Automic Automation Cloud Integrations: Google Cloud Run Agent Integration

Instantly inherit the advanced capabilities of your enterprise solution, enabling you to deliver your digital transformation more quickly and successfully. This video explains the Automic Automation Google Cloud Run agent integration and its benefits. It presents its components and demonstrates how to install, configure, and use it.

Video Transcript

Welcome to this video on the Automic Automation Google Cloud Run integration solution. In this video, we will explain the Google Cloud Run integration and what it brings to the Automic Automation user community.

Google Cloud Run is a fully managed compute platform that allows you to run stateless containers which can be invoked via HTTP requests or events. It's serverless, meaning it handles the infrastructure management, so you can focus on building and deploying your applications. Cloud Run abstracts away infrastructure concerns such as scaling, networking, and security, allowing you to deploy your code and containers and have it automatically scaled up or down based on demand.

Integrating Automic Automation with Google Cloud Run allows you to run Google Cloud Run jobs in your workspace from Automic Automation. We'll provide some technical insights so that the integration components are clearly identified and the deployment sequence is understood. We'll focus on the configuration of the agent and the design of the two core object templates: connections and jobs. Finally, we'll run through a demo.

The Role of Automic Automation

Automic Automation plays a central role in orchestrating operations across multiple environments, including the cloud. Automic Automation synchronizes these processes with other non-cloud operations. By integrating Google Cloud Run, we can configure process automation centrally in Automic Automation and then trigger, monitor, and supervise everything in one place.

Google Cloud Run processes can then be synchronized with all other environments routinely supported by Automic Automation. Google Cloud Run's role is reduced to executing the jobs; all other functions, especially those about automation, are delegated to Automic Automation. This means that we don't have to log in to the Google Cloud Run environment and keep on refreshing it by ourselves. Automic Automation manages all the execution and monitoring aspects.

Key Benefits for Operations

• Simplified View: Provides a streamlined interface to run jobs in Google Cloud Run.
• Intuitive Interfaces: Build configurations with drag-and-drop workflows.
• Native Supervision: Supervise processes in simple dashboard tools designed natively for operations.
• Visual Statuses: Statuses are color-coded, and retrieving logs is done with a basic right-click.
• Externalized Operations: Synchronize all cloud with non-cloud workloads using various agents and job object types.
• Sophisticated Configurations: Involve multiple applications, database packages, system processes (like backups and data consolidation), file transfers, web services, and other on-premise workloads.

Architecture and Deployment

A conventional architecture involves two systems: the Automic Automation host and a dedicated system for the agent. The agent is configured with a simple INI file containing standard values: system, agent name, connection, and TLS. When we start the agent, it connects to the engine and it adds two new objects to the repository:

1. Connection Object: To store the Google Cloud Run endpoint and login data.
2. Job Template: Designed to trigger Google Cloud Run jobs.

Deployment Procedure

The procedure to deploy the Google Cloud Run integration is as follows:

1. Download: Download the integration package from the marketplace.
2. Unzip: This produces a directory containing the agent, the INI configuration files, and several other items like the start command.
3. Configure: Use the appropriate INI file for your specific platform. It requires at least four values to be updated:
   • Agent name
   • Automic system
   • JCP connection and TLS port
   • TLS certificate
4. Start: When the agent is configured, we start it by invoking the JAR file via the Java command.
5. Templates: New object templates are deployed.
6. Connections: Create a connection to every Google Cloud Run instance we need to support by duplicating the connection template.
7. Jobs: Use the Google Cloud Run template job to create the jobs we need, matching them to the Google batch jobs.

Technical Demo: Creating and Executing Jobs

In our demo, we will create a connection object and a Cloud Run job. While there are three job types available—Create Run, Create Service, and Run Jobs—this demo will focus specifically on the Create Run job.

Google Cloud Console Overview

Before we look into the Automic system, the Google Cloud Console provides an overview of our existing jobs. Within this environment, you will see several key menus:

• Services Menu: Represents your deployed applications or APIs. Each service runs a container image and scales automatically.
• Jobs Menu: Used for background or batch processing tasks that do not serve HTTP requests. These can be triggered manually or scheduled.

We are using the specific DO01 Automic pre-sales project for the demo. All jobs that run through Automic will appear in the console under this project ID, regardless of whether they succeed or fail.

Configuring the Connection Object

If we open a connection object, we must enter the most important field: the Endpoint (the URL of the Google Cloud Run environment). Next, we specify an authentication type:

• VM Metadata Instance: Requires a service account email.
• Service Account Key: Requires the input type for the service account (either a JSON payload or a file path to the JSON file).
• Proxy Section: Optionally specify the proxy host name, port, username, and password.

Configuring the Job Object

1. Attributes Page: Select the agent that corresponds to the agent name configured in the INI file.
2. Connection: Select the connection object created previously.
3. Project ID: Enter the ID of the Google Cloud project.
4. Location: Define the region where resources are hosted.
5. Payload: Choose between a JSON file path or a JSON payload (which can be pasted directly from the console).
6. Delete on Completion: Optionally check this to delete the job from the local directory after completion.

Monitoring and Reports

Once executed, we can switch to the executions view. The details pane shows the Remote Status field, which tracks the job status in the target environment. The details panel also shows an object variable called an Execution Name Hash to help identify specific job executions.

Reports available include:

• Structured Output: Contains information sent to Automic Automation in JSON format for easy analysis.
• Agent Log (P Log): Shows all agent actions step-by-step, listing all parameters used and the response received from the target system.

This wraps up the demo on how Automic Automation can integrate with Google Cloud Run to run, execute, and monitor jobs. Thank you for watching this video.

Note: This transcript was generated with the assistance of an artificial intelligence language model. While we strive for accuracy and quality, please note that the transcription may not be entirely error-free. We recommend independently verifying the content and consulting with a product expert for specific advice or information. We do not assume any responsibility or liability for the use or interpretation of this content.