Track errors in daily scripts

Tracking errors in daily scripts is an essential practice, especially if you’re managing a dynamic workflow or working with complex code. Here’s a practical approach to track and manage errors in your daily scripts:

1. Use Logging

Logging is one of the most effective ways to track errors in your scripts. It helps you monitor the execution of the script and identifies where things might be going wrong.

• Python (Example):

python
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import logging

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

try:
    # Your script logic here
    result = 10 / 0  # Example error
except Exception as e:
    logging.error(f"Error occurred: {str(e)}")

This code will log any errors that occur into a file called script_errors.log.

• JavaScript (Example):

javascript
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try {
    // Your script logic here
    let result = 10 / 0; // Example error
} catch (error) {
    console.error('Error occurred:', error);
}

You can also use external logging tools like Winston (for Node.js) or Log4js (for JavaScript/Node.js).

2. Implement Error Notifications

Sometimes, it’s important to receive notifications when a critical error occurs. You can use services like Slack, Email, or SMS to get notified about script failures.

• Example: Integrating Python script with Slack notification:

python
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import logging
import requests

logging.basicConfig(filename='script_errors.log', level=logging.ERROR)

def send_slack_notification(message):
    url = 'https://hooks.slack.com/services/your/slack/webhook'
    payload = {'text': message}
    requests.post(url, json=payload)

try:
    # Your script logic
    result = 10 / 0
except Exception as e:
    error_message = f"Error occurred: {str(e)}"
    logging.error(error_message)
    send_slack_notification(error_message)

3. Use Error Handling Frameworks

For more structured error tracking, frameworks and tools like Sentry, Rollbar, and New Relic can be integrated into your scripts. These tools provide detailed reports, including stack traces, error frequency, and more.

• Example with Sentry (Python):

python
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import sentry_sdk

sentry_sdk.init("your_sentry_dsn")

try:
    # Your script logic
    result = 10 / 0
except Exception as e:
    sentry_sdk.capture_exception(e)

4. Track Errors Locally and Remotely

It’s essential to track errors both locally (in your logs) and remotely (in a dashboard or monitoring system) for faster diagnostics. Services like Datadog or Prometheus help with this type of error tracking in a real-time, more granular manner.

5. Implement Retry Mechanism

For certain types of errors (like network timeouts or temporary failures), you can implement a retry mechanism to minimize the impact of transient errors.

• Python Example: Using a retry mechanism:

python
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import time
import logging

logging.basicConfig(filename='script_errors.log', level=logging.ERROR)

def retry_request():
    for attempt in range(5):
        try:
            # Replace with your actual script logic
            result = 10 / 0
            return result
        except Exception as e:
            logging.error(f"Attempt {attempt+1} failed: {str(e)}")
            time.sleep(2)  # Wait before retrying
    return None

retry_request()

6. Unit Testing & Test Coverage

Before running scripts, ensure that they have proper test coverage. Unit testing can help identify errors before you run the script in a production environment.

• Example (Python): Use unittest or pytest for testing.

python
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import unittest

def divide(a, b):
    return a / b

class TestDivideFunction(unittest.TestCase):
    def test_divide(self):
        self.assertEqual(divide(10, 2), 5)
        with self.assertRaises(ZeroDivisionError):
            divide(10, 0)

if __name__ == '__main__':
    unittest.main()

7. Monitor Logs Regularly

If you’re running scripts every day, automate the log monitoring process. You can write a script that monitors your logs for certain error patterns and sends you a daily report.

8. Consider Error Levels

It helps to categorize errors based on severity:

• Critical: Errors that break the script and require immediate attention (e.g., system failures).

• Warning: Potential problems that could lead to issues, but do not stop execution.

• Info: General information about the script execution that might be useful for tracking its progress.

Conclusion

Tracking errors in your daily scripts is crucial for smooth operations and faster debugging. By combining logging, error notifications, and monitoring tools, you can streamline error management and reduce downtime.