optimize-comet-expression

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Optimize the native $ARGUMENTS scalar expression in native/spark-expr/.

The methodology, benchmark shapes, technique catalog, correctness traps, and PR conventions live in one place for both humans and agents: docs/source/contributor-guide/optimizing_expressions.md. Read it first — it is the source of truth. This skill only adds the agent execution loop and the discipline that keeps a change from being a no-op or a regression.

Execution loop

1. Read docs/source/contributor-guide/optimizing_expressions.md.
2. Locate the implementation under native/spark-expr/src/ and identify the per-row cost (allocation, kernel dispatch per element, UTF-8 decoding, regex compilation, bitmap reads).
3. Baseline first. Add or extend a criterion benchmark in native/spark-expr/benches/ (register it in native/spark-expr/Cargo.toml), covering the shapes from the guide: no-null / sparse-null / dense-null, short / long, valid / invalid, ASCII / non-ASCII. Run it on main before touching any source:

   cd native && cargo bench --bench <name> -- --save-baseline main
   

   The baseline must be built from unmodified source. cargo bench recompiles from whatever is on disk when it runs, so if your optimization is already in the working tree (or a slow build is still compiling when you start editing), the "baseline" measures the optimized code and every speedup looks like zero. Only the benchmark file and Cargo.toml bench registration may be present. If you have already edited the expression, git stash push -- <source file>, capture the baseline, then git stash pop. Confirm the run finished before editing.
4. Optimize, preserving exact semantics. Pick a technique from the catalog in the guide.
5. Prove correctness. Run the existing unit tests for the function; they must pass unchanged. Output must be bit-identical to main (values, null buffer, errors). There is no differential fuzz harness in this repo — the unit tests are the gate. If coverage is thin, add tests (or run audit-comet-expression) before claiming correctness.
6. Re-measure. cargo bench --bench <name> -- --baseline main. Criterion's "change" compares two separate process runs, so a small (±a few %) flag on a shape can be cross-run system noise (thermal, background load), not a real effect. Before trusting any flagged regression, take a second independent sample (--baseline main again) — real changes reproduce, noise does not. A shape whose code path you did not touch cannot truly regress: if it flags, it is noise, and the fix is another sample, not abandoning the change.
7. Apply the no-regression gate (below) before writing any PR.
8. Finish: make format, build, cargo clippy --all-targets --workspace -- -D warnings. PR title perf: optimize `$ARGUMENTS` (Nx faster), paste the criterion output for every shape.
9. Record the performance audit. Add a dated Performance (tuned ...) line under the expression's heading on the relevant page in docs/source/contributor-guide/expression-audits/ (naming the technique, speedup, PR, and benchmark file) so contributors can see what has already been tuned. Check this page before starting, too — it tells you whether the expression was already optimized.

The gate: what blocks a submission

• A shape got meaningfully and reproducibly slower. Do NOT submit. Even a 90%-faster-no-nulls win does not justify a 30%-slower-dense-nulls loss. Either gate the fast path behind a per-batch runtime check that picks the right path, or abandon the change. A Comet PR was closed for exactly this. "Reproducibly" matters: confirm a flagged regression on a second sample first (step 6) — a ~2% flag on a code path you did not touch is noise, not a blocker.
• No meaningful speedup on any shape. There is nothing to submit. A change inside criterion's noise threshold is not an improvement.
• Output differs from main on any input, including null placement or error behavior.
• You only benchmarked one shape. You have not shown the absence of a regression.

Red flags — STOP

• "It's obviously faster, I don't need to benchmark all shapes" → dense nulls / long values / the error path are where fast paths regress. Benchmark them.
• "The values are right, I'll assume nulls are fine" → null-buffer bugs are the most common correctness failure. Diff the null buffer.
• "I'll apply the fallible conversion to every slot" → a garbage value under a null must not raise an error Spark does not. Use try_unary or skip null slots.
• "One benchmark improved, ship it" → re-read the gate. One improvement plus one regression is not a win.

Rationalization table

Excuse	Reality
"Measuring the baseline is overhead, I'll benchmark once at the end"	Without a main baseline you cannot report a change %, and you cannot tell a win from noise. Baseline first.
"Dense-null regression is an edge case"	Real columns have dense nulls. A regression there is a regression. Gate the path or drop it.
"Existing tests are enough proof, no need to check the null buffer"	Tests may not assert null placement. Confirm bit-identical output explicitly.
"This trades a bit of the null case for a big win elsewhere"	That is a trade-off, not an optimization. Only submit a strict improvement (or a correctly-gated per-batch path).
"I'll edit the code, then run the baseline"	cargo bench compiles from disk. A baseline built with your change already applied measures the optimized code and hides the real speedup. Baseline on unmodified source; stash the edit if needed.
"One shape flagged a 2% regression, abandon it"	Criterion compares separate runs; small flags on an untouched code path are cross-run noise. Take a second sample before deciding — real regressions reproduce.

Related skills

• audit-comet-expression — shore up test coverage before optimizing when the gate is thin.
• implement-comet-expression — for a brand-new expression, not an existing one.
• wire-datafusion-function — for wiring an existing upstream function.