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.
Learning Objectives
Successfully complete this lab by achieving the following learning objectives:
- 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.
