fd032165d7
217: Improve the benchmarks readme r=Kerollmops a=irevoire - Move the Dataset part to the end of the readme so when peoples just want to run the benchmarks they are not tempted to download the benchmarks by hand (which are going to be downloaded anyway by the `build.rs` scritp) - Fix the links in the dataset -- wiki part Co-authored-by: Irevoire <tamo@meilisearch.com> |
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|---|---|---|
| .github | ||
| benchmarks | ||
| helpers | ||
| http-ui | ||
| infos | ||
| milli | ||
| search | ||
| .gitignore | ||
| bors.toml | ||
| Cargo.lock | ||
| Cargo.toml | ||
| LICENSE | ||
| qc_loop.sh | ||
| README.md | ||
a concurrent indexer combined with fast and relevant search algorithms
Introduction
This engine is a prototype, do not use it in production. This is one of the most advanced search engine I have worked on. It currently only supports the proximity criterion.
Compile and Run the server
You can specify the number of threads to use to index documents and many other settings too.
cd http-ui
cargo run --release -- --db my-database.mdb -vvv --indexing-jobs 8
Index your documents
It can index a massive amount of documents in not much time, I already achieved to index:
- 115m songs (song and artist name) in ~1h and take 107GB on disk.
- 12m cities (name, timezone and country ID) in 15min and take 10GB on disk.
All of that on a 39$/month machine with 4cores.
You can feed the engine with your CSV (comma-seperated, yes) data like this:
printf "name,age\nhello,32\nkiki,24\n" | http POST 127.0.0.1:9700/documents content-type:text/csv
Here ids will be automatically generated as UUID v4 if they doesn't exist in some or every documents.
Note that it also support JSON and JSON streaming, you can send them to the engine by using
the content-type:application/json and content-type:application/x-ndjson headers respectively.
Querying the engine via the website
You can query the engine by going to the HTML page itself.