noisepage VS peloton

> 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)

Proud to see my name (https://twitter.com/YingjunWu) mentioned in Andy's blog. I was Andy's visiting PhD at CMU and was the top 1 contributor to Peloton (https://github.com/cmu-db/peloton).

Today, building a database from scratch is extremely difficult, for several reasons:

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/).

I'll give you an example from my own industry. At my company we talk to university researchers, and one of the more famous university database researcher came to us to learn about our performance testing, because it turned out that the database they were working on for several years as a research project was completely unusable as they just forgot to think about that aspect. They had to abandon the project because there was no single feature that contributed to the performance issues and fixing the system was harder than starting from scratch. Here's the last commit if you don't believe me.