mirror of
https://github.com/meilisearch/MeiliSearch
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48 lines
2.3 KiB
Markdown
48 lines
2.3 KiB
Markdown
# MeiliDB
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A _full-text search database_ using a key-value store internally.
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It uses [RocksDB](https://github.com/facebook/rocksdb) like a classic database, to store documents and internal data. The key-value store power allow us to handle updates and queries with small memory and CPU overheads.
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You can [read the deep dive](deep-dive.md) if you want more informations on the engine, it describes the whole process of generating updates and handling queries.
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We will be proud if you send pull requests to help us grow this project, you can start with [issues tagged "good-first-issue"](https://github.com/Kerollmops/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) to start !
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At the moment this is a library only, this means that binaries are not part of this repository but since I'm still nice I have made some examples for you in the `examples/` folder that works with the data located in the `misc/` folder.
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In a near future MeiliDB we be a binary like any database: updated and queried using some kind of protocol. It is the final goal, [see the milestones](https://github.com/Kerollmops/MeiliDB/milestones). MeiliDB will just be a bunch of network and protocols functions wrapping the library which itself will be published to https://crates.io, following the same update cycle.
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## Performances
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_these informations have been made with a version dated of october 2018, we must update them_
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We made some tests on remote machines and found that we can handle with a dataset of near 280k products, on a server that cost 5$/month with 1vCPU and 1GB of ram and on the same index and with a simple query:
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- near 190 users with an average response time of 90ms
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- 150 users with an average response time of 70ms
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- 100 users with an average response time of 45ms
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Network is mesured, servers are located in amsterdam and tests are made between two different datacenters.
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## Usage and examples
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MeiliDB work with an index like most of the search engines.
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So to test the library you can create one by indexing a simple csv file.
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```bash
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cargo run --release --example create-database -- test.mdb misc/kaggle.csv
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```
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Once the command finished indexing the database should have been saved under the `test.mdb` folder.
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Now you can easily run the `query-database` example to check what is stored in it.
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```bash
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cargo run --release --example query-database -- test.mdb
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```
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