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Mo 26.10.2020 17:30 - 18:30 Uhr, Online: "Optimizing Cloud Query Engines at Microsoft" (Alekh Jindal, Microsoft Grey Systems Lab)

DBMS research colloquium: "Optimizing Cloud Query Engines at Microsoft" by Alekh Jindal

Cloud query engines have become increasingly complex making the job of a query optimizer incredibly difficult. This is due to more complicated decision making, more complex query plans seen, and more tedious objective functions in cloud workloads. As a result, production cloud query optimizers are often far from optimal. In this talk, we describe a learning platform for optimizing cloud query workloads at Microsoft. We present a micromodel approach for handling the scale and complexity of cloud workloads by characterizing them into smaller subsets and learning a large number of specialized models over them. The micromodel approach can scale to very large training inputs and yields smaller lightweight models that could be scored with efficiently within the query optimizer. We describe our journey towards productization, using learned cardinality as a concrete example, via performance over very large production workloads and illustrate the various challenges involved in deployment.

Alekh Jindal is a Principle Scientist at Gray Systems Lab (GSL), Microsoft and manages the Redmond site of the lab. His research focusses on improving the performance of large-scale data-intensive systems. Earlier, he was a postdoc associate in the Database Group at MIT CSAIL. Alekh received his PhD from Saarland University, working on flexible and scalable data storage for traditional databases as well as for MapReduce. In the past 10 years, Alekh has served as a chair, PC member and reviewer at top-tier conferences in the field including SIGMOD, VLDB, ICDE, and SOCC. He received best paper awards at VLDB 2014 and CIDR 2011.

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