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BigData / Apache Parquet Interview Questions

What is predicate pushdown in Parquet and how does it work end-to-end?

Predicate pushdown is the process of evaluating filter conditions as early as possible — before data reaches the compute layer — using metadata stored inside Parquet files.

End-to-end flow for WHERE amount > 1000:

  1. Reader fetches the Parquet footer and reads per-row-group statistics: min_amount, max_amount.
  2. For each row group, if max_amount ≤ 1000, the entire row group is skipped — no decompression, no I/O.
  3. For surviving row groups, the amount column chunk is decompressed and values are evaluated against > 1000 at the page level (with page-level indexes in Parquet 2.x).
  4. Only matching rows are returned to the engine.

Parquet 2.0 introduced column indexes (min/max per page, not just per row group), enabling finer-grained skipping within a row group.

Combined with partition pruning (directory skipping) and Bloom filters (equality skipping), predicate pushdown is the single most important performance feature of the Parquet format.

At what granularity does Parquet 1.x predicate pushdown (row group skipping) operate?
What feature introduced in Parquet 2.0 enables finer-grained skipping within a row group?

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