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DuckDB's columnar storage and vectorized execution engine are a deliberate engineering trade-off: optimize for analytical scan/aggregation throughput at the cost of point-lookup and transactional performance. Here's how to verify it works for your workload.
DuckDB’s vectorized engine and Parquet push‑down cut CPU work and I/O for analytical scans, as shown in a step‑by‑step example that creates a 100‑million‑row Parquet file, runs a filtered aggregation, and verifies push‑down with EXPLAIN.
Learn how DuckDB automatically prunes unneeded columns from Parquet files, see a concrete query example, verify the plan with EXPLAIN, and understand the limits of this optimization.
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