# crate/crate

CrateDB is a distributed and scalable SQL database for storing and analyzing massive amounts of data in near real-time, even with complex queries. It is PostgreSQL-compatible, and based on Lucene.

Repository: https://github.com/crate/crate
Canonical: https://ross.abutalabs.com/products/crate
Homepage: https://cratedb.com/database
Language: Java
License: Apache-2.0
License Family: permissive
Topics: sql, distributed, database, iot, cratedb, distributed-database, iot-database, iot-analytics, industrial-iot, olap, big-data, dbms, analytics, time-series, lucene, tsdb, postgresql, elasticsearch, distributed-sql-database, vector-database
Last push: 2026-08-26T17:10:06+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 4893, "days_push": 7, "days_rel": 14, "gap_med": 12.0, "n_releases_24m": 65}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4427, forks 611 (observed 2026-08-28T04:08:48.944111+00:00)

## What it is
CrateDB is a distributed, horizontally scalable SQL database built on Lucene, designed for real-time analytics on massive datasets. It supports the PostgreSQL wire protocol, full-text and geospatial search, time-series data, and vector search.

## Use cases
- ingesting tens of thousands of IoT sensor records per second
- running ad-hoc analytical SQL queries on large datasets in near real-time
- storing and querying time-series data from industrial equipment
- full-text search combined with relational SQL queries
- deploying a scalable database on Kubernetes or hybrid clouds
- querying semi-structured JSON documents with standard SQL

## When to choose
- you need horizontal scalability with a shared-nothing SQL cluster
- your workload mixes analytics, time-series, and full-text search
- you want PostgreSQL wire-protocol compatibility with distributed query execution
- you run in containerized or multi-region cloud/edge environments

## When to avoid
- you need strict ACID transactions with heavy OLTP write patterns
- you depend on advanced PostgreSQL features like stored procedures or full PL/pgSQL
- you want a simple single-node embedded database
- your dataset is small enough for a traditional RDBMS

## Facets
- artifact type: service
- maturity: active
- function: database, search-engine, vector-database, analytics
- domain: databases, big-data, analytics, iot, time-series
- platform: jvm, windows, self-hosted, cloud
- tags: distributed-sql, olap, time-series, lucene, postgresql-compatible, tsdb, iot-analytics, search, docker, kubernetes, linux, macos

## Member repositories
- crate/crate (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.944111+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 2026-08-29T18:20:56.179107+00:00, confidence not recorded.
  - readme: https://github.com/crate/crate (fetched 2026-08-28T04:08:48.944111+00:00, sha 602c6c13ec2e)
  - homepage: https://cratedb.com/database (fetched 2026-08-29T09:08:18.892742+00:00, sha 4b4e8fead74a)
- Data as of 2026-08-30T08:39:29.467469+00:00.