Does having the worker pool hold as many threads as cores work well alongside the async pool? It is basically oversubscribed by design.
replyI built a system once which had (this is Rust) a Rayon worker thread pool of 4 threads and a Tokio async pool of 2 (multithreaded runtime). On a system of 6 vCPU. This ended up working fine. Tokio was not starved so handled network requests at low latency.
One difference is DuckDB is a pure network client. If one of its async threads is starved it is not the end of the world (e.g. k8s does not kill your pod for failure of replying to health checks).
I've run quite a few benchmarks on that as well, on a few different machines, and oversubscribing ASYNC threads demonstrated very little performance downside. In the end, the memory governor also keeps these threads "in check" while still allowing full utilization when possible.
replyThere is still something to gain from tuning it further (as you can see in the async I/O tuned benchmark), but having that network saturation by default is still a work in progress.
(Disclaimer: I'm the author of the blogpost)
As long as you're scheduled by the kernel and not something like Kubernetes with a CPU limit, you can generally oversubscribe I/O threads without much of a problem. They're mostly parked waiting for syscalls anyway. Heck, even if they're mostly doing CPU work, the scheduler generally deals with it pretty gracefully.
replyUsing 512gb of ram for a 22gb remote file does feel a bit weird for a benchmark but maybe they couldn’t get a large number of cores without lots of memory?
replyThe main reason I decided to use a beefier machine is that it gives me flexibility when benchmarking, without the need to set up different environments. The CSV data, for example, is >80 GB. We can also “scale down the machine” for experiments where we want to stress-test lower-memory scenarios or use fewer threads by configuring DuckDB’s settings (e.g., SET memory_limit = '10GB'; or SET threads TO 1;).
reply(Disclaimer: I’m the author of the blog post.)
Most cloud providers start with a 2:1 ratio of memory in GiB to CPU cores and go up from there. Databases also are the most common workload for large-memory systems because they benefit so much from large buffer caches.
replyEven so you could use cgroups to cap the memory for the process (including its caches).
replyDeep dive into asynchronous I/O architectures like this is pure engineering gold for high-performance data processing. Excellent breakdown.
replyI wonder how this would work in trying to parallelize the worker threads (multiple duckdb instances) coordinating them via Quack.
replyDucks all the way down!
is there any aggregator for official docs such as this for db/systems/distributed arch at scale?
replyDuckDB is trending towards becoming a query engine, specifically the fastest analytical query engine. This is very good.
replyDo they have SSL updates yet? Signing is great, but using https means not fighting firewalls to start a job
replyThis is such a long waited feature!
replyBrilliant deep dive into asynchronous I/O and execution thread models. Essential reading for high-performance data engineering.
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