D4 · Code Duplication· Near-duplicate member family (3 members, 28 shared lines) · ×1
Near-duplicate member family (3 members, 28 shared lines) native/spark-expr/src/agg_funcs/sum_int.rs:300— native/spark-expr/src/agg_funcs/sum_int.rs:300-339 | native/spark-expr/src/agg_funcs/sum_int.rs:371-410 | native/spark-expr/src/agg_funcs/sum_int.rs:451-500 — These 3 members are variants of one another: a block of 28 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 3 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 3 times.
Near-duplicate member pair (72 shared lines) native/shuffle/src/spark_unsafe/row.rs:379— native/shuffle/src/spark_unsafe/row.rs:379-598 | native/shuffle/src/spark_unsafe/row.rs:832-1065 — These two members are variants of one another: 72 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
Near-duplicate member pair (31 shared lines) native/spark-expr/src/datetime_funcs/make_date.rs:119— native/spark-expr/src/datetime_funcs/make_date.rs:119-189 | native/spark-expr/src/datetime_funcs/make_time.rs:103-161 — These two members are variants of one another: 31 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
D4 · Code Duplication· Edited copy of a member (20 corresponding lines) · ×1
Edited copy of a member (20 corresponding lines) native/core/src/cloud/s3/credential_bridge.rs:228— native/core/src/cloud/s3/credential_bridge.rs:228-264 | native/core/src/cloud/s3/credential_bridge.rs:310-341 — These two members are one piece of code written twice and then edited apart: 20 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
Duplicated block (33 lines × 2) native/spark-expr/src/agg_funcs/sum_int.rs:614— native/spark-expr/src/agg_funcs/sum_int.rs:614-646 | native/spark-expr/src/agg_funcs/sum_int.rs:765-797 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (30 lines × 2) native/spark-expr/src/conversion_funcs/string.rs:384— native/spark-expr/src/conversion_funcs/string.rs:384-413 | native/spark-expr/src/conversion_funcs/string.rs:449-478 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (28 lines × 2) native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:65— native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:65-92 | native/spark-expr/src/static_invoke/char_varchar_utils/read_side_padding.rs:112-139 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (25–28 lines × 2) native/spark-expr/src/string_funcs/levenshtein.rs:186— native/spark-expr/src/string_funcs/levenshtein.rs:186-213 | native/spark-expr/src/string_funcs/levenshtein.rs:271-295 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (25–26 lines × 2) native/shuffle/src/spark_unsafe/row.rs:542— native/shuffle/src/spark_unsafe/row.rs:542-566 | native/shuffle/src/spark_unsafe/row.rs:1009-1034 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (14–19 lines × 2) native/core/src/execution/planner/join.rs:179— native/core/src/execution/planner/join.rs:179-197 | native/core/src/execution/planner/join.rs:319-332 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (18–19 lines × 2) native/shuffle/src/spark_unsafe/row.rs:511— native/shuffle/src/spark_unsafe/row.rs:511-528 | native/shuffle/src/spark_unsafe/row.rs:978-996 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (15–17 lines × 3) native/spark-expr/src/utils.rs:75— native/spark-expr/src/utils.rs:75-91 | native/spark-expr/src/utils.rs:94-108 | native/spark-expr/src/utils.rs:111-125 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (15–17 lines × 2) native/spark-expr/src/string_funcs/levenshtein.rs:77— native/spark-expr/src/string_funcs/levenshtein.rs:77-91 | native/spark-expr/src/string_funcs/levenshtein.rs:126-142 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (15 lines × 3) native/spark-expr/src/array_funcs/array_position.rs:145— native/spark-expr/src/array_funcs/array_position.rs:145-159 | native/spark-expr/src/array_funcs/array_position.rs:219-233 | native/spark-expr/src/array_funcs/array_position.rs:250-264 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (12–15 lines × 2) native/core/src/execution/planner/window.rs:422— native/core/src/execution/planner/window.rs:422-433 | native/core/src/execution/planner/window.rs:482-496 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (14–15 lines × 2) native/spark-expr/src/agg_funcs/collect.rs:429— native/spark-expr/src/agg_funcs/collect.rs:429-443 | native/spark-expr/src/agg_funcs/collect.rs:744-757 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (15 lines × 2) native/spark-expr/src/hash_funcs/murmur3.rs:160— native/spark-expr/src/hash_funcs/murmur3.rs:160-174 | native/spark-expr/src/hash_funcs/xxhash64.rs:189-203 — before extracting anything, compare `native/spark-expr/src/hash_funcs/murmur3.rs` and `native/spark-expr/src/hash_funcs/xxhash64.rs` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 32 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (12 lines × 4) native/spark-expr/src/array_funcs/array_position.rs:139— native/spark-expr/src/array_funcs/array_position.rs:139-150 | native/spark-expr/src/array_funcs/array_position.rs:173-184 | native/spark-expr/src/array_funcs/array_position.rs:213-224 | native/spark-expr/src/array_funcs/array_position.rs:244-255 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (11–12 lines × 2) native/core/src/execution/operators/scan.rs:132— native/core/src/execution/operators/scan.rs:132-143 | native/core/src/execution/operators/shuffle_scan.rs:134-144 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (11 lines × 4) native/spark-expr/src/agg_funcs/correlation.rs:221— native/spark-expr/src/agg_funcs/correlation.rs:221-231 | native/spark-expr/src/agg_funcs/correlation.rs:365-375 | native/spark-expr/src/agg_funcs/regr.rs:356-366 | native/spark-expr/src/agg_funcs/regr.rs:478-488 — there are 4 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 4 sites; resolving a subset leaves the remainder to drift apart.
