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Prune initially planned batch splits using runtime partition predicates for append, upsert, incremental, and lake-union Spark reads. Reuse original offsets, snapshots, partition metadata, and reader factories. Fixes apache#3344 Co-Authored-By: Codex <noreply@openai.com> AI-Model: gpt-6 AI-Contributed/Feature: 132/132 AI-Contributed/UT: 530/530
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Purpose
Linked issue: close #3344
Enable dynamic partition pruning for Spark batch reads through
SupportsRuntimeV2Filtering.Brief change log
Tests
FlussRuntimeFilteringTest: partition attributes, special column names, projected schemas, empty and null predicates, multiple partition keys, static pruning, repeated filtering, batch lifecycle, split boundaries, and lake partition metadata.SparkRuntimeFilteringTest: append/upsert joins with AQE enabled and disabled, incremental reads, DPP result equivalence and split pruning, and data written after initial planning.SparkLakeRuntimeFilteringTest: append/upsert joins with dynamic partition pruning for lake-only and combined lake/log reads.API and Format
No public Fluss API or storage format changes.
Documentation
Adds automatic runtime partition pruning for eligible Spark batch reads. No user documentation changes.