Diana Carroll created SPARK-9821: ------------------------------------ Summary: pyspark reduceByKey should allow a custom partitioner Key: SPARK-9821 URL: https://issues.apache.org/jira/browse/SPARK-9821 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 1.3.0 Reporter: Diana Carroll
In Scala, I can supply a custom partitioner to reduceByKey (and other aggregation/repartitioning methods like aggregateByKey and combinedByKey), but as far as I can tell from the Pyspark API, there's no way to do the same in Python. Here's an example of my code in Scala: {code}weblogs.map(s => (getFileType(s), 1)).reduceByKey(new FileTypePartitioner(),_+_){code} But I can't figure out how to do the same in Python. The closest I can get is to call repartition before reduceByKey like so: {code}weblogs.map(lambda s: (getFileType(s), 1)).partitionBy(3,hash_filetype).reduceByKey(lambda v1,v2: v1+v2).collect(){code} But that defeats the purpose, because I'm shuffling twice instead of once, so my performance is worse instead of better. -- 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