vscode-rust VS RocksDB

> it crashes process IDs more often than Justin Bieber crashes Maseratis: https://github.com/rust-lang/vscode-rust/issues/890

So -- this guy used an extension (named Rust) with rust-analyzer, which was known to not work, and it didn't work(!), and the Rust extension author recommends he tries the extension made for rust-analyzer. That extension doesn't have the features he likes (it works for me and has loads of features, so I have no idea what this is about?), and so they close the issue?

Hardly a case for the ages. Guy uses unsupported config and things don't work?

> What clangd does is work.

Don't doubt it. I'm just saying -- I haven't had any problems with the rust-analyzer extension since it became the Rust default. But, yes, I had a few hiccups and crashes beforehand, no doubt. I just have to imagine it's both younger, and doing more/different things than clangd.

I know. It's been argued for a long time. But at least if you follow the advice to install RA, it's fine. And if you don't, you won't be able to install RLS anyway, but instead get a helpful message pointing to RA.

But the last commit to the Code extension was almost one year ago (an URL update), a pull request for a pretty annoying issue was not merged in more than a year, and the only activity on the issue tracker is people complaining about old issues and sometimes me asking them nicely to try rust-analyzer instead (but only when I'm pretty certain that their issue does not happen in RA).

It's been asked before: https://github.com/rust-lang/vscode-rust/issues/927. Basically, rust-analyzer is not (yet?) a rust-lang.org project and it's not even going to be mentioned in the official docs until that happens.

Yeah, see e.g. https://github.com/rust-lang/vscode-rust/issues/880.

RA will be merged with the vscode-rust extension and aims to replace RLS (tracking issue on the vscode-rust repo).

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: