Autonomous / Self-Driving Databases utilize machine learning techniques to eliminate the manual labor associated with database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. This talk will focus specifically on how we implement a self-performing database with Oracle’s Database In-Memory product to automatically tune for query optimization, memory management, and storage management and data tiering. We will first present Oracle’s Database In-Memory architecture and various features built for optimizing analytics and mixed workload performance, and then describe in some detail the smarts we have to make it auto-performing in our self-driving database.
Shasank Chavan is the Vice President of the Data and In-Memory Technologies group at Oracle. He leads an amazing team of brilliant engineers in the Database organization who develop customer-facing, performance-critical features for an In-Memory Columnar Store which, as Larry Ellison proclaimed, “processes data at ungodly speeds”. His team implements novel SIMD kernels and hardware acceleration technology for blazing fast columnar data processing, optimized data formats and compression technology for efficient in-memory storage, algorithms and techniques for fast in-memory join and aggregation processing, and optimized in-memory data access and storage solutions in general. His team is currently hyper-focused on leveraging emerging hardware technologies to build Oracle's next-generation, highly distributed, data storage engine that powers the cloud. Shasank earned his BS/MS in Computer Science at the University of California, San Diego. He has accumulated 20+ patents over a span of 20 years working on systems software technology.
Faculty Host: Andy Pavlo