Hi @EWhitley, Youโre 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:
-
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., โDAYโ) as an argument.
-
Register the UDF:
- Register your custom UDF with Spark using
spark.udf.register
.
- Specify the return type (in your case,
IntegerType()
).
-
Handle Enum-Like Units:
- Since Spark SQL doesnโt 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., โDAYโ maps to the day difference).
Hereโs 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
spark = SparkSession.builder.appName("EnumUDFExample").getOrCreate()
def my_date_diff(unit, start_date, end_date):
if unit == "DAY":
return (end_date - start_date).days
elif unit == "MONTH":
return 0
spark.udf.register("udf_my_date_diff", my_date_diff, IntegerType())
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 (โDAYโ or โMONTHโ).
- You can replace the placeholder logic with your actual date difference calculation.
Remember that Spark SQL doesnโt 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. ๐
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