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.
You can add a Datadog verification step to your workflow and Datadog will be used by Harness to verify the performance and quality of your deployments using Harness machine-learning verification analysis.
Analysis with Datadog
Verification Setup Overview
You set up Datadog and Harness in the following way:
- Using Datadog, you monitor your microservice or application.
- In Harness, you connect Harness to your Datadog account, adding Datadog as a Harness Verification Provider.
- After you have built and run a successful deployment of your microservice or application in Harness, you then add Datadog verification steps to your Harness deployment workflow. You add Datadog after a successful deployment in order that Harness can use Datadog on the specific hosts/containers/pods on which the microservice is deployed, using the deployment environment tags or labels that identify the hosts/containers/pods.
- Harness uses Datadog to verify your future microservice/application deployments.
- Harness Continuous Verification uses unsupervised machine-learning to analyze your deployments and Datadog analytics/logs, discovering events that might be causing your deployments to fail. Then you can use this information to improve your deployments.
Before You Begin
Connect to Datadog
Connect Harness to Datadog to have Harness verify the success of your deployments. Harness will use your tools to verify deployments and use its machine learning features to identify sources of failures.
To add Datadog as a verification provider, do the following:
- Click Setup.
- Click Connectors.
- Click Verification Providers.
- Click Add Verification Provider, and select Datadog. The Datadog dialog for your provider appears.
The Add Datadog Verification Provider dialog has the following fields.
Enter the URL of the Datadog server. Simply take the URL from the Datadog dashboard, such as https://app.datadoghq.com/ and add the API and version: https://app.datadoghq.com/api/v1/.
Enter the API key for API calls.
To create an API key in Datadog, do the following:
Enter the application key.
To create an application key in Datadog, do the following:
Enter a display name for the provider. If you are going to use multiple providers of the same type, ensure you give each provider a different name.
If you want to restrict the use of a provider to specific applications and environments, do the following:
In Usage Scope, click the drop-down under Applications, and click the name of the application.
In Environments, click the name of the environment.
Verify with Datadog
The following procedure describes how to add Datadog as a verification step in a Harness workflow. For more information about workflows, see Add a Workflow.
Once you run a deployment and Datadog preforms verification, Harness' machine-learning verification analysis will assess the risk level of the deployment.
To verify your deployment with Datadog, do the following:
- Ensure that you have added Datadog as a verification provider, as described above.
- In your workflow, under Verify Service, click Add Verification, and then click Datadog. The Datadog dialog appears.
The Datadog dialog has the following fields.
Select the Datadog verification provider you added, as described above.
Datadog Service Name
For infrastructure and system-level metrics, you can leave this field empty. For any service-specific metrics, enter the service name that is emitting metrics to Datadog.
Select any of the Datadog API metrics. For a list of the API metrics, see Data Collected from Datadog.
Expression for Host/Container name
Enter an expression that evaluates to the host/container/pod name tagged in the Datadog events.
Analysis Time duration
Set the duration for the verification step. If a verification step exceeds the value, the workflow Failure Strategy is triggered. For example, if the Failure Strategy is Ignore, then the verification state is marked Failed but the workflow execution continues.
Baseline for Risk Analysis
Select Previous Analysis to have this verification use the previous analysis for a baseline comparison. If your workflow is a Canary workflow type, you can select Canary Analysis to have this verification compare old versions of nodes to new versions of nodes in real-time.
Execute with previous steps
Check this checkbox to run this verification step in parallel with the previous steps in Verify Service.
Specify the sensitivity of the failure criteria. When the criteria is met, the workflow Failure Strategy is triggered.
When you are finished, click SUBMIT. The Datadog verification step is added to your workflow.
Once you have deployed your workflow (or pipeline) using the Datadog verification step, you can automatically verify cloud application and infrastructure performance across your deployment. For more information, see Add a Workflow and Add a Pipeline.
To see the results of Harness machine-learning evaluation of your Datadog verification, in your workflow or pipeline deployment you can expand the Verify Service step and then click the Datadog step.
You can also see the evaluation in the Continuous Verification dashboard. The workflow verification view is for the DevOps user who developed the workflow. The Continuous Verification dashboard is where all future deployments are displayed for developers and others interested in deployment analysis.
To learn about the verification analysis features, see the following sections.
Verification phases and providers
Risk level analysis
Tune event capture