![]() The Catalyst optimizer in Spark tries as much as possible to optimize the queries but it can’t help you with scenarios like this when the query itself is inefficiently written. The overhead will directly increase with the number of columns being selected. There are a few common reasons also that would cause this failure:Įxample : Selecting all the columns from a Parquet/ORC table.Įxplanation : Each column needs some in-memory column batch state. Total executor memory = total RAM per instance / number of executors per instance In this case there arise two possibilities to resolve this issue: either increase the driver memory or reduce the value for. When performing a BroadcastJoin Operation,the table is first materialized at the driver side and then broadcasted to the executors. There could be another scenario where you may be working with Spark SQL queries and there could be multiple tables being broadcasted. repartition(1).write.csv(“/output/file/path”) Setting a proper limit using can protect the driver from OutOfMemory errors and repartitioning before saving the result to your output file can help too.ĭf. We can solve this problem with two approaches: either use or repartition. The Driver will try to merge it into a single object but there is a possibility that the result becomes too big to fit into the driver’s memory. Few unconscious operations which we might have performed could also be the cause of error.Ĭollect() operation will collect results from all the Executors and send it to your Driver. You should always be aware of what operations or tasks are loaded to your driver. The driver in the Spark architecture is only supposed to be an orchestrator and is therefore provided less memory than the executors. OutOfMemory error can occur here due to incorrect usage of Spark. It can also persist data in the worker nodes for re-usability.Įach of these requires memory to perform all operations and if it exceeds the allocated memory, an OutOfMemory error is raised. It runs an individual task and returns the result to the Driver. These can be dynamically launched and removed by the Driver as and when required. It executes the code and creates a SparkSession/ SparkContext which is responsible to create Data Frame, Dataset, RDD to execute SQL, perform Transformation & Action, etc.Įxecutors are launched at the start of a Spark Application with the help of Cluster Manager. Let us first understand what are Driver and Executors.ĭriver is a Java process where the main() method of our Java/Scala/Python program runs. The OutOfMemory Exception can occur at the Driver or Executor level. : Java heap spaceĮxception in thread “task-result-getter-0” : Java heap space This exception is of no surprise as Spark’s Architecture is completely memory-centric. And, out of all the failures, there is one most common issue that many of the spark developers would have come across, i.e. But it becomes very difficult when the spark applications start to slow down or fail and it becomes much more tedious to analyze and debug the failure. ![]() Also, you will get to know how to handle such exceptions in the real time scenarios.Īpache Spark applications are easy to write and understand when everything goes according to plan. You will be taken through the details that would have taken place in the background and raised this exception. The objective of this blog is to document the understanding and familiarity of Spark and use that knowledge to achieve better performance of Apache Spark. Through this blog post, you will get to understand more about the most common OutOfMemoryException in Apache Spark applications. Clairvoyant aims to explore the core concepts of Apache Spark and other big data technologies to provide the best-optimized solutions to its clients. ![]()
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