Tutorial / Cram Notes

Custom Metrics in CloudWatch

While Amazon CloudWatch provides many pre-defined metrics for AWS services, there might be instances where you need to monitor custom data, such as the performance of your application or specific networking components. Custom metrics allow you to extend CloudWatch beyond its default capabilities.

Creating Custom Metrics

To create a custom metric, you can use the put-metric-data command via the AWS Command Line Interface (CLI) or make use of the PutMetricData API operation.

Here’s a simple CLI example that sends a custom metric to CloudWatch:

aws cloudwatch put-metric-data –namespace “MyCustomMetrics” –metric-name “MyAppLatency” –value 123 –unit Milliseconds

In this example, MyCustomMetrics is the namespace used to group all relevant custom metrics, MyAppLatency is the name of the metric, and 123 is the value recorded in milliseconds.

Dimensions in Custom Metrics

Dimensions are name/value pairs that uniquely identify a metric. You can include dimensions in your custom metric to categorize and filter them appropriately. For example:

aws cloudwatch put-metric-data –namespace “MyCustomNetwork” –metric-name “PacketLoss” –dimensions “InstanceID=i-0abcd1234efgh5678” –value 1 –unit Count

High-Resolution Metrics

Custom metrics support high-resolution settings as low as one-second data points, which are useful for quick-reaction scenarios. To upload high-resolution metrics:

aws cloudwatch put-metric-data –namespace “MyCustomMetrics” –metric-name “HighResMetric” –value 10 –storage-resolution 1

Setting the –storage-resolution to 1 specifies that the data points are high resolution.

Monitoring Custom Metrics

Alarms

Once you’ve published custom metrics, you can set alarms to notify you if the metrics reach certain thresholds. This is configured in CloudWatch under the Alarms section. For example, you might set an alarm if MyAppLatency exceeds a particular value:

  1. Go to the CloudWatch dashboard.
  2. Navigate to ‘Alarms’ and select ‘Create Alarm’.
  3. Select the ‘MyCustomMetrics’ namespace and then choose the MyAppLatency metric.
  4. Define the threshold and the action based on that threshold.

Dashboards

You can also visualize custom metrics in CloudWatch Dashboards, allowing you to see real-time performance and trends.

  1. From the CloudWatch console, select ‘Dashboards’ and create a new dashboard.
  2. Add a ‘Widget’ and select the custom metric you want to plot.
  3. Configure the widget to display the data in your preferred format, such as a line chart or stacked area.

Integration with Other AWS Services

Custom metrics in CloudWatch can be integrated with other AWS services like AWS Lambda, AWS SNS for notifications, or AWS Auto Scaling to react to changes in performance or demand.

Example – Lambda Function Trigger

{
“source”: [“aws.cloudwatch”],
“detail-type”: [“CloudWatch Alarm State Change”],
“detail”: {
“alarmName”: [“MyAppLatencyAlarm”],
“state”: {
“value”: [“ALARM”]
}
}
}

This example event pattern in a CloudWatch Events rule would trigger a Lambda function whenever MyAppLatencyAlarm goes into the ALARM state.

Best Practices for Custom Metrics

  • Use naming conventions for metrics and namespaces for easier management.
  • Be consistent with metric units to avoid confusion.
  • Apply appropriate dimensions to categories metrics properly.
  • Clean up unused metrics to avoid unnecessary charges.
  • Set alarms for critical thresholds to maintain application performance.

In conclusion, by using CloudWatch custom metrics combined with alarms and dashboards, AWS users can gain comprehensive insights into the health and performance of their applications and infrastructure. These insights can enable swift responses to changing conditions, which might be crucial for networking specialists looking to pass the AWS Certified Advanced Networking – Specialty (ANS-C01) exam and maintain efficient network operations on AWS.

Practice Test with Explanation

True or False: Using CloudWatch, it is possible to create customized metrics by publishing your own metric data.

  • A) True
  • B) False

Answer: A) True

Explanation: With Amazon CloudWatch, you can publish your own metrics directly to Amazon CloudWatch, enabling you to create custom metrics unique to your application’s needs.

Which AWS service allows you to create custom dashboards to visualize your custom metrics?

  • A) AWS X-Ray
  • B) Amazon CloudWatch
  • C) AWS Data Pipeline
  • D) Amazon EC2

Answer: B) Amazon CloudWatch

Explanation: Amazon CloudWatch allows the creation of customizable dashboards to monitor and visualize metrics, including custom metrics.

