README.md

Simple Snowflake Python Logger

Purpose

Often organizations will have stringent role isolation levels within a Snowflake mesh like deployment. When the admin roles prevents access or creation of event tables in the current warehouse, it's often necessary to stream the logs in memory and collect the formatted data into defined schem to load post process into a custom python logging table.

Implementation

This requires an Snowflake integration and Snowflake Stage in the Cloud provider of choice. Additionally requires a role that had the ability to create tables in a Snoflake DataBase

Setting up the Logging Table

CREATE TABLE SF_DBNAME.SF.PYTHON_LOGS (

   LOG_TIMESTAMP TIMESTAMP_NTZ
  ,LOG_LEVEL VARCHAR
  ,LOG_LEVEL_DETAIL VARCHAR
  ,LOG_PROCESS VARCHAR
  ,ERROR_TRACE VARCHAR

);

Once the table is created, import the logger into the Procedure/Function in Snowflake

Logging in Python Stored Procedure in Snowflake

CREATE OR REPLACE DW.SCHEMA.PROCEDURE_NAME()
RETURNS VARCHAR(10)
LANGUAGE = PYTHON
RUNTIME_VERSION = '3.8'
PACKAGES = ()
HANDLER = 'main'
IMPORTS = ('@<YOUR_STAGE_NAME>/<PATH_TO_FILE>/sflogger.py')
EXECUTE AS CALLER
AS '

from sflogger import StreamLogger
import snowflake.snowpark as snowpark

sl = StreamLogger("PROCEDURE_NAME")
logger = sl.get_logger()

def failed_function():
  try:
    x = 1/0
  except Exception as e:
    logger.exception(e)

def main(session: snowpark.Session):

  logger.critical("CRITICAL MESSAGE!!")
  logger.info("INFORMATIONAL LOG MESSAGE")

  failed_function()

  log_data = sl.return_logs()

  log_df = session.createDataFrame(log_data["logs"],log_data["cols])
  session.write_pandas(log_df, sl.config.table, sl.config.databsde, sl.config.schema)

  return "logged"