Review Apache DataFusion pull requests using the project's review guide. Gather the proposed changes, discussion, and automated check results, or apply the same review criteria to your own changes before submitting a pull request.
Review Apache DataFusion pull requests following the project's PR review guide. Use whenever asked to review a DataFusion PR or PR URL, and whenever creating a PR, to check the changes against the same criteria before submitting.
Review the instructions the agent will receive before you install.
This skill describes the mechanics for doing PR reviews from the command line.
When creating a PR, skip the "Collect PR context" step and instead check the changes against each area of the PR review guide before submitting.
gh pr checkout <PR number> (ask first if the
working tree has other work in progress).gh pr view <PR number> --json title,body,comments,reviewsgh pr checks <PR number>.# find the remote that points at apache/datafusion (e.g. `apache`, `upstream`, or `origin`)
UPSTREAM=$(git remote -v | grep -m1 'apache/datafusion' | cut -f1)
git fetch $UPSTREAM main
git diff $(git merge-base HEAD $UPSTREAM/main)
Work through each area from the PR review guide.
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Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.
Audit one datafusion-spark function against Apache Spark 4.2.0. Compare source code, accepted types, and edge cases, verify expected results with PySpark, then fix differences and record remaining gaps as issues and disabled tests.
Guide changes and reviews in datafusion-ffi, which connects separately compiled libraries. Check binary compatibility, memory ownership, and method coverage, and choose unit and cross-library tests for new wrappers, trait extensions, or codec changes.