I also noticed quite a bit of parity between Hydra and Citus on data set size. Is Hydra a fork of Citus columnar storage?
0 - https://github.com/ClickHouse/ClickBench/blob/main/postgresq...
I found that is common among these types of databases (e.g. Citus, Timescale, etc.) which perform well under very specific conditions, and worse for many (most?) other things, sometimes significantly worse.
That said, Hydra does take up ~17.5G for that benchmark and "PostgreSQL tuned" about 120G, the insert time is ~9 times faster, and "cold run" is quite a bit faster too. It's only "hot run" that shows a fairly small difference. I think it's fair to say Hydra "wins" that benchmark.
> Is Hydra a fork of Citus columnar storage?
Yes: "Hydra Columnar is a fork from Citus Columnar c. April, 2022".
you can run pg on compressed filesystem
> it won't be free in terms of CPU cycles
it can reduce IO traffic significantly, and it can be very positive trade off depending on circumstances.
In general, I did very deep benchmarking of pg, clickhouse and duckdb, and I sure didn't make stupid mistakes like this: https://news.ycombinator.com/item?id=36990831
My dataset has 50B rows and 2tb of data, and I think columnar dbs are very overhiped and I chose pg because:
- pg performance is acceptable, maybe 2-5x times slower than clickhouse and duckdb on some queries if pg is configured correctly and run on compressed storage
- clickhouse and duckdb start falling apart very fast because they specialized on very narrow type of queries: https://github.com/ClickHouse/ClickHouse/issues/47520 https://github.com/ClickHouse/ClickHouse/issues/47521 https://github.com/duckdb/duckdb/discussions/6696
Feature request is not implemented yet: https://github.com/ClickHouse/ClickHouse/issues/40588
It can do size about 256 times larger than a memory because only one bucket has to be in memory while merging. It works for distributed query processing as well and is enabled by default.
About the linked issue - it looks like it is related to some extra optimization on top of what already exists.
its hard for me to judge about implementation details, but per that person reply memory is also multiplied by number of threads which do aggregation.
I just created large tables, and tried to join, group by, sort them in pg, clickhouse, duckdb, looked what failed or being slow, and tried to resolve it.
I am happy to answer specific questions, but I didn't use timescaledb.
I also "just" use PostgreSQL for all of this by the way, but the limitations are pretty obvious. You're much more limited in what you can query with good performance, unless you start creating tons of queries or pre-computed data and such, which have their own trade-offs. Columnar DBs are "overhyped" in the sense that everything in programming seems to be, but they do exist for good reasons (the reason I don't use it are because they also come with their own set of downsides, as well as just plain laziness).
that postgres config is very underpowered, it has only 8 workers per gather while machine has 192 vcpus.