Spark optimization tips articles

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Articles tagged with Spark optimization tips. There are 3 article(s) corresponding to the tag Spark optimization tips. If you don't find what you're looking for, please check related tags: access pattern, Ad-hoc polymorphism, Akka Distributed Data, Akka examples, algorithm analysis, algorithm complexity, Apache Beam configuration, Apache Beam internals, Apache Beam partitioning, Apache Beam PCollection.

Dynamic resource allocation in Spark

Defining the universal workload and associating corresponding resources is always difficult. Even if most of time expected resources will support the load, there always will be some interval in the year when data activity will grow (e.g. Black Friday). One of Spark's mechanisms helping to prevent processing failures in such situations is dynamic resource allocation. Continue Reading →

Tree aggregations in Spark

As every library, Spark has methods than are used more often than the others. As often used methods we could certainly define map or filter. In the other side of less popular transformations we could place, among others, tree-like methods that will be presented in this post. Continue Reading →

Per-partition operations in Spark

Spark was developed to work on big amount of data. If big means millions of items. For every item one or several costly operations are done, it'll lead quick to performance problems. It's one of the reasons why Spark proposes operations executed once per partition. Continue Reading →