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A query and indexing engine for Redis, providing secondary indexing, full-text search, and aggregations.

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RediSearch

Querying, secondary indexing, and full-text search for Redis

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Overview

RediSearch is a Redis module that provides querying, secondary indexing, and full-text search for Redis. To use RediSearch, you first declare indexes on your Redis data. You can then use the RediSearch query language to query that data.

RediSearch uses compressed, inverted indexes for fast indexing with a low memory footprint.

RediSearch indexes enhance Redis by providing exact-phrase matching, fuzzy search, and numeric filtering, among many other features.

Getting started

If you're just getting started with RediSearch, check out the official RediSearch tutorial. Also, consider viewing our RediSearch video explainer.

The fastest way to get up and running with RediSearch is by using the RediSearch Docker image.

Trying RediSearch

To try RediSearch, either use the RediSearch Docker image, or create a free Redis Cloud Essentials account to get a RediSearch instance in the cloud.

Docker image

The RediSearch Docker image makes it easy to try RediSearch.

To create a local RediSearch container, run:

$ docker run -p 6379:6379 redislabs/redisearch:latest

To connect to this instance, run:

$ redis-cli

Documentation

The RediSearch documentation provides a complete overview of RediSearch. Helpful sections include:

Mailing list and forum

Got questions? Join us in #redisearch on the Redis Discord server.

If you have a more detailed question, drop us a line on the RediSearch Discussion Forum.

Client libraries

You can use any standard Redis client library to run RediSearch commands, but it's easiest to use a library that wraps the RediSearch API.

Language Library Author License Stars
Python redisearch-py Redis Labs BSD redisearch-py-stars
Java (Jedis client library) JRediSearch Redis Labs BSD JRediSearch-stars
Java (Lettuce client library) lettusearch Redis Labs Apache-2.0 lettusearch-stars
Java spring-redisearch Redis Labs Apache-2.0 spring-redisearch-stars
Java redis-modules-java dengliming Apache-2.0 redis-modules-java-stars
Go redisearch-go Redis Labs BSD redisearch-go-stars
JavaScript RedRediSearch Kyle J. Davis MIT RedRediSearch-stars
JavaScript redis-redisearch Kyle J. Davis MIT redis-redisearch-stars
TypeScript redis-modules-sdk Dani Tseitlin BSD-3-Clause redis-modules-sdk-stars
C# NRediSearch Marc Gravell MIT NRediSearch-stars
PHP redisearch-php Ethan Hann MIT redisearch-php-stars
Rust redisearch-api-rs Redis Labs BSD redisearch-api-rs-stars
Ruby on Rails redi_search_rails Dmitry Polyakovsky MIT redi_search_rails-stars
Ruby redisearch-rb Victor Ruiz MIT redisearch-rb-stars
Ruby redi_search Nick Pezza MIT redi_search-stars

RediSearch features

  • Full-Text indexing of multiple fields in Redis hashes
  • Incremental indexing without performance loss
  • Document ranking (using tf-idf, with optional user-provided weights)
  • Field weighting
  • Complex boolean queries with AND, OR, and NOT operators
  • Prefix matching, fuzzy matching, and exact-phrase queries
  • Support for double-metaphone phonetic matching
  • Auto-complete suggestions (with fuzzy prefix suggestions)
  • Stemming-based query expansion in many languages (using Snowball)
  • Support for Chinese-language tokenization and querying (using Friso)
  • Numeric filters and ranges
  • Geospatial searches using Redis geospatial indexing
  • A powerful aggregations engine
  • Supports for all utf-8 encoded text
  • Retrieve full documents, selected fields, or only the document IDs
  • Sorting results (for example, by creation date)

Cluster support

RediSearch has a distributed cluster version that scales to billions of documents across hundreds of servers. At the moment, distributed RediSearch is available as part of Redis Enterprise Cloud and Redis Enterprise Software.

See RediSearch on Redis Enterprise for more information.

License

RediSearch is licensed under the Redis Source Available License Agreement.

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A query and indexing engine for Redis, providing secondary indexing, full-text search, and aggregations.

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