Pivot: High-Performance Python Pipeline Tool

Change your code. Pivot knows what to run.

Pivot is a Python pipeline tool with automatic code change detection. Stages are plain Python functions; you wire them together by calling them inside a Pipeline block, and Pivot works out what needs to re-run.

• Automatic code change detection using Python introspection
• No paths in pipeline code: Pivot chooses where every output is stored
• Per-stage lock files for fast parallel writes
• Warm worker pools with preloaded imports
• Content-addressable caching with S3 or Google Cloud Storage remotes

Python: 3.13+ | Platform: Unix (Linux/macOS)

Quick Start

pip install pivot
mkdir my_project && cd my_project
git init && pivot init

Put a raw input under data/raw/:

mkdir -p data/raw
printf 'name,value\nalice,100\nbob,200\ncarol,\n' > data/raw/records.csv

Define stages and the pipeline in pipeline.py:

# pipeline.py
import pandas as pd

from pivot import Pipeline, StageParams, stage


class CleanParams(StageParams):
    min_value: int = 0


@stage
def clean(records: pd.DataFrame, params: CleanParams) -> pd.DataFrame:
    kept = records.dropna()
    return kept[kept["value"] >= params.min_value]


@stage
def summarize(rows: pd.DataFrame) -> dict:
    return {"count": len(rows), "total": int(rows["value"].sum())}


with Pipeline("demo") as pipeline:
    records = pipeline.input("records.csv")
    cleaned = clean(records, params=CleanParams(min_value=50))
    summarize(cleaned)

Then run it; Pivot resolves the DAG from the handles passed between stages:

pivot repro           # runs clean, then summarize
pivot repro           # nothing changed: both stages are cached
pivot repro --watch   # re-run automatically on file changes

Pivot writes outputs to data/demo/clean.jsonl and data/demo/summarize.yaml.

Learn More

• Documentation
• Source code
• Interactive TUI: pip install pivot[tui], then pivot repro --tui