Duplicated block (9–11 lines × 2) native/spark-expr/src/datetime_funcs/make_date.rs:128— native/spark-expr/src/datetime_funcs/make_date.rs:128-138 | native/spark-expr/src/datetime_funcs/make_time.rs:111-119 — before extracting anything, compare `native/spark-expr/src/datetime_funcs/make_date.rs` and `native/spark-expr/src/datetime_funcs/make_time.rs` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 43 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (8–10 lines × 2) native/core/src/execution/columnar_to_row.rs:1514— native/core/src/execution/columnar_to_row.rs:1514-1523 | native/core/src/execution/columnar_to_row.rs:1858-1865 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8–9 lines × 2) native/core/src/cloud/s3/credential_bridge.rs:231— native/core/src/cloud/s3/credential_bridge.rs:231-239 | native/core/src/cloud/s3/credential_bridge.rs:311-318 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7–9 lines × 2) native/core/src/execution/columnar_to_row.rs:471— native/core/src/execution/columnar_to_row.rs:471-479 | native/core/src/execution/columnar_to_row.rs:517-523 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7–8 lines × 2) native/shuffle/src/partitioners/multi_partition.rs:282— native/shuffle/src/partitioners/multi_partition.rs:282-288 | native/shuffle/src/partitioners/multi_partition.rs:336-343 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 5) native/core/src/execution/columnar_to_row.rs:728— native/core/src/execution/columnar_to_row.rs:728-734 | native/core/src/execution/columnar_to_row.rs:743-749 | native/core/src/execution/columnar_to_row.rs:758-764 | native/core/src/execution/columnar_to_row.rs:773-779 | native/core/src/execution/columnar_to_row.rs:788-794 — all 5 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (6–7 lines × 2) native/spark-expr/src/utils.rs:224— native/spark-expr/src/utils.rs:224-229 | native/spark-expr/src/utils.rs:306-312 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (5 lines × 3) native/spark-expr/src/kernels/temporal.rs:201— native/spark-expr/src/kernels/temporal.rs:201-205 | native/spark-expr/src/kernels/temporal.rs:218-222 | native/spark-expr/src/kernels/temporal.rs:229-233 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (4–5 lines × 3) native/shuffle/src/writers/rss/rss_partition_writer.rs:667— native/shuffle/src/writers/rss/rss_partition_writer.rs:667-671 | native/shuffle/src/writers/rss/rss_partition_writer.rs:832-836 | native/shuffle/src/writers/rss/rss_partition_writer.rs:945-948 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (71–72 lines × 2) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:73— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:73-144 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:73-143 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:73` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (33–36 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:241— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:241-273 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:239-271 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:262-297 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:241` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (35 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:416— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:416-450 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:415-449 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:438-472 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (35 lines × 2) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:60— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:60-94 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:60-94 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (27–28 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:176— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:176-202 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:174-200 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:203-230 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:176` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (25–26 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:84— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:84-109 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:84-109 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:101-125 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:84` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (22 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala:97— spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala:97-118 | spark/src/main/scala/org/apache/spark/sql/comet/CometScanExec.scala:192-213 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala:97` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (20–21 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2877— spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2877-2897 | spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:3586-3605 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/operators.scala:2877` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (19 lines × 4) spark/src/main/spark-3.5/org/apache/comet/iceberg/IcebergDeltaLogicalShim.scala:28— spark/src/main/spark-3.5/org/apache/comet/iceberg/IcebergDeltaLogicalShim.scala:28-46 | spark/src/main/spark-4.0/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 | spark/src/main/spark-4.1/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 | spark/src/main/spark-4.2/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala:28-46 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/iceberg/IcebergDeltaLogicalShim.scala` and `spark/src/main/spark-4.0/org/apache/comet/iceberg/IcebergDeltaLogicalFieldsShim.scala` as WHOLE FILES: 95% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place.
