duckpipe-tuning

Optional, host-spec-based tuning suggestions for tasks that use a data engine directly — one module per engine, each a pure function of host specs (CPU count, total RAM, free disk space). None of them touch a connection, run a query, or look at your data — that's the boundary this package holds itself to (DESIGN.md §12), so it can never grow into the engine-aware core duckpipe deliberately avoids.

This is deliberately a separate package from duckpipe itself (DESIGN.md §6.1): the core orchestrator never imports psutil or knows anything about any engine's tuning knobs. Install this only if you want it:

uv add duckpipe-tuning

DuckDB

import duckdb
from duckpipe_tuning.duckdb import suggest_duckdb_settings

con = duckdb.connect()
settings = suggest_duckdb_settings()
con.execute(f"SET threads = {settings['threads']}")
con.execute(f"SET memory_limit = '{settings['memory_limit']}'")

suggest_thread_count(), suggest_duckdb_settings(mem_fraction=, workload=, threads=), suggest_temp_dir_limit(path, free_fraction=).

Polars

POLARS_MAX_THREADS is read once, at import time — apply this before import polars:

import os
from duckpipe_tuning.polars import suggest_polars_settings

os.environ["POLARS_MAX_THREADS"] = str(suggest_polars_settings()["POLARS_MAX_THREADS"])
import polars as pl

Dask

Mirrors LocalCluster's own default n_workers/threads_per_worker heuristic, but makes the numbers explicit and lets a mem_fraction budget replace Dask's own memory_limit="auto" (which otherwise claims the entire host):

from distributed import Client
from duckpipe_tuning.dask import suggest_dask_settings

settings = suggest_dask_settings()
client = Client(**settings)

Daft

import daft
from duckpipe_tuning.daft import suggest_num_threads

daft.context.set_runner_native(num_threads=suggest_num_threads())

Daft's native runner defaults to every visible core, which measurably hurts throughput on many-core hosts (the scheduler starts contending with itself); suggest_num_threads() caps it.

Pandas

No tuning surface here on purpose — pandas has no engine-level thread count or memory ceiling to configure, so there's nothing this package could suggest without inventing a knob pandas itself doesn't have.

Backward compatibility

suggest_thread_count, suggest_duckdb_settings, and suggest_temp_dir_limit are still importable from the top-level duckpipe_tuning package (DuckDB was the only engine before 0.2.0). New code should import from duckpipe_tuning.duckdb directly, the same as every other engine's module.