Summary

The database is the core of any system because it holds the data that drives your entire experience. We spend countless hours designing the data model, updating engine versions, and tuning performance. But how confident are you that you have configured it to be as performant as possible, given the dozens of parameters and how they interact with each other? Andy Pavlo researches autonomous database systems, and out of that research he created OtterTune to find the optimal set of parameters to use for your specific workload. In this episode he explains how the system works, the challenge of scaling it to work across different database engines, and his hopes for the future of database systems.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I’m interviewing Andy Pavlo about OtterTune, a system to continuously monitor and improve database performance via machine learning

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what OtterTune is and the story behind it?
  • How does it relate to your work with NoisePage?
• What are the challenges that database administrators, operators, and users run into when working with, configuring, and tuning transactional systems?
  • What are some of the contributing factors to the sprawling complexity of the configurable parameters for these databases?
• Can you describe how OtterTune is implemented?
  • What are some of the aggregate benefits that OtterTune can gain by running as a centralized service and learning from all of the systems that it connects to?
  • What are some of the assumptions that you made when starting the commercialization of this technology that have been challenged or invalidated as you began working with initial customers?
  • How have the design and goals of the system changed or evolved since you first began working on it?
• What is involved in adding support for a new database engine?
  • How applicable are the OtterTune capabilities to analytical database engines?
• How do you handle tuning for variable or evolving workloads?
• What are some of the most interesting or esoteric configuration options that you have come across while working on OtterTune?
  • What are some that made you facepalm?
• What are the most interesting, innovative, or unexpected ways that you have seen OtterTune used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on OtterTune?
• When is OtterTune the wrong choice?
• What do you have planned for the future of OtterTune?

Contact Info

• CMU Page
• apavlo on GitHub
• @andy_pavlo on Twitter

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

• OtterTune
• CMU (Carnegie Mellon University)
• Brown University
• Michael Stonebraker
• H-Store
• Learned Indexes
• NoisePage
• Oracle DB
• PostgreSQL
  • Podcast Episode
• MySQL
• RDS
• Gaussian Process Model
• Reinforcement Learning
• AWS Aurora
• MVCC (Multi-Version Concurrency Control)
• Puppet
• VectorWise
• GreenPlum
• Snowflake
  • Podcast Episode
• PGTune
• MySQL Tuner
• SIGMOD

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA