Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive, and Spark choose to use the Java Virtual Machine (JVM) despite debate on the overhead of doing so. By thoroughly studying the JVM performance overhead in the above-mentioned systems, we found that the warm-up overhead, i.e., class loading and interpretation of bytecode, is frequently the bottleneck. For example, even an I/O intensive, 1 GB read on HDFS spends 33% of its execution time in JVM warm-up, and Spark queries spend an average of 21 seconds in warmup. The findings on JVM warm-up overhead reveal a contradiction between the principle of parallelization, i.e., speeding up long-running jobs by parallelizing them into short tasks, and amortizing JVM warm-up overhead through long tasks. We therefore developed HotTub, a new JVM that reuses a pool of already warm JVMs across multiple applications. The speed-up is significant: for example, using HotTub results in up to 1.8x speed-ups for Spark queries, despite not adhering to the JVM specification in edge cases.
