# apache/flink

Apache Flink

Repository: https://github.com/apache/flink
Canonical: https://ross.abutalabs.com/products/flink
Homepage: https://flink.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: scala, java, big-data, flink, python, sql
Last push: 2026-08-26T10:45:27+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 4470, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 26294, forks 14013 (observed 2026-08-28T04:11:46.723522+00:00)

## What it is
Apache Flink is an open-source distributed stream processing framework for stateful computations over unbounded and bounded data streams, with APIs in Java, Scala, Python, and SQL. It provides exactly-once guarantees, event-time processing, and runs on common cluster environments like YARN and Kubernetes.

## Use cases
- process real-time event streams with exactly-once guarantees
- run continuous analytics queries on live data
- build ETL data pipelines between storage systems
- build event-driven applications with stateful processing
- run batch analytics on bounded datasets
- write streaming SQL jobs over Kafka topics
- implement complex event processing with windowing

## When to choose
- you need low-latency, high-throughput stream processing with exactly-once semantics
- you need unified stream and batch processing in one engine
- you need event-time processing and flexible windowing over out-of-order data
- you want SQL on top of streaming and batch data
- you need large-scale stateful computations with incremental checkpoints

## When to avoid
- you only need simple batch jobs where Spark or a data warehouse is simpler
- you need lightweight single-node data processing without a cluster
- your team has no JVM experience and prefers pure Python tooling
- you need ad-hoc analytics rather than continuously running jobs

## Facets
- artifact type: framework
- maturity: stable
- function: streaming, etl, data-science, machine-learning
- domain: big-data, microservices, analytics
- platform: jvm, python, cloud
- tags: stream-processing, batch-processing, event-time, exactly-once, stateful-computation, dataflow, apache, sql, data-engineering, real-time, docker, kubernetes

## Member repositories
- apache/flink (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:46.723522+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 2026-08-29T16:55:53.995620+00:00, confidence not recorded.
  - readme: https://github.com/apache/flink (fetched 2026-08-28T04:11:46.723522+00:00, sha 4110dd98afc3)
  - homepage: https://flink.apache.org/ (fetched 2026-08-29T07:52:18.704198+00:00, sha 1236764de676)
- Data as of 2026-08-30T08:39:29.467469+00:00.