Spark dataframe to nested map

Question

How can I convert a rather small data frame in spark (max 300 MB) to a nested map in order to improve spark's DAG. I believe this operation will be quicker than a join later on (Spark dynamic DAG is a lot slower and different from hard coded DAG) as the transformed values were created during the train step of a custom estimator. Now I just want to apply them really quick during predict step of the pipeline.

val inputSmall = Seq(
    ("A", 0.3, "B", 0.25),
    ("A", 0.3, "g", 0.4),
    ("d", 0.0, "f", 0.1),
    ("d", 0.0, "d", 0.7),
    ("A", 0.3, "d", 0.7),
    ("d", 0.0, "g", 0.4),
    ("c", 0.2, "B", 0.25)).toDF("column1", "transformedCol1", "column2", "transformedCol2")

This gives the wrong type of map

val inputToMap = inputSmall.collect.map(r => Map(inputSmall.columns.zip(r.toSeq):_*))

I would rather want something like:

Map[String, Map[String, Double]]("column1" -> Map("A" -> 0.3, "d" -> 0.0, ...), "column2" -> Map("B" -> 0.25), "g" -> 0.4, ...)

Daniel de Paula · Accepted Answer · 2016-12-20T09:34:45.590

3

Edit: removed collect operation from final map

If you are using Spark 2+, here's a suggestion:

val inputToMap = inputSmall.select(
  map($"column1", $"transformedCol1").as("column1"),
  map($"column2", $"transformedCol2").as("column2")
)

val cols = inputToMap.columns
val localData = inputToMap.collect

cols.map { colName => 
  colName -> localData.flatMap(_.getAs[Map[String, Double]](colName)).toMap
}.toMap

edited Dec 20 '16 at 09:34

answered Dec 20 '16 at 09:27

Daniel de Paula

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score 0 · Answer 2 · answered Dec 20 '16 at 09:25

I'm not sure I follow the motivation, but I think this is the transformation that would get you the result you're after:

// collect from DF (by your assumption - it is small enough)
val data: Array[Row] = inputSmall.collect()

// Create the "column pairs" -
// can be replaced with hard-coded value: List(("column1", "transformedCol1"), ("column2", "transformedCol2"))
val columnPairs: List[(String, String)] = inputSmall.columns
  .grouped(2)
  .collect { case Array(k, v) => (k, v) }
  .toList

// for each pair, get data and group it by left-column's value, choosing first match
val result: Map[String, Map[String, Double]] = columnPairs
  .map { case (k, v) => k -> data.map(r => (r.getAs[String](k), r.getAs[Double](v))) }
  .toMap
  .mapValues(l => l.groupBy(_._1).map { case (c, l2) => l2.head })

result.foreach(println)
// prints: 
// (column1,Map(A -> 0.3, d -> 0.0, c -> 0.2))
// (column2,Map(d -> 0.7, g -> 0.4, f -> 0.1, B -> 0.25))

Spark dataframe to nested map

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