bustub VS RocksDB

Introduction to Computing"

https://dcic-world.org/

# Programming Language Theory:

"Programming Languages: Application and Interpretation"

https://www.plai.org/

# Compilation:

"Essentials of Compilation: An Incremental Approach in Python"

https://github.com/IUCompilerCourse/Essentials-of-Compilatio...

# Database Systems:

"CMU: Intro to Database Systems"

https://15445.courses.cs.cmu.edu/

"CMU: Advanced Database Systems"

https://15721.courses.cs.cmu.edu/

# Calculus I/II & Real Analysis

"A Course in Calculus and Real Analysis"

https://link.springer.com/book/10.1007/978-3-030-01400-1

"A Course in Multivariable Calculus and Analysis"

https://link.springer.com/book/10.1007/978-1-4419-1621-1

# Linear Algebra & ML:

* A Series of books by prof. Joe Suzuki without using any external library for the implementations *

"Statistical Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-15-7877-9

"Sparse Estimation with Math and Python"

https://link.springer.com/book/10.1007/978-981-16-1438-5

"Kernel Methods for Machine Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-19-0401-1

# Discrete Mathematics:

"CMU 21-228 Discrete Mathematics (prof. Poh-Shen Loh"

https://www.math.cmu.edu/~ploh/2021-228.shtml

# Cryptography:

"Serious Cryptography: A Practical Introduction to Modern Encryption"

https://nostarch.com/seriouscrypto

# Problem Solving:

"Math 235: Mathematical Problem Solving"

https://www.cip.ifi.lmu.de/~grinberg/t/20f/

The relevant classes are: https://github.com/cmu-db/bustub/blob/master/src/primer/trie.cpp and the header https://github.com/cmu-db/bustub/blob/master/src/include/primer/trie.h (you can look at the root github's repo README how to compile)

Take for example CMU's bustub DB. Great lecture material, but their own pedagogical database where you implement parts of the database.

This seems like a fairly shallow course: if you’re interested in some real awesome database hacking, I highly recommend bustub. It’s great and educational.

Not a tutorial but I completed all the assignments for CMU Database System course (link) and watched all their youtube videos before I started it (I highly recommend it, it's a great course and it's possible to submit the solutions even if you're not a CMU student. The entry code to gradescope is in the FAQ). Though, what I do is not re-writing bustub in Rust, as bustub uses 2 phase locking to achieve transaction isolation, and this uses MVCC, pretty much like Postgres (though currently much simpler). I used this resource as a starting point how it works.

There is also BusTub from CMU which I stumbled upon earlier today:

https://github.com/cmu-db/bustub

check this course from cmu

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: