NumbDuck

Adapting DuckDB database API to the Numba JIT context.

Leverages bindings toolkit in the NumbOx project.

Inspired by the NumbSQL project.

Examples

Runnable narrative-style examples comparing numbduck against the closest stock DuckDB Python+Arrow approaches live in examples/. Each script is self-contained, generates its own data, and prints the measured numbers.

Highlights:

• Throughput (haversine.py): JIT chunk callback is ~400× faster than a per-row Python scalar UDF (10K rows) and ~100× faster than a PyArrow expression UDF at 1M rows. The win comes from no Python crossings per chunk and LLVM-fused math with no intermediate arrays.

• Latency + GIL-free (online_scoring.py): ~2.2× lower per-event latency vs pure-Python conn.execute, and monotonic parallel scaling to ~2.4× on 8 threads while the Python loop plateaus under GIL contention.

• Branchy logic (fraud_score.py): Arrow's pc.if_else chain beats the Python scalar UDF by ~60× (Arrow is the right stock-DuckDB tool here). The JIT chunk callback then beats Arrow by ~16× at 10K and ~1750× at 1M rows — the gap grows because each Arrow UDF chunk crosses the Python boundary and allocates intermediates, while the JIT computes in registers.