# Dask

Parallel computing with task scheduling

Repository: https://github.com/dask/dask
Canonical: https://ross.abutalabs.com/products/dask
Homepage: https://dask.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: dask, python, pydata, numpy, pandas, scikit-learn, scipy
Last push: 2026-08-24T18:46:39+00:00
Link (homepage): https://dask.org
Link (site_page): https://docs.dask.org/en/stable
Link (site_page): https://docs.dask.org/en/stable/dataframe.html
Link (site_page): https://docs.dask.org/en/stable/futures.html
Link (site_page): https://docs.dask.org/en/stable/array.html

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 4259, "days_push": 9, "days_rel": 9, "gap_med": 19, "n_releases_24m": 32}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 13896, forks 1937 (observed 2026-08-28T04:11:05.009447+00:00)

## What it is
Dask is a flexible parallel and distributed computing library for Python that scales pandas, NumPy, scikit-learn, and other PyData tools to larger-than-memory and multi-machine workloads. It provides task scheduling with arbitrary dependencies, parallel dataframes and arrays, and a distributed scheduler deployable from a laptop to clusters on cloud or Kubernetes.

## Use cases
- process a 100 GB CSV that doesn't fit in RAM with pandas-like code
- parallelize a for loop that processes thousands of files
- analyze terabytes of multi-dimensional array data with xarray
- scale machine learning training and hyperparameter optimization across a cluster
- run distributed computations on Kubernetes or cloud infrastructure
- speed up groupby and merge operations on large parquet datasets

## When to choose
- you already use pandas/NumPy and hit memory or performance limits
- you need flexible arbitrary task graphs, not just data-parallel jobs
- you want a lighter, more Pythonic alternative to Spark
- you need to scale from a laptop to a cluster with the same API

## When to avoid
- your datasets fit comfortably in memory and pandas is fast enough
- you need heavy SQL-based ETL where a query engine or Spark is better suited
- you need strict low-latency streaming rather than batch/task computation
- your team requires a JVM-based ecosystem with mature enterprise tooling

## Facets
- artifact type: library
- maturity: stable
- function: concurrency, data-science, etl, machine-learning, streaming, developer-tools
- domain: data-science, big-data, analytics, machine-learning, microservices
- platform: python, cross-platform, cloud
- tags: parallel-computing, task-scheduling, dataframes, numpy, pandas, distributed-computing, pydata, larger-than-memory, data-engineering, kubernetes

## Member repositories
- dask/dask (main) score 99
- dask/dask-tutorial (examples) score 55
- dask/distributed (backend) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.009447+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:12:58.224635+00:00, confidence not recorded.
  - readme: https://github.com/dask/dask (fetched 2026-08-28T04:11:05.009447+00:00, sha 3f78e7b7da74)
  - homepage: https://dask.org (fetched 2026-08-29T08:07:20.262444+00:00, sha 34b9cbb5e7b5)
  - site_page: https://docs.dask.org/en/latest/install.html (fetched 2026-08-29T08:07:20.285949+00:00, sha 32c67fac1399)
  - site_page: https://docs.dask.org/en/stable (fetched 2026-08-29T08:07:20.271631+00:00, sha 0d0ce7f9e85b)
  - site_page: https://docs.dask.org/en/stable/dataframe.html (fetched 2026-08-29T08:07:20.273609+00:00, sha 9bc13202cb57)
  - site_page: https://docs.dask.org/en/stable/futures.html (fetched 2026-08-29T08:07:20.275750+00:00, sha 3201414d3e54)
  - site_page: https://docs.dask.org/en/stable/array.html (fetched 2026-08-29T08:07:20.280272+00:00, sha bf98e4441811)
  - site_page: https://docs.dask.org/en/stable/ml.html (fetched 2026-08-29T08:07:20.282067+00:00, sha e469f18117eb)
  - site_page: https://docs.dask.org/en/stable/deploying.html (fetched 2026-08-29T08:07:20.283882+00:00, sha 22c58261f8a7)
  - registry_pypi: https://pypi.org/pypi/dask/json (fetched 2026-08-29T08:07:20.287730+00:00, sha a28366a8fadc)
- Data as of 2026-08-30T08:39:29.467469+00:00.