go-pg VS Netty

Might have been this one but I can't be sure https://github.com/go-pg/pg

I usually work with PostgreSQL and have used https://github.com/go-pg/pg. https://github.com/uptrace/bun is its successor.

And it is go-pg. I personally really like it as a query builder (rather than an ORM) and it has good performance. It has one feature which I'll tell you about next, without which I would have struggled to implement what I had in mind. But this feature is available in other libraries too, so don't rush to rewrite your code.

bun and go-pg both seem to work fairly well for these kind of purposes.

For database communication we will use go-pg module. You can install it by running go get github.com/go-pg/pg/v10. This will install version 10 of go-pg module, which is latest version at the time of writing this guide. Now create new directory internal/database/ and file database.go inside of it.

gorm.io/gorm`, or `github.com/go-pg/pg` if you need something more feature-rich. I would avoid using `go-kit` and `go-micro` "frameworks" unless you have some special requirement for it. But you can take a look to have an idea of what Go "framework" may look like.

This one is mostly excellent: https://github.com/go-pg/pg

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.