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If you have a Python program that is currently running you may want to understand what the real-world performance profile of the code is. This program could be in a production environment or just on your local machine. You will want to understand where the running program spends its time and if any “hot spots” exist that should be investigated further for improvement. You may be dealing with a production system that is misbehaving and you may want to profile it in an unobtrusive way that doesn’t further impact production performance or require code modifications. What’s a good way to do this? This article will talk about py-spy, a tool that allows you to profile Python programs that are already running.
I’m using a Coinbase Pro API for Python to access data from the WebSocket feed. Here’s a first cut that has some debugging code left in place (along with two ways to generate the VWAP, one inefficient (the _vwap method) and one more efficient). Let’s see if py-spy reveals how much time this code uses.