How do you create merge_asof functionality in PySpark?

Question

Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive.

I need to join B to A under the condition that a given element a of A.datetime corresponds to

B[B['datetime'] <= a]]['datetime'].max()

There are a couple ways to do this, but I would like the most efficient way.

Option 1

Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that creates a pandas DataFrame for each row merges with the large dataset using merge_asof.

Option 2

Use the broadcast join functionality of Spark SQL: set up a theta join on the following condition

B['datetime'] <= A['datetime']

Then eliminate all the superfluous rows.

Option B seems pretty terrible... but please let me know if the first way is efficient or if there is another way.

EDIT: Here is the sample input and expected output:

A =
+---------+----------+
| Column1 | Datetime |
+---------+----------+
|    A    |2019-02-03|
|    B    |2019-03-14|
+---------+----------+

B =
+---------+----------+
|   Key   | Datetime |
+---------+----------+
|    0    |2019-01-01|
|    1    |2019-01-15|
|    2    |2019-02-01|
|    3    |2019-02-15|
|    4    |2019-03-01|
|    5    |2019-03-15|
+---------+----------+

custom_join(A,B) =
+---------+----------+
| Column1 |   Key    |
+---------+----------+
|    A    |     2    |
|    B    |     4    |
+---------+----------+

Answer 1

I doubt that it is faster, but you could solve it with Spark by using union and last together with a window function.

from pyspark.sql import functions as f
from pyspark.sql.window import Window

df1 = df1.withColumn('Key', f.lit(None))
df2 = df2.withColumn('Column1', f.lit(None))

df3 = df1.unionByName(df2)

w = Window.orderBy('Datetime', 'Column1').rowsBetween(Window.unboundedPreceding, -1)
df3.withColumn('Key', f.last('Key', True).over(w)).filter(~f.isnull('Column1')).show()

Which gives

+-------+----------+---+
|Column1|  Datetime|Key|
+-------+----------+---+
|      A|2019-02-03|  2|
|      B|2019-03-14|  4|
+-------+----------+---+

It's an old question but maybe still useful for somebody.