RocksDB VS Spring Boot

RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.

[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)

To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.

The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.

I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.

Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.

If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.

- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...

It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.

LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.

https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...

Here are some bugs in JeMalloc:

- Usually manually wired and configured vs the spring boot "starter" pattern of having libraries that automatically do some of the manual setup work for you: https://github.com/spring-projects/spring-boot/blob/main/spr...

I wish more client library sets had the feature-matrix that the pulsar one does, because in practice most end up being the same: Java supports everything because it's either built in the same codebase or is the most used client and gets the most support, while the dotnet client codebase has many feature-requests or performance improvement issues, often leading to a "third-party client" being created.

https://github.com/spring-projects/spring-boot/pull/39754/co...

If you’re currently running with an earlier version of Spring Boot, I recommend that you upgrade to Spring Boot 2.7 before migrating to Spring Boot 3.0. It minimizes compatibility issues as much as possible.

Okay, we need to build the game? No problem, we will use Spring Boot and Swing!

It's weird that some people including you directly attack my competence. As a power user you should have plenty of experience getting something to work that is not properly document, does not work how the documentation promised it to, or has weird problems on top of it. Look at idiotic things like this:

https://github.com/spring-projects/spring-boot/issues/33044

Take any similar issue and you'll see a bunch of people who try to find a solution for them because they just aren't repeatable at all. The underlying issue is the auto configuration doing things you can't follow quite properly. It's like it wasn't mean to be understood. Issues like the one I linked above also show me that the spring dev crowd also doesn't understand the ecosystem anymore. The problem is complexity and automagic.

An interested reader can decide for themselves:

https://github.com/spring-projects/spring-boot/tree/main/spr...

Explicitly decoding URL query parameters occurs less often because many frameworks, including Spring Boot, handle decoding automatically.

https://aws.amazon.com/lambda/ https://aws.amazon.com/api-gateway/ https://aws.amazon.com/serverless/sam/ https://aws.amazon.com/cloudformation/ https://aws.amazon.com/s3/ https://spring.io/projects/spring-boot https://start.spring.io