test(spark): add nested schema pruning coverage - #19406
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Extend TestNestedSchemaPruningOptimization to cover the previously untested branches of HoodieNestedSchemaPruning (around 30 percent line coverage): - Prune a nested struct when array<struct> and map<string,struct> columns are also present, exercising the array and map branches of countLeaves and the full successful-pruning path (getPrunedOutput, buildNewProjection, buildPrunedRelation). - No-op when every nested sub-field is selected, so the pruned schema has the same leaf count as the data schema. - No-op when the nested schema pruning optimizer flag is disabled. Factor the plan-level schema assertion shared with the existing successful test into a single helper.
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## master #19406 +/- ##
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+ Coverage 74.83% 74.89% +0.06%
- Complexity 32332 32369 +37
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Files 2574 2574
Lines 142978 142984 +6
Branches 17527 17536 +9
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+ Hits 106992 107086 +94
+ Misses 27921 27832 -89
- Partials 8065 8066 +1
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Describe the issue this Pull Request addresses
HoodieNestedSchemaPruning, the Catalyst optimizer rule that prunes nested (struct) schemas for Hudi relations, sat at roughly 30 percent line coverage.TestNestedSchemaPruningOptimizationonly exercised a single-nested-struct table, leaving several branches of the rule untested: the array and map cases ofcountLeaves, the successful-pruning path (getPrunedOutput/buildNewProjection/buildPrunedRelation), the no-op when the pruned schema has the same leaf count as the data schema, and the no-op when the optimizer flag is off.Summary and Changelog
Test-only change. Extends
TestNestedSchemaPruningOptimizationwith three cases that assert the optimized plan's read schema, pinning the exact pruned/unpruned schema rather than just that the query returns rows:prunes nested struct when array and map columns are present: a MOR table withitem struct<...>,tags array<struct<...>>, andprops map<string, struct<...>>; projectingid, item.nameprunesitemdown tonameand drops the unreferenced complex columns. BecausecountLeavesis called on the full data schema, this exercises itsArrayTypeandMapTypebranches along with the whole successful-pruning path.no-op when all nested sub-fields are selected: selecting every leaf keepsitemasstruct<name, price>(the leaf-count comparison is an equality, so the rule does not rewrite).no-op when nested schema pruning is disabled: withspark.sql.optimizer.nestedSchemaPruning.enabled=falsethe rule short-circuits anditemretainspriceeven though onlyitem.nameis projected.The plan-level schema assertion that the existing successful test performed inline is factored into a shared
assertPrunedReadSchemahelper used by both.Impact
None. Adds test coverage only, no production code changes.
Risk Level
none
Documentation Update
none
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