Duplicated block (12–19 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:382— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:382-400 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:381-399 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:407-418 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:382` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:419` calls `Some`, `CometPythonException`, `params` and `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:406` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (16 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:121— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:121-136 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:121-136 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:138-153 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:121` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:137` calls `setSemanticMetricsRequired` and `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:120` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (11–15 lines × 3) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:388— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:388-402 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:448-462 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:534-544 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:388` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it.
Duplicated block (8–15 lines × 3) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:446— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:446-460 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:512-519 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:553-560 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:445` calls `createSizeMetric` and `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:511` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (14–15 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:400— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:400-413 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:399-412 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:421-435 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:400` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:419` calls `Some`, `CometPythonException`, `params` and `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:398` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (13–15 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:454— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:454-468 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:472-484 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11–14 lines × 3) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:385— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:385-398 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:467-478 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:532-542 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:385` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (13–14 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:352— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:352-364 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:351-363 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:381-394 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:352` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (14 lines × 3) spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:143— spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:143-156 | spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala:143-156 | spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala:160-173 — before extracting anything, compare `spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala` and `spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala` as WHOLE FILES: this scan already matched 12 separate duplicated blocks between them, totalling at least 163 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
Duplicated block (7–13 lines × 3) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:452— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:452-464 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:470-480 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:555-561 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (12–13 lines × 3) spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:118— spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:118-129 | spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:118-129 | spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:143-155 — before extracting anything, compare `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` and `spark/src/main/spark-3.5/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala` as WHOLE FILES: this scan already matched 13 separate duplicated blocks between them, totalling at least 456 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. The two sit in different directories, so one cannot simply be deleted in favour of the other while both are reached separately: hoist the shared part into a location both already depend on and have each file call it, and retire whichever file turns out to have no caller of its own left. Extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/spark-3.4/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala:118` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9–13 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:432— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:432-444 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:510-518 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:432` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (8–11 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:514— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:514-521 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:536-546 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:514` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:513` calls `createSizeMetric` and `spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:535` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (6–11 lines × 2) spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:526— spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:526-536 | spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:557-562 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (10 lines × 5) contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1135— contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1135-1147 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1240-1249 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1296-1305 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1354-1363 | contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1652-1661 — all 5 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:1135` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (5 lines × 9) spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:393— spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:393-397 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:403-407 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:413-417 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:423-427 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:433-437 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:443-447 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:454-458 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:466-470 | spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:475-479 — all 9 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `spark/src/main/java/org/apache/comet/DirectColumnarToRowConverter.java:393` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (12–16 lines × 2) dev/ci/check-ci-config.py:690— dev/ci/check-ci-config.py:690-701 | dev/ci/check-ci-config.py:718-733 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Off the main sequence: comet-udf-sdk — comet-udf-sdk: abstractness 0.17, instability 0.00, distance 0.83 — zone of pain — concrete and depended on by 1 project(s), so it's rigid to change.
