Provides 6 default ranking criteria used to bucket sort documents
Accepts custom criteria and can apply them in any custom order
Support 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

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

You can read the deep dive if you want more information on the engine, it describes the whole process of generating updates and handling queries or you can take a look at the typos and ranking rules if you want to know the default rules used to sort the documents.

We will be proud if you submit issues and pull requests. You can help to grow this project and start contributing by checking issues tagged "good-first-issue". It is a good start!

Quick Start

You can deploy your own instant, relevant and typo-tolerant MeiliDB search engine by yourself too. Something similar to the demo above can be achieved by following these little three steps first. You will need to create your own web front display to make it pretty though.

Deploy the Server

You can deploy the server on your own machine, it will listen to HTTP requests on the 8080 port by default.

cargo run --release

Create an Index and Upload Some Documents

MeiliDB can serve multiple indexes, with different kinds of documents, therefore, it is required to create the index before sending documents to it.

curl -i -X POST 'http://127.0.0.1:8080/indexes/movies'

Now that the server knows about our brand new index, we can send it data. We provided you a little dataset, it is available in the datasets/ directory.

curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \
  --header 'content-type: application/json' \
  --data @datasets/movies/movies.json

Search for Documents

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

curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman'

{
  "hits": [
    {
      "id": "29751",
      "title": "Batman Unmasked: The Psychology of the Dark Knight",
      "poster": "https://image.tmdb.org/t/p/w1280/jjHu128XLARc2k4cJrblAvZe0HE.jpg",
      "overview": "Delve into the world of Batman and the vigilante justice tha",
      "release_date": "2008-07-15"
    },
    {
      "id": "471474",
      "title": "Batman: Gotham by Gaslight",
      "poster": "https://image.tmdb.org/t/p/w1280/7souLi5zqQCnpZVghaXv0Wowi0y.jpg",
      "overview": "ve Victorian Age Gotham City, Batman begins his war on crime",
      "release_date": "2018-01-12"
    }
  ],
  "offset": 0,
  "limit": 2,
  "processingTimeMs": 1,
  "query": "botman"
}

Performances

With a database composed of 100 353 documents with 352 attributes each and 3 of them indexed. So more than 300 000 fields indexed for 35 million stored we can handle 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

Notes

With Rust 1.32 the allocator has been changed to use the system allocator. We have seen much better performances when using jemalloc as the global allocator.

Usage and Examples

MeiliDB also provides an example binary that is mostly used for features testing. Notice that the example binary is faster to index data as it does read direct CSV files and not JSON HTTP payloads.

The index subcommand has been made to create an index and inject documents into it. Using the command line below, the index will be named movies and the 19 700 movies of the datasets/ will be injected in MeiliDB.

cargo run --release --example from_file -- \
    index example.mdb datasets/movies/movies.csv \
    --schema datasets/movies/schema.toml

Once the first command is done, you can query the freshly created movies index using the search subcomand. In this example we filtered the dataset to only show non-adult movies using the non-definitive !adult syntax filter.

cargo run --release --example from_file -- \
    search example.mdb \
    --number 4 \
    --filter '!adult' \
    id popularity adult original_title