sled VS RocksDB

How do you compare this to sled? https://github.com/spacejam/sled

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.

Memgraph uses RocksDB as a key-value store for extending the capabilities of the in-memory database. Not to go into too many details about RocksDB, but let’s just briefly mention that it is based on a data structure called Log-Structured Merge-Tree (LSMT) (instead of B-Trees, typically the default option in databases), which are saved on disk and because of the design come with a much smaller write amplification than B-Trees.

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.

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:

Streamiz wrap a consumer, a producer, and execute the topology for each record consumed in the source topic. You can easily create stateless and stateful application. By default, each state store is a RocksDb state store persisted on disk.

LMDB is much more same in the sense that it supports real ACID transaction 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.