Skyway: connecting managed heaps in distributed big data systems
Skyway: connecting managed heaps in distributed big data systems Nguyen et al., ASPLOS’18
Yesterday we saw how to make Java objects persistent using NVM-backed heaps with Espresso. One of the drawbacks of using that as a persistence mechanism is that they’re only stored in the memory of a single node. If only there was some way to create a cluster of JVMs, and efficiently copy objects across remote heaps in the cluster… Meet Skyway!
Skyway is aimed at JVM-based big data systems (think Spark, Flink) that end up spending a lot of their time serializing and deserializing objects to move them around the cluster (e.g., to and from workers – see ‘Making sense of performance in data analytics frameworks’). Java comes with a default serialization mechanism, and there are also many third party libraries. Kyro is the recommended library for use with Spark.
Consider a small Spark cluster (3 worker nodes each with a 20 GB heap) running a triangle counting algorithm over the LiveJournal graph (about 1.2GB). With both the standard Java serializers and Kyro, serialization and deserialization combined account for a significant portion of the overall execution time (more than 30%).

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