X15 · Unvalidated length from an untrusted reader· Length read from the parsed input is spent without ever being bounded · ×1
Length read from the parsed input is spent without ever being bounded native/spark-expr/src/agg_funcs/quantile_summaries.rs:426— `n` is read from the bytes being parsed at line 425 (`u32::from_le_bytes`), then used to size a `Vec::with_capacity` at line 426 — and nowhere in its scope is `n` compared to anything, clamped, or bounded against the input that remains. Whoever wrote those bytes therefore chooses the allocation directly: it reserves the whole length before a single byte of the payload is known to exist, so a 32-bit length asks for up to 4 GiB, and an allocation the allocator refuses aborts the process with `memory allocation of … bytes failed` — an abort, not a panic, so no `catch_unwind` intercepts it and the whole process dies; a 64-bit length past `isize::MAX` panics with `capacity overflow` instead, out of a parser whose failure path was written for malformed input, not for hostile input. Bound `n` against the bytes actually remaining and a maximum the format allows before allocating.
Duplicated predicate spark/src/main/java/org/apache/spark/shuffle/comet/CometUnboundedShuffleMemoryAllocator.java:149— `block.pageNumber == MemoryBlock.FREED_IN_ALLOCATOR_PAGE_NUMBER || block.pageNumber == MemoryBlock.FREED_IN_TMM_PAGE_NUMBER` appears character-identically in 2 files — spark/src/main/java/org/apache/spark/shuffle/comet/CometUnboundedShuffleMemoryAllocator.java, spark/src/main/java/org/apache/spark/shuffle/comet/CometUnifiedShuffleMemoryAllocator.java. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:247— `spill.file.exists() && !spill.file.delete()` appears character-identically in 2 files — spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java, spark/src/main/java/org/apache/spark/sql/comet/execution/shuffle/CometUnsafeShuffleWriter.java. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate native/spark-expr/src/conversion_funcs/string.rs:398— `matches!(e, arrow::error::ArrowError::InvalidArgumentError(_)) && e.to_string().contains("too large to store in a Decimal128")` appears character-identically in 2 files — native/spark-expr/src/conversion_funcs/string.rs, native/spark-expr/src/math_funcs/internal/checkoverflow.rs. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate native/core/src/parquet/parquet_support.rs:2695— `msg.contains("_LEGACY_ERROR_TEMP_2094") && msg.contains("id=1 matches [x, y]")` appears character-identically in 2 files — native/core/src/parquet/parquet_support.rs, native/core/src/parquet/schema_adapter.rs. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate native/spark-expr/src/agg_funcs/avg.rs:250— `opt_filter.is_none() && values.null_count() == 0` appears character-identically in 3 files — native/spark-expr/src/agg_funcs/avg.rs, native/spark-expr/src/agg_funcs/avg_decimal.rs, native/spark-expr/src/agg_funcs/sum_decimal.rs. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate native/spark-expr/src/agg_funcs/correlation.rs:325— `values[0].null_count() == 0 && values[1].null_count() == 0` appears character-identically in 2 files — native/spark-expr/src/agg_funcs/correlation.rs, native/spark-expr/src/agg_funcs/regr.rs. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:210— `!isSpark35Plus && scanExec.partitionFilters.exists(isAqeDynamicPruningFilter)` appears character-identically in 2 files — spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala, spark/src/main/scala/org/apache/comet/serde/operator/CometNativeScan.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:177— `!op.checkCardinality || rowIdOrdinal(op).isDefined` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:213— `(expectedNulls || !actualNulls) && dataTypeCompatible(actualElement, expectedElement)` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:217— `(expectedNulls || !actualNulls) && dataTypeCompatible(actualKey, expectedKey) && dataTypeCompatible(actualValue, expectedValue)` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala:58— `Utils.isInRunningSparkTask || child.supportsColumnar` appears character-identically in 2 files — spark/src/main/scala/org/apache/spark/sql/comet/CometColumnarToRowExec.scala, spark/src/main/scala/org/apache/spark/sql/comet/CometNativeColumnarToRowExec.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:223— `actualField.name == expectedField.name && (expectedField.nullable || !actualField.nullable) && dataTypeCompatible(actualField.dataType, expe…` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:109— `childOp.nonEmpty && matched.forall(_.isDefined) && notMatched.forall(_.isDefined) && notMatchedBySource.forall(_.isDefined) && isSourcePrese…` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:78— `condition.isDefined && outputs.forall(_.isDefined)` appears character-identically in 2 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala:280— `d != null && (d.isInstanceOf[ArrayBasedMapData] || d .isInstanceOf[GenericInternalRow] || d.isInstanceOf[GenericArrayData])` appears character-identically in 2 files — contrib/delta-spark/src/main/scala/org/apache/comet/contrib/delta/DeltaScanSupport.scala, spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala:97— `key.equals("DataFilters") || key.equals("Format")` appears character-identically in 2 files — spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala, spark/src/main/scala/org/apache/spark/sql/comet/CometScanExec.