How To Debug Performance Issues in Python With Profilers

How To Debug Performance Issues in Python With Profilers

Using flame graphs to get to the root of the problem .How To Debug Performance Issues in Python With Profilers

I know from personal experience that debugging performance issues on Python servers can be incredibly frustrating. Usually, increased traffic or a transient bug would cause end-users to report that something was wrong.

More often than not, it’s impossible to exactly replicate the conditions under which the bug occurred, so I would be stuck trying to figure out which part of our code/infrastructure was responsible for the performance issue on our server.

This article explains how to use flame graphs to continuously profile your code and reveal exactly which lines are responsible for those pesky performance issues.

Why You Should Care About CPU Performance

CPU utilization is a metric of application performance commonly used by companies that run their software in the cloud (e.g. on AWS, Google Cloud, etc.).

“Netflix cares very much about CPU utilization that’s the key metric for which we scale our cloud. And if we can reduce CPU utilization by 1% or 5% its a very major win.” — Brendan Gregg, Senior Performance Architect at Netflix

However, smaller companies can see similar benefits when improving performance because regardless of size, CPU is often directly correlated with two very important facets of running software:

  • How much money you’re spending on servers — The more CPU resources you need, the more it costs to run servers.
  • End-user experience — The more load placed on your server’s CPUs, the slower your website or server becomes.

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