Spark: Transpose DataFrame Without Aggregating

Unfortunately there is no case when:

• Spark DataFrame is justified considering amount of data.
• Transposition of data is feasible.

You have to remember that DataFrame, as implemented in Spark, is a distributed collection of rows and each row is stored and processed on a single node.

You could express transposition on a DataFrame as pivot:

val kv = explode(array(df.columns.tail.map { 
  c => struct(lit(c).alias("k"), col(c).alias("v")) 
}: _*))

df
  .withColumn("kv", kv)
  .select($"segment_id", $"kv.k", $"kv.v")
  .groupBy($"k")
  .pivot("segment_id")
  .agg(first($"v"))
  .orderBy($"k")
  .withColumnRenamed("k", "vals")

but it is merely a toy code with no practical applications. In practice it is not better than collecting data:

val (header, data) = df.collect.map(_.toSeq.toArray).transpose match {
  case Array(h, t @ _*) => {
    (h.map(_.toString), t.map(_.collect { case x: Int => x }))
  }
}

val rows = df.columns.tail.zip(data).map { case (x, ys) => Row.fromSeq(x +: ys) }
val schema = StructType(
  StructField("vals", StringType) +: header.map(StructField(_, IntegerType))
)

spark.createDataFrame(sc.parallelize(rows), schema)

For DataFrame defined as:

val df = Seq(
  (1, 100, 0, 0, 0, 0, 0),
  (2, 0, 50, 0, 0, 20, 0),
  (3, 0, 0, 0, 0, 0, 0),
  (4, 0, 0, 0, 0, 0, 0)
).toDF("segment_id", "val1", "val2", "val3", "val4", "val5", "val6")

both would you give you the desired result:

+----+---+---+---+---+
|vals|  1|  2|  3|  4|
+----+---+---+---+---+
|val1|100|  0|  0|  0|
|val2|  0| 50|  0|  0|
|val3|  0|  0|  0|  0|
|val4|  0|  0|  0|  0|
|val5|  0| 20|  0|  0|
|val6|  0|  0|  0|  0|
+----+---+---+---+---+

That being said if you need an efficient transpositions on distributed data structure you’ll have to look somewhere else. There is a number of structures, including core CoordinateMatrix and BlockMatrix, which can distribute data across both dimensions and can be transposed.