Make Database Performance Optimization A Playful Experience With OtterTune

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 When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. 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 Support Data Engineering Podcast