[jira] [Created] (SPARK-6968) Make maniuplating an underlying RDD of a DataFrame easier

["John Muller (JIRA)" <ji...@apache.org>]

John Muller created SPARK-6968:
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             Summary: Make maniuplating an underlying RDD of a DataFrame easier
                 Key: SPARK-6968
                 URL: https://issues.apache.org/jira/browse/SPARK-6968
             Project: Spark
          Issue Type: Improvement
          Components: Spark Core
    Affects Versions: 1.3.0
         Environment: AWS EMR
            Reporter: John Muller
            Priority: Minor


Use case:  let's say you want to coalesce the RDD underpinning a DataFrame so that you get a certain number of partitions when you go to save it:

{code:title=RDDsAndDataFrames.scala|borderStyle=solid}
val sc: SparkContext // An existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val df = sqlContext.load("hdfs://examples/src/main/resources/people.avro", "avro")
val coalescedRowRdd = df.rdd.coalesce(8)
// Now the tricky part, you have to get the schema of the original dataframe:
val originalSchema = df.schema
val finallyCoalescedDF = sqlContext.createDataFrame(coalescedRowRdd , originalSchema )
{code}

Basically, it would be nice to have an "attachRDD" method on DataFrames, that requires a RDD[Row], so long as it has the same schema, we should be good:

{code:title=SimplierRDDsAndDataFrames.scala|borderStyle=solid}
val sc: SparkContext // An existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val df = sqlContext.load("hdfs://examples/src/main/resources/people.avro", "avro")
val finallyCoalescedDF = df.attachRDD(df.rdd.coalesce(8)
{code}




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