Bedrock VS RocksDB

Also Expensify's Bedrock, which powers their famous "Scaling SQLite to 4M QPS" article:

https://bedrockdb.com/

https://use.expensify.com/blog/scaling-sqlite-to-4m-qps-on-a...

Lots of things don't need failover, but if you do, you can use Bedrock, which is built on sqlite.

At first, I thought Amazon was launching their own SQLite hosted database.

BedrockDB is a SQLite based database with MySQL compatible drivers.

https://bedrockdb.com

Vapor[0] based on Swift. Advantage of this is that you don't have to evaluate multiple frameworks for Swift and suffer paralysis by analysis. All the Swift community is behind one framework.

The next is Actix[1] based on Rust. There are many frameworks in Rust and most of them have not reached 1.0 And which framework will survive becomes a question.

Other not so well-known is Wt[2] based on C++. This actually is created for programmers who are not web developers. The development experience is similar to desktop app development like Qt.

If that is not acceptable then Django[3], based on Python, is the one that will be good for you.

For the front-end I would recommend Flutter[4]. As much as I dislike getting tied to a single company for whom the framework is not their bread-and-butter, I don't see any other viable options to Flutter that will cover all web, mobile and desktop out of the box.

For databases, I would recommend BedrockDB[5], if you are not averse to SQLite. Or FoundationDB[6], if you want NoSQL. But if you are not concerned about horizontal scalability or okay with self-managing database availability, then PostgreSQL[7] is a very good option.

For push notifications, PushPin[8] is a good option.

[0] https://vapor.codes

[1] https://actix.rs

[2] https://webtoolkit.eu

[3] https://www.djangoproject.com

[4] https://flutter.dev

[5] https://bedrockdb.com

[6] https://www.foundationdb.org

[7] https://postgresql.org

[8] https://pushpin.org

Recently I stumbled upon BedrockDB[0] from Expensify. It is based on SQLite and has very interesting idea on HA and distributed DB.

[0] https://bedrockdb.com

This is not SQLite though, also the test is trivial compared to TPC: https://github.com/Expensify/Bedrock/blob/dbarrett_perftest/...

Don’t forget BedrockDB (built on SQLite) that’s used in production at Expensify.

How it scales as well.

https://bedrockdb.com/

https://blog.expensify.com/2018/01/08/scaling-sqlite-to-4m-q...

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: