hydra VS paradedb

How does this compare to Hydra? https://www.hydra.so/

Don't feel bad, lots of people get bitten by not reading all the way down to the bottom of their readme: https://github.com/hydradatabase/hydra/blob/v1.1.2/README.md... While Hydra may very well license their own code Apache 2, they ship the AGPLv3 columnar which to my very best IANAL understanding taints the whole stack and AGPLv3's everything all the way through https://github.com/hydradatabase/hydra/blob/v1.1.2/columnar/...

Columnar store PostgreSQL extension exists, here are two but I think I’m missing at least another one:

https://github.com/citusdata/cstore_fdw

https://github.com/hydradatabase/hydra

You can also connect other stores using the foreign data wrappers, like parquet files stored on an object store, duckdb, clickhouse… though the joins aren’t optimised as PostgreSQL would do full scan on the external table when joining.

New column store alternative : https://github.com/hydradatabase/hydra

HN: https://news.ycombinator.com/item?id=37571974

some previous discussions:

https://news.ycombinator.com/item?id=37247945

https://news.ycombinator.com/item?id=36987920

and a relevant observation is that there are actually multiple license files in the repo so the consumer should read their explicit licensing section of the readme <https://github.com/hydradatabase/hydra#license> since the GitHub sidebar is misleading

Hydra DB Link to Github -> Worked well for aggregated query usecases but not for queries that build reports. Also, data insertion and updation is abyssmal on columnar dbs.

There's a lot of experience about db operation and how to approach MVCC encoded in PostgreSQL that shouldn't be underestimated.

[0]: https://github.com/hydradatabase/hydra

Postgres's FTS is actually quite solid! You can get very far with just the built-in tsvector. The ranking could be improved, though, which was one of the reasons for creating pg_search in the first place: https://github.com/paradedb/paradedb/tree/dev/pg_search (disclaimer: I work on pg_search @ ParadeDB)

Elasticsearch is good, and it does scale, but it is much more cumbersome and expensive to scale and operate than Postgres. If you use the managed service, you'll pay for the operational pain in the form of higher pricing.

The Postgres movement is strong and extensions like ParadeDB https://github.com/paradedb/paradedb are designed specifically to solve this pain point (Disclaimer: I work for ParadeDB)

No timeline yet, but we know it's a high-priority feature and are working hard on it. Best way would be to join our Slack (link here: https://github.com/paradedb/paradedb/blob/dev/README.md) to follow along. It will be in the coming weeks/months, though.

You're right. We're working on this currently. You can track the issue here: https://github.com/paradedb/paradedb/issues/717

There are definitely ways to cleanly make Postgres scale for analytics. We didn't discuss in this blog, but we will be writing about them in the future. For example, check out what the folks at ParadeDB are doing. https://github.com/paradedb/paradedb. Neon is doing an awesome job separating compute from storage. Supabase contributed foreign data wrappers make it super easy to read from S3 into Postgres. Lots of great work going out there :)