Spark optimizations: what are the optimizations that can be done for the below snippet code: shoppers_df (customers description DF) 250MB, 15M records: schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("retailer_id", StringType, nullable = True), StructField("shopper_group_id", StringType, nullable = True), StructField("join_date", DateType, nullable = True), StructField("shopper_type", StringType, nullable = True), StructField("gender", StringType, nullable = True))) sku_df (dimension DF): 15 MB, 90K records purchase_df (transactions DF): 50GB of parquet compressed files 5,000,000,000 records. schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("product_id", LongType, nullable = True), StructField("pos_id", IntegerType, nullable = True), StructField("purchase_date", DateType, nullable = True), StructField("units", DoubleType, nullable = True), StructField("total_spent", DoubleType, nullable = True))) Current code: products_purchased_df = purchase_df.alias("purchase").join(shoppers_df, on = "shopper_id", how = "left outer").join(sku_df.alias("sku"), on = "product_id").select(Col("purchase.*"), Col("sku.*")) usage: status_df = products_purchased_df.groupBy(["shopper_id", "product_id"]).agg(...) Optimize join statement
