Spark 3.2.0 - How to effectively use broadcast variables for large dataset joins?
I'm writing unit tests and Hey everyone, I'm running into an issue that's driving me crazy. I'm currently working with Apache Spark 3.2.0 and working with performance optimization when performing joins between a large DataFrame and a smaller reference DataFrame. I read that using broadcast variables can significantly enhance the performance for such scenarios, but I'm unsure how to implement this correctly. I've already tried the traditional join methods, and while they work, the performance is subpar, especially when the size of the larger DataFrame is in the millions of rows. Here’s a simplified version of the code I’m using: ```python from pyspark.sql import SparkSession spark = SparkSession.builder.appName('BroadcastExample').getOrCreate() # Load large DataFrame large_df = spark.read.csv('large_data.csv', header=True, inferSchema=True) # Load small reference DataFrame small_df = spark.read.csv('small_reference_data.csv', header=True, inferSchema=True) # Performing join without broadcast joined_df = large_df.join(small_df, 'key_column') joined_df.show() ``` This works, but it takes a long time to execute, and I suspect that I can speed it up by broadcasting `small_df`. I attempted to use `broadcast` from `pyspark.sql.functions`, but I’m not sure about the proper syntax or placement. Here’s what I tried: ```python from pyspark.sql import functions as F # Attempting broadcast join broadcasted_small_df = F.broadcast(small_df) joined_df = large_df.join(broadcasted_small_df, 'key_column') joined_df.show() ``` After making this change, I’m running into the following behavior: `AnalysisException: Resolved attribute(s) key_column#123 missing from ...`. It seems like the broadcast variable isn’t being recognized in the join condition. Am I missing something crucial here? Is there a particular way to reference the broadcast variable in the join condition? Any insights or examples on best practices for using broadcast joins in Spark would be greatly appreciated! How would you solve this? My team is using Python for this microservice.