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feat: Add support for round-robin partitioning in native shuffle #3076
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## main #3076 +/- ##
============================================
+ Coverage 56.12% 59.55% +3.42%
- Complexity 976 1382 +406
============================================
Files 119 167 +48
Lines 11743 15572 +3829
Branches 2251 2585 +334
============================================
+ Hits 6591 9274 +2683
- Misses 4012 4998 +986
- Partials 1140 1300 +160 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Which issue does this PR close?
Closes #3067
Builds on #3077
Rationale for this change
When a native plan has round-robin partitioning, we do columnar-to-row, fall back to Spark for partitioning, then row-to-columnar before writing the shuffle file. This is extremely inefficient.
What changes are included in this PR?
Comet's native shuffle now supports
df.repartition(n)(round-robin partitioning). However, it is disabled by default because it produces different partition assignments than Spark. Spark's round-robin implementation sorts rows by their binary UnsafeRow representation before assigning partitions to ensure deterministic output for fault tolerance. Since Comet uses Arrow format internally (which has a completely different binary layout), we cannot match Spark's exact partition assignments.Instead of true round-robin, Comet implements it as hash partitioning on ALL columns. This achieves the same semantic goals:
To enable, set
spark.comet.native.shuffle.partitioning.roundrobin.enabled=true.How are these changes tested?