# alibaba/Alink

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

Repository: https://github.com/alibaba/Alink
Canonical: https://ross.abutalabs.com/products/alink
Language: Java
License: Apache-2.0
License Family: permissive
Topics: machine-learning, flink, classification, clustering, regression, graph-algorithms, xgboost, recommender, recommender-system, feature-engineering, statistics, kafka, data-mining, apriori, word2vec, flink-ml, fm, flink-machine-learning, graph-embedding
Last push: 2024-06-07T14:56:29+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2894, "days_push": 817, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3611, forks 782 (observed 2026-08-28T04:08:11.655698+00:00)

## What it is
Alink is a machine learning algorithm platform built on Apache Flink, developed by Alibaba's PAI team. It provides a large library of batch and streaming ML algorithms (classification, clustering, regression, recommendation, graph embedding) usable from Java and Python (PyAlink).

## Use cases
- run machine learning pipelines on flink
- train classification and regression models on streaming data
- build recommender systems at scale
- feature engineering on big data
- distributed clustering and graph algorithms
- use xgboost with flink
- word2vec and graph embedding on flink

## When to choose
- you already use Apache Flink and want ML on the same platform
- you need both batch and streaming machine learning
- you want a broad algorithm library with Java or Python APIs

## When to avoid
- you don't run Flink and don't want a JVM/Flink dependency
- you need a lightweight single-node scikit-learn-style workflow
- you need cutting-edge deep learning training rather than classic ML

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, etl, streaming, data-science, nlp
- domain: machine-learning, data-science, big-data
- platform: jvm, python, cross-platform
- tags: flink, apache-flink, distributed-ml, feature-engineering, recommender-systems, xgboost, pyalink, alibaba, data-engineering

## Member repositories
- alibaba/Alink (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.655698+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 2026-08-29T18:33:38.881785+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/Alink (fetched 2026-08-28T04:08:11.655698+00:00, sha 8405093f9f1d)
- Data as of 2026-08-30T08:39:29.467469+00:00.