fugue
An abstraction layer for distributed computing
Decision gist · record as of 2026-08-14
Yes. Fugue is actively maintained, has low install friction, permissive licensing, and solves a real problem—writing portable distributed code. It is well-suited if you want to scale workflows without major rewrites. Install the base package for the core API; add extras only for the backends you need.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- FugueSQL requires the sql extra.
- Backend support (Spark, Dask, Ray, DuckDB, Polars) requires corresponding optional extras.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you can use, modify, and distribute Fugue freely in commercial and private projects with minimal restrictions.
last release 2026-02-20 (175 days) · last repo commit 2026-05-19 · 2,170 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,393,234 downloads/mo, #3,086 on PyPI
Alternatives
Verify before relying
pip install fugue
import pandas as pd
from fugue import transform
def my_func(df: pd.DataFrame) -> pd.DataFrame:
return df
df = pd.DataFrame({"a": [1, 2]})
result = transform(df, my_func, schema="*")- Whether the CPP antlr parser extra is pre-built for your Python version and platform, or requires a C++ compiler.
- Performance characteristics of the pure Python SQL parser versus the optional CPP parser in your workload.
- Which specific backends (Spark, Dask, Ray, DuckDB, Polars) you need and whether pre-built binaries exist for your environment.
What it is and what it does
Fugue is an abstraction layer that lets you write Python and pandas code once and run it on multiple distributed computing backends without rewriting the core logic. It has two main interfaces: the Fugue API (functions like transform(), load(), save()) that work across all backends, and FugueSQL, an enhanced SQL dialect that can invoke Python functions and run on any backend.
The package solves the problem of code portability in data workflows. Instead of writing separate code for different execution engines, you write it once and Fugue handles the distribution. The library depends on triad, adagio, and pandas; additional backends require optional extras. It is actively maintained, supports current Python versions (3.10+), and has no known vulnerabilities.
Use it for
- Scale an existing pandas transformation by wrapping it in transform() without rewriting the function logic.
- Define an end-to-end data pipeline in FugueSQL that can run locally for testing or on a distributed backend for production.
- Parallelize a custom Python function across partitions by passing it to Fugue's API instead of writing backend-specific code.
- Load, transform, and save data in a workflow that is agnostic to the execution backend.
- Combine SQL queries with Python-defined transformations in a single FugueSQL statement across multiple backends.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Fugue is actively maintained, has low install friction, permissive licensing, and solves a real problem—writing portable distributed code. It is well-suited if you want to scale workflows without major rewrites. Install the base package for the core API; add extras only for the backends you need.
Install
fugue on PyPI
Before you install
Low friction installation with a pure-Python wheel. Maintenance is active with recent commits and a release 175 days ago. Core functionality works without extras; optional extras unlock support for additional backends.
Requires Python 3.10 or later. FugueSQL requires the sql extra. Backend support (Spark, Dask, Ray, DuckDB, Polars) requires corresponding optional extras.
License in practice
Apache-2.0 is permissive; you can use, modify, and distribute Fugue freely in commercial and private projects with minimal restrictions.
Quickstart
pip install fugue
import pandas as pd
from fugue import transform
def my_func(df: pd.DataFrame) -> pd.DataFrame:
return df
df = pd.DataFrame({"a": [1, 2]})
result = transform(df, my_func, schema="*")Verify before relying
- Whether the CPP antlr parser extra is pre-built for your Python version and platform, or requires a C++ compiler.
- Performance characteristics of the pure Python SQL parser versus the optional CPP parser in your workload.
- Which specific backends (Spark, Dask, Ray, DuckDB, Polars) you need and whether pre-built binaries exist for your environment.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagestriadadagiopandas |
| Maintenance | Actively maintained 175 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,393,234 / month, #3,086 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries :: Python Modules |
Evidence: fugue-0.9.7-py3-none-any.whl
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See also fugue-sql-antlr · triad · adagio · pyspark · daft · ibis-framework · pyspark-client · raydp · qpd