---------- Forwarded message ---------
From: Mamillapalli, Purna Pradeep <purnapradeep.mamillapa...@capitalone.com>
Date: Tue, Aug 29, 2017 at 8:08 PM
Subject: Spark question
To: purna pradeep <purna2prad...@gmail.com>
Below is the input Dataframe(In real this is a very large Dataframe)
EmployeeID
INCOME
INCOME AGE TS
JoinDate
Dept
101
19000
4/20/17
4/20/13
ES
101
10000
10/3/15
4/20/13
OS
102
13000
5/9/17
4/20/12
DS
102
12000
5/8/17
4/20/12
CS
103
10000
5/9/17
4/20/10
EQ
103
9000
5/8/15
4/20/10
MD
Get the latest income of an employee which has Income_age ts <10 months
Expected output Dataframe
EmployeeID
INCOME
INCOME AGE TS
JoinDate
Dept
101
19000
4/20/17
4/20/13
ES
102
13000
5/9/17
4/20/12
DS
103
10000
5/9/17
4/20/10
EQ
Below is what im planning to implement
case class employee (*EmployeeID*: Int, *INCOME*: Int, INCOMEAGE: Int,
*JOINDATE*: Int,DEPT:String)
*val *empSchema = *new *StructType().add(*"EmployeeID"*,*"Int"*).add(
*"INCOME"*, *"Int"*).add(*"INCOMEAGE"*,*"Date"*) . add(*"JOINDATE"*,*"Date"*).
add(*"DEPT"*,*"String"*)
*//Reading from the File **import *sparkSession.implicits._
*val *readEmpFile = sparkSession.read
.option(*"sep"*, *","*)
.schema(empSchema)
.csv(INPUT_DIRECTORY)
*//Create employee DataFrame **val *custDf = readEmpFile.as[employee]
*//Adding Salary Column **val *groupByDf = custDf.groupByKey(a => a.*
EmployeeID*)
*val *k = groupByDf.mapGroups((key,value) => performETL(value))
*def *performETL(empData: Iterator[employee]) : new employee = {
*val *empList = empData.toList
*//calculate income has Logic to figureout latest income for an account and
returns latest income val *income = calculateIncome(empList)
*for *(i <- empList) {
*val *row = i
*return new *employee(row.EmployeeID, row.INCOMEAGE , income)
}
*return "Done"*
}
Is this a better approach or even the right approach to implement the
same.If not please suggest a better way to implement the same?
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