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https://issues.apache.org/jira/browse/SPARK-6968?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin closed SPARK-6968.
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Resolution: Later
> 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: SQL
> Affects Versions: 1.3.0
> Environment: AWS EMR
> Reporter: John Muller
> Priority: Minor
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> 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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