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:1203— `leftExpr.isDefined && rightExpr.isDefined` appears character-identically in 2 files — spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala, spark/src/main/scala/org/apache/comet/serde/arithmetic.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:238— `op.conf.resolver(attr.name, MergeRows.ROW_ID) && attr.dataType == LongType` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala:188— `op.matchedInstructions.forall(instructionShapeSatisfied(_, outputTypes)) && op.notMatchedInstructions.forall(instructionShapeSatisfied(_, ou…` appears character-identically in 3 files — spark/src/main/spark-3.5/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.0/org/apache/comet/serde/operator/CometMergeRows.scala, spark/src/main/spark-4.1+/org/apache/comet/serde/operator/CometMergeRows.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CometArrowConverters.scala:75— `rowIter.hasNext && rowCount < maxRecordsPerBatch` appears character-identically in 2 files — spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CometArrowConverters.scala, spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/RowArrowReader.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/rules/CometCacheColumnarRule.scala:94— `scan.relation.cacheBuilder.serializer.isInstanceOf[ArrowCachedBatchSerializer] && ArrowCachedBatchSerializer.supportsSchema(scan.relation.ou…` appears character-identically in 2 files — spark/src/main/scala/org/apache/comet/rules/CometCacheColumnarRule.scala, spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala:1015— `showTransformations && !newPlan.fastEquals(plan)` appears character-identically in 3 files — spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala, spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala, spark/src/main/scala/org/apache/comet/rules/EliminateRedundantTransitions.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/shuffle/CelebornShufflePusherFactory.scala:47— `stageAttempt >= 0 && stageAttempt < MAX_STAGE_ATTEMPTS` appears character-identically in 2 files — spark/src/main/scala/org/apache/comet/shuffle/CelebornShufflePusherFactory.scala, spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleReader.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate spark/src/main/scala/org/apache/comet/shuffle/CelebornShufflePusherFactory.scala:50— `taskAttempt >= 0 && taskAttempt < MAX_TASK_ATTEMPTS` appears character-identically in 2 files — spark/src/main/scala/org/apache/comet/shuffle/CelebornShufflePusherFactory.scala, spark/src/main/scala/org/apache/spark/sql/comet/execution/shuffle/CometCelebornShuffleReader.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Orphaned file native/spark-expr/src/nondetermenistic_funcs/randn.rs:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file native/spark-expr/src/nondetermenistic_funcs/monotonically_increasing_id.rs:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file spark/src/main/scala/org/apache/spark/sql/comet/CometCollectLimitExec.scala:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file native/spark-expr/src/predicate_funcs/is_nan.rs:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 39 significant file(s) lose their only recent owner: native/core/src/execution/operators/iceberg_write.rs, dev/ci/check-ci-config.py, native/spark-expr/src/agg_funcs/collect.rs, native/spark-expr/src/agg_funcs/max_min_by.rs, spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/CachedBatchIpc.scala, native/core/src/execution/operators/iceberg_partition_path.rs, native/spark-expr/src/agg_funcs/mode.rs, native/common/src/schema.rs (+31 more). Pair on, review, or document these before any departure.
Off-boarding risk: anonymized user #2 — If anonymized user #2 becomes unavailable, 8 significant file(s) lose their only recent owner: spark/src/main/java/org/apache/comet/shuffle/CelebornTransportCallbackTracker.java, spark/src/main/java/org/apache/comet/shuffle/CelebornShufflePartitionPusher.java, native/shuffle/src/remote_schema.rs, native/jni-bridge/src/shuffle_partition_pusher.rs, native/core/src/execution/operators/dynamic_filter/parquet_reader/schema_adapter/tests.rs, native/core/src/execution/operators/dynamic_filter/parquet_reader/schema_adapter.rs, spark/src/main/java/org/apache/comet/shuffle/ExecutorShufflePushAdmission.java, native/core/src/execution/operators/dynamic_filter/parquet_reader/tests.rs. Pair on, review, or document these before any departure.
