Replicate Spark Row N-times

Question

I want to duplicate a Row in a DataFrame, how can I do that?

For example, I have a DataFrame consisting of 1 Row, and I want to make a DataFrame with 100 identical R

Answer 1

You could use a flatMap, or a for-comprehension, like it is described here.

I encourage you to use DataSets every time you can, but if it's not possible, the last example in the link works with DataFrames as well:

val df = Seq(
  (0, "Lorem ipsum dolor", 1.0, List("prp1", "prp2", "prp3"))
).toDF("id", "text", "value", "properties")

val df2 = for {
  row <- df
  p <- row.getAs[Seq[String]]("properties")
} yield (row.getAs[Int]("id"), row.getAs[String]("text"), row.getAs[Double]("value"), p)

Also keep in mind that explode is deprecated, see here.

Answer 2

You could pick out the single row, make a list with a hundred elements, populated with that row and convert it back into a dataframe.

import org.apache.spark.sql.DataFrame

val testDf = sc.parallelize(Seq(
    (1,2,3), (4,5,6)
)).toDF("one", "two", "three")

def replicateDf(n: Int, df: DataFrame) = sqlContext.createDataFrame(
    sc.parallelize(List.fill(n)(df.take(1)(0)).toSeq), 
    df.schema)

val replicatedDf = replicateDf(100, testDf)

Answer 3

You can add a column with a literal value of an Array with size 100, and then use explode to make each of its elements create its own row; Then, just get rid of this "dummy" column:

import org.apache.spark.sql.functions._

val result = singleRowDF
  .withColumn("dummy", explode(array((1 until 100).map(lit): _*)))
  .selectExpr(singleRowDF.columns: _*)