# apache/fluss

Apache Fluss is a streaming storage built for real-time analytics.

Repository: https://github.com/apache/fluss
Canonical: https://ross.abutalabs.com/products/fluss
Homepage: https://fluss.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: streaming, fluss, lakehouse, real-time-analytics, big-data, hacktoberfest
Last push: 2026-08-26T19:49:16+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 70, longevity 47
- inputs: {"age_days": 671, "days_push": 7, "days_rel": 121, "gap_med": 89.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2113, forks 611 (observed 2026-08-28T04:06:14.610656+00:00)

## What it is
Apache Fluss is a lakehouse-native streaming storage built for real-time analytics and AI, serving as the real-time data layer for Lakehouse architectures. It combines a message broker, online KV store, stream-processing state backend, and lakehouse cold store into one system, integrating with compute engines like Apache Flink and Apache Spark.

## Use cases
- build real-time analytics dashboards with sub-second data freshness
- serve as the real-time data layer for a lakehouse with Paimon, Iceberg, or Hudi
- power real-time fraud detection and alerting systems
- run streaming ETL and real-time data warehouses with Flink SQL
- build feature stores and AI/ML pipelines with low-latency lookups
- ingest CDC streams from Postgres, MySQL, Oracle, or MongoDB
- track real-time shipment updates and geolocation services

## When to choose
- you need sub-second fresh data feeding lakehouse analytics
- you want to unify message queue, KV store, and lakehouse cold storage
- your stack uses Apache Flink or Spark for stream processing
- you need updatable primary-key tables plus append-only log tables

## When to avoid
- you only need batch analytics with no real-time requirements
- you want a simple managed message queue without lakehouse integration
- your infrastructure cannot run a distributed cluster with coordinator and tablet servers

## Facets
- artifact type: service
- maturity: active
- function: streaming, database, message-queue, caching, etl, analytics
- domain: big-data, analytics, databases
- platform: jvm, self-hosted, cloud
- tags: streaming-storage, lakehouse, real-time-analytics, apache-flink, apache-spark, data-lake, cdc, log-tables, primary-key-tables, hot-tier, data-engineering, real-time, docker

## Member repositories
- apache/fluss (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:14.610656+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 2026-08-30T02:53:39.445538+00:00, confidence not recorded.
  - readme: https://github.com/apache/fluss (fetched 2026-08-28T04:06:14.610656+00:00, sha 0c25986efcc0)
  - homepage: https://fluss.apache.org/ (fetched 2026-08-29T10:33:35.823790+00:00, sha 418917749529)
  - site_page: https://fluss.apache.org/docs (fetched 2026-08-29T10:33:35.826611+00:00, sha 6ef103b3a145)
  - site_page: https://fluss.apache.org/docs/quickstart/flink (fetched 2026-08-29T10:33:35.828391+00:00, sha aebb3afb6dc4)
- Data as of 2026-08-30T08:39:29.467469+00:00.