[ https://issues.apache.org/jira/browse/SPARK-17020?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15417250#comment-15417250 ]
Roi Reshef commented on SPARK-17020: ------------------------------------ val ab = SomeReader.read(...) //some reader function that uses spark-csv with inferSchema=true filter(!isnull($"name")). alias("revab") val meta = SomeReader.read(...) //same but different schema and data val udaf = ... //some UserDefinedAggregateFunction val features = ab.groupBy(...).agg(udaf(...)) val data = features. join(meta, $"meta.id" === $"features.id"). select(...) //only relevant fields val rdd = data.rdd.setName("rdd").cache() rdd.count > Materialization of RDD via DataFrame.rdd forces a poor re-distribution of data > ------------------------------------------------------------------------------ > > Key: SPARK-17020 > URL: https://issues.apache.org/jira/browse/SPARK-17020 > Project: Spark > Issue Type: Bug > Components: Spark Core, SQL > Affects Versions: 1.6.1, 1.6.2, 2.0.0 > Reporter: Roi Reshef > Attachments: dataframe_cache.PNG, rdd_cache.PNG > > > Calling DataFrame's lazy val .rdd results with a new RDD with a poor > distribution of partitions across the cluster. Moreover, any attempt to > repartition this RDD further will fail. > Attached are a screenshot of the original DataFrame on cache and the > resulting RDD on cache. -- 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