Since benchmarks can be misleading I want to point out that the differences between Hydra and the "tuned"[0] PostgreSQL (which are some very basic settings) are a lot less convincing, with plain old PG coming ahead on quite a few: https://tinyurl.com/eju9tht2
I also noticed quite a bit of parity between Hydra and Citus on data set size. Is Hydra a fork of Citus columnar storage?
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".
I'm sure you can, but AFAIK neither uses compression in that benchmark so it's a fair comparison. Even if filesystem compression would reduce that to 17.5G (doubtable), it won't be free in terms of CPU cycles, and no matter what it's still ~120G to load in memory, bytes to scan/update, etc.
I had assumed that PostgreSQL is so much larger because it creates heaps of indexes (which is probably also why inserts are so much slower for it), but I don't really have a good way to confirm that quickly.
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
"2-5x times slower" can mean the difference from 2 seconds to 4 to 10 seconds. Two seconds is still (barely) acceptable for interactive usage, ten seconds: not so much. You're also going to need less beefy servers, or fewer servers.
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).
ClickHouse uses "grace hash" GROUP BY with the number of buckets = 256.
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.
Do you have happen to have any documentation about your benchmarking? I'm also considering these options at the moment (currently using pg+timescaledb) and interested in what you found.
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 don’t see why the GitHub link is any harder to click than the tiny url link in that post.
I’m pretty sure the only reason url shorteners exist with purpose is because of Twitter limits (and software that doesn’t visually hide egregiously long urls), but continues to be used outside of those places due to cargo culting
