Hi,
given your dataset:
val df=Seq(
(1, 20230523, "M01"), (2, 20230523, "M01"), (3, 20230523, "M01"), (4, 20230523, "M02"), (5, 20230523, "M02"), (6, 20230523, "M02"), (7, 20230523,
"M01"), (8, 20230523, "M01"), (9, 20230523, "M02"), (10, 20230523, "M02"), (11, 20230523, "M02"), (12, 20230523
Given, you are already stating the above can be imagined as a partition, I
can think of mapPartitions iterator.
val inputSchema = inputDf.schema
val outputRdd = inputDf.rdd.mapPartitions(rows => new SomeClass(rows))
val outputDf = sparkSession.createDataFrame(outputRdd,
inputSchema.add("coun
Hi all,
This is the sample set of data that I used for this task
[image: image.png]
My expected output is as below
[image: image.png]
My scenario is if Type is M01 the count should be 0 and if Type is M02 it
should be incremented from 1 or 0 until the sequence of M02 is finished.
Imagine this
