Apache Spark - Best Practices and Tuning
  • Introduction
  • RDD
    • Don’t collect large RDDs
    • Don't use count() when you don't need to return the exact number of rows
    • Avoiding Shuffle "Less stage, run faster"
    • Picking the Right Operators
      • Avoid List of Iterators
      • Avoid groupByKey when performing a group of multiple items by key
      • Avoid groupByKey when performing an associative reductive operation
      • Avoid reduceByKey when the input and output value types are different
      • Avoid the flatMap-join-groupBy pattern
      • Use TreeReduce/TreeAggregate instead of Reduce/Aggregate
      • Hash-partition before transformation over pair RDD
      • Use coalesce to repartition in decrease number of partition
    • TreeReduce and TreeAggregate Demystified
    • When to use Broadcast variable
    • Joining a large and a small RDD
    • Joining a large and a medium size RDD
  • Dataframe
    • Joining a large and a small Dataset
    • Joining a large and a medium size Dataset
  • Storage
    • Use the Best Data Format
    • Cache Judiciously and use Checkpointing
  • Parallelism
    • Use the right level of parallelism
    • How to estimate the size of a Dataset
    • How to estimate the number of partitions, executor's and driver's params (YARN Cluster Mode)
  • Serialization and GC
    • Tuning Java Garbage Collection
    • Serialization
  • References
    • References
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  1. RDD

Picking the Right Operators

When you try to write an application with Spark, you can usually choose from many arrangements of actions and transformations that will produce the same results. However, not all these arrangements will result in the same performance: avoiding common pitfalls and picking the right arrangement can make a world of difference in an application’s performance.

The following rules and insights that I've collected will help you orient yourself when these choices come up.

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Last updated 2 years ago

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