Comment by cle

> The issue is that "83 per second" is multiple orders of magnitude off the expected level of performance on any RDBMS running on anything resembling modern hardware.

This is just not true, there are so many scenarios where 83/sec would be the limit. That number by itself is almost meaningless, similar to benchmarks which also make a bunch of assumptions about workloads and runtime environments.

As a simple example if your queue has a large backlog, you have a large worker fleet aggressively pulling work to minimize latency, your payloads are large, you have not optimized indexing, and/or you have many jobs scheduled for the future, every acquire can be an expensive table scan.

(This is a specific example because this is one of many failure scenarios I’ve encountered with Graphile that can cause your DB to meltdown. The same workload in Redis barely causes a blip in Redis CPU, without having to fiddle with indexes and auto vacuuming and worker backoffs.)