Jedis VS Chronicle Map

It's important to note that it's possible to use any Redis-compatible client with Valkey. I used the go-redis client, but (at the time of writing) there is work underway to build Valkey specific client libraries. Check the Valkey GitHub org to take look at the forks of existing Redis client libraries such as valkey-go (corresponding to rueidis), iovalkey (corresponding to ioredis), Jackey (corresponding to jedis) etc. These are very early days (at the time of writing), and it will be interesting to see the progress here!

Finding out what's causing that was not that simple as Jedis does not write a lot of log information when the log level is set to debug. From what we could tell from the Jedis source code it seems that when the Jedis client connects to the configuration endpoint it fetches all known hosts. It stores that in a local cache and figures out the primary node from the set of nodes. Once the primary node is known it will execute operations against the primary node. It does so by connecting to the Amazon-generated domain name/entry and when that entry is down / unreachable it seems it does not use the configuration endpoint to rediscover the nodes or fetch the new cluster topology. We initially thought this had to do with the DNS caching of the JVM, so we also tried to disable caching, but we did not see any effect after that change.

jedis is used to interact with Redis.

Como um bônus, deixo aqui uma classe SessionManager pra ajudar na implementação em Java usando Jedis e o gerador de token do Tomcat, que já é normalmente incluído no Spring Boot:

We’re using JVector for the vector index and Chronicle Map for the article data. There are several things I don’t love about Chronicle Map, but nothing else touches it for simple disk-based key/value performance.

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:

Chronicle-Map: Chronicle Map is an in-memory, key-value store, designed for low-latency, and/or multi-process applications.

I've wrangled data sets in the ~600gb range using nothing but plain old Java and a few beefy boxes. This can all be kept in memory, but you have to go off-heap. You can use Chronicle Map and Chronicle Values to model this data and work with it off-heap in a way that's still very clean and object oriented. 128gb of RAM is cheap these days, whether you're in the cloud or not.