Hi @seefoods ,

You can write decorator function and use it to perform custom logging.

from functools import wraps
from pyspark.sql import SparkSession
from datetime import datetime
import traceback

spark = SparkSession.builder.getOrCreate()

def logger(success_table: str, error_table: str):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            try:
                result = func(*args, **kwargs)

                # Success log
                log_df = spark.createDataFrame([{
                    "function_name": func.__name__,
                    "status": "SUCCESS",
                    "timestamp": datetime.now()
                }])
                log_df.write.format("delta").mode("append").saveAsTable(success_table)

                return result
            except Exception as e:
                # Error log
                error_df = spark.createDataFrame([{
                    "function_name": func.__name__,
                    "status": "ERROR",
                    "timestamp": datetime.now(),
                    "error_message": str(e),
                    "stack_trace": traceback.format_exc()
                }])
                error_df.write.format("delta").mode("append").saveAsTable(error_table)

                raise  # Optional: re-raise exception
        return wrapper
    return decorator

#function definition
@logger(success_table="LOG_SUCCESS", error_table="LOG_ERROR")
def load_data(a, b):
    print(a/b)
    return "Done"

#calling the function
load_data(20, 10) #this will log the success message to LOG_SUCCESS
load_data(10, 0) #this will log the exception message to LOG_ERROR

 Check below article for more details:

Custom Logging to Delta Tables in Databricks: Reduce Code Redundancy | by Krishnanand A | Medium