Datadog 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 Datadog.
Walk through this guide in the following order:
Datadog delivers real-time and trending data about application performance by seamlessly aggregating metrics and events across the full DevOps stack. Datadog automatically collects logs from all your services, applications, and platforms.
Harness Continuous Verification integrates with Datadog 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.
This document describes how to set up these Harness Continuous Verification features and monitor your deployments and production applications using its unsupervised machine-learning functionality.
Analysis with Datadog
You set up Datadog and Harness in the following way:
- Datadog - Monitor your application using Datadog. In this article, we assume that you are using Datadog to monitor your application already.
- Verification Provider Setup - In Harness, you connect Harness to your Datadog account, adding Datadog as a Harness Verification Provider.
- Harness Application - Create a Harness Application with a Service and an Environment. We do not cover Application set up in this article. See Application Checklist.
- 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 Datadog analytics 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.