Why does join fail with “java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]”?

Question

I am using Spark 1.5.

I have two dataframes of the form:

scala> libriFirstTable50Plus3DF
res1: org.apache.spark.sql.DataFrame = [basket_id: string         


        

Answer 1

This happens because Spark tries to do Broadcast Hash Join and one of the DataFrames is very large, so sending it consumes much time.

You can:

1. Set higher spark.sql.broadcastTimeout to increase timeout - spark.conf.set("spark.sql.broadcastTimeout", newValueForExample36000)
2. persist() both DataFrames, then Spark will use Shuffle Join - reference from here

PySpark

In PySpark, you can set the config when you build the spark context in the following manner:

spark = SparkSession
  .builder
  .appName("Your App")
  .config("spark.sql.broadcastTimeout", "36000")
  .getOrCreate()