Asynchronous I/O in DuckDB: Work, Thread, Work

The 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;).

(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.

Even so you could use cgroups to cap the memory for the process (including its caches).

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.

There 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)

Network IO is heavily NIC queue bound, if your NIC only has one queue it just makes it slower to do any threading workload against it.

On my ryzen 9 it needs around 8 cores to do the same work in a threaded io loop than you can do single threaded. And the mechanism doesnt matter, you could share an fd, use SO_REUSEPORT or just share memory between threads.

Just doing the sharing makes everything extremely slow. One context switch becomes more expensive than just doing it single threaded.

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.

Should also mention that it is of the most convenient!

What is it missing to be called a query engine?

far from the fastest - our work on Presto on GPUs would like a word ;-)

DuckDB uses a speculative parallel CSV parsing technique. The basic idea is that the parser speculates about the state the CSV parser is in at a random byte (e.g., whether it is inside a quoted field) and tries to figure out where the next row starts based on that.There are validation steps during finalization as well, to ensure the parser did not got anything wrong in its speculation.

I've never gotten around to writing a blog post about it, but I go quite in-depth on the technique in this presentation: https://www.youtube.com/watch?v=YrqSp8m7fmk

(Disclaimer: I'm the author of the blog post and also the developer who implemented the entire CSV parser in DuckDB.)

In general, yes. But Hive, for example, doesn't support embedded newlines or record separators that are not newlines, which means that a lot of older CSV files containing big data do not allow quoted newlines.

most likely - they read large chunks and decode them