Does collect_list() maintain relative ordering of rows? - scala

Question

Imagine that I have the following DataFrame df:
+---+-----------+------------+
| id|featureName|featureValue|
+---+-----------+------------+
|id1| a| 3|
|id1| b| 4|
|id2| a| 2|
|id2| c| 5|
|id3| d| 9|
+---+-----------+------------+
Imagine that I run:
df.groupBy("id")
.agg(collect_list($"featureIndex").as("idx"),
collect_list($"featureValue").as("val"))
Am I GUARANTEED that "idx" and "val" will be aggregated and keep their relative order? i.e.
GOOD GOOD BAD
+---+------+------+ +---+------+------+ +---+------+------+
| id| idx| val| | id| idx| val| | id| idx| val|
+---+------+------+ +---+------+------+ +---+------+------+
|id3| [d]| [9]| |id3| [d]| [9]| |id3| [d]| [9]|
|id1|[a, b]|[3, 4]| |id1|[b, a]|[4, 3]| |id1|[a, b]|[4, 3]|
|id2|[a, c]|[2, 5]| |id2|[c, a]|[5, 2]| |id2|[a, c]|[5, 2]|
+---+------+------+ +---+------+------+ +---+------+------+
NOTE: e.g. It's BAD because for id1 [a, b] should have been associated with [3, 4] (and not [4, 3]). Same for id2

Answer 1

I think you can rely on "their relative order" as Spark goes over rows one by one in order (and usually does not re-order rows if not explicitly needed).
If you are concerned with the order, merge these two columns using struct function before doing groupBy.
struct(colName: String, colNames: String*): Column Creates a new struct column that composes multiple input columns.
You could also use monotonically_increasing_id function to number records and use it to pair with the other columns (perhaps using struct):
monotonically_increasing_id(): Column A column expression that generates monotonically increasing 64-bit integers.
The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive.