# hail-is/hail

Cloud-native genomic dataframes and batch computing

Repository: https://github.com/hail-is/hail
Canonical: https://ross.abutalabs.com/products/hail-is-hail
Homepage: https://hail.is
Language: Python
License: MIT
License Family: permissive
Topics: genetics, vcf, genomics, gwas, bioinformatics, python, software, hail
Last push: 2026-09-02T21:23:47+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 73, longevity 100
- inputs: {"age_days": 3963, "days_push": 0, "days_rel": 23, "gap_med": 113.0, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1070, forks 266 (observed 2026-09-03T02:15:12.567206+00:00)

## What it is
Hail is an open-source Python library for scalable exploration and analysis of genomic data, built on Spark, Scala, and C++ primitives for distributed queries and linear algebra. It provides the MatrixTable data structure for multi-dimensional genomic data and Hail Batch for massively parallel pipeline execution in the cloud.

## Use cases
- run a genome-wide association study (GWAS) on biobank-scale data
- parse and query VCF, BGEN, PLINK, and other genomic file formats at scale
- perform PCA and linear regression across millions of variants
- build massively parallel computational pipelines with dependency management
- analyze petabyte-scale datasets in the cloud or small datasets on a laptop
- compute variant QC statistics like call rates and allele frequencies
- visualize GWAS results with Manhattan and QQ plots

## When to choose
- you need to analyze large-scale genomic or multi-dimensional structured data beyond what Pandas or NumPy can handle
- you are running GWAS or population genetics analyses on biobank-scale datasets
- you want a unified Python API for distributed genomic queries and batch job orchestration
- you need to process VCF/BGEN/PLINK files with scalable, cloud-native tooling

## When to avoid
- you only need simple dataframe operations on small tabular data - use Pandas or Polars instead
- your project requires a stable, long-term-support API - Hail uses continuous deployment with frequent changes
- you cannot install Java 11 or lack access to Spark clusters or cloud resources for large workloads
- your analysis is unrelated to genomics and does not involve matrix-like structured data

## Facets
- artifact type: library
- maturity: active
- function: data-science, machine-learning, etl, workflow-automation, streaming
- domain: bioinformatics, data-science, big-data
- platform: python, cloud
- tags: genomics, gwas, vcf, spark, distributed-computing, matrixtable, batch-computing, cloud-native, genetics, linux, macos, docker

## Member repositories
- hail-is/hail (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:12.567206+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 2026-08-30T06:54:02.023672+00:00, confidence not recorded.
  - readme: https://github.com/hail-is/hail (fetched 2026-09-03T02:15:12.567206+00:00, sha b1983ff90f6a)
  - homepage: https://hail.is (fetched 2026-08-29T12:56:37.695498+00:00, sha afbc03b03b96)
  - site_page: https://hail.is/docs/0.2/index.html (fetched 2026-08-29T12:56:37.704708+00:00, sha 545bed95f38c)
  - site_page: https://hail.is/docs/batch/index.html (fetched 2026-08-29T12:56:37.706391+00:00, sha dbf92e9276b5)
  - site_page: https://hail.is/docs/0.2/getting_started.html (fetched 2026-08-29T12:56:37.707873+00:00, sha e3e862d3a916)
  - site_page: https://hail.is/docs/0.2/overview/matrix_table.html?highlight=matrix+table (fetched 2026-08-29T12:56:37.709276+00:00, sha 719fafe2f422)
  - registry_pypi: https://pypi.org/pypi/hail/json (fetched 2026-08-29T12:56:37.711049+00:00, sha cbb67ca00c58)
- Data as of 2026-08-30T08:39:29.467469+00:00.