Often I find myself wanting more information about the Python functions I’m running, whether it’s because I want to debug, log or even time their completion. All of these are relatively well-defined problems (debugging excepted). Unfortunately no tool makes it easy enough from my research to truly see the input, output, time elapsed, errors, warnings, etc about a function in a simple interface. So I wrote a simple python decorator compatible with Python 2.7+ (and probably earlier versions), including Py3.

What does the meta_func decorator actually do?

It stores all arguments for every function call, both positional and keyword arguments, error information (including the ability to catch and not raise errors), warnings, time elements (time started, ended and elapsed), and the returned value as a standard python dictionary.

What’s the point of tracking all this [meta]data?

Debugging, Logging, Timing… The use cases are nearly endless, because it tells us a lot of what’s going on in one easily interpreted structure.

Important Notes

This decorator should be expected to add a good deal of overhead to many function calls due to the handling of so many dimensions.

Arguments (Positional and Keywords), Return value, Warnings and Exceptions will be stored in their raw form, so any transformations (such as stringifying errors and traceback) would need to be done post-processing.

The error_info field will return a tuple from sys.exc_info() with error details.

Github Repo


Converting CSVs with Headers to AVRO The Power of Hadoop in under 10 lines!

Leave a Reply

Your email address will not be published.