[romange]

You are an exception, then :) But I still stand by the claim that fragmenting your stateful workload (i.e. Redis) into bunch of processes instead of having a single endpoint per instance is an acceptable approach in 2023. When your processes are excessively tiny, their load variability overshadows their average load. This imbalance results in unpredictable pauses, latencies, and Out of Memory (OOM) issues. This primarily occurs due to the absence of resource pooling under a single process. While it's challenging to exhibit this issue via synthetic benchmarks, it's certainly present.

[arp242]

I think you forgot "not" there before "an acceptable approach in 2023".

These are all fair and reasonable opinions to have, and to some degree I even agree with it, but none of that is captured in the rather simplistic benchmark. Everyone understands that even with the best of efforts it's hard to capture everything in a benchmark, but in this case it's just missing a very obvious way to run Redis.

It's like benchmarking PostgreSQL connections and coming to the conclusion there is no way PostgreSQL can handle more than n connections and that OtherSQL is much better. Is this true? Yes. But it's also true that half the world is running pg_bouncer and that this is widely seen as the way to run PostgreSQL if you need loads of connections. Is it a pain you need to run this and something that should be addressed in PostgreSQL? Absolutely. Such a benchmark would be correct in a strict narrow technical sense, but at the same time also misrepresentative of the real-world situation.

[romange]

I understand what you are saying. How would you suggest to present it then? Dragonfly is not faster than Redis when running on a single cpu. It can not be, just because it has the overhead of the internal virtualization layer that composes all the operations over multiple shards (in general case). But Dragonfly can scale vertically with low latency and high throughput unlike other attempts of making multi-threaded Redis that used spinlocks or mutexes. So how do we demonstrate the added value?

[arp242]

> But Dragonfly can scale vertically with low latency and high throughput unlike other attempts of making multi-threaded Redis that used spinlocks or mutexes. So how do we demonstrate the added value?

Provide more advanced benchmarks which demonstrates those types of differences better.

The situation is that the differences are complex, both in terms of performance and operationally (e.g. running multiple instances is not a huge obstacle, but it is harder). That's always going to be hard to capture in a single graph or a single tagline; I appreciate this isn't easy.

It's your website; you can do what you want with it. And maybe I'm just a grumpy old curmudgeon who has seen too many hype cycles, but to me it just comes off as "too good to be true" – which it kind of is – and leaves a more negative than positive impression. The same applies to "The most performant in-memory data store on Earth" tagline, which seems a bit hyperbolic (what is "fastest" depends, as you mentioned that Redis will always be faster on a single core – some people only need a single core!)

I have the business acumen of a goat, so what do I know? But it seems to me that a lot of people appreciate when products are straight-forward about their weaker points as well, and even straight-up say they're not the best fit for all scenarios, and in that in the long run this is more beneficial.