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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