CODEWITHSUNDEEP
dataframeapache-sparkpysparkapache-spark-sql
gael
gael

Reputation: 177

Pyspark - Join with null values in right dataset

Let's say I have a dataset with the following:

# dataset_left
 #+-----------------+--------------+---------------+
 #|         A       |       B      |      C        |
 #+-----------------+--------------+---------------+
 #|   some_value_1  | some_value_3 | some_value_5  |
 #+-----------------+--------------+---------------+
 #|   some_value_2  | some_value_4 | some_value_6  |
 #+-----------------+--------------+---------------+

I also have another dataset like the following:

# dataset_rules
 #+-----------------+--------------+---------------+
 #|         A       |       B      |  result_col   |
 #+-----------------+--------------+---------------+
 #|        null     | some_value_3 |    result_1   |
 #+-----------------+--------------+---------------+
 #|   some_value_2  |      null    |    result_2   |
 #+-----------------+--------------+---------------+

My goal is to join the two datasets with this rule:

For the dataset_rules : null values in column A & column B can match any value from the dataset_left. The join should only take into account non-null values from the dataset_rules.

So for the 1st row in dataset_rule, only column B should be used as a condition. And for the 2nd row, only column A should be used as a condition.

I want to achieve the following desired result :

# dataset_result
 #+-----------------+--------------+---------------+------------+
 #|         A       |       B      |      C        | result_col |
 #+-----------------+--------------+---------------+------------+
 #|   some_value_1  | some_value_3 | some_value_5  |   result_1 |
 #+-----------------+--------------+---------------+------------+
 #|   some_value_2  | some_value_4 | some_value_6  |   result_2 |
 #+-----------------+--------------+---------------+------------+

The goal is to avoid hard coding the rules in dataset_rules to make it easy to add new rules and more maintainable.

Upvotes: 4

Views: 3180

Answers (1)

blackbishop
blackbishop

Reputation: 32640

You can join using when or coalesce expression like this:

from pyspark.sql import functions as F

join_cond = (
        (F.coalesce(dataset_rules["A"], dataset_left["A"]) == dataset_left["A"])
        & (F.coalesce(dataset_rules["B"], dataset_left["B"]) == dataset_left["B"])
)

result = dataset_left.join(dataset_rules, join_cond, "left").select(
    dataset_left["*"],
    dataset_rules["result_col"]
)

result.show()
#+------------+------------+------------+----------+
#|           A|           B|           C|result_col|
#+------------+------------+------------+----------+
#|some_value_1|some_value_3|some_value_5|  result_1|
#|some_value_2|some_value_4|some_value_6|  result_2|
#+------------+------------+------------+----------+

Upvotes: 5

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