pg_jsonschema VS zombodb

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

> In other words, there is still a (lossy) translation layer, it just happens to be in the RDBMS rather than in-app.

It's not lossy if your application can guarantee a json <-> datatype roundtrip and the json is validated with jsonschema (generated by your application)

In Rust it's something like this

https://serde.rs/ to do the data type <-> json mapping

https://docs.rs/schemars/latest/schemars/ to generate jsonschema from your types

https://github.com/supabase/pg_jsonschema to validate jsonschema in your database (postgres). with this setup it's interesting (but not required) to also use https://docs.rs/jsonschema/latest/jsonschema/ to validate the schema in your application

Pretty exciting!

What about optionally validating some columns with jsonschema? Perhaps using https://github.com/supabase/pg_jsonschema - is using other postgres extensions supported in FerretDB? (if not, maybe it's feasible to incorporate the code of pg_jsonschema in FerretDB?)

I'm not currently using it, but I'm strongly considering validating json in postgres with https://github.com/supabase/pg_jsonschema - which uses the https://docs.rs/jsonschema/latest/jsonschema/ Rust crate

So I'm not sure if my feedback is valid but, I sure hope that the jsonschema crate follows the spec! Otherwise I'll never use jsonschema but instead something-not-exactly-jsonschema. In other words.. you better not break anything.

Sounds dumb, but I want JSON field schema validation. I added a JSON column for flexible data, and although I'm happy with its flexibility, I kinda hope I can validate the JSON data structure. Recently I just found an extension [1] and will try soon.

[1] https://github.com/supabase/pg_jsonschema

https://github.com/supabase/pg_jsonschema is new and looks good

Born as an excuse to play with pgx, pg_jsonschema is a solution we're exploring to allow enforcing more structure on json and jsonb typed postgres columns. Only 10 lines of code 😎

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.