What is partitioning in Parquet and how does it improve query performance?

Partitioning organises files into a directory hierarchy based on column values, following the Hive partition layout:

s3://bucket/events/
  date=2026-01-01/
    part-0000.parquet
  date=2026-01-02/
    part-0001.parquet
  region=US/
    ...

When a query filters on a partition column, the engine lists only matching subdirectories and skips all others — this is called partition pruning. No Parquet files in the skipped partitions are opened at all.

Example savings: a table with 3 years of daily data (1,095 partitions) filtering on a single date skips 99.9% of files before any Parquet-level statistics are consulted.

Trade-offs:

• Partition on columns with moderate cardinality (dates, regions, categories) — not user IDs (too many small files).
• Avoid over-partitioning: thousands of tiny files hurt throughput more than they help with pruning.

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