zfs VS RocksDB

> The only non-junk PCIe3 option that's even advertised here recently is the overpriced WD Red SN700.

Those WD drives seem to have some real issues, at least with ZFS and btrfs. :(

https://github.com/openzfs/zfs/discussions/14793

IMO the "next fs" is just zfs. They somewhat recently merged RAIDZ expansion feature https://github.com/openzfs/zfs/pull/12225 and make regular improvements. If no file system has what you need today, zfs will probably be the first one to have it "tomorrow," imo.

https://github.com/openzfs/zfs/issues/15526#issuecomment-181...

> zpool get all tank | grep bclone

> kc3000 bcloneused 442M

> kc3000 bclonesaved 1.42G

> kc3000 bcloneratio 4.30x

> My understanding is this: If the result is 0 for both bcloneused and bclonesaved then it's safe to say that you don't have silent corruption.

A couple years ago, I had an idea for convincing a filesystem to go faster using 2 compression steps instead of one. I couldn't see why it wouldn't work, and I also couldn't convince myself it should.

It seems to have worked out. [1]

[1] - https://github.com/openzfs/zfs/commit/f375b23c026aec00cc9527...

https://github.com/openzfs/zfs/issues/7631

This is a long-standing issue with zvols which affects overall system stability, and has no real solution as of yet.

TIL. Thank you! https://github.com/openzfs/zfs/pull/15022

Difference may be significant, yes https://github.com/openzfs/zfs/pull/14243

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: