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Each executor is assigned 10 CPU cores.ĥ executors and 10 CPU cores per executor = 50 CPU cores available in total. Let’s say, you have 5 executors available for your application.
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Number of CPU cores available for an executor determines the number of tasks that can be executed in parallel for an application for any given time. property controls the number of partitions during a shuffle and the default value of this property is 200.Ĭhange the value of to change the number of partitions during a shuffle. Spark optimizer tries to pick the “right” number of partitions during a shuffle but most often you will see Spark creates 200 tasks for stages executing wide transformation operations like JOIN, GROUP BY etc. Third stage executes a JOIN and JOIN operation triggers a wide transformation and wide transformation will result in a shuffle. Second stage reads dataset_Y and dataset_Y has 5 partitions. The default for defaultMinPartitions is 2. If your dataset is very small, you might see Spark still creates 2 tasks and this is because Spark looks at the defaultMinPartitions property and this property decides the minimum number of tasks Spark can create. Third stage – Instructions 6, 7 and 8 Number of tasks in first stageįirst stage reads dataset_X and dataset_X has 10 partitions. Let’s also assume dataset_Y has 10 partitions and dataset_Y has 5 partitions.ĭo you like us to send you a 47 page Definitive guide on Spark join algorithms? => Send me the guide Stages and number of tasks per stage Let’s see how Spark decides on the number of tasks with the below set of instructions. In this post we will see how Spark decides the number of tasks and number of tasks to execute in parallel in a job.