Does collect_list() maintain relative ordering of rows?

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.