zombodb VS vitess

And lots of interesting extensions use it, like

https://github.com/tembo-io/pgmq

https://github.com/zombodb/zombodb

https://github.com/supabase/pg_jsonschema

Point 2 is generally solvable via engineering effort and careful dedicated code. From the existing tools, PGSync is an open source project that aims to specifically solve this problem. ZomboDB is an interesting Postgres extension that tackles point 2 (and I think partially point 3), by controlling and querying Elasticsearch through Postgres. I haven't yet tried either of these two projects, so I can't comment on their trade-offs, but I wanted to mention them.

Curious, did you try zombodb? [https://www.zombodb.com/]

What about zombodb, do you think that quickwit has all the necessary APIs?

No. Haha. Was just the right name for https://github.com/zombodb/zombodb at the time. Software where the only limit is yourself!

> The hardest part of building any search engine is keeping the index up-to-date with changes made to the underlying data store.

This deserves mention, as it solves that problem: https://github.com/zombodb/zombodb

From the README:

> ZomboDB brings powerful text-search and analytics features to Postgres by using Elasticsearch as an index type. Its comprehensive query language and SQL functions enable new and creative ways to query your relational data.

> From a technical perspective, ZomboDB is a 100% native Postgres extension that implements Postgres' Index Access Method API. As a native Postgres index type, ZomboDB allows you to CREATE INDEX ... USING zombodb on your existing Postgres tables. At that point, ZomboDB takes over and fully manages the remote Elasticsearch index and guarantees transactionally-correct text-search query results.

I find other things also hard in search engines: dealing with the plethora of human languages and all the requirements we may have to processing them. A mature solution like ES therefor is almost a must in the more demanding cases.

You can read more about it on our GitHub repo, but basically it brings most of the power of elasticsearch’s searching and analytics abilities straight into Postgres.

With Vitess likely merging a lot of its binaries into a single unified binary: https://github.com/vitessio/vitess/issues/7471#issuecomment-...

... it would be a wild future if Vitess replaced the underlying MySQL engine with this as long as the performance is good enough.

Thank you for the compliment!

We recently started adding support for CTEs in Vitess! You can check out https://github.com/vitessio/vitess/pull/14321 if you want to see some technical details of the implementation.

For now, we have added preliminary support by converting them to derived tables internally, but we believe that we need to make CTEs first-class citizens themselves of query planning. Once we make that change, we can look towards supporting recursive CTEs.

This however will take some time, but then, all good things do!

Why would it be a Google project? https://github.com/vitessio/vitess

This is great news. I strolled around https://github.com/vitessio/vitess/issues/12967.

Are there any public discussions of more trade-offs vitess has to make to enable fks?

MySQL + Vitess I noticed Slack gets by using MySQL because they're using Vites. From Slack's post (https://slack.engineering/scaling-datastores-at-slack-with-vitess/) it seems like they choose Vites because it facilitated a smooth transition because it's built on top of MySQL.

This sounds very similar to the connection pooling done by vitess https://vitess.io/.

https://github.com/vitessio/vitess/issues/12967

A lot of this is possible thanks to the magic of Vitess.

"Vitess is a database clustering system for horizontal scaling of MySQL."

https://github.com/vitessio/vitess