Seastar VS Folly

I used glommio as the async executor (instead of something like tokio), and it is wonderful. For people wondering whether it's "good enough" or to use C++ and seastar (as I have thought about a lot before starting this project), take the leap of faith, it's fast - both in terms of run time and to code.

b) It's proven now e.g Seastar, Glommio that the fastest way to run a multi-threaded application is to have one instance with one thread pinned per CPU core. Then to have fibers/lightweight threads on top handling all of the asynchronous code. Your approach of lots of instances is the slowest so there will be a ton of unnecessary thread context-switching.

The most common example is DPDK [1]. It's a framework for building bespoke networking stacks that are usable from userspace, without involving the kernel.

You'll find DPDK mentioned a lot in the networking/HPC/data center literature. An example of a backend framework that uses DPDK is the seastar framework [2]. Also, I recently stumbled upon a paper for efficient RPC networks in data centers [3].

If you want to learn more, the p99 conference by ScyllaDB has tons of speakers talking about some interesting challenges.

[1] https://www.dpdk.org/.

[2] https://github.com/scylladb/seastar

[3] https://github.com/erpc-io/eRPC

I assume Tokio itself, see e.g monoio or glommio, but also Seastar for C++.

https://core.dpdk.org/supported/ lists supported nics. You're best just reading material from the dpdk website for figuring out roughly what it is. It is used for a lot of different goals. For most web C++ stuff it's mainly used because you can avoid round trips of data passing through the kernel and can reference network data without tons of copying. For an example check out the SeaStar framework, https://seastar.io/, which is under the hood of ScyllaDB.

As this is a Kafka sub, this may be a good opportunity to mention that Redpanda is based on the same framework (seastar) as Scylla. The idea of sharding work to CPU cores turns out to apply very well to the Kafka data model, too!

Seastar which is a thread per core runtime written by the Scylla devs thats used in both Redpanda and Scylla as the underlying runtime. https://github.com/scylladb/seastar

ScyllaDB is, ironically, maybe one of the worst examples the author could have come up with for "abstraction" in the article.

If folks aren't familiar with their work/internal tech, go check out some of their repos like Seastar. They have some of the most talented systems programmers on the planet writing thin veneers over kernel and hardware API's to squeeze every ounce out of performance.

https://github.com/scylladb/seastar

I know it's beside the point, but I just had to share because I thought that was funny

I’ve seen frameworks for c++ (https://seastar.io/) and rust (https://github.com/actix/actix) which support what you’re describing out of the box.

If you're interested in scaling and asynchronous programming in c++ I highly recommend you investigate the SeaStar application framework. You wouldn't build a web service with SeaStar, rather you would build the infrastructure that you would use to build the web service on top of. https://github.com/scylladb/seastar

https://github.com/facebook/folly/commit/b1391e1c57be71c1e2a...

https://github.com/facebook/folly/pull/2153

To set a HP on Linux, Folly just does a relaxed load of the src pointer, release store of the HP, compiler-only barrier, and acquire load. (This prevents the compiler from reordering the 2nd load before the store, right? But to my understanding does not prevent a hypothetical CPU reordering of the 2nd load before the store, which seems potentially problematic!)

Then on the GC/reclaim side of things, after protected object pointers are stored, it does a more expensive barrier[0] before acquire-loading the HPs.

I'll admit, I am not confident I understand why this works. I mean, even on x86, loads can be reordered before earlier program-order stores. So it seems like the 2nd check on the protection side could be ineffective. (The non-Linux portable version just uses an atomic_thread_fence SeqCst on both sides, which seems more obviously correct.) And if they don't need the 2nd load on Linux, I'm unclear on why they do it.

[0]: https://github.com/facebook/folly/blob/main/folly/synchroniz...

(This uses either mprotect to force a TLB flush in process-relevant CPUs, or the newer Linux membarrier syscall if available.)

folly provides functions to resize std::string & std::vector without initialization [0].

[0] https://github.com/facebook/folly/blob/3c8829785e3ce86cb821c...

My implementation was quite similar to Boost's spsc_queue and Facebook's folly/ProducerConsumerQueue.h.

> How is that relevant?

Roaring bitmaps and similar data structures get their speed from decoding together consecutive groups of elements, so if you do sequential decoding or decode a large fraction of the list you get excellent performance.

EF instead excels at random skipping, so if you visit a small fraction of the list you generally get better performance. This is why it works so well for inverted indexes, as generally the queries are very selective (otherwise why do you need an index?) and if you have good intersection algorithms you can skip a large fraction of documents.

I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block: https://github.com/facebook/folly/blob/main/folly/experiment...

C++ with folly's SCOPE_EXIT {} construct:

https://github.com/facebook/folly/blob/main/folly/ScopeGuard...

Seems like github issues taking a long time to get any response: https://github.com/facebook/folly

Can't speak for abseil and tbb, but in folly there are a few solutions for the common problem of sharing state between a writer that updates it very infrequently and concurrent readers that read it very frequently (typical use case is configs).

The most performant solutions are RCU (https://github.com/facebook/folly/blob/main/folly/synchroniz...) and hazard pointers (https://github.com/facebook/folly/blob/main/folly/synchroniz...), but they're not quite as easy to use as a shared_ptr [1].

Then there is simil-shared_ptr implemented with thread-local counters (https://github.com/facebook/folly/blob/main/folly/experiment...).

If you absolutely need a std::shared_ptr (which can be the case if you're working with pre-existing interfaces) there is CoreCachedSharedPtr (https://github.com/facebook/folly/blob/main/folly/concurrenc...), which uses an aliasing trick to transparently maintain per-core reference counts, and scales linearly, but it works only when acquiring the shared_ptr, any subsequent copies of that would still cause contention if passed around in threads.

[1] Google has a proposal to make a smart pointer based on RCU/hazptr, but I'm not a fan of it because generally RCU/hazptr guards need to be released in the same thread that acquired them, and hiding them in a freely movable object looks like a recipe for disaster to me, especially if paired with coroutines https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p05...

Not sure if it's still the case but about 6 years ago Facebook's folly C++ library was something I'd point to for my junior engineers to get a sense of "good" C++ https://github.com/facebook/folly