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28 March 2019

Intro At Taboola we use Spark extensively throughout the pipeline. Regularly faced with Spark-related scalability challenges, we look for optimisations in order to squeeze the most out of the library. Often, the problems we encounter are related to shuffles. In this post we will present a technique we discovered which gave us up to 8x boost in performance for jobs with huge data shuffles.   Shuffles Shuffling is a process of redistributing data across partitions (aka repartitioning) that may or may not cause moving data across JVM processes or even over the wire (between executors on separate machines).Shuffles, despite their drawbacks, are sometimes inevitable. In our case, here are some of the problems we faced: Performance hit – Jobs run longer because shuffles use network and IO resources intensively. Cluster stability – Heavy shuffles fill scratch disks of cluster machines. This affects other jobs on the same cluster , since […]

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