I am using spark 1.5.2. I have a question regarding plan generated by spark. I have 3 data-frames which has the data for different countries. I have around 150 countries and data is skewed.
My 95% queries will have country as criteria. However, I have seen issues with the plans generated for queries which has country as join column. Data-frames are partitioned based on the country.Not only these dataframes are co-partitioned, these are co-located as well. E.g. Data for UK in data-frame df1, df2 df3 will be at on same hdfs datanode. Then when i join these 3 tables and country is one of the join column. I assume that the join should be the map side join but it shuffles the data from 3 dataframes and then join using shuffled data. Apart from country there are other columns in join. Is this correct behavior? If it is an issue is it fixed in latest versions? Thanks -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Plan-issue-with-spark-1-5-2-tp26681.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
