Besides being an in-memory-oriented computing framework, Spark runs on top of Java Virtual Machines (JVMs), so JVM parameters must be tuned to improve Spark application performance. Misconfigured parameters and settings degrade performance. For example, using Java heaps that are too large often causes a long garbage collection pause time, which accounts for over 10–20% of application execution time. Moreover, recent computing nodes have many cores with simultaneous multi-threading technology and the processors on the node are connected via NUMA, so it is difficult to exploit best performance without taking into account of these hardware features. Thus, optimization in a full stack is also important. Not only JVM parameters but also OS parameters, Spark configuration, and application code based on CPU characteristics need to be optimized to take full advantage of underlying computing resources. In this paper, we used the TPC-H benchmark as our optimization case study and gathered many perspective logs such as application, JVM (e.g. GC and JIT), system utilization, and hardware events from a performance monitoring unit. We discuss current problems and introduce several JVM and OS parameter optimization approaches for accelerating Spark performance. As a result, our optimization exhibits 30–40% increase in speed on average and is up to 5x faster than the naive configuration.
['Tatsuhiro Chiba', 'Tamiya Onodera']
Workload characterization and optimization of TPC-H queries on Apache Spark