Spark 3.3.1 - implementing memory errors when using Window functions on large datasets
I'm prototyping a solution and I've tried everything I can think of but I'm dealing with I've encountered a strange issue with I'm working with Apache Spark 3.3.1 and trying to implement a solution that requires calculating running totals using Window functions on a large dataset (around 50 million records)... Despite tuning my Spark configuration, I'm continuously hitting memory errors. The specific behavior message I'm working with is `java.lang.OutOfMemoryError: Java heap space`, which seems to occur during the execution of my Window function. Here’s the code snippet I’m using: ```python from pyspark.sql import SparkSession from pyspark.sql import Window from pyspark.sql import functions as F spark = SparkSession.builder.appName('WindowFunctionExample').getOrCreate() data = spark.read.csv('path/to/large_dataset.csv', header=True, inferSchema=True) window_spec = Window.partitionBy('category').orderBy('date') result = data.withColumn('running_total', F.sum('amount').over(window_spec)) result.show() ``` I have tried increasing the executor memory by setting `--executor-memory 4G` and `spark.executor.memory 4g` in my configuration, but it doesn't seem to help. Additionally, I've tried using the `continue()` method on the DataFrame before applying the Window function to cache the intermediate results, but the scenario continues. Is there a better way to handle this? Should I consider using `coalesce()` to reduce the number of partitions before the Window operation? Or are there other performance tuning tips or configurations that could help alleviate the memory pressure during this operation? Any advice would be greatly appreciated! I'm working on a API that needs to handle this. What's the best practice here? I recently upgraded to Python latest. I'm open to any suggestions. What am I doing wrong? This issue appeared after updating to Python LTS.