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Resolving Out of Memory Error on Spark Driver When Reading Large Avro Files

Discover how to fix the common `out of memory error` in Spark driver while handling large Avro files with effective partition management strategies. --- This video is based on the question https://stackoverflow.com/q/74306482/ asked by the user 'dsumner' ( https://stackoverflow.com/u/6681494/ ) and on the answer https://stackoverflow.com/a/74316285/ provided by the user 'dsumner' ( https://stackoverflow.com/u/6681494/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Why am I getting out of memory error on spark driver when trying to read lots of Avro files? No collect operation happening Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Introduction: Understanding the Out of Memory Error in Spark Are you grappling with an out of memory error on your Spark driver while trying to read a substantial amount of Avro files? This challenging situation typically arises when working with large data sets on Apache Spark, especially in environments like Databricks. You might find yourself confused, particularly if you are not conducting any operations that would normally strain memory, such as a collect() operation. This guide will take you through the problem and offer a comprehensive solution to prevent your Spark driver from crashing. The Problem: Why Is Your Spark Driver Running Out of Memory? When you attempt to read a considerable amount of Avro files stored on S3 via spark.read.load, you may encounter the following issues: Out of Memory Error: This happens when the size of the result exceeds the memory allocation specified by spark.driver.maxResultSize. Driver Crashes: Even if you increase the memory limit, the driver may still run out of memory, especially if there are too many partitions created from the data files. You became aware that attempts to adjust configurations such as spark.sql.files.maxPartitionBytes for fewer partitions didn’t yield any results. Even increasing the cluster's memory wasn’t helpful. You even noticed that specifying the Avro schema improved the situation but did not completely solve the problem. Solution: Managing Partitions Effectively After investigating the cause of the memory issues, it was found that the underlying problem stemmed from the setting of spark.sql.sources.parallelPartitionDiscovery.parallelism. Here’s how to address it: Step 1: Increase Parallelism for Partition Discovery The key issue was that the parallelism setting for partition discovery was too low, making it difficult for Spark to handle the large number of files effectively. By increasing this setting, you enable the Spark driver to better handle the partitioning of your data. How to Increase Parallelism You can change the setting by inserting the following configuration in your code: [[See Video to Reveal this Text or Code Snippet]] Replace <desired_value> with a number that reflects a higher parallelism level (for example, start with 100 or 200). This adjustment allows Spark to better manage the reading of multiple files simultaneously without overwhelming the driver memory. Step 2: Test Your Configuration with Smaller Batches Before attempting to process all your Avro files, consider testing your new configuration with smaller batches. This approach helps you monitor memory usage effectively and ensures the modifications yield better results without crashing the driver. Testing Tips Start with a small subset of your Avro files. Gradually increase the number of files to determine the point at which your driver remains stable. Monitor memory consumption and performance metrics. Step 3: Continuously Monitor and Adjust Over time, as you work with larger and larger data sets, keep a close eye on how your configurations affect Spark's memory usage. Be prepared to adjust settings as needed to prevent any future issues. Revisit the spark.driver.memory setting if necessary. Adjust the maxPartitionBytes to balance the number of partitions and memory usage. Conclusion Confronting an out of memory error while working with large Avro files on Spark can be frustrating, but by focusing on proper partition management and adjusting the right configurations, you can alleviate these issues effectively. Increasing the parallelism for partition discovery is a crucial step in optimizing your Spark jobs, allowing for smoother proce