A Cross-Layer Memory Tracing Toolkit for Big Data Application Based on Spark

Spark has been increasingly employed by industries for big data analytics recently, due to its efficient in-memory distributed programming model. Most existing optimization and analysis tool of Spark perform at either application layer or operating system layer separately, which makes Spark semantics separate from the underlying actions. For example, unknowing the impaction of operating system parameters on performance of Spark layer will lead unknowing of how to use OS parameters to tune system performance. In this paper, we propose SMTT, a new Spark memory tracing toolkit, which establishes the semantics of the upper application and the underlying physical hardware across Spark layer, JVM layer and OS layer. Based on the characteristics of Spark memory, we design the tracking scheme of execution memory and storage memory respectively. Then we analyze the Spark iterative calculation process and execution/storage memory usage by SMTT. The experiment of RDD memory assessment analysis shows our toolkit could be effectively used on performance analysis and provide guides for optimization of Spark memory system.