Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic**: DPP in Spark pushes dimension filters to fact table to skip partitions—type mismatch or missing broadcast breaks it. **Mechanism**: When dimension is filtered and broadcast, Spark pushes partition filter to fact. **Error Causes**: Partition column type...
Architectural Logic: DPP in Spark pushes dimension filters to fact table to skip partitions—type mismatch or missing broadcast breaks it. Mechanism: When dimension is filtered and broadcast, Spark pushes partition filter to fact. Error Causes: Partition column type mismatch (e.g., INT vs STRING); filter not pushed; broadcast hint missing. Fix: (1) Align join key types with partition column. (2) broadcast(dim_df) for small dimension. (3) Enable spark.sql.optimizer.dynamicPartitionPruning.enabled (default true). (4) Ensure partition column in join condition. Scalability: DPP reduces I/O significantly; failure = full scan. Best Practice: Check query plan for DPP; align types; use broadcast for dimensions.
Red Flag: Ignoring DPP in join plan—missing optimization. Pro-Move: Mention type alignment; broadcast hint; verify DPP in plan (Filter with subquery); cite partition pruning improvement.
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