Error and Exception Handling in Python and to maintain a log table

Error and Exception Handling:

Python uses exceptions to handle errors that occur during program execution. There are two main ways to handle exceptions:

1. try-except Block:

• The try block contains the code you expect to execute normally.
• The except block handles exceptions that might occur within the try block. You can specify the type of exception to handle or use a general except clause for any exception.
• Optionally, an else block can be used to execute code if no exceptions occur in the try block.
• Finally, a finally block will always execute, regardless of exceptions, and is commonly used for cleanup tasks like closing files or database connections.

try:
  # Code that might raise an exception
  result = 10 / 0  # This will cause a ZeroDivisionError
except ZeroDivisionError:
  print("Error: Division by zero!")
else:
  print("The result is:", result)  # Won't execute in this case
finally:
  # Clean up code (e.g., closing files)
  print("Cleaning up resources...")

try:
    # Code that may raise an exception
    result = 10 / 0
except ZeroDivisionError as e:
    # Handle the specific exception
    print(f"Error: {e}")
except Exception as e:
    # Handle any other exceptions
    print(f"An unexpected error occurred: {e}")
else:
    # Code to run if no exception occurs
    print("Operation succeeded")
finally:
    # Code to run regardless of whether an exception occurred
    print("Operation complete")

2. Raising Exceptions:

• You can use the raise statement to explicitly raise an exception when encountering an error condition in your code. This allows you to control the flow of execution and potentially provide more specific error messages.

def check_age(age):
  if age < 18:
    raise ValueError("Age must be 18 or older.")
  # Rest of the code...

Logging Errors to a Table:

Here’s how you can integrate exception handling with logging to a database table:

1. Choose a Logging Library:

Popular options include:

• Python’s built-in logging module: Offers basic logging functionalities.
• External libraries like loguru or structlog: Provide more advanced features like structured logging and custom formatting.

2. Connect to your Database:

Use a connector library like psycopg2 (PostgreSQL) or mysql.connector (MySQL) to establish a connection to your database.

3. Create a Log Table:

Design a table in your database to store error information. Common columns might include:

• timestamp: Time of the error occurrence.
• error_type: Type of exception raised (e.g., ZeroDivisionError, ValueError).
• error_message: The specific error message associated with the exception.
• filename: The Python file where the error occurred.
• lineno: The line number in the code where the error occurred.

4. Implement Logging within Exception Handling:

Within your except block, use the chosen logging library to create a log message with details about the exception and write it to the database table.

Here’s an example using the logging module:

import logging

# Configure logging
logging.basicConfig(filename='errors.log', level=logging.ERROR)

try:
  # Code that might raise an exception
  result = 10 / 0
except ZeroDivisionError as e:
  # Log the error to the database
  logging.error(f"Error: Division by zero! ({e})")

5. Periodically Review Logs:

Regularly review the logs in the database table to identify recurring errors or patterns. This can help you improve your code’s robustness and prevent similar issues in the future.

Additional Considerations:

• Choose the logging level (e.g., DEBUG, INFO, ERROR) based on the verbosity of information you want to capture.
• Implement proper error handling for user input or external dependencies to ensure a smooth user experience.
• Consider using context managers (like with statements) for automatic resource management and exception handling in specific code blocks.

By effectively combining error and exception handling with logging, you can create more robust and maintainable Python applications while keeping track of errors in a structured format within your database.

Logging Errors to a Log Table

To maintain a log of errors, you can write a function that inserts error information into a database table. Here’s an example using SQLite, but you can adapt it to any database system you are using.

1. Set up the Log Table

First, create a log table in your database to store error information.

CREATE TABLE error_log (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    error_type TEXT NOT NULL,
    error_message TEXT NOT NULL,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
);

2. Python Function to Log Errors

Next, create a Python function to insert error details into this log table.

import sqlite3
from datetime import datetime

def log_error(error_type, error_message):
    # Connect to the SQLite database (or another database)
    conn = sqlite3.connect('errors.db')
    cursor = conn.cursor()
    
    # Insert error details into the log table
    cursor.execute("""
        INSERT INTO error_log (error_type, error_message, timestamp)
        VALUES (?, ?, ?)
    """, (error_type, error_message, datetime.now()))
    
