afb4133bd2
212: Introduce integration test on criteria r=Kerollmops a=ManyTheFish - add pre-ranked dataset - test each criterion 1 by 1 - test all criteria in several order 222: Move the `UpdateStore` into the http-ui crate r=Kerollmops a=Kerollmops We no more need to have the `UpdateStore` inside of the mill crate as this is the job of the caller to stack the updates and sequentially give them to milli. 223: Update dataset links r=Kerollmops a=curquiza Co-authored-by: many <maxime@meilisearch.com> Co-authored-by: Many <legendre.maxime.isn@gmail.com> Co-authored-by: Kerollmops <clement@meilisearch.com> Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
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.