# apache/inlong

Apache InLong - a one-stop, full-scenario integration framework for massive data

Repository: https://github.com/apache/inlong
Canonical: https://ross.abutalabs.com/products/inlong
Homepage: https://inlong.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: inlong, one-stop-service, data-streaming, event-streaming, framework, massive-data-integration, full-scenario-service
Last push: 2026-08-26T07:32:24+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 74, longevity 100
- inputs: {"age_days": 2434, "days_push": 7, "days_rel": 12, "gap_med": 149.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1497, forks 570 (observed 2026-08-28T04:04:53.906241+00:00)

## What it is
Apache InLong is a one-stop, full-scenario integration framework for massive data, supporting data ingestion, synchronization, and subscription with reliable high-performance transmission. Originally TubeMQ and donated by Tencent, it graduated as an Apache top-level project and handles trillion-scale data streams for batch and stream processing.

## Use cases
- ingest massive event streams from files, HTTP, SDKs, and databases
- synchronize data between sources and sinks in real time
- subscribe to data streams via topics
- run ETL and sorting on streaming data
- integrate with different message queue backends
- build real-time analytics and modeling pipelines
- monitor and alert on data flow health

## When to choose
- you need a unified platform for high-volume data ingestion, sync, and subscription
- you process trillion-scale event streams and need proven production reliability
- you want pluggable MQ integration and rule-based ETL in one system
- you need both batch and streaming data processing

## When to avoid
- you only need a simple single-source connector or lightweight ETL
- your data volumes are small and a basic message queue suffices
- you need a non-JVM-native stack with no Java operations expertise
- you want a fully managed cloud service rather than self-hosted infrastructure

## Facets
- artifact type: framework
- maturity: stable
- function: message-queue, etl, streaming, data-science, monitoring, alerting
- domain: big-data, analytics
- platform: jvm, self-hosted, cross-platform
- tags: data-ingestion, data-synchronization, data-subscription, tubemq, apache-project, massive-data, event-streaming, data-engineering, messaging, real-time, docker, kubernetes

## Member repositories
- apache/inlong (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:53.906241+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 2026-08-30T04:33:08.239583+00:00, confidence not recorded.
  - readme: https://github.com/apache/inlong (fetched 2026-08-28T04:04:53.906241+00:00, sha 432ae5262592)
  - homepage: https://inlong.apache.org/ (fetched 2026-08-29T11:38:22.113898+00:00, sha 511f06010bdc)
  - site_page: https://inlong.apache.org/docs/introduction (fetched 2026-08-29T11:38:22.116907+00:00, sha 9f3b766b5b12)
  - site_page: https://inlong.apache.org/docs/next/introduction (fetched 2026-08-29T11:38:22.119361+00:00, sha 985f9dd6915d)
  - site_page: https://inlong.apache.org/docs/2.3.0/introduction (fetched 2026-08-29T11:38:22.121740+00:00, sha 1f7413bb5ce2)
  - site_page: https://inlong.apache.org/docs/contact (fetched 2026-08-29T11:38:22.123518+00:00, sha ac3ab9b526be)
- Data as of 2026-08-30T08:39:29.467469+00:00.