👀 Views: 0 💬 Answers: 1 📅 Created: 2025-07-11

apache-spark pyspark memory-management

I'm currently working on a Spark application using version 3.4.1 and I'm facing an `OutOfMemoryError` when processing large RDDs within a lambda function. The dataset I'm working with is approximately 500 GB, and I've set the executor memory to 16 GB. Despite this, I keep getting errors like this: ``` java.lang.OutOfMemoryError: Java heap space ``` I have tried increasing the executor memory to 32 GB, but the problem persists. My current code that processes the RDD looks something like this: ```python from pyspark import SparkContext sc = SparkContext(appName="LargeRDDProcessing") rdd = sc.textFile("hdfs://path/to/large/dataset.txt") # This lambda function processes each line and results in a large output. result = rdd.map(lambda line: complex_processing_function(line)) result.saveAsTextFile("hdfs://path/to/output") ``` The `complex_processing_function` involves some heavy computations, including multiple nested operations that involve filtering and aggregating data. I've also configured the Spark settings to use: ```properties spark.executor.memory=32g spark.driver.memory=16g spark.memory.fraction=0.8 ``` I suspect that the issue might be related to the way Spark manages memory for the lambda functions, but I'm not sure how to optimize this or if there are any specific configurations I should be applying. Any insights on how to resolve this memory issue would be greatly appreciated!