How to subtract two DataFrames keeping duplicates in Spark 2.3.0

Question

Spark 2.4.0 introduces new handy function exceptAll which allows to subtract two dataframes, keeping duplicates.

Example

  val df1 = Seq(
    ("a", 1L),
    ("a", 1L),
    ("a", 1L),
    ("b", 2L)
  ).toDF("id", "value")
  val df2 = Seq(
    ("a", 1L),
    ("b", 2L)
  ).toDF("id", "value")

df1.exceptAll(df2).collect()
// will return

Seq(("a", 1L),("a", 1L))

However I can only use Spark 2.3.0.

What is the best way to implement this using only functions from Spark 2.3.0?

Answer 1

One option is to use row_number to generate a sequential number column and use it on a left join to get the missing rows.

PySpark solution shown here.

 from pyspark.sql.functions import row_number
 from pyspark.sql import Window
 w1 = Window.partitionBy(df1.id).orderBy(df1.value)
 w2 = Window.partitionBy(df2.id).orderBy(df2.value)
 df1 = df1.withColumn("rnum", row_number().over(w1))
 df2 = df2.withColumn("rnum", row_number().over(w2))
 res_like_exceptAll = df1.join(df2, (df1.id==df2.id) & (df1.val == df2.val) & (df1.rnum == df2.rnum), 'left') \
                         .filter(df2.id.isNull()) \ #Identifies missing rows 
                         .select(df1.id,df1.value)
 res_like_exceptAll.show()