When creating a custom metric in CloudWatch, what is the maximum resolution you can specify for the data points?

  • A) 1 minute
  • B) 10 seconds
  • C) 1 second
  • D) 5 minutes

Answer: C) 1 second

Explanation: Custom metrics in CloudWatch support high-resolution metrics, allowing data points with a period of as little as 1 second.

What method can be used to send custom metric data to CloudWatch?

  • A) Upload a CSV file to an S3 bucket
  • B) Use the `put-metric-data` CLI command
  • C) Send metrics via email to an AWS-provided email address
  • D) Configure an EC2 instance to automatically report the metrics

Answer: B) Use the `put-metric-data` CLI command

Explanation: The `put-metric-data` command in the AWS CLI is used to publish custom metric data to CloudWatch.

True or False: CloudWatch Logs can trigger a CloudWatch alarm.

  • A) True
  • B) False

Answer: A) True

Explanation: CloudWatch Logs can be analyzed with CloudWatch Metrics filters to create metric data points, which in turn can be used to trigger alarms.

Which dimension is automatically assigned by AWS when custom metrics are published without specifying a dimension?

  • A) InstanceId
  • B) Namespace
  • C) Default
  • D) None

Answer: D) None

Explanation: If you do not specify a dimension when publishing a custom metric, no dimensions will be assigned to that metric.

True or False: When publishing a custom metric in CloudWatch, you can backfill historical data for any past timestamps.

  • A) True
  • B) False

Answer: B) False

Explanation: CloudWatch rejects data points with timestamps that are more than two weeks in the past or more than two hours into the future.

What is the time-to-live (TTL) for custom metrics in CloudWatch with no associated alarms?

  • A) 15 days
  • B) 3 months
  • C) 15 months
  • D) They are kept indefinitely

Answer: C) 15 months

Explanation: Custom metrics data points with no associated alarms are retained for 15 months, allowing you to access historical data over a long period.

Which AWS service can you use in conjunction with CloudWatch to automatically react to changes in your AWS resources?

  • A) AWS Lambda
  • B) AWS Direct Connect
  • C) Amazon Route 53
  • D) AWS Config

Answer: A) AWS Lambda

Explanation: AWS Lambda can be triggered by CloudWatch events or alarms to execute functions in response to changes in your AWS environment.

True or False: It is possible to create custom metrics for on-premises servers with the CloudWatch agent.

  • A) True
  • B) False

Answer: A) True

Explanation: The CloudWatch agent can be installed on both AWS EC2 instances and on-premises servers to collect and send both logs and custom metric data to CloudWatch.

Which statistic indicates the proportion of data points in a specified period that are in the highest one-eighth of the data point range for CloudWatch custom metrics?

  • A) Sum
  • B) Average
  • C) Minimum
  • D) p90

Answer: D) p90

Explanation: The p90 statistic gives you the value below which 90% of the data points fall, representing the threshold for the highest one-eighth if metric data is uniformly distributed.

What is the default period for a CloudWatch metric upon which alarms can be created?

  • A) 1 minute
  • B) 5 minutes
  • C) 10 minutes
  • D) 15 minutes

Answer: B) 5 minutes

Explanation: By default, the period for a CloudWatch alarm is 5 minutes, but this can be changed depending on the needs for monitoring and resolution.

Interview Questions

What is Amazon CloudWatch, and how is it relevant to custom metrics implementation in AWS?

Amazon CloudWatch is a monitoring service for AWS cloud resources and the applications running on AWS. It provides data and actionable insights to monitor applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch is relevant to custom metrics implementation, as it allows users to publish their own metrics using the PutMetricData API call. This is useful for monitoring application-specific data or any metric that is not natively captured by CloudWatch.

Can you explain the process of creating and sending custom metrics to CloudWatch?

To create and send custom metrics to CloudWatch, you first need to gather the data you want to monitor, then use the CloudWatch PutMetricData API or AWS CLI put-metric-data command to publish these metrics to CloudWatch. You can also use the AWS SDKs to programmatically send custom metrics. The data must be formatted in a way that CloudWatch accepts, including necessary dimensions and timestamps.

How can you use dimensions to organize and filter custom metrics in CloudWatch?

Dimensions are name-value pairs that are part of the identity of a metric. You can assign up to 10 dimensions to a metric, and they help to give it a specific meaning by grouping and filtering metrics. For example, if you are tracking the memory utilization of different EC2 instances, you can use the instance ID as a dimension to distinguish between metrics from different EC2 instances.

