Stackdriver Verification Overview
This guide describes how to set up Harness' Continuous Verification features, and monitor your deployments and production applications using its unsupervised machine-learning functionality, on Stackdriver.
Walk through this guide in the following order:
Google Stackdriver aggregates metrics, logs, and events from infrastructure, giving developers and operators a rich set of observable signals that speed root-cause analysis and reduce mean time to resolution (MTTR).
Harness Continuous Verification integrates with Stackdriver to verify your deployments and live production applications using the following Harness features:
- 24/7 Service Guard – Monitors your live, production applications.
- Deployment Verification – Monitors your application deployments, and performs automatic rollback according to your criteria.
Verification with Stackdriver
You can read more about Harness and Stackdriver integration on the Harness Blog.
You set up Stackdriver and Harness in the following way:
- Stackdriver – Monitor your application using Stackdriver. In this article, we assume that you are using Stackdriver to monitor your application already.
- Cloud Provider Setup – In Harness, you connect Harness to your Google account, adding Google Cloud Platform as a Harness Cloud Provider. For more informations, see Add Cloud Providers.
- Harness Application – Create a Harness Application with a Service, Environment, and Workflow. We do not cover Application setup in this sequence. See Application Components.
- 24/7 Service Guard Setup – In the Environment, set up 24/7 Service Guard to monitor your live, production application.
- Verify Deployments:
- Add a Workflow to your Harness Application and deploy your microservice or application to the service infrastructure/Infrastructure Definition in your Environment.
- After you have run a successful deployment, you then add verification steps to the Workflow using your Verification Provider.
- Harness uses unsupervised machine-learning and Stackdriver monitoring to analyze your future deployments, discovering events that might be causing your deployments to fail. Then you can use this information to set rollback criteria and improve your deployments.