Logging Levels in Python are Unnecessary – Just Log Everything

I've spent countless hours debugging Python issues that could have been solved faster if I had just had more information. Today, I want to challenge a fundamental concept in Python logging: the idea of logging levels.

The Traditional Approach: Python Logging Levels

If you're familiar with Python's logging module, you know about the five standard logging levels:

1. DEBUG: Detailed information, typically only valuable when diagnosing problems.
2. INFO: Confirmation that things are working as expected.
3. WARNING: An indication that something unexpected happened, or there may be a problem in the near future.
4. ERROR: Due to a more serious problem, the software hasn't been able to perform some function.
5. CRITICAL: A serious error, indicating that the program itself may be unable to continue running.

These levels serve as a way to categorize log messages and filter them based on their perceived importance. It's a system that has served us well for years, but I believe it's time for a change.

The Case for Logging Everything

Here's my controversial take: we should stop using logging levels and instead log everything. Yes, everything. Here's why:

1. Storage is cheap: The original rationale for logging levels was to conserve disk space. In today's world of cheap storage, this is no longer a significant concern.
2. Information is invaluable: How many times have you wished you had more context when debugging an issue? By logging everything, you ensure you always have the information you need.
3. Shift decision-making left: Instead of deciding what's important at coding time, we can make that decision during analysis. This gives us more flexibility and prevents information loss.
4. Catch the unexpected: Often, it's the things we don't think are important that end up being crucial. By logging everything, we catch those unexpected insights.

Let me share a real-world example that drove this last point home for me:

A few years ago, I was working on a large web application that was experiencing intermittent performance issues. Users would occasionally report slow response times, but we couldn't replicate the problem in our staging environment. We had ERROR and WARNING level logs, but they showed nothing out of the ordinary.

Out of desperation, we decided to enable DEBUG level logging across the entire application for a short period. Among the sea of logs, we found our culprit: a seemingly innocuous database query that was sometimes taking much longer than expected.

The DEBUG logs revealed that this query's execution time spiked when it was handling a specific type of user input – something we hadn't considered in our initial optimization efforts. This input was causing the query planner to choose a suboptimal execution plan.

If we had been logging everything from the start, we could have identified and fixed this issue much earlier. Instead, we spent weeks chasing red herrings and trying to replicate the problem.

This whole ordeal really opened my eyes to why logging everything matters. What seems unimportant during development can become critical when you're trying to understand complex, real-world behavior.

Addressing the Counterarguments

Now, I know what you're thinking. "Won't this impact performance? What about privacy concerns?" These are valid points, but they're not insurmountable:

• Performance: Use asynchronous logging or offload to a separate process to minimize impact.
• Privacy: Implement robust anonymization techniques to protect sensitive data.
• Data overload: Utilize modern log analysis tools and techniques to efficiently process large volumes of data.

Enter Rollbar: Making "Log Everything" Feasible

This is where a tool like Rollbar comes into play. Rollbar is an error monitoring solution, but it can be so much more in a "log everything" world:

1. Centralized aggregation: Rollbar can serve as a hub for all your logged events, not just errors.
2. Intelligent filtering: While you log everything, Rollbar's algorithms can help prioritize and surface the most important information.
3. Rich context: With more data available, Rollbar can provide deeper, more meaningful context for each event.
4. Performance mitigation: Rollbar's asynchronous reporting helps offset the potential performance impact of increased logging.
5. Advanced analysis: With a wealth of data, Rollbar's pattern recognition capabilities become even more powerful, potentially catching subtle issues you might otherwise miss.

Implementing "Log Everything" with Rollbar

Now, let's get practical. Here's a quick example of how you can implement the "log everything" approach using Rollbar's Python SDK:

import rollbar
from functools import wraps

# Initialize Rollbar
rollbar.init('YOUR_ROLLBAR_ACCESS_TOKEN', environment='production')

def log_everything(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        try:
            # Log the function call
            rollbar.report_message(f"Calling {func.__name__} with args: {args}, kwargs: {kwargs}", 'info')

            # Execute the function
            result = func(*args, **kwargs)

            # Log the function result
            rollbar.report_message(f"{func.__name__} returned: {result}", 'info')

            return result
        except Exception as e:
            # Log any exceptions
            rollbar.report_exc_info()
            raise
    return wrapper

@log_everything
def my_function(x, y):
    return x + y

# Usage
result = my_function(5, 3)

In this example, we're using a decorator to automatically log every function call, its arguments, and its return value. We're also catching and logging any exceptions that might occur.

This approach allows you to:

• Log all function calls and their contexts.
• Capture return values for later analysis.
• Ensure all exceptions are logged, even if they're re-raised.

By applying this decorator to key functions in your application, you can quickly implement a "log everything" strategy without cluttering your code with numerous log statements.

Remember, while this example uses 'info' level for simplicity, Rollbar allows you to use custom log levels if needed. You can also add more context or customize the logging behavior within the decorator to suit your specific needs.

Logging Everything is the Future of Debugging

Logging everything is a paradigm shift, and it's not without its challenges. But with the right tools and approach, I believe it's the future of debugging and system understanding. By combining comprehensive logging with a powerful analysis tool like Rollbar, we can gain unprecedented insight into our systems.

What do you think? Are you ready to log everything? Try Rollbar today!