Create Application Insights

This step allows you to create an Azure Application Insights service. It allows you to monitor any running APIs or streaming Spark jobs running on Databricks.

Deployment

Add the following task to deployment.yaml

field	description	values
`task`	`"create_application_insights"`
`kind` [optional]	Used to customize the UI	One of `web`, `ios`, `other`, `store`, `java`, `phone`
`application_type` [optional]	Type of application being monitored	One of `web`, `other`
`create_databricks_secret` [optional]	Postfix for the image name, will be added before the tag	One of `true`, `false`

Takeoff config

Credentials for an Azure Active Directory (AAD) user (username, password) must be available in your cloud vault. If create_databricks_secret := true credentials for Databricks (host, token) must also be available in your cloud vault.

Make sure .takeoff/config.yaml contains the following keys:

azure:
    keyvault_keys:
        active_directory_user:
          username: "aad-username"
          password: "aad-password"

Examples

Assume an application name myapp and version 1.2.0. The resouce_group_naming parameter is rg{env}, where releases go to env =: prd

Minimum configuration example. This constructs an Application Insights service in the rgprd resource group with the name myapp.

steps:
  - task: create_application_insights

Full configuration example. This constructs an Application Insights service in the rgprd resource group with the name myapp with the UI tailored to Java applications, it also creates a Databricks secret under the scope myapp with name instrumentation-key.

steps:
  - task: create_application_insights
    kind: java
    application_type: other
    create_databricks_secret: true 