The content you've shared is a comprehensive overview of how to create and manage custom metrics in Amazon CloudWatch, which is a crucial aspect of monitoring and optimizing AWS resources and applications. Custom metrics allow AWS users to monitor application-specific metrics that are not captured by default AWS service metrics, providing a more granular view of the application's performance and health. Here's a summary and some additional insights on key points mentioned:

Creating Custom Metrics in CloudWatch

1. Use of PutMetricData API: This API call is central to pushing custom metrics to CloudWatch. It allows for the specification of metric names, dimensions, timestamps, and values. Custom metrics can capture a wide variety of data points, such as memory usage, disk space, or the number of active users.
2. Dimensions: Dimensions are name-value pairs that uniquely identify a metric. You can use dimensions to segment metrics into categories, making them more meaningful and easier to manage. Common dimensions include InstanceId for EC2 instances or EnvironmentName for distinguishing between production, development, or testing environments.
3. Metric Resolution: CloudWatch supports two resolutions for custom metrics: standard (with data points every minute) and high-resolution (with data points every 1, 5, 10, or 30 seconds). High-resolution metrics are beneficial for applications requiring detailed monitoring in short intervals.
4. Timestamps: An interesting feature of CloudWatch is its ability to accept metrics with timestamps up to two weeks in the past or two hours in the future. This flexibility allows for the correction of time discrepancies and ensures that metrics are accurately represented.
5. Configuring Time Settings on EC2 Instances: Since CloudWatch can accept past or future timestamps, it's crucial to ensure that your EC2 instances are correctly time-synced. This ensures that metrics correlate accurately with real-world events and timeframes.

Practical Application

• Pushing a Custom Metric: The process involves constructing a CLI command with the necessary parameters (metric name, dimensions, value, timestamp, etc.) and executing it. This can be automated with scripts running on EC2 instances or other AWS resources, allowing for regular and automated metric collection.
• Viewing Custom Metrics: After pushing custom metrics to CloudWatch, they are accessible in the CloudWatch console under a custom namespace. These metrics can then be visualized, monitored with alarms, or used in dashboards alongside AWS service metrics for comprehensive monitoring.

CloudWatch Agent for Automated Metric Collection

• The CloudWatch Agent simplifies the process of collecting custom metrics by automating data collection and submission to CloudWatch. It can collect system-level metrics, application logs, and custom metrics specified in its configuration using the PutMetricData API call under the hood.

Conclusion

Custom metrics in CloudWatch offer a powerful way to monitor and optimize AWS applications beyond the default metrics provided by AWS services. By understanding and utilizing the PutMetricData API, dimensions, and metric resolutions, you can gain deeper insights into your applications' performance and operational health.

The standard resolution for custom metrics in CloudWatch is 1 minute, which means you can publish data points with a one-minute granularity.

Using high-resolution custom metric, your applications can publish metrics to CloudWatch with 1-second resolution.