How to add columns into org.apache.spark.sql.Row inside of mapPartitions

Question

I am a newbie at scala and spark, please keep that in mind :)

Actually, I have three questions

1. How should I define function to pass it into df.rdd.mapPartitions, if I want to create new Row with few additional columns
2. How can I add few columns into Row object(or create a new one)
3. How create DataFrame from created RDD

Thank you at advance

Answer 1

Usually there should be no need for that and it is better to use UDFs but here you are:

How should I define function to pass it into df.rdd.mapPartitions, if I want to create new Row with few additional columns

It should take Iterator[Row] and return Iterator[T] so in your case you should use something like this

import org.apache.spark.sql.Row  def transformRows(iter: Iterator[Row]): Iterator[Row] = ??? 

How can I add few columns into Row object(or create a new one)

There are multiple ways of accessing Row values including Row.get* methods, Row.toSeq etc. New Row can be created using Row.apply, Row.fromSeq, Row.fromTuple or RowFactory. For example:

def transformRow(row: Row): Row =  Row.fromSeq(row.toSeq ++ Array[Any](-1, 1)) 

How create DataFrame from created RDD

If you have RDD[Row] you can use SQLContext.createDataFrame and provide schema.

Putting this all together:

import org.apache.spark.sql.types.{IntegerType, StructField, StructType}  val  df = sc.parallelize(Seq(     (1.0, 2.0), (0.0, -1.0),     (3.0, 4.0), (6.0, -2.3))).toDF("x", "y")  def transformRows(iter: Iterator[Row]): Iterator[Row] = iter.map(transformRow)  val newSchema = StructType(df.schema.fields ++ Array(   StructField("z", IntegerType, false), StructField("v", IntegerType, false)))  sqlContext.createDataFrame(df.rdd.mapPartitions(transformRows), newSchema).show  // +---+----+---+---+ // |  x|   y|  z|  v| // +---+----+---+---+ // |1.0| 2.0| -1|  1| // |0.0|-1.0| -1|  1| // |3.0| 4.0| -1|  1| // |6.0|-2.3| -1|  1| // +---+----+---+---+