Hi @Yaacoub ,
The reason why your UDF gets registered every time you call it is because Spark registers UDFs at the session level, not the notebook level. This meansIf you are registering a UDF multiple times, it can result in exceeding the maximum number UDFs allowed per query, which is 5 in the public preview of Databricks SQL. To resolve this issue, you can define the UDF outside of the loop or function that is calling it.
Here is an example of how you can modify your code to register the UDF only once:
from pyspark.sql.functions import udf
@udf(returnType=IntegerType())
def ends_with_one(value, bit_position):
if bit_position + len(value) < 0:
return 0
else:
return int(value[bit_position] == '1')
spark.udf.register("ends_with_one", ends_with_one)
# You can call the registered UDF in a loop, function or elsewhere
df = df.withColumn('Ends_With_One', ends_with_one(col('Column_To_Check'), lit(-1)))
By defining the UDF outside of the loop or function that is calling it, you will register the UDF only once and prevent it from being registered multiple times by repeated calls to the function.
Also, keep in mind that using too many UDFs can negatively impact query performance, so it's generally a good practice to use built-in Spark functions or DataFrame API operations whenever possible to achieve the same results for better performance.
