you can always use array+explode, I don't know if its the most
elegant/optimal solution (would be happy to hear from the experts)
code example:
//create data
Dataset<Row> test= spark.createDataFrame(Arrays.asList(new
InternalData("bob", "b1", 1,2,3),
new InternalData("alive", "c1", 3,4,6),
new InternalData("eve", "e1", 7,8,9)
), InternalData.class);
+-----+---------+----+----+----+
| name|otherName|val1|val2|val3|
+-----+---------+----+----+----+
| bob| b1| 1| 2| 3|
|alive| c1| 3| 4| 6|
| eve| e1| 7| 8| 9|
+-----+---------+----+----+----+
Dataset<Row> expandedTest = test.selectExpr("name", "otherName",
"explode(array(val1, val2, val3)) as time");
expandedTest.show();
+-----+---------+----+
| name|otherName|time|
+-----+---------+----+
| bob| b1| 1|
| bob| b1| 2|
| bob| b1| 3|
|alive| c1| 3|
|alive| c1| 4|
|alive| c1| 6|
| eve| e1| 7|
| eve| e1| 8|
| eve| e1| 9|
+-----+---------+----+
On Wed, Aug 1, 2018 at 11:05 PM, nookala <srinook...@gmail.com> wrote:
> Pivot seems to do the opposite of what I want, convert rows to columns.
>
> I was able to get this done in python, but would like to do this in Java
>
> idfNew = idf.rdd.flatMap((lambda row: [(row.Name, row.Id, row.Date,
> "0100",row.0100),(row.Name, row.Id, row.Date, "0200",row.0200),row.Name,
> row.Id, row.Date, "0300",row.0300),row.Name, row.Id, row.Date,
> "0400",row.0400)])).toDF()
>
>
>
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