Top N items from a Spark DataFrame/RDD

Question

My requirement is to get the top N items from a dataframe.

I've this DataFrame:

val df = List(
      ("MA", "USA"),
      ("MA", "USA"),
      ("OH", "USA"),
      ("OH", "USA"),
      ("OH", "USA"),
      ("OH", "USA"),
      ("NY", "USA"),
      ("NY", "USA"),
      ("NY", "USA"),
      ("NY", "USA"),
      ("NY", "USA"),
      ("NY", "USA"),
      ("CT", "USA"),
      ("CT", "USA"),
      ("CT", "USA"),
      ("CT", "USA"),
      ("CT", "USA")).toDF("value", "country")

I was able to map it to an RDD[((Int, String), Long)] colValCount: Read: ((colIdx, value), count)

((0,CT),5)
((0,MA),2)
((0,OH),4)
((0,NY),6)
((1,USA),17)

Now I need to get the top 2 items for each column index. So my expected output is this:

RDD[((Int, String), Long)]

((0,CT),5)
((0,NY),6)
((1,USA),17)

I've tried using freqItems api in DataFrame but it's slow.

Any suggestions are welcome.

Answer 1

For example:

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

df.select(lit(0).alias("index"), $"value")
   .union(df.select(lit(1), $"country"))
   .groupBy($"index", $"value")
   .count
  .orderBy($"count".desc)
  .limit(3)
  .show

// +-----+-----+-----+
// |index|value|count|
// +-----+-----+-----+
// |    1|  USA|   17|
// |    0|   NY|    6|
// |    0|   CT|    5|
// +-----+-----+-----+

where:

df.select(lit(0).alias("index"), $"value")
  .union(df.select(lit(1), $"country"))

creates a two column DataFrame:

// +-----+-----+
// |index|value|
// +-----+-----+
// |    0|   MA|
// |    0|   MA|
// |    0|   OH|
// |    0|   OH|
// |    0|   OH|
// |    0|   OH|
// |    0|   NY|
// |    0|   NY|
// |    0|   NY|
// |    0|   NY|
// |    0|   NY|
// |    0|   NY|
// |    0|   CT|
// |    0|   CT|
// |    0|   CT|
// |    0|   CT|
// |    0|   CT|
// |    1|  USA|
// |    1|  USA|
// |    1|  USA|
// +-----+-----+

If you want specifically two values for each column:

import org.apache.spark.sql.DataFrame

def topN(df: DataFrame, key: String, n: Int)  = {
   df.select(
        lit(df.columns.indexOf(key)).alias("index"), 
        col(key).alias("value"))
     .groupBy("index", "value")
     .count
     .orderBy($"count")
     .limit(n)
}

topN(df, "value", 2).union(topN(df, "country", 2)).show
// +-----+-----+-----+ 
// |index|value|count|
// +-----+-----+-----+
// |    0|   MA|    2|
// |    0|   OH|    4|
// |    1|  USA|   17|
// +-----+-----+-----+

So like pault said - just "some combination of sort() and limit()".