peloton

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: 1. it anyways takes a long time; 2. there are so many successful (open-source) databases; 3. hiring top engineers are so expensive. 4. you won't get enough attention unless your system is drastically better than existing ones. An interesting observation is that very few database was built since 2020 - almost all the newly built databases were developed on top of existing databases (PostgreSQL, ClickHouse, etc). I started building RisingWave (https://github.com/risingwavelabs/risingwave) in early 2021. The only reason we built the system from scratch was that none of th

From the GitHub README: https://github.com/cmu-db/peloton UPDATE 2019-03-17 The Peloton project is dead. We have abandoned this repository and moved on to build a new DBMS. There are a several engineering techniques and designs that we learned from this first system on how to support autonomous operations that we are doing a much better job at implementing in the second system. We will not accept pull requests for this repository. We will also not respond to questions or problems that you may have with running with this software.

lol "Latest development updates" > Added notice that the project is dead https://github.com/cmu-db/peloton/commit/484d76df9344cb5c153...

CMU has a "self driving" db with the same name https://github.com/cmu-db/peloton

For a moment, I thought that CMU's and Andy Pavlo's amazing database project got serious funding. https://github.com/cmu-db/peloton I'm a bit disappointed now

I know for the advanced database course the students end up writing a new feature for an existing database called Peloton, which is a research project at CMU[0]. You obviously aren't writing the whole thing from scratch though. [0]: https://github.com/cmu-db/peloton

Good summary. Some systems groups are already going in this direction. PeletonDB is trying to use DL to build a self-tuning DB https://github.com/cmu-db/peloton We have been trying to self-tune resource management decisions in Hadoop YARN using deep learning.

ML with human-in-the-loop (HITL). There is a feedback loop that learns based on human expectation. Peloton[1] is already learning based on data access patterns, but doesn't consider subjective feedback. Maybe there could be a query planner that learns which query plans are better via reinforcement learning (the reward is given by the human). [1]: https://github.com/cmu-db/peloton

The first commit was: commit 35823950d500314811212282bd68c101e34b9a06 Author: jarulraj <jarulraj@cs.cmu.edu> Date: Thu Dec 18 16:41:48 2014 -0500 Take a look at the different graphs on GitHub, like code frequency, to get a better idea: https://github.com/cmu-db/peloton/graphs/code-frequency