MeiliSearch is a powerful, fast, open-source, easy to use and deploy search engine. Both searching and indexing are highly customizable. Features such as typo-tolerance, filters, and synonyms are provided out-of-the-box. For more information about features go to our documentation.

Meili helps the Rust community find crates on crates.meilisearch.com

Features

Search as-you-type experience (answers < 50 milliseconds)
Full-text search
Typo tolerant (understands typos and miss-spelling)
Supports Kanji
Supports Synonym
Easy to install, deploy, and maintain
Whole documents are returned
Highly customizable
RESTful API

Get started

Deploy the Server

Run it using Docker

docker run -it -p 7700:7700 --rm getmeili/meilisearch

Installing with Homebrew

brew update && brew install meilisearch
meilisearch

Installing with APT

echo "deb [trusted=yes] https://apt.fury.io/meilisearch/ /" > /etc/apt/sources.list.d/fury.list
apt update && apt install meilisearch-http
meilisearch

Download the binary

curl -L https://install.meilisearch.com | sh
./meilisearch

Run it on heroku

Compile and run it from sources

If you have the Rust toolchain already installed on your local system, clone the repository and change it to your working directory. In the cloned repository, compile MeiliSearch.

git clone https://github.com/meilisearch/MeiliSearch.git
cd MeiliSearch
cargo run --release

Create an Index and Upload Some Documents

Let's create an index! If you need a sample dataset, use this movie database. You can also find it in the datasets/ directory.

curl -L 'https://bit.ly/2PAcw9l' -o movies.json

MeiliSearch can serve multiple indexes, with different kinds of documents. It is required to create an index before sending documents to it.

curl -i -X POST 'http://127.0.0.1:7700/indexes' --data '{ "name": "Movies", "uid": "movies" }'

Now that the server knows about your brand new index, you're ready to send it some data.

curl -i -X POST 'http://127.0.0.1:7700/indexes/movies/documents' \
  --header 'content-type: application/json' \
  --data-binary @movies.json

Search for Documents

In command line

The search engine is now aware of your documents and can serve those via a HTTP server.
The jq command-line tool can greatly help you read the server responses.

curl 'http://127.0.0.1:7700/indexes/movies/search?q=botman+robin&limit=2' | jq

{
  "hits": [
    {
      "id": "415",
      "title": "Batman & Robin",
      "poster": "https://image.tmdb.org/t/p/w1280/79AYCcxw3kSKbhGpx1LiqaCAbwo.jpg",
      "overview": "Along with crime-fighting partner Robin and new recruit Batgirl...",
      "release_date": "1997-06-20",
    },
    {
      "id": "411736",
      "title": "Batman: Return of the Caped Crusaders",
      "poster": "https://image.tmdb.org/t/p/w1280/GW3IyMW5Xgl0cgCN8wu96IlNpD.jpg",
      "overview": "Adam West and Burt Ward returns to their iconic roles of Batman and Robin...",
      "release_date": "2016-10-08",
    }
  ],
  "offset": 0,
  "limit": 2,
  "processingTimeMs": 1,
  "query": "botman robin"
}

Use the Web Interface

We also deliver an out-of-the-box web interface in which you can test MeiliSearch interactively.

You can access the web interface in your web browser at the root of the server. The default URL is http://127.0.0.1:7700. All you need to do is open your web browser and enter MeiliSearch’s address to visit it. This will lead you to a web page with a search bar that allows you to search in a given set of documents.

Documentation

Now that your MeiliSearch server is up and running, you can learn more about how to tune your search engine in the documentation.

How it works

MeiliSearch uses LMDB as the internal key-value store. The key-value store allows to handle updates and queries with small memory and CPU overheads. The whole ranking system is data oriented and ensures great performances.

You can read this document if you want to dive deeper into the engine. The whole process of generating updates and handling queries is described in it. Besides, to learn the default rules used for sorting documents, you can take a look at this typos and ranking rules explanation.

Technical features

Provides 6 default ranking criteria used to bucket sort documents
Accepts custom criteria and can apply them in any custom order
Supports ranged queries, useful for paginating results
Can distinct and filter returned documents based on context defined rules
Searches for concatenated and splitted query words to improve the search quality.
Can store complete documents or only user schema specified fields
The default tokenizer can index Latin and Kanji based languages
Returns the matching text areas, useful to highlight matched words in results
Accepts query time search config like the searchable attributes
Supports runtime incremental indexing

Performances

When processing a dataset composed of 100 353 documents with 352 attributes each and 3 of them indexed, which means more than 300 000 fields indexed for 35 million stored, MeiliSearch is able to carry out more than 2.8k req/sec with an average response time of 9 ms on an Intel i7-7700 (8) @ 4.2GHz.

Requests are made using wrk and scripted to simulate real users' queries.

Running 10s test @ http://localhost:2230
  2 threads and 25 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency     9.52ms    7.61ms  99.25ms   84.58%
    Req/Sec     1.41k   119.11     1.78k    64.50%
  28080 requests in 10.01s, 7.42MB read
Requests/sec:   2806.46
Transfer/sec:    759.17KB

We also indexed a dataset containing about 12 millions cities names in 24 minutes on a 8 cores, 64 GB of RAM, and a 300 GB NMVe SSD machine.
The size of the resulting database reached 16 GB and search results were presented between 30 ms and 4 seconds for short prefix queries.

Notes

In Rust 1.32, the allocator has been changed to use the system allocator. We observed significant performance improvements when using jemalloc as the global allocator.

Contributing

Hey! We're glad you're thinking about contributing to MeiliSearch! If you think something is missing or could be improved, please open issues and pull requests. If you'd like to help this project grow, we'd love to have you! To start contributing, checking issues tagged as "good-first-issue" is a good start!

Analytic Events

Once a day, events are being sent to our Amplitude instance so we can know how many people are using MeiliSearch.
Only information concerning the platform on which the server runs is concerned. No other information is being sent.
If this doesn't suit you, you can disable these analytics by using the MEILI_NO_ANALYTICS env variable.

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MeiliSearch