mirror of
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160 lines
8.2 KiB
Markdown
160 lines
8.2 KiB
Markdown
# MeiliSearch
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[](https://github.com/meilisearch/MeiliSearch/actions)
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[](https://deps.rs/repo/github/meilisearch/MeiliSearch)
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[](https://github.com/meilisearch/MeiliSearch/blob/master/LICENSE)
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⚡ Ultra relevant and instant full-text search API 🔍
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MeiliSearch is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.
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For more [details about those features, go to our documentation](https://docs.meilisearch.com/).
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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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## Features
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* Search as-you-type experience (answers < 50ms)
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* Full-text search
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* Typo tolerant (understands typos and spelling mistakes)
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* Supports Kanji
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* Supports Synonym
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* Easy to install, deploy, and maintain
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* Whole documents returned
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* Highly customizable
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* RESTfull API
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## Quick Start
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### Deploy the Server
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```bash
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# If you have the Rust toolchain already installed, you can compile from the source
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git clone https://github.com/meilisearch/MeiliSearch.git
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cd MeiliSearch
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cargo run --release
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# You can also use Docker
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docker run -it -p 7700:7700 --rm getmeili/MeiliSearch
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# You can also download the binary
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curl -L https://install.meilisearch.com | sh
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./meilisearch
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```
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### Create an Index and Upload Some Documents
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We provide a movie dataset that you can use for testing purposes.
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```bash
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curl -L 'https://bit.ly/33MKvk4' -o movies.json
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```
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MeiliSearch 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://127.0.0.1:7700/indexes' --data '{ "name": "Movies", "uid": "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 small dataset that is available in the `datasets/` directory.
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```bash
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curl -i -X POST 'http://127.0.0.1:7700/indexes/movies/documents' \
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--header 'content-type: application/json' \
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--data-binary @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 significantly help you read the server responses.
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```bash
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curl 'http://127.0.0.1:7700/indexes/movies/search?q=botman+robin&limit=2' | jq
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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": "415",
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"title": "Batman & Robin",
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"poster": "https://image.tmdb.org/t/p/w1280/79AYCcxw3kSKbhGpx1LiqaCAbwo.jpg",
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"overview": "Along with crime-fighting partner Robin and new recruit Batgirl...",
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"release_date": "1997-06-20",
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},
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{
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"id": "411736",
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"title": "Batman: Return of the Caped Crusaders",
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"poster": "https://image.tmdb.org/t/p/w1280/GW3IyMW5Xgl0cgCN8wu96IlNpD.jpg",
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"overview": "Adam West and Burt Ward returns to their iconic roles of Batman and Robin...",
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"release_date": "2016-10-08",
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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 robin"
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}
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```
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### Documentation
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Now, that you have a running MeiliSearch, you can learn more and tune your search engine using [the documentation](https://docs.meilisearch.com).
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## How it works
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MeiliSearch 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/MeiliSearch/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. Also, 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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### Technical features
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- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L106-L111) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
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- Accepts [custom criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L20-L29) and can apply them in any custom order
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- Support [ranged queries](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L342), useful for paginating results
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- Can [distinct](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L324-L329) and [filter](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L313-L318) returned documents based on context defined rules
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- Searches for [concatenated](https://github.com/meilisearch/MeiliSearch/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliSearch/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/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/datasets/movies/schema.toml)
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- The [default tokenizer](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-tokenizer/src/lib.rs) can index latin and kanji based languages
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- Returns [the matching text areas](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-types/src/lib.rs#L49-L65), useful to highlight matched words in results
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- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L331-L336)
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- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/store/mod.rs#L143-L212)
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## Performances
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With a dataset 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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We also indexed a dataset containing something like _12 millions_ cities names in _24 minutes_ on a machine with _8 cores_, _64 GB of RAM_, and a _300 GB NMVe_ SSD.<br/>
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The resulting database was _16 GB_ and search results were between _30 ms_ and _4 seconds_ for short prefix queries.
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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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## Contributing
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We will be glad 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/MeiliSearch/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
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### Analytic Events
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We send events to our Amplitude instance to be aware of the number of people who use MeiliSearch.<br/>
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We only send the platform on which the server runs once by day. No other information is sent.<br/>
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If you do not want us to send events, you can disable these analytics by using the `MEILI_NO_ANALYTICS` env variable.
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