Auto-Scaling Web Applications for AI Dashboards on App Engine

Question

Pulumi · Accepted Answer

To create an auto-scaling web application for AI dashboards on Google App Engine, you will need to define an `App` resource which stands for an App Engine application, and a `Version` resource which represents the different versions of the application that can be deployed.

The `Version` resource will contain the configuration for `automaticScaling`, where you can specify how the application should scale based on factors like CPU usage, request latency, concurrent requests, and more.

Here's a Pulumi Python program that defines an App Engine app with auto-scaling capabilities:

```python
import pulumi
import pulumi_google_native.appengine as appengine

# Define the App Engine application
app = appengine.Application("my-app",
    location_id="us-central",  # Use the appropriate location for your app
    # Additional properties like iap (Identity-Aware Proxy) can be configured here
)

# Define a version of your app that will be auto-scaled
version = appengine.Version("v1",
    # Link to the application defined above
    apps_id=app.name,
    # The service within the App Engine application to associate with this version
    service_id="default",
    # Define the runtime and environment for the deployment
    runtime="python27",  # Use an appropriate runtime for your application
    env="standard",
    # The actual deployment configuration
    deployment=appengine.VersionDeploymentArgs(
        # Specify your application files or container image here
        files={
            "main.py": appengine.VersionDeploymentFilesArgs(
                sha1_sum="hash_of_main_py_file",
                source_url="gs://bucket_name/main.py"
            ),
            # Additional files can be included similarly
        },
    ),
    # Configure automatic scaling for the app
    automatic_scaling=appengine.VersionAutomaticScalingArgs(
        cool_down_period="120s",  # Cool down period between changes to the number of instances
        cpu_utilization=appengine.VersionAutomaticScalingCpuUtilizationArgs(
            target_utilization=0.6  # Target CPU utilization to trigger scaling
        ),
        # Further scaling parameters like max and min instances can be set below
        max_total_instances=10,  # Maximum number of instances for auto-scaling
        min_total_instances=2,   # Minimum number of instances for auto-scaling
        # Target latencies and concurrent requests configurations can also be included
    ),
    # Additional configurations like env_variables, entrypoint, handlers etc. can be added
)

# Export the URL of the deployed application
pulumi.export("app_url", pulumi.Output.concat("https://", app.location_id, ".appspot.com"))
```

In this code:

- We create an `appengine.Application` resource named "my-app", specifying the location where the app will reside in App Engine.
- We define a `appengine.Version` resource "v1" which is connected to the "my-app" application through the `apps_id` parameter.
- We specify the runtime and environment details that our application will run on. In the example above, we use Python 2.7 in the standard environment. Update the `runtime` parameter to match your application's requirements.
- The deployment details are provided with a simple dictionary specifying the source URLs of our application files stored in Google Cloud Storage.
- The `automatic_scaling` configuration details of the application are set within `appengine.VersionAutomaticScalingArgs`. Here, we've configured the app to scale automatically based on CPU utilization, with specific minimum (2) and maximum (10) instance counts.
- For simplicity, the `pulumi.export` statement at the end provides the URL where the app will be accessible once deployed.

Remember to replace placeholder values like `sha1_sum`, `source_url`, `bucket_name`, and `hash_of_main_py_file` with actual values that correspond to your application's deployment details. Additionally, the example uses the deprecated Python 2.7 runtime for demonstration purposes; be sure to use a current, supported runtime environment for your implementation.