MeiliSearch

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⚡ Lightning Fast, Ultra Relevant, and Typo-Tolerant Search Engine 🔍

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 -p 7700:7700 -v $(pwd)/data.ms:/data.ms 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

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.

git clone https://github.com/meilisearch/MeiliSearch.git
cd MeiliSearch

In the cloned repository, compile 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 an 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 will allow you to search in the selected index.

Documentation

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

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

Performance

When processing a dataset composed of 5M books, each with their own titles and authors, MeiliSearch is able to carry out more than 553 req/sec with an average response time of 21 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://1.2.3.4:7700
  2 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    21.45ms   15.64ms 214.10ms   85.95%
    Req/Sec   256.48     37.66   330.00     69.50%
  5132 requests in 10.05s, 2.31MB read
Requests/sec:    510.46
Transfer/sec:    234.77KB

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.

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 about the platform on which the server runs is stored. 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.

Contact

Feel free to contact us about any questions you may have:

• At bonjour@meilisearch.com: English or French is welcome! 🇬🇧 🇫🇷
• Via the chat box available on every page of our documentation and on our landing page.
• Join our Slack community.
• By opening an issue.

Any suggestion or feedback is highly appreciated. Thank you for your support!