Apache Spark 3.4.1 - Issues with Window Functions Returning Incorrect Aggregated Results on Grouped Data
I need some guidance on I'm performance testing and I've been struggling with this for a few days now and could really use some help... I'm trying to implement I'm facing an issue with window functions in Apache Spark 3.4.1 where the results seem to be incorrect when applying an aggregation over a grouped dataset... I have a DataFrame that contains user activity logs and I want to calculate the running total of user actions within each user group. However, the output does not match my expected results. Here is a simplified version of my DataFrame: ```python from pyspark.sql import SparkSession from pyspark.sql import functions as F spark = SparkSession.builder.appName("WindowFunctionIssue").getOrCreate() data = [ (1, '2023-10-01', 5), (1, '2023-10-02', 3), (1, '2023-10-03', 8), (2, '2023-10-01', 2), (2, '2023-10-02', 1) ] columns = ['user_id', 'date', 'actions'] activity_df = spark.createDataFrame(data, columns) ``` I created a window specification to partition by `user_id` and order by `date`: ```python from pyspark.sql import Window window_spec = Window.partitionBy('user_id').orderBy('date') ``` Then, I applied the running total aggregation: ```python activity_df = activity_df.withColumn('running_total', F.sum('actions').over(window_spec)) activity_df.show() # This is where I expect cumulative totals ``` The output I receive is: ``` +-------+----------+-------+-------------+ |user_id| date|actions|running_total| +-------+----------+-------+-------------+ | 1|2023-10-01| 5| 5| | 1|2023-10-02| 3| 8| | 1|2023-10-03| 8| 16| | 2|2023-10-01| 2| 2| | 2|2023-10-02| 1| 3| +-------+----------+-------+-------------+ ``` The total seems correct, but when I run a filter on the DataFrame post-aggregation to find records where running total exceeds 10, I end up with no results where I expect some for user_id 1. I've also tried re-ordering the data and using different aggregations, but the issue persists. Any insights into why the aggregated results might be incorrect or how the window function behaves differently than expected in this scenario? I've confirmed that my date format is consistent and that there are no nulls in the actions column. I'm also running this with a local Spark session to test the logic. Thanks for your help! Any examples would be super helpful. I'm developing on Debian with Python. The project is a REST API built with Python. I'm on Windows 11 using the latest version of Python. Is there a better approach?