Performance Scaling of Cassandra on High-Thread Count Servers

Abstract

NoSQL databases are commonly used today in cloud deployments due to their ability to scale-out and effectively use distributed computing resources in a data center. At the same time, cloud servers are also witnessing rapid growth in CPU core counts, memory bandwidth, and memory capacity. Hence, apart from scaling out effectively, it's important to consider how such workloads scale-up within a single system, so that they can make the best use of available resources. In this paper, we describe our experiences studying the performance scaling characteristics of Cassandra, a popular open-source, column-oriented database, on a single high-thread count dual socket server. We demonstrate that using commonly used benchmarking practices, Cassandra does not scale well on such systems. Next, we show how by taking into account specific knowledge of the underlying topology of the server architecture, we can achieve substantial improvements in performance scalability. We report on how, during the course of our work, we uncovered an area for performance improvement in the official open-source implementation of the Java platform with respect to NUMA awareness. We show how optimizing this resulted in 27% throughput gain for Cassandra under studied configurations. As a result of these optimizations, using standard workload generators, we obtained up to 1.44x and 2.55x improvements in Cassandra throughput over baseline single and dual-socket performance measurements respectively. On wider testing across a variety of workloads, we achieved excellent performance scaling, averaging 98% efficiency within a socket and 90% efficiency at the system-level.