👀 Views: 79 💬 Answers: 1 📅 Created: 2025-07-11

apache-spark pyspark memory-management Python

I've spent hours debugging this and This might be a silly question, but I'm working through a tutorial and I'm currently working on a Spark application using version 3.4.1 and I'm working with 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 question continues. 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 scenario 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 scenario would be greatly appreciated! This is my first time working with Python 3.11. I'd love to hear your thoughts on this. For reference, this is a production CLI tool. Any advice would be much appreciated. I recently upgraded to Python 3.10. What would be the recommended way to handle this?