- A Cloud Guru
Discovering Latency with Google Cloud Trace
Latency is one of Google Cloud's Four Golden Signals when it comes to Service Level Indicators – and it's easy to see why. When an application suffers from increased latency, the user experience is a highly negative one: suddenly what was regarded as a reliable application becomes, at best, suspect and, at worst, dismissed as unreliable. Google Cloud's Cloud Trace makes it possible for developers and SREs to keep a close eye on latency analytics. In this hands-on lab, you'll create the infrastructure for the app and then trigger it so you can see how Cloud Trace works, first-hand.
Table of Contents
Set up Necessary Infrastructure
- Create the needed Kubernetes cluster and work with existing YAML files to create a Docker image of an app and push it to the Container Registry.
Deploy a Python App With Additional Services
- Customize a bash script and then run it to deploy the app and three load balancers.
Execute a Request
- Using Cloud Shell, execute a curl command in the proper syntax to send a request to one of the load balancers and begin the tracing.
Review Cloud Trace Analytics
- Identify traces and spans resulting from the submitted request and response and review the latency data.
What's a lab?
Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.