Hi I have Spark job which does group by and I cant avoid it because of my use case. I have large dataset around 1 TB which I need to process/update in DataFrame. Now my jobs shuffles huge data and slows things because of shuffling and groupby. One reason I see is my data is skew some of my group by keys are empty. How do I avoid empty group by keys in DataFrame? Does DataFrame avoid empty group by key? I have around 8 keys on which I do group by.
sourceFrame.select("blabla").groupby("col1","col2","col3",..."col8").agg("bla bla"); How do I change above code into using reduceByKey() can we apply aggregation on reduceByKey()? Please guide. Thanks in advance. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/How-to-change-Spark-DataFrame-groupby-col1-coln-into-reduceByKey-tp26998.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org