Rolling join using dates in PySpark? - sql

Question

I'm trying to do a join between two PySpark dataframes, joining on a key, however the date of the first table should always come after the date of the second table. As an example. We have two tables that we're trying to join:
Table 1:
Date1 value1 key
13 Feb 2020 1 a
01 Mar 2020 2 a
31 Mar 2020 3 a
15 Apr 2020 4 a
Table 2:
Date2 value2 key
10 Feb 2020 11 a
15 Mar 2020 22 a
After the join, the result should be something like this:
Date1 value1 value2 key
13 Feb 2020 1 11 a
01 Mar 2020 2 null a
31 Mar 2020 3 22 a
15 Apr 2020 4 null a
Any ideas?

Answer 1

This is an interesting join. My approach is to join on the key first, select the earliest date, and do a self join after the earliest date is found.
from pyspark.sql import functions as F, Window
# Clean up date format first
df3 = df1.withColumn('Date1', F.to_date('Date1', 'dd MMM yyyy'))
df4 = df2.withColumn('Date2', F.to_date('Date2', 'dd MMM yyyy'))
result = (df3.join(df4, 'key')
.filter('Date1 > Date2')
.withColumn('rn', F.row_number().over(Window.partitionBy('Date2').orderBy('Date1')))
.filter('rn = 1')
.drop('key', 'rn', 'Date2')
.join(df3, ['Date1', 'value1'], 'right')
)
result.show()
+----------+------+------+---+
|Date1 |value1|value2|key|
+----------+------+------+---+
|2020-02-13|1 |11 |a |
|2020-03-01|2 |null |a |
|2020-03-31|3 |22 |a |
|2020-04-15|4 |null |a |
+----------+------+------+---+

Answer 2

You can try window lag function, it's scala but python version will be similar.
// change col names for union all and add extra col to indentify dataset
val df1A = df1.toDF("Date","value","key").withColumn("df",lit(1))
val df2A = df2.toDF("Date","value","key").withColumn("df",lit(2))
import org.apache.spark.sql.expressions.Window
df1A.unionAll(df2A)
.withColumn("value2",lag(array('value,'df),1) over Window.partitionBy('key).orderBy(to_date('Date,"dd MMM yyyy")))
.filter('df===1)
.withColumn("value2",when(element_at('value2,2)===2,element_at('value2,1)))
.drop("df")
.show
output:
+-----------+-----+---+------+
| Date|value|key|value2|
+-----------+-----+---+------+
|13 Feb 2020| 1| a| 11|
|01 Mar 2020| 2| a| null|
|31 Mar 2020| 3| a| 22|
|15 Apr 2020| 4| a| null|
+-----------+-----+---+------+