Spark: how to perform loop fuction to dataframes - sql

Question

I have two dataframes as below, I'm trying to search the second df using the foreign key, and then generate a new data frame. I was thinking of doing a spark.sql("""select history.value as previous_year 1 from df1, history where df1.key=history.key and history.date=add_months($currentdate,-1*12)""" but then I need to do it multiple times for say 10 previous_years. and join them back together. How can I create a function for this? Many thanks. Quite new here.
dataframe one:
+---+---+-----------+
|key|val| date |
+---+---+-----------+
| 1|100| 2018-04-16|
| 2|200| 2018-04-16|
+---+---+-----------+
dataframe two : historical data
+---+---+-----------+
|key|val| date |
+---+---+-----------+
| 1|10 | 2017-04-16|
| 1|20 | 2016-04-16|
+---+---+-----------+
The result I want to generate is
+---+----------+-----------------+-----------------+
|key|date | previous_year_1 | previous_year_2 |
+---+----------+-----------------+-----------------+
| 1|2018-04-16| 10 | 20 |
| 2|null | null | null |
+---+----------+-----------------+-----------------+

Answer 1

To solve this, the following approach can be applied:
1) Join the two dataframes by key.
2) Filter out all the rows where previous dates are not exactly years before reference dates.
3) Calculate the years difference for the row and put the value in a dedicated column.
4) Pivot the DataFrame around the column calculated in the previous step and aggregate on the value of the respective year.
private def generateWhereForPreviousYears(nbYears: Int): Column =
(-1 to -nbYears by -1) // loop on each backwards year value
.map(yearsBack =>
/*
* Each year back count number is transformed in an expression
* to be included into the WHERE clause.
* This is equivalent to "history.date=add_months($currentdate,-1*12)"
* in your comment in the question.
*/
add_months($"df1.date", 12 * yearsBack) === $"df2.date"
)
/*
The previous .map call produces a sequence of Column expressions,
we need to concatenate them with "or" in order to obtain
a single Spark Column reference. .reduce() function is most
appropriate here.
*/
.reduce(_ or _) or $"df2.date".isNull // the last "or" is added to include empty lines in the result.
val nbYearsBack = 3
val result = sourceDf1.as("df1")
.join(sourceDf2.as("df2"), $"df1.key" === $"df2.key", "left")
.where(generateWhereForPreviousYears(nbYearsBack))
.withColumn("diff_years", concat(lit("previous_year_"), year($"df1.date") - year($"df2.date")))
.groupBy($"df1.key", $"df1.date")
.pivot("diff_years")
.agg(first($"df2.value"))
.drop("null") // drop the unwanted extra column with null values
The output is:
+---+----------+---------------+---------------+
|key|date |previous_year_1|previous_year_2|
+---+----------+---------------+---------------+
|1 |2018-04-16|10 |20 |
|2 |2018-04-16|null |null |
+---+----------+---------------+---------------+

Answer 2

Let me "read through the lines" and give you a "similar" solution to what you are asking:
val df1Pivot = df1.groupBy("key").pivot("date").agg(max("val"))
val df2Pivot = df2.groupBy("key").pivot("date").agg(max("val"))
val result = df1Pivot.join(df2Pivot, Seq("key"), "left")
result.show
+---+----------+----------+----------+
|key|2018-04-16|2016-04-16|2017-04-16|
+---+----------+----------+----------+
| 1| 100| 20| 10|
| 2| 200| null| null|
+---+----------+----------+----------+
Feel free to manipulate the data a bit if you really need to change the column names.
Or even better:
df1.union(df2).groupBy("key").pivot("date").agg(max("val")).show
+---+----------+----------+----------+
|key|2016-04-16|2017-04-16|2018-04-16|
+---+----------+----------+----------+
| 1| 20| 10| 100|
| 2| null| null| 200|
+---+----------+----------+----------+