MapDB VS Redisson

I decided to start with an overview of what open-source options are currently available. When it comes to the implementation of the on-heap cache mechanism, the options are numerous – there is well known: guava, ehcache, caffeine and many other solutions. However, when I began researching cache mechanisms offering the possibility of storing data outside GC control, I found out that there are very few solutions left. Out of the popular ones, only Terracotta is supported. It seems that this is a very niche solution and we do not have many options to choose from. In terms of less-known projects, I came across Chronicle-Map, MapDB and OHC. I chose the last one because it was created as part of the Cassandra project, which I had some experience with and was curious about how this component worked:

So, it's an object database, like Zope's ZODB on Python?

I like the idea, but I'd like to learn about use cases for it.

Otherwise, in Java, MapDB is about as far as I'd be willing to go: https://github.com/jankotek/mapdb/

Anyway, without further ado, I found MapDB (https://github.com/jankotek/mapdb) which does exactly that. Of course, they also provide their own Java collection implementations as well, so I suspect using it with Vavr would be a poor idea, but it is very cool in its own right anyway. Of course, there is also Apache Derby and HSQLDB, and those great options with a long history as well. I haven't played with these in a while though, so I might give them a try again soon for some personal stuff.

In Java I like

https://mapdb.org/

It is more feature rich than you want but in Python I'd probably just use sqlite3 since it is in the standard library.

I have had good results with mapdb

In the final verdict, Reliable Queue is the more durable and feature-rich option. Standard Valkey/Redis streams will suffice for smaller applications, but Reliable Queue provides the enterprise-grade reliability that businesses depend on. To learn more, visit the Redisson PRO website today.

Redis Official Documentation Redisson GitHub Repository

Local caches, like Caffeine, provide extremely fast data retrieval because they operate in memory and are close to the application. These are ideal for reducing response times for frequently accessed data. On the other hand, distributed caches, such as those powered by Redisson with Redis, offer scalability and consistency across multiple instances of an application. Distributed caches ensure all nodes in a distributed system access the same up-to-date data, which is crucial in multi-node environments.

Source code you can find in the GitHub repository. he main idea of this project is the implementation of microservice using Kotlin, Spring WebFlux, PostgresSQL, and Redis with metrics and monitoring and deploying it to k8s. For interacting with PostgresSQL we will use reactive Spring Data R2DBC and for Redis caching using Redisson.

I came up empty handed in search of an alternative to Quartz for scheduling Crons in a clustered/distributed environment. Redisson has a scheduler, but it came with its own issues: - https://github.com/redisson/redisson/issues/4020 - https://github.com/redisson/redisson/issues/3991 - https://github.com/redisson/redisson/issues/4321

4. We use the open-source library Redisson to connect to Redis instance and use Redis as Hibernate 2nd level cache. Please see their Github repo for more information.