Understanding the internal state of a running system can be vital to maintaining a high performance, stable system, but conventional approaches such as logging and error handling only expose so much. This talk will touch on how to instrument Python programs in order to observe the state of the system, measure performance, and identify ongoing problems.
Something is wrong with your web application. The time it’s taking to serve requests is growing. Your logs don’t contain enough. Your database appears bored. How do you know what’s going wrong?
In high-performance production servers it’s vital to know as much about the internals of your system as possible. Traditionally this is done by simple methods like logging anything of potential interest or sending error emails with unexpected exceptions. These methods are insufficient, both due to the level of noise inherent in such systems and because of the difficulty in anticipating what metrics are important during an incident.
Environments such as the JVM and .Net VM have advanced tools for communicating with the VM and for applications to expose internal state, but CPython has lacked similar tooling.
This talk will cover what options CPython application developers have for introspecting their programs; new tools for instrumenting, exposing, and compiling performance and behavior metrics; and techniques for diagnosing runtime issues without restarting the process.
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