Application Performance Benchmarking Focused On Users
Google Kubernetes Engine (GKE) also demonstrates how to replicate complicated user behavior using the open-source Locust tool for use in your end-to-end benchmarking exercises
By identifying and addressing performance bottlenecks early in the development cycle, early and frequent benchmarking can help developers save money and speed up time to market
Furthermore, by quickly identifying and resolving performance regressions, benchmarking can be incorporated into testing procedures to provide a vital safety net that protects code quality and user experience
Frequent benchmarking makes it easier to track performance trends over time, enabling developers to assess the effects of updates, new features, and system changes
A realistic evaluation of application performance in a production setting can be obtained as part of a development process by benchmarking real-world workloads, images, and scaling patterns
This aids in the comprehension of how autoscaling systems adapt to changing demands while optimizing resource use and preserving peak application performance
To provide an end-to-end benchmark of a pre-release autoscaling cluster setup, it has included a GitHub repository with this blog post. It is advised that you modify it to meet your unique needs