Welcome to another post in our series of interview blogs for the upcoming Percona Live Europe 2017 in Dublin. This series highlights a number of talks that will be at the conference and gives a short preview of what attendees can expect to learn from the presenter.
This blog post is with Dana Van Aken, a Ph.D. student in Computer Science at Carnegie Mellon University. Her talk is titled Automatic Database Management System Tuning Through Large-Scale Machine Learning. DBMSs are difficult to manage because they have hundreds of configuration “knobs” that control factors such as the amount of memory to use for caches and how often to write data to storage. Organizations often hire experts to help with tuning activities, but experts are prohibitively expensive for many. In this talk, Dana will present OtterTune, a new tool that can automatically find good settings for a DBMS’s configuration knobs. In our conversation, we discussed how machine learning helps DBAs manage DBMSs:
Percona: How did you get into database technology? What do you love about it?
Dana: I got involved with research as an undergrad and ended up working on a systems project with a few Ph.D. students. It turned out to be a fantastic experience and is what convinced me to go for my Ph.D. I visited potential universities and chatted with many faculty members. I met with my current advisor at Carnegie Mellon University, Andy Pavlo, for a half hour and left his office excited about databases and the research problems he was interested in. Three years later, I’m even more excited about databases and the progress we’ve made in developing smarter auto-tuning techniques.
Percona: You’re presenting a session called “Automatic Database Management System Tuning Through Large-Scale Machine Learning”. How does automation make DBAs life easier in a DBMS production environment?
Dana: The role of the DBA is becoming more challenging due to the advent of new technologies and increasing scalability requirements of data-intensive applications. Many DBAs are constantly having to adjust their responsibilities to manage more database servers or support new platforms to meet an organization’s needs as they change over time. Automation is critical for reducing the DBA’s workload to a manageable size so that they can focus on higher-value tasks. Many organizations now automate at least some of the repetitive tasks that were once DBA responsibilities: several have adopted public/private cloud-based services whereas others have built their own automated solutions internally.
The problem is that the tasks that have now become the biggest time sinks for DBAs are much harder to automate. For example, DBMSs have dozens of configuration options. Tuning them is an essential but tedious task for DBAs, because it’s a trial and error approach even for experts. What makes this task even more time-consuming is that the best configuration for one DBMS may not be the best for another. It depends on the application’s workload and the server’s hardware. Given this, successfully automating DBMS tuning is a big win for DBAs since it would streamline common configuration tasks and give DBAs more time to deal with other issues. This is why we’re working hard to develop smarter tuning techniques that are mature and practical enough to be used in a production environment.
Percona: What do you want attendees to take away from your session? Why should they attend?
Dana: I’ll be presenting OtterTune, a new tool that we’re developing at Carnegie Mellon University that can automatically find good settings for a DBMS’s configuration knobs. I’ll first discuss the practical aspects and limitations of the tool. Then I’ll move on to our machine learning (ML) pipeline. All of the ML algorithms that we use are popular techniques that have both practical and theoretical work backing their effectiveness. I’ll discuss each algorithm in our pipeline using concrete examples from MySQL to give better intuition about what we are doing. I will also go over the outputs from each stage (e.g., the configuration parameters that the algorithm find to be the most impactful on performance). I will then talk about lessons I learned along the way, and finally wrap up with some exciting performance results that show how OtterTune’s configurations compared to those created by top-notch DBAs!
My talk will be accessible to a general audience. You do not need a machine learning background to understand our research.
Percona: What are you most looking forward to at Percona Live Europe 2017?
Dana: This is my first Percona Live conference, and I’m excited about attending. I’m looking forward to talking with other developers and DBAs about the projects they’re working on and the challenges they’re facing and getting feedback on OtterTune and our ideas.
Want to find out more about Dana and machine learning for DBMS management? Register for Percona Live Europe 2017, and see his talk Automatic Database Management System Tuning Through Large-Scale Machine Learning. Register now to get the best price! Use discount code SeeMeSpeakPLE17 to get 10% off your registration.
Percona Live Open Source Database Conference Europe 2017 in Dublin is the premier European open source event for the data performance ecosystem. It is the place to be for the open source community as well as businesses that thrive in the MySQL, MariaDB, MongoDB, time series database, cloud, big data and Internet of Things (IoT) marketplaces. Attendees include DBAs, sysadmins, developers, architects, CTOs, CEOs, and vendors from around the world.
The Percona Live Open Source Database Conference Europe will be September 25-27, 2017 at the Radisson Blu Royal Hotel, Dublin.