How does DuckDB query Parquet files and what makes it fast?

DuckDB can query Parquet files directly without loading them into a database, using SQL:

-- Query a local Parquet file
SELECT region, SUM(revenue)
FROM read_parquet('events.parquet')
GROUP BY region;

-- Glob pattern for multiple files / partitioned directory
SELECT * FROM read_parquet('s3://bucket/data/**/*.parquet')
WHERE date = '2026-01-01';

DuckDB is fast on Parquet for several reasons:

• Columnar execution engine — DuckDB processes data in column vectors natively, aligning perfectly with Parquet's columnar layout.
• Push-down filters — WHERE conditions are pushed into the Parquet reader, exploiting row-group statistics and Bloom filters.
• Projection pushdown — only referenced columns are read from disk.
• Parallel I/O — multiple row groups are read in parallel across CPU cores.
• Zero-copy reads — avoids deserialising data into intermediate objects.

This makes DuckDB an excellent tool for ad-hoc analytics on Parquet data lakes without a cluster.

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