spring-data-relational VS Netty

What do you think of Spring Data JDBC (https://spring.io/projects/spring-data-jdbc)?

What do you think of Spring Data JDBC?

Link to GitHub: https://github.com/spring-projects/spring-data-relational/blob/main/spring-data-relational/src/main/java/org/springframework/data/relational/repository/Lock.java

I would ignore the hipster jOOQ and similar and start with Spring Data JDBC https://spring.io/projects/spring-data-jdbc

This makes Spring Data JDBC a simple, limited, opinionated ORM.

Though JPA is fine, and yes hibernate can be used under the hood. When it comes to spring it is mostly what level of abstraction you want. Or you could go with JDBC. And you have a spring data JDBC for having a similar abstraction as to the JPA one.

This is not correct. You're thinking Spring Data JPA [1]. Spring Data JDBC [2] does _not_ use any Hibernate nonsense.

[1] https://spring.io/projects/spring-data-jdbc

The data moved from the awesome-but-confusing DynamoDB... into PostgreSQL✨, using Spring Data JDBC.

I didn't have much experience with Hibernate and Spring (was using JavaEE prior), it could very possibly be the case, that the team simply misused Hibernate. We might have used n+1 queries, eager loading (although I mildly remember we fixed this), oh and we had the old id generation via sequence in Hibernate, that was really pain to optimize properly. Oracle 11 does not have identity generation. I remembered this only becuase I created an issue at the time. Still not implemented, but can't blame them, who the hell uses sequences as ID generation nowadays.

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.