SIGMOD’16, June 26-July 01, 2016, San Francisco, CA, USA.
Joy Arulraj, Andrew Pavlo, Prashanth Menon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Data-intensive applications seek to obtain trill insights in real-time by analyzing a combination of historical data sets alongside recently collected data. This means that to support such hybrid workloads, database management systems (DBMSs) need to handle both fast ACID transactions and complex analytical queries on the same database. But the current trend is to use specialized systems that are optimized for only one of these workloads, and thus require an organization to maintain separate copies of the database. This adds additional cost to deploying a database application in terms of both storage and administration overhead.
To overcome this barrier, we present a hybrid DBMS architecture that efficiently supports varied workloads on the same database. Our approach differs from previous methods in that we use a single execution engine that is oblivious to the storage layout of data without sacrificing the performance benefits of the specialized systems. This obviates the need to maintain separate copies of the database in multiple independent systems. We also present a technique to continuously evolve the database’s physical storage layout by analyzing the queries’ access patterns and choosing the optimal layout for different segments of data within the same table. To evaluate this work, we implemented our architecture in an in-memory DBMS. Our results show that our approach delivers up to 3X higher throughput compared to static storage layouts across different workloads. We also demonstrate that our continuous adaptation mechanism allows the DBMS to achieve a near-optimal layout for an arbitrary workload without requiring any manual tuning.
FULL PAPER: pdf