mirror of
https://github.com/meilisearch/MeiliSearch
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149 lines
7.7 KiB
Markdown
149 lines
7.7 KiB
Markdown
# MeiliDB
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[](https://dev.azure.com/thomas0884/thomas/_build/latest?definitionId=1&branchName=master)
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[](https://deps.rs/repo/github/meilisearch/MeiliDB)
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[](https://commonsclause.com/)
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A _full-text search database_ based on the fast [LMDB key-value store](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database).
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## Features
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- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/criterion/mod.rs#L107-L113) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
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- Accepts [custom criteria](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/criterion/mod.rs#L24-L33) and can apply them in any custom order
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- Support [ranged queries](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L283), useful for paginating results
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- Can [distinct](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L265-L270) and [filter](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L246-L259) returned documents based on context defined rules
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- Searches for [concatenated](https://github.com/meilisearch/MeiliDB/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliDB/pull/232) to improve the search quality.
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- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-schema/src/lib.rs#L265-L279)
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- The [default tokenizer](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-tokenizer/src/lib.rs) can index latin and kanji based languages
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- Returns [the matching text areas](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/lib.rs#L66-L88), useful to highlight matched words in results
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- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L272-L275)
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- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/store/mod.rs#L143-L173)
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It uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) 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](https://github.com/meilisearch/MeiliDB/issues/82) and provides great performances.
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You can [read the deep dive](deep-dive.md) 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](typos-ranking-rules.md) if you want to know the default rules used to sort the documents.
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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"](https://github.com/meilisearch/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
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[](https://crates.meilisearch.com)
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> Meili helps the Rust community find crates on [crates.meilisearch.com](https://crates.meilisearch.com)
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## Quick Start
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You can deploy your own instant, relevant and typo-tolerant MeiliDB search engine by yourself too.
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Something similar to the demo above can be achieve by following these little three steps first.
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You will need to create your own web front display to make it pretty though.
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### Deploy the Server
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You can deploy the server on your own machine, it will listen to HTTP requests on the 8080 port by default.
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```bash
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cargo run --release
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```
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### Create an Index and Upload Some Documents
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MeiliDB can serve multiple indexes, with different kinds of documents,
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therefore, it is required to create the index before sending documents to it.
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```bash
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curl -i -X POST 'http://127.0.0.1:8080/indexes/movies'
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```
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Now that the server knows about our brand new index, we can send it data.
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We provided you a little dataset, it is available in the `datasets/` directory.
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```bash
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curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \
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--header 'content-type: application/json' \
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--data @datasets/movies/movies.json
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```
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### Search for Documents
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The search engine is now aware of our documents and can serve those via our HTTP server again.
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The [`jq` command line tool](https://stedolan.github.io/jq/) can greatly help you read the server responses.
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```bash
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curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman'
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```
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```json
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{
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"hits": [
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{
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"id": "29751",
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"title": "Batman Unmasked: The Psychology of the Dark Knight",
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"poster": "https://image.tmdb.org/t/p/w1280/jjHu128XLARc2k4cJrblAvZe0HE.jpg",
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"overview": "Delve into the world of Batman and the vigilante justice tha",
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"release_date": "2008-07-15"
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},
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{
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"id": "471474",
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"title": "Batman: Gotham by Gaslight",
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"poster": "https://image.tmdb.org/t/p/w1280/7souLi5zqQCnpZVghaXv0Wowi0y.jpg",
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"overview": "ve Victorian Age Gotham City, Batman begins his war on crime",
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"release_date": "2018-01-12"
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}
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],
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"offset": 0,
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"limit": 2,
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"processingTimeMs": 1,
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"query": "botman"
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}
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```
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## Performances
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With a database composed of _100 353_ documents with _352_ attributes each and _3_ of them indexed.
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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.
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Requests are made using [wrk](https://github.com/wg/wrk) and scripted to simulate real users queries.
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```
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Running 10s test @ http://localhost:2230
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2 threads and 25 connections
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Thread Stats Avg Stdev Max +/- Stdev
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Latency 9.52ms 7.61ms 99.25ms 84.58%
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Req/Sec 1.41k 119.11 1.78k 64.50%
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28080 requests in 10.01s, 7.42MB read
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Requests/sec: 2806.46
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Transfer/sec: 759.17KB
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```
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### Notes
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With Rust 1.32 the allocator has been [changed to use the system allocator](https://blog.rust-lang.org/2019/01/17/Rust-1.32.0.html#jemalloc-is-removed-by-default).
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We have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
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## Usage and Examples
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MeiliDB also provides an example binary that is mostly used for features testing.
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Notice that the example binary is faster to index data as it does read direct CSV files and not JSON HTTP payloads.
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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.
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```bash
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cargo run --release --example from_file -- \
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index example.mdb datasets/movies/movies.csv \
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--schema datasets/movies/schema.toml
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```
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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.
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```bash
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cargo run --release --example from_file -- \
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search example.mdb \
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--number 4 \
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--filter '!adult' \
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id popularity adult original_title
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```
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