Custom CloudWatch metrics allow developers to publish application-specific or business-specific data points to Amazon CloudWatch for monitoring and alerting purposes beyond the default AWS service metrics.
When troubleshooting and optimizing applications, custom metrics provide visibility into app…Custom CloudWatch metrics allow developers to publish application-specific or business-specific data points to Amazon CloudWatch for monitoring and alerting purposes beyond the default AWS service metrics.
When troubleshooting and optimizing applications, custom metrics provide visibility into application-level performance indicators that standard metrics cannot capture. Examples include tracking user login counts, API response times, queue depths, cache hit ratios, or any business KPI relevant to your application.
To publish custom metrics, you can use the AWS SDK's PutMetricData API call or the CloudWatch agent. Each metric requires a namespace (logical container), metric name, dimensions (optional key-value pairs for categorization), timestamp, value, and unit.
Key considerations for optimization:
1. **Resolution**: Standard resolution stores data at 1-minute intervals, while high resolution captures data at 1-second intervals. High resolution provides more granular data but incurs additional costs.
2. **Batching**: Use batch operations when publishing multiple data points to reduce API calls and costs. PutMetricData supports up to 1000 values per call.
3. **Dimensions**: Carefully design dimensions to enable meaningful aggregation and filtering. Each unique dimension combination creates a separate metric stream.
4. **Cost Management**: Custom metrics are charged per metric per month. Optimize by consolidating metrics where possible and avoiding excessive unique dimension combinations.
5. **Retention**: CloudWatch retains metric data based on granularity - 3 hours for 1-second data, 15 days for 1-minute data, 63 days for 5-minute data, and 455 days for 1-hour data.
For troubleshooting, create CloudWatch alarms based on custom metrics to receive notifications when thresholds are breached. Combine with CloudWatch dashboards for real-time visualization and use CloudWatch Logs Insights to correlate metric anomalies with log events for root cause analysis.
Custom CloudWatch Metrics
Why Custom CloudWatch Metrics Are Important
AWS CloudWatch automatically collects standard metrics like CPU utilization, network traffic, and disk I/O for many AWS services. However, these default metrics often don't capture application-specific data that matters most to your business. Custom CloudWatch Metrics allow you to extend monitoring capabilities to track business-critical data points, application performance indicators, and system-level metrics that AWS doesn't natively provide.
For the AWS Developer Associate exam, understanding custom metrics is essential because they represent a core component of building observable, production-ready applications on AWS.
What Are Custom CloudWatch Metrics?
Custom CloudWatch Metrics are user-defined metrics that you publish to CloudWatch from your applications, scripts, or on-premises servers. These metrics can represent any time-series data relevant to your workload, such as:
• Number of logged-in users • Page load times • Memory utilization (not a default EC2 metric) • Application-specific error counts • Business KPIs like orders per minute
How Custom CloudWatch Metrics Work
1. Publishing Metrics You can publish custom metrics using: • AWS CLI: Using the aws cloudwatch put-metric-data command • AWS SDKs: Programmatically from your application code • CloudWatch Agent: For system-level metrics from EC2 or on-premises servers
2. Key Components • Namespace: A container for your metrics (e.g., MyApplication/Production) • Metric Name: The identifier for your metric (e.g., ActiveUsers) • Dimensions: Name-value pairs that uniquely identify a metric (e.g., InstanceId, Environment) • Timestamp: When the data point occurred (optional, defaults to current time) • Value: The actual metric data point • Unit: The unit of measurement (Seconds, Bytes, Count, Percent, etc.)
3. Resolution • Standard Resolution: 1-minute granularity (default, no additional cost) • High Resolution: 1-second granularity (additional cost applies)
4. Data Retention • Data points with period less than 60 seconds: Available for 3 hours • 1-minute data points: Available for 15 days • 5-minute data points: Available for 63 days • 1-hour data points: Available for 455 days (15 months)
Exam Tips: Answering Questions on Custom CloudWatch Metrics
Tip 1: Memory and Disk Space Metrics Remember that memory utilization and disk space are NOT default EC2 metrics. If a question asks how to monitor these, the answer involves either custom metrics or the CloudWatch Agent.
Tip 2: High Resolution Metrics When questions mention monitoring at intervals less than 1 minute, think high-resolution custom metrics. These support granularity down to 1 second but incur additional costs.
Tip 3: API Call and Costs The PutMetricData API is used to publish custom metrics. Be aware that you can batch up to 20 data points per API call and can include up to 10 dimensions per metric.
Tip 4: CloudWatch Agent vs. SDK For system-level metrics (memory, disk), use the CloudWatch Agent. For application-level metrics within your code, use the AWS SDK.
Tip 5: Namespace Best Practices Custom namespaces should NOT start with "AWS/" as this prefix is reserved for AWS services.
Tip 6: Dimensions Matter Dimensions create unique metric identities. The same metric name with different dimensions creates separate metrics. Questions may test your understanding of how dimensions affect metric aggregation.
Tip 7: Statistic Sets You can publish aggregated statistics (Sum, SampleCount, Minimum, Maximum) rather than individual data points to reduce API calls and costs.
Common Exam Scenarios
• Scenario: Monitor memory on EC2 → Answer: CloudWatch Agent or custom metrics • Scenario: Track application-specific business metrics → Answer: Custom metrics via SDK • Scenario: Need sub-minute monitoring → Answer: High-resolution custom metrics • Scenario: Reduce monitoring costs for custom metrics → Answer: Use statistic sets to batch data