tantivy VS sonic

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.

Two years after, we are finally reaching a version that can deliver our promise. Two years is both very long for a startup and very short when building a distributed engine. And we decided to do it the hard way: we implemented our own OSS gossip library, our own {S3,JSON}-friendly columnar format for schemaless analytics, and of course, we maintain our own search library, tantivy. This is a lot of engineering investment and obviously, it takes some time to finally reach the end users.

Sonic - a project written in Rust, uses custom network communication protocol for fast communication between the client and the server.

This is uncanny, I just discovered ArchiveBox earlier today and set up a self-hosted instance on some home hardware for a collection of bookmarks of useful guides, tutorials, and references I've collected over the years.

Setting it up on K8s with sonic [1] as the search backend and importing a few hundred URLs only took ~an hour or so, and the cached pages look great for the most part.

[1] https://github.com/valeriansaliou/sonic

If you don't need advanced search features, you can use Sonic (https://github.com/valeriansaliou/sonic). It's blazing fast and you can save lot of money on servers.

What about Sonic? Maybe it requires a bit of integration, but it's simple and blazing fast.

git clone https://github.com/valeriansaliou/sonic.git

- Sonic (https://github.com/valeriansaliou/sonic)

There isn't enough out there comparing all these for the simple typical fuzzy search/search box usecase -- which I think is 80% of people doing search today.

Like other people are pointing out, most of these engines won't have all the features of ES (or more accurately Lucene) but I am pretty convinced that most of the time it doesn't actually matter and if someone is searching on your site excessively maybe there's a problem with your UX (unless you're a search engine or repository of information).

[0]: https://supabase.com/blog/postgres-full-text-search-vs-the-r...