CloudWatch Embedded Metric Format (EMF) is a JSON specification that enables you to generate custom metrics asynchronously from your application logs. Instead of making separate API calls to publish metrics, EMF allows you to embed metric data within your log entries, which CloudWatch automatically…CloudWatch Embedded Metric Format (EMF) is a JSON specification that enables you to generate custom metrics asynchronously from your application logs. Instead of making separate API calls to publish metrics, EMF allows you to embed metric data within your log entries, which CloudWatch automatically extracts and processes as metrics.
Key Benefits:
1. **Cost Efficiency**: EMF reduces the number of PutMetricData API calls, lowering costs since you only pay for log ingestion rather than metric API calls.
2. **Correlation**: Metrics and logs are linked together, making troubleshooting easier. You can trace from a metric anomaly to the corresponding log entry.
3. **High Cardinality**: EMF supports dimensions with high cardinality, allowing detailed metric segmentation.
EMF Structure:
The format includes a special _aws object containing CloudWatchMetrics array that defines namespaces, dimensions, and metric definitions. Example structure:
{
"_aws": {
"Timestamp": 1234567890,
"CloudWatchMetrics": [{
"Namespace": "MyApplication",
"Dimensions": [["Service", "Environment"]],
"Metrics": [{"Name": "ProcessingTime", "Unit": "Milliseconds"}]
}]
},
"Service": "OrderService",
"Environment": "Production",
"ProcessingTime": 150
}
Implementation Options:
- **Lambda**: Use the aws-embedded-metrics library for Node.js, Python, or Java
- **EC2/ECS/EKS**: Install CloudWatch agent configured to parse EMF logs
- **Manual**: Write EMF-formatted JSON to stdout or log files
For AWS Lambda, EMF logs written to stdout are automatically processed by CloudWatch. The CloudWatch Logs agent extracts metrics and publishes them to CloudWatch Metrics.
Best Practices:
- Use meaningful namespaces and dimensions for metric organization
- Batch multiple metrics in single log entries when possible
- Include relevant context in log entries for debugging
- Set appropriate timestamps for accurate metric timing
EMF is particularly valuable for serverless applications where traditional metric publishing adds latency and complexity.
CloudWatch Embedded Metric Format (EMF) - Complete Guide
Why CloudWatch Embedded Metric Format (EMF) is Important
CloudWatch Embedded Metric Format is crucial for modern serverless and containerized applications because it allows developers to generate custom metrics from structured log data. This eliminates the need to make separate API calls to publish metrics, reducing complexity, latency, and costs. For AWS Developer Associate exam candidates, understanding EMF demonstrates knowledge of efficient observability practices in AWS environments.
What is CloudWatch Embedded Metric Format?
EMF is a JSON specification that enables you to embed custom metrics within your application logs. When logs containing EMF-formatted data are sent to CloudWatch Logs, the service automatically extracts the metric values and creates CloudWatch metrics from them. This provides a seamless way to generate high-cardinality, high-resolution metrics alongside your application logs.
Key characteristics: - JSON-based format with a specific schema - Metrics are extracted asynchronously from logs - Supports dimensions, units, and namespaces - Works with Lambda, ECS, EC2, and other compute services - No additional CloudWatch PutMetricData API calls required
How EMF Works
1. Structure your log output - Format your log entries using the EMF JSON schema, including the _aws object with CloudWatchMetrics array
2. Send logs to CloudWatch Logs - Output the EMF-formatted JSON to stdout or use the CloudWatch Logs agent
3. Automatic metric extraction - CloudWatch Logs automatically parses the EMF data and creates metrics in CloudWatch Metrics
4. Query and visualize - Use CloudWatch dashboards, alarms, and insights to analyze your metrics
AWS provides client libraries to simplify EMF usage: - aws-embedded-metrics for Node.js, Python, Java, and .NET - These libraries handle the JSON formatting and flushing of metrics - Particularly useful in AWS Lambda where they integrate with the execution context
Exam Tips: Answering Questions on CloudWatch EMF
Scenario Recognition: - Look for scenarios involving custom metrics from Lambda functions or containers - Questions about reducing latency or cost when publishing metrics - Situations requiring high-cardinality metrics with many dimension values - Scenarios asking for metrics and logs to be correlated
Key Points to Remember: - EMF generates metrics from structured logs, not from separate API calls - Metrics are extracted asynchronously after logs are ingested - The _aws reserved key indicates EMF-formatted data - EMF supports up to 100 metrics per log event - Maximum of 30 dimensions per metric - Works best with Lambda, ECS, EKS, and EC2
Common Exam Distractors: - PutMetricData API is for synchronous metric publishing, not EMF - CloudWatch Logs Insights is for querying logs, not generating metrics - Metric filters create metrics from log patterns, but EMF provides more flexibility and structure
When EMF is the Right Answer: - Generating custom metrics in serverless applications - Need to correlate metrics with log data - High-cardinality metrics with unique dimension combinations - Reducing operational overhead of metric publishing - Batch processing of metrics through logs