Spark 3.4.1 - Issues with Custom Partitioning Leading to Skewed Data Distribution
I'm trying to figure out I'm testing a new approach and I'm experiencing significant data skew when applying a custom partitioning strategy on a large DataFrame in Spark 3.4.1. I've defined my custom partitioner using a UDF that hashes certain columns and maps them to partition IDs. However, after executing my transformations, I've noticed that one of the partitions ends up with a disproportionately high number of records, leading to performance degradation during subsequent actions like `write` or `groupBy`. Here’s the code snippet where I define my custom partitioner: ```python from pyspark.sql import SparkSession from pyspark.sql.functions import udf from pyspark.sql.types import IntegerType spark = SparkSession.builder.appName('CustomPartitioning').getOrCreate() def custom_partitioner(col1, col2): return hash((col1, col2)) % 10 # Assuming 10 partitions partition_udf = udf(custom_partitioner, IntegerType()) # Create DataFrame data = [1, 2, 3, 4, 5] # This is just sample data columns = ['value'] df = spark.createDataFrame(data, columns) # Apply the custom partition partitioned_df = df.withColumn('partition_id', partition_udf(df.value, df.value)) # Repartitioning based on the custom id partitioned_df = partitioned_df.repartition('partition_id') ``` After repartitioning, I try to perform an action: ```python result = partitioned_df.groupBy('partition_id').count().collect() ``` The output shows that one partition has 90% of the records while others are almost empty, which is quite unexpected. I've confirmed that the input data is reasonably distributed. I've tried using the `coalesce()` method after the repartitioning, but it hasn't improved the situation. Additionally, I verified that the data types being passed to the UDF are correct and consistent. What can I do to achieve a better distribution of data across partitions? Are there any best practices or alternative strategies to implement custom partitioning in Spark? For reference, this is a production mobile app. What's the best practice here? How would you solve this?