roaring-rs VS tantivy

There are issues and pull requests but I advise you to look at the milli folder in the Meilisearch repository, it’s where all the logic is done. We extensively use RoaringBitmaps, heed the LMDB wrapper and grenad when indexing.

On some benchmarks, we are faster but most of the time we aren't. We absolutely need to introduce benchmarks with the croaring-rs library and most of those performances gain could also be achieved with other methods to do multi-ops for example.

We tried to release the new version of roaring-rs, better compressed bitset in Rust, but found out that the core library simd module was blocking us. We now have to work on std::simd to release the blocking features.

Yeah, it can be attributed to using the roaring-rs library, but not just that, we have done so much to improve the search performances by reducing the number of set-operations we do.

Given the shoutouts to Burntsushi and Lemire this is almost certainly a bitmap trigram index based engine similar to https://github.com/google/zoekt

The index is likely based on Roaring bitmaps, presumably https://github.com/RoaringBitmap/roaring-rs in this case.

Nice architecture, exactly how I would have done it also.

I have worked on roaring-rs, a very fast library to do set operations like unions and intersections, and improved the four operations by using the standard ops traits.

I have implemented a better way of specifying ranges to be inserted or removed from a RoaringBitmap by using the RangeBounds trait. The roaring-rs library exposes fast data-structures to do set operations, like intersections and unions.

Tantivy - a full-text indexing library written in Rust. Has a great performance and featureset.

By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea :)

Both libraries offer multilingual support for keywords, I believe, so that's a benefit to vector search where multilingual embedding models are rather expensive.

We also implemented our schemaless columnar storage optimized for object storage.

The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it.

[0] tantivy github repo: https://github.com/quickwit-oss/tantivy

The issue for geo search is here: https://github.com/quickwit-oss/tantivy/issues/44

Search index : Custom-built using tantivy.

The roaring bitmap variant is used only for the optional index (1 docid => 0 or 1 value) in the columnar storage (DocValues), not for the inverted index. Since this is used for aggregation, some queries may be a full scan.

The inverted index in tantivy uses bitpacked values of 128 elements with a skip index on top.

> I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block

The select for the sparse codec is a [simple array index access](https://github.com/quickwit-oss/tantivy/blob/main/columnar/s...), that is hard to beat. Compression is not good near the 5k threshold though.

Hi /r/rust, I’m a SWE on Etsy’s Retrieval Systems team where we’re building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). We’re looking to bring two new engineers onto the team.

Quick Search: Easily find specific notes with Velo's fuzzy-search feature, powered by tantivy. tantivy might have been a little overkill, but it was really easy to integrate.