hydra VS chdb

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

Hey HN, creator of DB Pilot here.

I first announced DB Pilot on HN back in April: https://news.ycombinator.com/item?id=35761979.

Since then a lot has improved: More databases are supported, most of the product can now be used for free, and most importantly:

The app now comes with an analytics workspace powered by an embedded ClickHouse instance, running locally on your machine. This allows you to query local files, files on S3, PostgreSQL, SQLite & more - and all of those at once.

Embedding ClickHouse was possible thanks to chDB (https://github.com/chdb-io/chdb). A recent discussion on HN about it: https://news.ycombinator.com/item?id=37985005

I recommend using ClickHouse instead of DuckDB.

It has been around since 2016, and it covers and extends the feature set of DuckDB with a huge margin. Worth noting that it never has breaking changes in its table format MergeTree.

I'm tracking the progress of DuckDB and see that it is modeled after ClickHouse, but does not approach it in terms of feature completeness, stability, or performance.

The closest to DuckDB option is to use its self-contained version, clickhouse-local: https://clickhouse.com/blog/extracting-converting-querying-l... or an embedded version, chdb: https://github.com/chdb-io/chdb

Different beasts, but if by any chance you love ClickHouse already and just want to run OLAP queries in-process, there's chdb: https://github.com/chdb-io/chdb

> Can you embed it in Python as a library?

https://github.com/chdb-io/chdb

    pip install chdb

New kid on the block that I prefer over DuckDB is CHDB (https://github.com/chdb-io/chdb). Embedded ClickHouse, so once you out grow your laptop you can simply move to an actual OLAP that's Open-source.