Off-boarding risk: anonymized user #3 — If anonymized user #3 becomes unavailable, 3 significant file(s) lose their only recent owner: dev/ci/check-iceberg-shards.py, dev/ci/test-iceberg-shards.py, dev/ci/iceberg-test-shards.gradle. Pair on, review, or document these before any departure.
Silent fallback default on Err native/core/src/execution/c_udf/policy.rs:121— `from_spark_config` turns every `Err` into `false` — a failure becomes a plausible value and a silent behavior change with no trail. Log the error or propagate it with `?`. If that constant is the correct answer rather than a stand-in for a value that could not be read, say so in a comment on the arm or in the doc comment, and the row stops firing.
Silent fallback default on Err native/core/src/execution/operators/dynamic_filter/parquet_reader/schema_adapter.rs:73— `create` turns every `Err` into `false` — a failure becomes a plausible value and a silent behavior change with no trail. Log the error or propagate it with `?`. If that constant is the correct answer rather than a stand-in for a value that could not be read, say so in a comment on the arm or in the doc comment, and the row stops firing.
D16 · Bus Factor· Further sole-owners (lower concentration) · ×1
Further sole-owners (lower concentration) — 7 other contributor(s) are each the sole owner of a small amount of code below the off-boarding threshold — folded into the bus-factor score and metrics (57 single-owned of 572 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated, example/demo and scaffolding-template trees and test files identified by path convention, largest first; 572 of the 812 production source files in this repository met that bar). They are anonymized user #4 (1 file(s)), anonymized user #5 (1 file(s)), anonymized user #6 (1 file(s)), anonymized user #7 (1 file(s)), anonymized user #8 (1 file(s)), anonymized user #9 (1 file(s)) (+1 more) — spread or document their files in the same way, at lower priority than the named off-boarding risks above. The files folded here: native/spark-expr/src/array_funcs/flatten.rs, dev/ci/local-ci-config.py, spark/src/main/java/org/apache/comet/cloud/s3/CometS3LocationScopedCredentialProvider.java, native/spark-expr/src/string_funcs/pattern_cache.rs, native/spark-expr/src/math_funcs/internal/unscaled_value.rs, native/spark-expr/src/string_funcs/concat_ws.rs, native/core/src/execution/planner/lance_scan.rs.
No ADRs found — No ADRs found. No recognised ADR directory (`docs/adr/`, `docs/decisions/`, `adr/`, `docs/rfcs/`, an `ADR0001/` folder, or their siblings) exists anywhere in this tree. What was searched, so you can tell an empty log from a search that missed one: every directory under the tree (build output, dependencies and VCS metadata excepted), for a document that is either any non-index page inside a recognised ADR directory, whatever its name and however deeply nested (`docs/adr/use-postgres.md`, `docs/adr/2024/0001-x.md`); or a file anywhere whose name is ADR-shaped (`0001-use-postgres.md`, `adr-012-caching.md`); or, when neither turned anything up, a document carrying the decision-record signature (an "Architecture Decision Record" heading, or Status / Context / Decision / Consequences as section headings). A decision log that clears none of these — unnumbered files outside any recognised directory, without those headings — is not seen by this check and this row is then wrong. If that is your case, say so rather than renaming anything; otherwise, consider recording architectural decisions in `docs/adr/`.
D26 · Project Cohesion· Projects may be oversized for their cohesion · ×1
Projects may be oversized for their cohesion — 3 of 16 project(s) overshoot their size bounds, lowering Project Cohesion to 6.3/10. The most over is `native/spark-expr` (77993 LoC, 239 module-visible types across 25 directories). Review these for cohesion — draw the boundary inside the module first (group each responsibility into its own package or directory and keep the cross-boundary members non-public), since splitting a published package moves types between packages and breaks consumers.
