[riku_iki]

> that async programming aims to avoid (performance, deadlocks, your business logic being cluttered with low level implementation details).

I disagree with you, my code looks safe and simple with explicit blocking threading, and at the same time is much simpler to reason about what is going on and tune in contrast to async frameworks which hide most of the details under the hood.

You can argue about performance, that async/epoll/etc allows to avoid spawning thousands of threads and remove some overhead, but there is no much benchmarks in internet (per my research) which would say that this performance overhead is large.

[jupp0r]

If you are using explicit blocking, share data between threads and have not run into deadlocks then your application is trivial (which is great if it solves your problem).

[riku_iki]

Could you explain how sharing data between threads is different in async programming and blocking programming?

[jupp0r]

You can minimize sharing data between threads because it's easier to have data affinity with threads (ie only thread A will read or write to a piece of data). You can still access that data from multiple modules because the whole thread is never blocked waiting for IO (because of async). An extreme example is nodejs, where you only have one thread, can concurrently do thousands of things and never have to coordinate (ie via mutexes) data access.

[riku_iki]

that may be true if you are Ok to have only one thread and not utilize parallelism.

[jupp0r]

It's not either or, you can combine the two. I've worked on a system that did real time audio mixing for 10000s of concurrent connections, utilizing >50 cores, mostly with one thread each. Each thread had thread-local data, was receiving/sending audio packets to hundreds/thousands of different IP addresses just fine without worrying about mutexes at all. Try that with tens of thousands of actual OS threads and the associated scheduling overhead.

Having data affinity to cores is also great for cache hit rates.

Here is part of the C++ runtime this is based on: https://github.com/goto-opensource/asyncly. I was the principal author of it when it was created (before it was open sourced).

[riku_iki]

> Each thread had thread-local data, was receiving/sending audio packets to hundreds/thousands of different IP addresses just fine without worrying about mutexes at all.

it doesn't sound they really sharing data with each other, it looks like your logic is well lineralizable and data localized, and you can't implement access to some global hashmap in that way for example.

> Try that with tens of thousands of actual OS threads and the associated scheduling overhead.

I run this(10k threads blocked by DB access) in prod and it works fine for my needs. There are lots of statements in internet about overhead, but not much benchmarks how large this overhead is.

> Here is part of the C++ runtime this is based on

yeah, I need one runtime on top of another runtime, with unknown quality, support, longevity and number of gotchas.