Find Most Common Value and Corresponding Count Using Spark Groupby Aggregates - scala

Question

I am trying to use Spark (Scala) dataframes to do groupby aggregates for mode and the corresponding count.
For example,
Suppose we have the following dataframe:
Category Color Number Letter
1 Red 4 A
1 Yellow Null B
3 Green 8 C
2 Blue Null A
1 Green 9 A
3 Green 8 B
3 Yellow Null C
2 Blue 9 B
3 Blue 8 B
1 Blue Null Null
1 Red 7 C
2 Green Null C
1 Yellow 7 Null
3 Red Null B
Now we want to group by Category, then Color, and then find the size of the grouping, count of number non-nulls, the total size of number, the mean of number, the mode of number, and the corresponding mode count. For letter I'd like the count of non-nulls and the corresponding mode and mode count (no mean since this is a string).
So the output would ideally be:
Category Color CountNumber(Non-Nulls) Size MeanNumber ModeNumber ModeCountNumber CountLetter(Non-Nulls) ModeLetter ModeCountLetter
1 Red 2 2 5.5 4 (or 7)
1 Yellow 1 2 7 7
1 Green 1 1 9 9
1 Blue 1 1 - -
2 Blue 1 2 9 9 etc
2 Green - 1 - -
3 Green 2 2 8 8
3 Yellow - 1 - -
3 Blue 1 1 8 8
3 Red - 1 - -
This is easy to do for the count and mean but more tricky for everything else. Any advice would be appreciated.
Thanks.

Answer 1

As far as I know - there's no simple way to compute mode - you have to count the occurrences of each value and then join the result with the maximum (per key) of that result. The rest of the computations are rather straight-forward:
// count occurrences of each number in its category and color
val numberCounts = df.groupBy("Category", "Color", "Number").count().cache()
// compute modes for Number - joining counts with the maximum count per category and color:
val modeNumbers = numberCounts.as("base").join(numberCounts.groupBy("Category", "Color").agg(max("count") as "_max").as("max"),
$"base.Category" === $"max.Category" and
$"base.Color" === $"max.Color" and
$"base.count" === $"max._max")
.select($"base.Category", $"base.Color", $"base.Number", $"_max")
.groupBy("Category", "Color")
.agg(first($"Number", ignoreNulls = true) as "ModeNumber", first("_max") as "ModeCountNumber")
.where($"ModeNumber".isNotNull)
// now compute Size, Count and Mean (simple) and join to add Mode:
val result = df.groupBy("Category", "Color").agg(
count("Color") as "Size", // counting a key column -> includes nulls
count("Number") as "CountNumber", // does not include nulls
mean("Number") as "MeanNumber"
).join(modeNumbers, Seq("Category", "Color"), "left")
result.show()
// +--------+------+----+-----------+----------+----------+---------------+
// |Category| Color|Size|CountNumber|MeanNumber|ModeNumber|ModeCountNumber|
// +--------+------+----+-----------+----------+----------+---------------+
// | 3|Yellow| 1| 0| null| null| null|
// | 1| Green| 1| 1| 9.0| 9| 1|
// | 1| Red| 2| 2| 5.5| 7| 1|
// | 2| Green| 1| 0| null| null| null|
// | 3| Blue| 1| 1| 8.0| 8| 1|
// | 1|Yellow| 2| 1| 7.0| 7| 1|
// | 2| Blue| 2| 1| 9.0| 9| 1|
// | 3| Green| 2| 2| 8.0| 8| 2|
// | 1| Blue| 1| 0| null| null| null|
// | 3| Red| 1| 0| null| null| null|
// +--------+------+----+-----------+----------+----------+---------------+
As you can imagine - this might be slow, as it has 4 groupBys and two joins - all requiring shuffles...
As for the Letter column statistics - I'm afraid you'll have to repeat this for that column separately and add another join.