pgx VS tantivy

It uses pgx (https://github.com/tcdi/pgx) which is our more generalized framework for developing Postgres extensions with Rust.

It's a great question, considering I've been using Rust for a number of years now, and I generally advocate its use for its rich ecosystem, safety and tooling. I actively contribute to pgx, a library for building Postgres extensions in Rust. Yet, Omnigres appears to be all done in C.

Our release today is a framework which extends this functionality to other databases/systems. If you’re familiar with Multicorn[1] or Steampipe[2], then it’s very similar. The framework is written in Rust, using the excellent pgx[3].

We have developed FDWs for Stripe, Firebase, BigQuery, Clickhouse, and Airtable (all in various “pre-release” states). The plan is to focus on the tools we’re using internally while we stabalize the framework.

There’s a lot in the blog post into our goals for this release. It’s early, but one of the things I’m most excited about.

[0] Postgres FDW: [https://www.postgresql.org/docs/current/sql-createforeigndat...

[1] Multicorn: https://multicorn.org/

[2] Steampipe: https://steampipe.io/

[2] pgx: [https://github.com/tcdi/pgx](https://github.com/tcdi/pgx)

BTW one nice easter egg is that with pgx there is actually no reason that we can't build even better search solutions inside the database itself.

> That thread led me to a project/product idea where you take an existing Postgres instance used for normal products or whatever, replicate it to various read only clusters with a custom search extension loaded and some orchestrator sitting on top (I’ve written most of one in rust that uses 0mq to communicate with it’s nodes) and create drop in search from existing databases with a nice guided web gui for automatic tuning suitable for most business use cases.

Very interesting idea -- just want to add one thing, write it in rust (with pgx?[0]) :)

[0]: https://github.com/tcdi/pgx

Hey HN,

It turns out choosing a good database-optimized UUID (and deciding whether to use serial, etc) isn't quite so simple, and I finally got a chance to do some exploration, write about it[0].

One of the reasons Postgres is the best open source database out there is it's extensibility -- so I hacked up a small extension for generating some of the more exotic (but crucially, lexicograhically sortable) UUID generation mechanisms:

https://github.com/t3hmrman/pg_idkit

This idea has been bumbling around my head for a while, but I finally got a chance to build it while working with Supabase on a post about IDs[0]!

Most of the heavy lifting is done by pgx[1] which is an amazing framework for building Postgres extensions in Rust. I think we are very early to the trend of amazing postgres extensions built in Rust, and it's yet another reason that it's an exciting time to be all-in on Postgres.

[0]: https://supabase.com/blog/choosing-a-postgres-primary-key

[1]: https://github.com/tcdi/pgx

[0]: https://supabase.com/blog/choosing-a-postgres-primary-key

The extension is still WIP but for those of ya'll that like Rust it's built on pgx which has excellent DX. The rust involved isn't complicated -- I'm basically laundering the functionality from other crates that are listed in the README.md.

Seems to be using this: https://github.com/tcdi/pgx

- https://github.com/furstenheim/is_jsonb_valid

pgx[0] is going to be pretty revolutionary for the postgres ecosystem I think -- there is so much functionality that can be utilized at the database level and I can't think of a language I want to do it with more than Rust.

[0]: https://github.com/tcdi/pgx

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.