D34 · Knowledge Freshness· Orphaned files with no living knowledge · ×1
Orphaned files with no living knowledge — 4 of 572 analysed file(s) have no living knowledge left — their last meaningful change has decayed away, so if one breaks, no one currently understands it (counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated, example/demo and scaffolding-template trees and test files identified by path convention, largest first; 572 of the 812 production source files in this repository met that bar). None is large enough to earn a read-through of its own, so this row stands in for the per-file rows rather than raising one each — most significant first: native/spark-expr/src/nondetermenistic_funcs/randn.rs, native/spark-expr/src/nondetermenistic_funcs/monotonically_increasing_id.rs, spark/src/main/scala/org/apache/spark/sql/comet/CometCollectLimitExec.scala, native/spark-expr/src/predicate_funcs/is_nan.rs. Attach the read to the next change that touches one of them: have a second person review that change, and leave behind a short comment or test recording what the file is for, so the knowledge comes back at the cost of a change you were making anyway.
No ADRs — No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
Logging is not universal — Only 5/8 runnable modules use logging (modules with no entry point or server are excluded — they are libraries a runnable module hosts). Silent: `.`, `dev/release`, `docs`.
No security response headers detected REDACTED:18— None of the 11 security response headers the OWASP Secure Headers Project recommends (Content-Security-Policy, Strict-Transport-Security, X-Content-Type-Options, X-Frame-Options, …) is configured — defense in depth, even when a reverse proxy could set them. This is reported because `REDACTED` builds the web-server image that serves this application, so the configuration that would carry these headers is in this repository and was read in full. (−2.0 on this card.)
Interpolated log message defeats structured logging spark/src/main/java/org/apache/spark/shuffle/sort/CometShuffleExternalSorter.java:306— `info` at line 306 is handed a message assembled at the call — a string concatenated with a value, or `String.format` — through SLF4J or log4j 2, so the logger receives one finished string: the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the level is filtered out. Pass a constant pattern and the values beside it instead: `log.info("Order {} moved to {}", orderId, status)`, which keeps the pattern and its arguments apart on the event for a structured encoder (logstash-logback-encoder, log4j 2's JsonTemplateLayout, OpenTelemetry), or `log.atInfo().addKeyValue("order_id", orderId).log("order moved")` with SLF4J 2's fluent API. An exception goes last, as the throwable argument, not into the text.
X5 · Nullable reference types· Scala's null is not held to the type system everywhere · ×1
Scala's null is not held to the type system everywhere — No build over this repository's 362 Scala product file(s) passes `-Yexplicit-nulls`, enables WartRemover's `Null` wart or runs Scalafix's `DisableSyntax.noNulls`, so every reference type admits null and nothing reports a `null` written into the code or returned from a Java API — the first one to reach a method call is a `NullPointerException`. `Option` by convention is a habit no tool here holds the code to.
Silent fallback default on parse failure spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala:1476— `inspectLocation` (in `validateIcebergFileScanTasks`) answers `` when `URISyntaxException` is thrown, with no log or throw — a malformed value silently becomes that default. Log the input or fail validation. If that constant is the correct answer rather than a stand-in for a value that could not be read, say so in a comment in the catch case or in the Scaladoc, and the row stops firing.
Off the main sequence: comet-common-spark${spark.version.short}_${scala.binary.version} — comet-common-spark${spark.version.short}_${scala.binary.version}: abstractness 0.00, instability 0.00, distance 1.00 — the shape a shared-kernel / building-block library has BY DESIGN — concrete and widely depended-on is what makes it useful, and this dimension does not penalise it (the distance is reported for completeness, not as a defect). Worth a look only if it has grown past one coherent kernel into an everything-bucket.
Off the main sequence: datafusion-comet-common — datafusion-comet-common: abstractness 0.00, instability 0.00, distance 1.00 — the shape a shared-kernel / building-block library has BY DESIGN — concrete and widely depended-on is what makes it useful, and this dimension does not penalise it (the distance is reported for completeness, not as a defect). Worth a look only if it has grown past one coherent kernel into an everything-bucket.
Appendix B — Reproduction & audit trail
Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.
semgrep: not applicable — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that. semgrep could not parse 1 file(s) — `spark/src/main/scala/org/apache/spark/sql/comet/execution/arrow/ArrowWriters.scala` — so the PII/GDPR sweep did not cover them at all.
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
Run 01a17742-90dd-7352-9942-b2888691262a · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 173 · Warnings: 840 · Recommendations: 50 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 26-10-2026 @ 08:10 UTC.
Downloadable artifacts
Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.