Spark dataframe 1 -:
+------+-------+---------+----+---+-------+
|city |product|date |sale|exp|wastage|
+------+-------+---------+----+---+-------+
|cit
Check out MegaSparkDiff its an open source project on GitHub that helps compare dataframes .. the project is not yet published in maven central but you can look at the SparkCompare scala class that compares 2 dataframes
the below code snippet will give you 2 dataframes one has rows inLeftButNotInRight and another one having InRightButNotInLeft.
if you do a JOIN between both then you can apply some logic to identify the missing primary keys (where possible) and then those keys would constitute the deleted records.
We are working on adding the use case that you are looking for in the project. https://github.com/FINRAOS/MegaSparkDiff
https://github.com/FINRAOS/MegaSparkDiff/blob/master/src/main/scala/org/finra/msd/sparkcompare/SparkCompare.scala
private def compareSchemaDataFrames(left: DataFrame , leftViewName: String
, right: DataFrame , rightViewName: String) :Pair[DataFrame, DataFrame] = {
//make sure that column names match in both dataFrames
if (!left.columns.sameElements(right.columns))
{
println("column names were different")
throw new Exception("Column Names Did Not Match")
}
val leftCols = left.columns.mkString(",")
val rightCols = right.columns.mkString(",")
//group by all columns in both data frames
val groupedLeft = left.sqlContext.sql("select " + leftCols + " , count(*) as recordRepeatCount from " + leftViewName + " group by " + leftCols )
val groupedRight = left.sqlContext.sql("select " + rightCols + " , count(*) as recordRepeatCount from " + rightViewName + " group by " + rightCols )
//do the except/subtract command
val inLnotinR = groupedLeft.except(groupedRight).toDF()
val inRnotinL = groupedRight.except(groupedLeft).toDF()
return new ImmutablePair[DataFrame, DataFrame](inLnotinR, inRnotinL)
}