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Custom ENUM input as parameter for SQL UDF?

EWhitley
New Contributor III

Hello  - 

We're migrating from T-SQL to Spark SQL. We're migrating a significant number of queries.


"datediff(unit, start,end)" is different between these two implementations (in a good way).  For the purpose of migration, we'd like to stay as consistent with possible, so I was evaluating writing a custom migration-specific UDF that emulated the t-sql behavior - so people could transition over time with minimal disruption.
I'm bumping into something I have no idea how to achieve - allowing ENUMs to be exposed to SQL from a Python function.
For example - 

SELECT
DATEDIFF(
    DAY,  -- <---- the enum unit
    TIMESTAMP'1999-12-31 23:59:59',
    TIMESTAMP'2000-01-01 23:59:58'
) diff_day

Note the "DAY" there is NOT a string - it's some for of ENUM exposed to Spark SQL. I'd like to achieve that similar behavior if at all possible, but I'm not sure how to go about doing it. I'm writing the function in Python and then registering the UDF ala:

 
def my_date_diff(unit, start_date, end_date😞
    ...

 

spark.udf.register("udf_my_date_diff", my_date_diff, IntegerType())

When I call this in SQL, though, I get an error (which I kind of expected):

%sql

select udf_my_date_diff(DAY, TIMESTAMP'1999-12-31 23:59:59', TIMESTAMP'2000-01-01 23:59:58')
 
[UNRESOLVED_COLUMN.WITHOUT_SUGGESTION] A column, variable, or function parameter with name `DAY` cannot be resolved. SQLSTATE: 42703
 
Is there a way to expose my custom ENUM as a type when calling the UDF from SQL?

 

 

 

1 REPLY 1

Kaniz_Fatma
Community Manager
Community Manager

Hi @EWhitley, You’re on the right track with creating a custom UDF in Python for your migration.

To achieve similar behaviour to the T-SQL DATEDIFF function with an enum-like unit parameter, you can follow these steps:

  1. Create a Custom UDF:

    • Define your custom Python function that emulates the behavior of DATEDIFF.
    • In your case, you want to pass an enum-like unit (e.g., “DAY”) as an argument.
  2. Register the UDF:

    • Register your custom UDF with Spark using spark.udf.register.
    • Specify the return type (in your case, IntegerType()).
  3. Handle Enum-Like Units:

    • Since Spark SQL doesn’t directly support enums, you can use string literals to represent the units.
    • Modify your Python function to handle these string literals and map them to the appropriate behavior (e.g., “DAY” maps to the day difference).

Here’s an example of how you can achieve this:

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType

# Create a Spark session
spark = SparkSession.builder.appName("EnumUDFExample").getOrCreate()

# Define your custom Python function
def my_date_diff(unit, start_date, end_date):
    # Handle different units (e.g., "DAY", "MONTH", etc.)
    if unit == "DAY":
        # Calculate day difference
        # You can implement your custom logic here
        return (end_date - start_date).days
    elif unit == "MONTH":
        # Calculate month difference
        # Implement your logic here
        return 0  # Placeholder for demonstration

# Register the UDF
spark.udf.register("udf_my_date_diff", my_date_diff, IntegerType())

# Example usage in SQL
spark.sql("""
    SELECT udf_my_date_diff('DAY', TIMESTAMP'1999-12-31 23:59:59', TIMESTAMP'2000-01-01 23:59:58') AS diff_day
""").show()

In the example above:

  • We handle the unit parameter as a string literal (“DAY” or “MONTH”).
  • You can replace the placeholder logic with your actual date difference calculation.

Remember that Spark SQL doesn’t directly support enums, so using string literals is a common workaround. If you need additional units, extend the logic in your custom function accordingly12.

I hope this helps! Let me know if you have any further questions or need more assistance. 😊

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