Imran Rashid created SPARK-5785: ----------------------------------- Summary: Pyspark does not support narrow dependencies Key: SPARK-5785 URL: https://issues.apache.org/jira/browse/SPARK-5785 Project: Spark Issue Type: Improvement Components: PySpark Reporter: Imran Rashid
joins (& cogroups etc.) are always considered to have "wide" dependencies in pyspark, they are never narrow. This can cause unnecessary shuffles. eg., this simple job should shuffle rddA & rddB once each, but it also will do a third shuffle of the unioned data: {code} rddA = sc.parallelize(range(100)).map(lambda x: (x,x)).partitionBy(64) rddB = sc.parallelize(range(100)).map(lambda x: (x,x)).partitionBy(64) joined = rddA.join(rddB) joined.count() >>> rddA._partitionFunc == rddB._partitionFunc True {code} (Or the docs should somewhere explain that this feature is missing from spark.) -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org