Summary
Apache Spark defaults perform decently on large data sets, but tuning parameters to the resources and job can bring significant gains. Best practices from real-world problems cover garbage collector selection, serialization, executor counts, partitioning and skew, fair scheduler pools, Java heap settings, reading Spark UI DAGs for bottlenecks, and optimizing joins.
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