Debugging in Python - the Zen way

Debugging is no less than an art, as critical as development of complex logic is, and is essential to know the right ways to debug so that we do not give in to bugs or to the frustration of cracking the CPU open or to create your own complier, creating alternate ways to debug.

In summary, we’ve identified the below few points as the Zen of debugging in Python which may sometimes also extend to other languages, however our focus remains Python. Please note, this list is not exhaustive and is subject to change with suggestions. But before all that, a little brief on debugging -

Debugging is a critical process in software development, aimed at identifying, isolating, and fixing issues or bugs in code. The general steps in debugging can be outlined as follows:

Identify and Reproduce: Detect the issue from errors or unexpected behavior and reproduce it to confirm its consistency.

Analyze and Diagnose: Use debugging tools to understand the problem's context, inspect variables, and isolate the root cause.

Fix and Test: Implement a solution, then thoroughly test to ensure the issue is resolved without side effects.

Review, Refactor, Document and Monitor: Assess and improve the fix and related code for better quality and future issue prevention. Record the issue and fix details for future reference, and monitor the system post-fix to ensure stability.

There may be other steps involved however, generally, well mostly this is a SOP in some sense. Having developed some complex custom monitoring solutions and also later having built custom protocols and logging engines, I have come to realize some of this to be more universal than not.

In this blog, we are interested in the first two steps of debugging and the debugging principles outlined earlier are relevant for these steps. The below elaboration will be labelled accordingly.

Further dividing this blog into 5 Parts -

Favor logging for detailed analysis; fallback to pprint for clarity, and print for simplicity.

Traceback module for dissecting errors, providing clear insights into execution paths leading to issues.

Debuggers reveal runtime behavior, essential for complex async or multi-threaded code.

Attach debugger(debugpy) for flexible, non-intrusive debugging without altering code.

Use Call graphs visualize program flow and function timings

Conclusion

Each of these tools, methodologies, and approaches addresses a different aspect of debugging, allowing us to tackle various challenges effectively. For example, pprint helps in understanding complex data structures by providing a more readable output. Traceback is essential for analyzing stack traces and identifying the execution paths leading to errors. For granular, step-by-step debugging, debuggers are indispensable, offering control over the execution flow. Finally, call graphs provide a visual representation of the code's execution and resource utilization, highlighting invocation counts and interdependencies. By mastering these tools, we can enhance our debugging efficiency and resolve issues faster.