Abstract

Database systems have traditionally optimized performance for write-intensive workloads. Recently, there has been renewed interest in architectures that optimize read performance by using column-oriented data representation and light-weight compression. This previous work has shown that under certain broad classes of workloads, column-based systems can outperform rowbased systems. Previous work, however, has not characterized the precise conditions under which a particular query workload can be expected to perform better on a column-oriented database. In this paper we first identify the distinctive components of a read-optimized DBMS and describe our implementation of a high-performance query engine that can operate on both row and column-oriented data. We then use our prototype to perform an in-depth analysis of the tradeoffs between column and row-oriented architectures. We explore these tradeoffs in terms of disk bandwidth, CPU cache latency, and CPU cycles. We show that for most database workloads, a carefully designed column system can outperform a carefully designed row system, sometimes by an order of magnitude. We also present an analytical model to predict whether a given workload on a particular hardware configuration is likely to perform better on a row or column-based system. 1.

Citations