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94 lines
5.7 KiB
Markdown
94 lines
5.7 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/Kerollmops/MeiliDB)
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[](https://github.com/Kerollmops/MeiliDB)
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[](
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https://www.rust-lang.org)
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A _full-text search database_ using a key-value store internally.
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## Features
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- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/criterion/mod.rs#L95-L101) 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/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/criterion/mod.rs#L22-L29) and can apply them in any custom order
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- Support [ranged queries](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/query_builder.rs#L146), useful for paginating results
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- Can [distinct](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/query_builder.rs#L68) and [filter](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/query_builder.rs#L57) returned documents based on context defined rules
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- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/examples/movies/schema-movies.toml)
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- The [default tokenizer](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-tokenizer/src/lib.rs#L99) can index latin and kanji based languages
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- Returns [the matching text areas](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/lib.rs#L117-L120), useful to highlight matched words in results
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- Accepts query time search config like the [searchable fields](https://github.com/meilisearch/MeiliDB/blob/3d85cbf0cfa3a3103cf1e151a75a443719cdd5d7/meilidb-core/src/query_builder.rs#L79)
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- Supports run time indexing (incremental indexing)
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It uses [sled](https://github.com/spacejam/sled) 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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The project is only a library yet. It means that there is no binary provided yet. To get started, you can check the examples wich are made to work with the data located in the `misc/` folder.
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MeiliDB will be a binary in a near future so you will be able to use it as a database out-of-the-box. We should be able to query it using a [to-be-defined](https://github.com/meilisearch/MeiliDB/issues/38) protocol. This is our current goal, [see the milestones](https://github.com/meilisearch/MeiliDB/milestones). In the end, the binary will be a bunch of network protocols and wrappers around the library - which will also be published on [crates.io](https://crates.io). Both the binary and the library will follow the same update cycle.
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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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The default Rust allocator has recently been [changed to use the system allocator](https://github.com/rust-lang/rust/pull/51241/).
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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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You can try a little part of MeiliDB with the following commands.
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It creates an index named _movies_ and insert two great Tarantino movies in it.
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```bash
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cargo run --release
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curl -XPOST 'http://127.0.0.1:8000/movies' \
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-d '
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identifier = "id"
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[attributes.id]
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stored = true
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[attributes.title]
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stored = true
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indexed = true
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'
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curl -H 'Content-Type: application/json' \
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-XPUT 'http://127.0.0.1:8000/movies' \
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-d '{ "id": 123, "title": "Inglorious Bastards" }'
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curl -H 'Content-Type: application/json' \
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-XPUT 'http://127.0.0.1:8000/movies' \
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-d '{ "id": 456, "title": "Django Unchained" }'
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```
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Once the database is initialized you can query it by using the following command:
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```bash
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curl -XGET 'http://127.0.0.1:8000/movies/search?q=inglo'
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```
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