How to iterate over each row of an Dataframe / RDD in PySpark for a group.?

Question

I want to set the value of column based on the value of that column in the previous row for a group. Then this updated value will be used in the next row.

I have the following dataframe

id | start_date|sort_date | A | B |
-----------------------------------
1 | 1/1/2017 | 31-01-2015 | 1 | 0 | 
1 | 1/1/2017 | 28-02-2015 | 0 | 0 | 
1 | 1/1/2017 | 31-03-2015 | 1 | 0 | 
1 | 1/1/2017 | 30-04-2015 | 1 | 0 | 
1 | 1/1/2017 | 31-05-2015 | 1 | 0 | 
1 | 1/1/2017 | 30-06-2015 | 1 | 0 | 
1 | 1/1/2017 | 31-07-2015 | 1 | 0 | 
1 | 1/1/2017 | 31-08-2015 | 1 | 0 | 
1 | 1/1/2017 | 30-09-2015 | 0 | 0 | 
2 | 1/1/2017 | 31-10-2015 | 1 | 0 | 
2 | 1/1/2017 | 30-11-2015 | 0 | 0 | 
2 | 1/1/2017 | 31-12-2015 | 1 | 0 | 
2 | 1/1/2017 | 31-01-2016 | 1 | 0 | 
2 | 1/1/2017 | 28-02-2016 | 1 | 0 | 
2 | 1/1/2017 | 31-03-2016 | 1 | 0 | 
2 | 1/1/2017 | 30-04-2016 | 1 | 0 | 
2 | 1/1/2017 | 31-05-2016 | 1 | 0 | 
2 | 1/1/2017 | 30-06-2016 | 0 | 0 | 

Output :

id | start_date|sort_date | A | B | C
---------------------------------------
1 | 1/1/2017 | 31-01-2015 | 1 | 0 | 1
1 | 1/1/2017 | 28-02-2015 | 0 | 0 | 0
1 | 1/1/2017 | 31-03-2015 | 1 | 0 | 1
1 | 1/1/2017 | 30-04-2015 | 1 | 0 | 2
1 | 1/1/2017 | 31-05-2015 | 1 | 0 | 3
1 | 1/1/2017 | 30-06-2015 | 1 | 0 | 4
1 | 1/1/2017 | 31-07-2015 | 1 | 0 | 5
1 | 1/1/2017 | 31-08-2015 | 1 | 0 | 6
1 | 1/1/2017 | 30-09-2015 | 0 | 0 | 0
2 | 1/1/2017 | 31-10-2015 | 1 | 0 | 1
2 | 1/1/2017 | 30-11-2015 | 0 | 0 | 0
2 | 1/1/2017 | 31-12-2015 | 1 | 0 | 1
2 | 1/1/2017 | 31-01-2016 | 1 | 0 | 2
2 | 1/1/2017 | 28-02-2016 | 1 | 0 | 3
2 | 1/1/2017 | 31-03-2016 | 1 | 0 | 4
2 | 1/1/2017 | 30-04-2016 | 1 | 0 | 5
2 | 1/1/2017 | 31-05-2016 | 1 | 0 | 6
2 | 1/1/2017 | 30-06-2016 | 0 | 0 | 0

Group is of id and date

Column C is to derived based on column A and B.

If A == 1 and B == 0 then C is derived C from previous row + 1.
There are some other conditions as well but I am struggling with this part.

Assuming we have a column sort_date in dataframe.

I tried the following query :

SELECT
id,
date,
sort_date,
lag(A) OVER (PARTITION BY  id, date ORDER BY sort_date) as prev,
CASE
   WHEN A=1 AND B= 0  THEN 1
   WHEN  A=1 AND B> 0 THEN prev +1
   ELSE 0
 END AS A
FROM
Table

This Is what I did for UDAF

val myFunc = new MyUDAF
val w = Window.partitionBy(col("ID"), col("START_DATE")).orderBy(col("SORT_DATE"))
val df = df.withColumn("C", myFunc(col("START_DATE"), col("X"),
  col("Y"), col("A"),
  col("B")).over(w))

PS : I am using Spark 1.6

Answer 1

First define a window:

import org.apache.spark.sql.expressions.Window
val winspec = Window.partitionBy("id","start_date").orderBy("sort_date")

Next create a UDAF which recieves A and B and basically calculates C by starting with 0, changing to 0 whenever the condition appears (A=1,B=0) and increasing by 1 any other time. To see how to write a UDAF see examples in here , here and here

EDIT Here is a sample implementation of the UDAF (not really tested so there may be typos):

import org.apache.spark.sql.Row
import org.apache.spark.sql.expressions.{MutableAggregationBuffer,UserDefinedAggregateFunction}
 import org.apache.spark.sql.types._

 class myFunc() extends UserDefinedAggregateFunction {

  // Input Data Type Schema
  def inputSchema: StructType = StructType(Array(StructField("A", IntegerType), StructField("A", IntegerType)))

   // Intermediate Schema
  def bufferSchema = StructType(Array(StructField("C", IntegerType)))

  // Returned Data Type .
  def dataType: DataType = IntegerType

  // Self-explaining
  def deterministic = true

  // This function is called whenever key changes
  def initialize(buffer: MutableAggregationBuffer) = {
    buffer(0) = 0 // set number of items to 0
  }

  // Iterate over each entry of a group
  def update(buffer: MutableAggregationBuffer, input: Row) = {
    buffer(0) = if (input.getInt(0) == 1 && input.getInt(1) == 0) buffer.getInt(0) + 1 else 0
  }

  // Merge two partial aggregates - doesn't really matter because the window will make sure the buffer remains in a
  // single partition
  def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
    buffer1(0) = buffer1.getInt(0) + buffer2.getInt(0)
  }

  // Called after all the entries are exhausted.
  def evaluate(buffer: Row) = {
    buffer.getInt(0)
  }

}

Last apply it to your dataframe. Let's assume you named your UDAF myFunc:

val f = new myFunc()
val newDF = df.withColumn("newC", f($"A",$"B").over(winspec))