RocksDB VS sled

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:

a few times, seems interesting. The author's also built a lot of other cool concurrency primitives for Rust as well.

[0] https://github.com/spacejam/sled

- Dropbox [3] uses a similar approach but they talk about it a bit more abstractly.

Sans-IO is more documented in Python [4], but str0m [5] and quinn-proto [6] are the best examples in Rust I’m aware of. Note that sans-IO is orthogonal to deterministic test frameworks, but it composes well with them.

With the disclaimer that my opinions are mine and mine alone, and don’t reflect the company I work at —— I do work at a rust shop that has utilized these techniques on some projects.

TigerBeetle is an amazing example and I’ve looked at it before! They are really the best example of this approach outside of FoundationDB I think.

[0]: https://risingwave.com/blog/deterministic-simulation-a-new-e...

[1]: https://risingwave.com/blog/applying-deterministic-simulatio...

[2]: https://dropbox.tech/infrastructure/-testing-our-new-sync-en...

[3]: https://github.com/spacejam/sled

[4]: https://fractalideas.com/blog/sans-io-when-rubber-meets-road...

[5]: https://github.com/algesten/str0m

[6]: https://docs.rs/quinn-proto/0.10.6/quinn_proto/struct.Connec...

Sled uses bw-tree actually https://github.com/spacejam/sled/wiki/sled-architectural-outlook

There is a valid argument to be made for threads over async in a large percentage of use cases where async is considered the default. If this is what you are referring to however, I don't think they ever referred to async as completely useless: https://github.com/spacejam/sled/issues/1123.

Have you looked into other pure-Rust databases as well, such as sled or GlueSQL which has an SQL interface on top of sled? I wonder how those would compare to Persy.

I'm not sure what you need. Are these of any use? https://github.com/meilisearch/heed https://github.com/spacejam/sled

There is also Sled but as I understand it that is being reworked to use the author's new DB core Marble

There are some databases like sled/FlashDB designed to be embedded to other applications even bare metal microcontrollers. But I do doubt the potential bussiness value of a pure embedded database.

https://github.com/spacejam/sled

To add transaction support, you probably need a good understanding of how the memtable works in Log Structured Merge trees: