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https://issues.apache.org/jira/browse/SPARK-17020?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15417250#comment-15417250
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Roi Reshef commented on SPARK-17020:
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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.
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