    # Commit and close the connection
    conn.commit()
    conn.close()

3. Using the Log Function in Exception Handling

Now, use the log_error function within your exception handling blocks.

try:
    # Code that may raise an exception
    result = 10 / 0
except ZeroDivisionError as e:
    log_error('ZeroDivisionError', str(e))
    print(f"Logged Error: {e}")
except Exception as e:
    log_error('GeneralException', str(e))
    print(f"Logged Unexpected Error: {e}")
else:
    print("Operation succeeded")
finally:
    print("Operation complete")

Implementing structured error and exception handling along with maintaining an error log is essential for developing robust and maintainable Python applications. Here is a step-by-step guide to create a sample project that demonstrates these concepts.

Project Overview

We will create a sample project that reads data from a CSV file, processes it, and handles various types of errors. The project will log any errors to a SQLite database.

Project Structure

error_handling_project/
│
├── data/
│   └── sample_data.csv
│
├── logs/
│   └── error_log.db
│
├── src/
│   ├── __init__.py
│   ├── error_logger.py
│   ├── data_processor.py
│   └── main.py
│
└── requirements.txt

Step 1: Set Up the Environment

Create a new directory for your project and set up the structure as shown above. Install necessary dependencies using requirements.txt.

requirements.txt

Copy codepandas

Step 2: Create the Log Table

Create a SQLite database and an error log table.

src/error_logger.py

import sqlite3
from datetime import datetime

def create_log_table():
    conn = sqlite3.connect('logs/error_log.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS error_log (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            error_type TEXT NOT NULL,
            error_message TEXT NOT NULL,
            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
        )
    ''')
    conn.commit()
    conn.close()

def log_error(error_type, error_message):
    conn = sqlite3.connect('logs/error_log.db')
    cursor = conn.cursor()
    cursor.execute('''
        INSERT INTO error_log (error_type, error_message, timestamp)
        VALUES (?, ?, ?)
    ''', (error_type, error_message, datetime.now()))
    conn.commit()
    conn.close()

Step 3: Implement Data Processing with Error Handling

Create a data processor that reads data from a CSV file and handles errors.

src/data_processor.py

import pandas as pd
from src.error_logger import log_error

def process_data(file_path):
    try:
        # Attempt to read a CSV file
        data = pd.read_csv(file_path)
        
        # Simulate a potential KeyError for demonstration
        result = data['value'] / data['divider']
        
        return result
    except FileNotFoundError as e:
        log_error('FileNotFoundError', str(e))
        print(f"Logged File Error: {e}")
    except KeyError as e:
        log_error('KeyError', str(e))
        print(f"Logged Key Error: {e}")
    except ZeroDivisionError as e:
        log_error('ZeroDivisionError', str(e))
        print(f"Logged Division Error: {e}")
    except Exception as e:
        log_error('GeneralException', str(e))
        print(f"Logged Unexpected Error: {e}")

Step 4: Main Application

Create a main script to run the data processing.

src/main.py

from src.error_logger import create_log_table
from src.data_processor import process_data

def main():
    # Create the log table
    create_log_table()
    
    # Define the file path
    file_path = 'data/sample_data.csv'
    
    # Process the data
    result = process_data(file_path)
    
    if result is not None:
        print("Data processed successfully.")
    else:
        print("Data processing failed.")

if __name__ == "__main__":
    main()

Step 5: Sample Data

Create a sample CSV file for testing.

data/sample_data.csv

value,divider
10,2
20,4
30,0
40,8

Step 6: Run the Project

Run the main script to see how errors are handled and logged.

python src/main.py

Checking the Error Log

You can query the error log to see the recorded errors.

src/main.py (add this to check the log after processing)

def get_error_log():
    conn = sqlite3.connect('logs/error_log.db')
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM error_log")
    rows = cursor.fetchall()
    conn.close()
    return rows

if __name__ == "__main__":
    main()
    
    # Print the error log
    log_entries = get_error_log()
    for entry in log_entries:
        print(entry)

Summary

This project demonstrates how to implement structured error and exception handling in Python and maintain an error log using SQLite. The key steps include:

1. Creating a log table to store error details.
2. Writing a function to log errors.
3. Implementing data processing with error handling.
4. Running the main application and checking the error log.

This approach helps you keep track of errors systematically and provides valuable insights into the types of issues your application encounters.