noisepage VS bustub

> Carne

Okay, so, noisepage appears to be open source https://github.com/cmu-db/noisepage/

But I can't find the Ottertune Github page

Is any part of Ottertune open source?

I was one of the main authors of a research project called Peloton (https://github.com/cmu-db/peloton) which was later rebranded to NoisePage (https://github.com/cmu-db/noisepage). The initial version of RisingWave actually borrowed a lot from Peloton (fun fact: that's also how DuckDB https://duckdb.org/ started!), but we decided to rewrite in Rust due to development cost and security (e.g., memory leakage) considerations (more info: https://www.risingwave-labs.com/blog/building-a-cloud-database-from-scratch-why-we-moved-from-cpp-to-rust/).

> If I may, can you please shed light on why Peloton had to be archived and in essence re-done with OtterTune. Interested in your team's learnings from it from a software engineering point of view.

Peloton and OtterTune are completely different projects. Peloton was abandoned and rewritten as NoisePage (https://noise.page). OtterTune has always been OtterTune.

See this recent interview where I discuss why we gave up on Peloton:

https://www.ibm.com/cloud/blog/database-deep-dives-with-andy...

> - How did the team ensure this project doesn't suffer from the same disadvantages as its predecessor?

Again, different projects. OtterTune is all about not having to modify the internals of Postgres, MySQL, and any other DBMS. This is why we were able to support Oracle in the academic version in a short amount of time:

https://ottertune.com/blog/vldb-autonomous-database-tuning-i...

> - What would you advise other teams undertaking a rewrite to pay off their tech debts?

It is hard for to provide general advice for this question because every situation is different.

> How does this project compare to / contrast with Google's and SingleStore's efforts in this space?

I am not familiar with Google or SingleStore using ML in the manner that we are with OtterTune to tune configuration knobs. Or at least I have not seen anything public about it.

These days Oracle is the most aggressive with pushing automated tuning capabilities (Oracle's autonomous DBaaS, AutoPilot for MySQL Heatwave). The difference with these approaches and OtterTune is that right now we are focused on configuration tuning (to avoid data privacy issues) and our core approach is platform/DBMS agnostic.

> Any chance we see you do a Peter Bailis and Sisu Data this? (:

I don't know what you mean by this? Peter Bailis is the Ryan Gosling of databases.

The state of the art for this sort of thing is (Leanstore/Umbra - https://umbra-db.com/) or the new NoisePage database (https://github.com/cmu-db/noisepage/tree/master/src/storage). There is also the HyRise database, but that one focuses more on datasets that fit entirely in memory (https://hpi.de/plattner/projects/hyrise.html)

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