77de82aaa4
254: Improve the facet string distribution speed r=Kerollmops a=Kerollmops
This pull request creates a data structure similar to the one we use for the faceted numbers, a tetratomic decision tree but this time for the facet strings. This PR also changes the facet distribution behavior by returning one of the original facet values, fixes #260.
This data structure defines bucket-like structures where documents ids are stored under their facet value and helps the search decide if it wants to move to a lower level under a given bucket or not, depending on if the current bucket contains interesting documents or not. The whole format, algorithm, and previous attempts are explained in the [`facet_string.rs` file](ec1cfdd42b/milli/src/search/facet/facet_string.rs).
Note that this data structure **could** be used to sort by string lexicographically, that hypothetically possible. We need more testing, in terms of performance and quality, as we will sort on lowercased versions of the facet values.
- [x] Implement a faster and more precise way to fetch the facet distribution.
- [x] Store and return the original facet string value. We currently return the lowercased version.
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clément Renault <clement@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.
Contributing
You can setup a git-hook to stop you from making a commit too fast. It'll stop you if:
- Any of the workspaces does not build
- Your code is not well-formatted
These two things are also checked in the CI, so ignoring the hook won't help you merge your code.
But if you need to, you can still add --no-verify when creating your commit to ignore the hook.
To enable the hook, run the following command from the root of the project:
cp script/pre-commit .git/hooks/pre-commit