1. Analyzing AI Service Latencies with AWS CloudWatch RUM


    AWS CloudWatch Real User Monitoring (RUM) is a service that allows you to monitor the performance of your web applications. It collects and analyzes browser performance data from end users in real-time, which helps you pinpoint and resolve issues that could affect the user experience. By using AWS CloudWatch RUM, you can track the latencies and other metrics that your AI services may be affecting, and make necessary adjustments to improve performance.

    To set up AWS CloudWatch RUM with Pulumi, we'll go through the following steps:

    1. Creating a RUM App Monitor: First, we need to create an App Monitor, which defines the application we want to monitor.

    2. Configuring RUM App Monitor: Next, we configure the App Monitor with details such as domain, session sample rate, etc.

    3. Enabling RUM Metrics Destination: Optionally, we can configure a destination for our RUM metrics so we can analyze them in other services if needed.

    4. Creating a CloudWatch Dashboard: To visualize and analyze these metrics, we can create a CloudWatch Dashboard.

    Let's set up a program to accomplish the task:

    import pulumi import pulumi_aws as aws # Create an AWS CloudWatch RUM App Monitor app_monitor = aws.rum.AppMonitor("appMonitor", # The domain that the RUM client will monitor domain="example.com", # Configuration for the App Monitor app_monitor_configuration={ "telemetries": ["performance"], "session_sample_rate": 1.0, # Fully sample for illustration purposes }, # Include this field and set to True if you want to enable CloudWatch Logs cw_log_enabled=True, # Provide additional details for your monitor, such as a name name="MyAppMonitor" ) # Optionally, if you want to send RUM metrics to a specific destination # such as an S3 bucket or Kinesis stream, you can create a Metrics Destination metrics_destination = aws.rum.MetricsDestination("metricsDestination", # IAM role ARN for permissions iam_role_arn="<IAM-ROLE-ARN>", # Specify the destination for the metrics, like S3 or Kinesis destination="s3", # Reference to your AppMonitor's name app_monitor_name=app_monitor.name ) # Export the ARN of the App Monitor to use elsewhere if needed pulumi.export('app_monitor_arn', app_monitor.arn) # Now, let's create a CloudWatch Dashboard to visualize the RUM data dashboard = aws.cloudwatch.Dashboard("dashboard", # JSON definition of the dashboard layout and widgets dashboard_body=pulumi.Output.all(app_monitor.name).apply(lambda name: f''' {{ "widgets": [ {{ "type": "metric", "x": 0, "y": 0, "width": 12, "height": 6, "properties": {{ "metrics": [ ["AWS/RUM", "Latency", "AppMonitorName", "{name}"] ], "period": 300, "stat": "Average", "title": "Latency Overview" }} }} ] }} '''), # Optionally, give your dashboard a meaningful name dashboard_name="MyRUMDashboard" ) # Export the URL of the CloudWatch Dashboard to view it in the AWS console dashboard_url = pulumi.Output.all(dashboard.dashboard_arn).apply( lambda arn: f"https://console.aws.amazon.com/cloudwatch/home?dashboard={arn}" ) pulumi.export('dashboard_url', dashboard_url)


    In the program above, we start by importing the required Pulumi AWS SDK modules. Then we proceed to:

    • Create a AppMonitor resource which will capture the real user monitoring data based on the configuration we provide.

    • Optionally set up a MetricsDestination where our RUM data can be sent for analysis. This is useful if you're integrating with a workflow outside AWS CloudWatch.

    • Export the ARN of our App Monitor in case we need to reference it elsewhere.

    • Construct a CloudWatch Dashboard resource to visualize the collected RUM data. We format the JSON body of the dashboard to display metrics such as latency. We're dynamically including the name of the AppMonitor within the dashboard body using Pulumi's Output combinator to correctly reference the App Monitor by name.

    • Finally, we export the URL of the Dashboard so we can easily access the dashboard from the AWS console.

    Using Pulumi, we are able to define our monitoring stack as code, which makes the process repeatable and version-controllable. This approach aids in creating a reliable and maintainable infrastructure for monitoring application performance.