Why would you need to use custom metrics in CloudWatch, give an example of a scenario where they are necessary?

Custom metrics are necessary when you need to monitor application-specific or business-specific metrics that are not provided out-of-the-box by AWS. For example, if you want to monitor the number of active sessions on a web server or track the queue length of a processing pipeline, you would need to create custom metrics because these are specific to your application’s functionality and not natively available in CloudWatch.

What is the importance of setting the correct namespace for custom metrics in CloudWatch?

Namespaces are containers for CloudWatch metrics that help you to organize and isolate them logically. Setting the correct namespace for custom metrics is important because it allows you to group related metrics and avoid metric naming collisions with other CloudWatch metrics. Typically, you should define a namespace that represents your company and application to ensure metrics are well organized and easily identifiable, such as “MyCompany/MyApplication”.

What is the highest resolution for custom metrics that you can define in CloudWatch, and how can you configure it?

The highest resolution for custom metrics in CloudWatch is 1 second, often referred to as high-resolution metrics. To configure a custom metric with this resolution, you must specify the ‘StorageResolution’ parameter as ‘1’ when using the PutMetricData API or include the `–storage-resolution 1` flag when using the AWS CLI put-metric-data command.

Explain the difference between Standard and High-Resolution custom metrics in CloudWatch.

The key difference is the granularity of the data points they allow. Standard resolution custom metrics have a minimum granularity of 60 seconds, while high-resolution custom metrics support a minimum granularity of 1 second. High-resolution metrics are designed for applications that need to collect and react to data in near real-time.

What are the costs associated with publishing custom metrics to CloudWatch?

Publishing custom metrics to CloudWatch incurs costs depending on the number of metrics sent and their resolution. Standard resolution custom metrics are charged per metric per month, while high-resolution metrics (which have a data point every second) cost more due to their increased frequency and resolution. Additionally, costs can accrue for custom metric data storage, API requests, and additional features like alarms based on custom metrics.

How do you secure the data of your custom metrics in CloudWatch?

To secure custom metrics data, you should follow AWS best practices such as using IAM roles and policies to control access to your CloudWatch data, enabling encryption with CloudWatch Logs if sensitive data is collected, and ensuring proper API call logs through AWS CloudTrail for auditing and monitoring purposes.

Can you set alarms on custom metrics in CloudWatch? If so, explain how you would approach this.

Yes, you can set alarms on custom metrics in CloudWatch, similar to standard AWS metrics. To do this, navigate to the CloudWatch console, select ‘Alarms,’ then ‘Create Alarm.’ You can then select the respective custom metric from the list, define the condition that should trigger the alarm (such as a threshold value), and specify the action to be taken when the alarm state is reached.

Describe a method to visually represent and analyze custom metrics within CloudWatch.

You can visually represent and analyze custom metrics within CloudWatch by creating custom dashboards. These dashboards allow you to add widgets such as graphs, numbers, or text to visualize your metrics data. You can create and customize the widgets to display your custom metrics over different time periods and use statistical functions like averages, sums, and maximums to analyze trends.

In what ways can you integrate CloudWatch custom metrics with other AWS services?

CloudWatch custom metrics can be integrated with other AWS services in various ways. For example, you can set CloudWatch Alarms based on custom metrics to trigger AWS Lambda functions, send notifications via Amazon SNS, or automatically adjust resources through Auto Scaling policies. Additionally, custom metrics data can be retrieved and used by AWS services like AWS CloudFormation for infrastructure monitoring and by AWS Step Functions for workflow-driven functionalities.

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Christina Rose
6 months ago

Great post on implementing customized metrics using CloudWatch! It really helped me understand the intricacies.

Teresa Richardson
7 months ago

Does anyone know if it’s possible to create custom metrics for VPC Flow Logs in CloudWatch?

Janna Haufe
6 months ago

Thanks for this tutorial. It was very helpful for my preparation for the AWS Certified Advanced Networking – Specialty exam.

Natalie Walker
6 months ago

Can anyone share some advanced use cases where custom metrics have significantly helped in monitoring?

Ana María Candelaria

Amazing tutorial. Can someone explain how to store the custom metrics data for longer retention?

Bently Denys
6 months ago

Appreciate the detailed steps provided in the blog. It made learning quite easy!

Micaela Navarrete
5 months ago

I’ve noticed that custom metrics can become expensive with large-scale implementations. Any cost-optimization tips?

Lance Bell
6 months ago

Thanks for the post! It was very informative.

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