# ClickHouse

ClickHouse® is a real-time analytics database management system

Repository: https://github.com/ClickHouse/ClickHouse
Canonical: https://ross.abutalabs.com/products/clickhouse
Homepage: https://clickhouse.com
Language: C++
License: Apache-2.0
License Family: permissive
Topics: dbms, olap, analytics, sql, big-data, mpp, clickhouse, hacktoberfest, cpp, rust, ai, cloud-native, database, distributed, embedded, lakehouse, self-hosted
Last push: 2026-08-27T00:38:53+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3744, "days_push": 7, "days_rel": 7, "gap_med": 0.0, "n_releases_24m": 259}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 49470, forks 8860 (observed 2026-08-28T04:12:11.606749+00:00)

## What it is
ClickHouse is an open-source, column-oriented database management system designed for real-time analytical queries over large datasets. It is written in C++ and supports distributed, self-hosted, and cloud deployments with SQL access and official client drivers for multiple languages.

## Use cases
- run fast analytical SQL queries over billions of rows
- build real-time dashboards and reporting on event data
- store and query application logs and metrics at scale
- power user-facing analytics with sub-second query latency
- analyze clickstream and time-series data
- self-host a columnar OLAP data warehouse

## When to choose
- you need high-performance OLAP analytics on large volumes of data
- you want real-time reporting and dashboards over streaming events
- you need a self-hosted or cloud-native columnar database with SQL support
- your workload is read-heavy, append-mostly analytical queries

## When to avoid
- you need frequent row-level updates or deletes on transactional data
- you need a general-purpose OLTP database for transactional workloads
- your dataset is small and a traditional relational database suffices
- you require strict multi-row ACID transactions

## Facets
- artifact type: application
- maturity: stable
- function: database, search-engine, analytics
- domain: databases, big-data, analytics
- platform: windows, cross-platform, self-hosted, cloud
- tags: olap, column-oriented, dbms, mpp, distributed, real-time-analytics, sql-database, lakehouse, sql, data-engineering, real-time, linux, macos, docker, kubernetes

## Member repositories
- ClickHouse/ClickHouse (main) score 95
- ClickHouse/clickhouse-go (sdk) score 98
- ClickHouse/clickhouse-java (sdk) score 99
- ClickHouse/clickhouse-presentations (docs) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:11.606749+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 2026-08-29T16:20:13.421814+00:00, confidence not recorded.
  - readme: https://github.com/ClickHouse/ClickHouse (fetched 2026-08-28T04:12:11.606749+00:00, sha cb3745900216)
- Data as of 2026-08-30T08:39:29.467469+00:00.