Spark Scala: User defined aggregate function that calculates median - scala

Question

I´m trying to find a way, to calculate the Median for a given Dataframe.
val df = sc.parallelize(Seq(("a",1.0),("a",2.0),("a",3.0),("b",6.0), ("b", 8.0))).toDF("col1", "col2")
+----+----+
|col1|col2|
+----+----+
| a| 1.0|
| a| 2.0|
| a| 3.0|
| b| 6.0|
| b| 8.0|
+----+----+
Now I want to do sth like that:
df.groupBy("col1").agg(calcmedian("col2"))
the result should look like this:
+----+------+
|col1|median|
+----+------+
| a| 2.0|
| b| 7.0|
+----+------+`
therefore calcmedian() has to be a UDAF, but the problem is, the "evaluate" method of the UDAF only takes a Row, but i need the whole table to sort the values and return the median...
// Once all entries for a group are exhausted, spark will evaluate to get the final result
def evaluate(buffer: Row) = {...}
Is this possible somehow? or is there another nice workaround? I want to stress, that i know how to calculate the median on a dataset with "one group". But i don´t want to use this algorithm in a "foreach" loop as this is inefficient!
Thank you!
edit:
that´s what i tried so far:
object calcMedian extends UserDefinedAggregateFunction {
// Schema you get as an input
def inputSchema = new StructType().add("col2", DoubleType)
// Schema of the row which is used for aggregation
def bufferSchema = new StructType().add("col2", DoubleType)
// Returned type
def dataType = DoubleType
// Self-explaining
def deterministic = true
// initialize - called once for each group
def initialize(buffer: MutableAggregationBuffer) = {
buffer(0) = 0.0
}
// called for each input record of that group
def update(buffer: MutableAggregationBuffer, input: Row) = {
buffer(0) = input.getDouble(0)
}
// if function supports partial aggregates, spark might (as an optimization) comput partial results and combine them together
def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
buffer1(0) = input.getDouble(0)
}
// Once all entries for a group are exhausted, spark will evaluate to get the final result
def evaluate(buffer: Row) = {
val tile = 50
var median = 0.0
//PROBLEM: buffer is a Row --> I need DataFrame here???
val rdd_sorted = buffer.sortBy(x => x)
val c = rdd_sorted.count()
if (c == 1){
median = rdd_sorted.first()
}else{
val index = rdd_sorted.zipWithIndex().map(_.swap)
val last = c
val n = (tile/ 100d) * (c*1d)
val k = math.floor(n).toLong
val d = n - k
if( k <= 0) {
median = rdd_sorted.first()
}else{
if (k <= c){
median = index.lookup(last - 1).head
}else{
if(k >= c){
median = index.lookup(last - 1).head
}else{
median = index.lookup(k-1).head + d* (index.lookup(k).head - index.lookup(k-1).head)
}
}
}
}
} //end of evaluate

Answer 1

try this:
import org.apache.spark.functions._
val result = data.groupBy("col1").agg(callUDF("percentile_approx", col("col2"), lit(0.5)))