Query: Optimize schema traversal for non-leading tag IN predicates - #18369
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Caideyipi wants to merge 3 commits into
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Query: Optimize schema traversal for non-leading tag IN predicates#18369Caideyipi wants to merge 3 commits into
Caideyipi wants to merge 3 commits into
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Description
Problem
Table-model tag predicates are converted to OR-concatenated precise-filter branches. Both an
INpredicate and its equivalentORexpression therefore produce multiple device patterns.When the predicate is on a non-leading tag and an earlier tag level is not fixed, every expanded pattern traverses the same wildcard prefix independently. With N values, the schema tree prefix is visited N times. Rewriting
INasORdoes not avoid this behavior.Solution
DeviceFilterUtilonly when they differ by a precise value at a non-leading tag behind an unfixed earlier tag level.InFilterattached toExtendedPartialPath.SimpleNFAso the shared wildcard prefix is traversed only once.The comparison intentionally uses a stable 1:1 candidate-count heuristic rather than a wall-clock threshold. An opt-in performance UT confirmed that direct memory-tree child iteration wins the equal-count tie because it avoids one direct child lookup per transition.
Leading-tag predicates and fully fixed prefixes remain as separate precise paths. Branches with null precise values, additional filters on the same tag, or correlated conditions such as
(card = a AND device = x) OR (card = b AND device = y)are not merged.For the motivating case, six values on the second tag are reduced from six schema readers over the same first-tag wildcard subtree to one adaptive reader.
Manual performance UT
SchemaRegionTableDevicePerformanceTestis disabled by default with a JUnit assumption. It compares the legacy N-reader expansion with the merged adaptive reader, verifies identical results, and reports median CPU time, thread allocation, peak-heap delta, and speedup. There are no wall-clock performance assertions.The
valuesproperty accepts a comma-separated sweep, andhit.values=0models the important empty-result case:mvn -pl iotdb-core/datanode \ -Dtest=SchemaRegionTableDevicePerformanceTest \ -Diotdb.schema.non.leading.in.perf.enabled=true \ -Diotdb.schema.non.leading.in.perf.meters=50000 \ -Diotdb.schema.non.leading.in.perf.children.per.meter=8 \ -Diotdb.schema.non.leading.in.perf.values=7,8,9 \ -Diotdb.schema.non.leading.in.perf.hit.values=0 \ -Diotdb.schema.non.leading.in.perf.warmups=2 \ -Diotdb.schema.non.leading.in.perf.iterations=3 \ -Diotdb.schema.non.leading.in.perf.rounds=5 \ testRepresentative local Windows results with 400,000 devices and zero matches:
For the equal-count case, switching from precise lookup to child-key iteration reduced the optimized path from 46.875 ms/op to 31.250 ms/op in the same 50,000-by-8 empty-result workload (about 1.5x faster).
Tests
DeviceFilterUtilTest: 10 tests passed, including value-smaller, child-smaller, and equal-count adaptive directionsSchemaPredicateUtilTest: 2 tests passed, covering both expandedINand equivalentORSchemaRegionTableDeviceTest: 20 tests passedSchemaRegionTableDevicePerformanceTest: enabled benchmark passed; disabled mode skips all 4 parameterized instancesThis PR has:
Key changed/added classes
DeviceFilterUtilExtendedPartialPathSimpleNFAAbstractTreeVisitorTraverserDeviceFilterUtilTestSchemaPredicateUtilTestSchemaRegionTableDevicePerformanceTest