cannonpalms

Disclosure: I'm a software engineer on the core team at a different database company, but we focus on time-series use cases and do not compete in the ACID/OLTP space ParticleDB is targeting.

I decided to dig into these claims a bit.

> Its also faster than Redis for KV

ParticleDB's own benchmarks[0] come with several caveats. Much of the win over Redis seems to come from comparing ParticleDB on 48 cores to Redis's single execution thread, from what I can tell.

- On a single client in async mode, ParticleDB loses on writes at 0.86-0.90x and ties on GET.

- SET at 512 clients reaches 4.81x Redis, but that's about 18.7k ops/s per allocated core vs Redis's 186k on one thread. For comparison, GET at 512 clients works out to about 33.8k ops/s per allocated core vs Redis's 228k.

- Tail latency on SET is much worse, e.g. p99 of 49ms vs 5.3ms for async SET at 512 clients and 306ms vs 41ms for durable mode with pipelining.

- Each test was 8 seconds long on 100k keys, but the Jepsen report saw throughput decline linearly over an hour, even on small datasets.

> also numbers show this should be top on clickbench

Would this claim survive after validating query results?

Regardless, it's moot, because it doesn't survive on its face either. On hot runs, by my calculations from the numbers in the Clickbench PR[1], ParticleDB scores about 1.5x better than ClickHouse, however Umbra still scores about 1.5x better than ParticleDB.

The fact that `GROUP BY URL` (Q33/Q34) beats `GROUP BY UserID` (Q15) by 4x on a `c6a` is also puzzling to me. Only a handful of 100+ ClickBench results that I checked do this, and ParticleDB's ratio is the most extreme. It also speeds up only 2.2x when going from 16 to 192 vCPUs, while Umbra, ClickHouse, and ElosDB manage 7-10x. This makes me wonder whether the ParticleDB submission may inadvertently be using some precomputation/intermediate caching, despite final result caching seemingly having been disabled.

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