Finagle VS Netty

Not sure about now but a few years back the company I worked for was heavily vested in Finagle [1] using Future pools. I'm sure virtual threads would only enhance this framework. Also, Spring and it's reactive webflux would probably benefit as well [2].

[1] https://twitter.github.io/finagle/

[2] https://docs.spring.io/spring-framework/reference/web/webflu...

Don't really see how "enterprise scala" has anything to do with this, scala is meant to be parallelized , that's like it's whole thing with akka / actors / twitter's finagle (https://twitter.github.io/finagle/)

So biased. Twitter dropping Scala3 is simply untrue, ticket is there and still is open https://github.com/twitter/finagle/issues/932

Thank you. I only took a quick look, but this looks like a goldmine of info if you are interested in using bazel to build a scala monorepo: https://github.com/twitter/finagle

Finagle

And the engineering team are far from imbeciles because they built one of the worlds' best cache and RPC microservice components. As well as the fact that up until Musk took over the website was running just fine (other than your issue with product decisions).

Bro it's their fucking project lolhttps://twitter.github.io/finagle/

RPC generally means server side calls, probably this https://twitter.github.io/finagle/, and XHR is not RPC.

not familiar with twitter internals but it would be atypical to use gql for internal communications even if you expose it as a public api. twitter also develops a widely-used RPC system https://github.com/twitter/finagle

Code of netty is here and using following library

We use Netty (https://netty.io/) as the source of the MQTT communication, and we build the MQTT features the MQTT broker should support ourselves on top of that.

In this space, we also have the somewhat related term blocking. Java's NIO library is one well-known non-blocking tool used for managing multiple tasks on a single Java thread. When listening to sockets, most of the time a thread is just blocked, doing nothing until it receives some data. So, it's efficient to use a single thread for monitoring many sockets, to increase the likelihood of the thread having some actual work to do. The Selector API does this but is notoriously challenging to program well. Instead, developers use frameworks like Netty which abstract some of NIO's complexity and layer on some best practices.

DirectByteBuffer exhibits an intriguing behavior: it deallocates its backing memory during the finalization process, which occurs after garbage collection (GC) cycles. This poses an issue if your system is conservative with on-heap allocations, leading to infrequent GC cycles. In such cases, there could be a significant delay between the time the memory becomes unreferenced and when it is actually deallocated. This behavior could, in some respects, mimic a memory leak.

This is why some libraries hacks into DirectByteBuffer to deallocate memory explicitly, bypassing the finalizer altogether. For instance, the Netty library has implemented such a workaround, see Netty as an [example](https://github.com/netty/netty/blob/795db4a866401aa172757b95...).

Apologies replying to myself, but Netty, which underpins many of the popular Java backend frameworks, see backward compatibility as more important than supporting green threads.

https://github.com/netty/netty/issues/12816

It'll be interesting to see who (if anyone) picks up Netty's mantle in the Project Loom world.

Looks fairly recent so I'm glad I had a pre oct22 build https://github.com/netty/netty/issues/12737

This is better? https://github.com/netty/netty/blob/4.1/pom.xml

Given the fact that Lettuce is built with Netty, we also immediately noticed quite an impact on the initialization time (cold start) of our lambda function. Netty is really fast while executing, but takes a bit of time to initialize. The new Lambda Snapstart functionality might help with that.