Python code optimization may seem easy or hard depending on the performance target. If the target is “best effort”, carefully choosing the algorithm and applying well-known common practices is usually enough. If the target is dictated by the UX, you have to go down a few abstraction layers and hack the system sometimes. Or rewrite the underlying libraries. Or change the language, really. This post is about our experience in Python code optimizations when whatever you do is not fast enough.
Feature Highlights is a new addition to our ongoing series of webinars. As the name suggests, it’ll focus on specific product features with anonymized customer use cases taking center stage. In other words, how Cribl customers actually use the features to get the job done, sometimes in unintended ways. QuickConnect was the first act with a session “Streamline Connections w/ LogStream QuickConnect”.