# Redis

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

Repository: https://github.com/redis/redis
Canonical: https://ross.abutalabs.com/products/redis
Homepage: http://redis.io
Language: C
License: NOASSERTION
License Family: other
Topics: database, key-value, nosql, redis, caching, distributed-systems, open-source, real-time, time-series, cache, in-memory, in-memory-database, key-value-store, message-broker, no-sql, message-queue, realtime, vector-search, vector-databases, json
Last push: 2026-08-26T22:16:48+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": 6374, "days_push": 7, "days_rel": 16, "gap_med": 0.0, "n_releases_24m": 67}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 76114, forks 24765 (observed 2026-08-28T04:12:21.562932+00:00)

## What it is
Redis is an open-source, in-memory data structure server written in C that doubles as a cache, NoSQL data store, message broker, and document/vector query engine. It supports strings, lists, sets, hashes, sorted sets, JSON, time series, and provides full-text and vector search with transactions and scripting.

## Use cases
- cache frequently accessed data to speed up web applications
- store user sessions in a distributed application
- build leaderboards with sorted sets
- implement rate limiting for APIs
- use as a message broker or pub/sub event bus
- run vector similarity search for AI applications
- store and query JSON documents
- track time-series metrics in real time

## When to choose
- you need sub-millisecond reads and writes for real-time applications
- you want a versatile cache with multiple eviction policies and key expiration
- you need a lightweight message broker or event store
- you want combined document, full-text, and vector search in one engine
- you need high-throughput data structures like counters, queues, and leaderboards

## When to avoid
- your dataset must exceed available RAM and cannot fit in memory
- you need complex multi-table relational queries and joins
- you require strict durability guarantees for every write without performance trade-offs
- you need a primary system of record with rich ACID transactional semantics

## Facets
- artifact type: service
- maturity: stable
- function: database, caching, message-queue, vector-database, search-engine, streaming, rate-limiting
- domain: databases, microservices, big-data
- platform: windows, bsd, self-hosted, cloud
- tags: in-memory-database, key-value-store, nosql, data-structure-server, message-broker, pub-sub, time-series, vector-search, document-database, session-store, real-time, search, linux, macos, docker

## Member repositories
- redis/redis (main) score 95
- redis/redis-doc (docs) score 10
- redis/docker-library-redis (infra) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:21.562932+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 2026-08-29T16:14:27.656173+00:00, confidence not recorded.
  - readme: https://github.com/redis/redis (fetched 2026-08-28T04:12:21.562932+00:00, sha 2c99015dba3e)
- Data as of 2026-08-30T08:39:29.467469+00:00.