Amazon CloudWatch
A fully managed monitoring service that provides unified monitoring and analysis of metrics, logs, and events for AWS resources and applications
Overview
Amazon CloudWatch is a monitoring service that provides real-time visibility into the performance and operational health of AWS resources and applications. It automatically collects metrics from AWS services such as EC2 CPU utilization, RDS database connections, Lambda execution duration, and ALB request counts. CloudWatch Logs aggregates, searches, and analyzes application and system logs. CloudWatch Alarms triggers SNS notifications or Auto Scaling actions when metrics exceed defined thresholds. CloudWatch Dashboards lets you create custom dashboards for at-a-glance operational visibility. CloudWatch Logs Insights provides a SQL-like query language for rapid log data analysis, useful for incident investigation and performance analysis. Custom metrics let you track application-specific indicators (order counts, error rates, etc.) within CloudWatch's unified management.
Alarm Design and Composite Alarms
CloudWatch Alarms execute actions when a metric value exceeds a specified threshold. Alarms have three states - OK, ALARM, and INSUFFICIENT_DATA - and can trigger actions on state transitions including SNS topic notifications, Auto Scaling policy execution, EC2 instance stop/terminate/reboot, and Systems Manager OpsItem creation. Composite Alarms combine multiple alarms using logical operators (AND, OR, NOT) to execute actions based on complex conditions - for example, triggering only when both CPU utilization exceeds 80% and request error rate exceeds 5%, reducing false positives from single-metric spikes. The Anomaly Detection feature uses machine learning to automatically learn the normal range of a metric and detect anomalies, enabling monitoring that adapts to seasonal variations and trend changes that fixed thresholds cannot capture. Metric resolution defaults to 1-minute intervals, but high-resolution metrics enable monitoring at 1-second intervals. While Azure Monitor provides similar alerting capabilities, CloudWatch's Composite Alarms offer more flexible multi-condition logic than Azure's action group rules.
Log Analysis with Logs Insights
CloudWatch Logs Insights provides a purpose-built query language for rapid log data analysis across multiple log groups simultaneously. You can write queries to parse, filter, aggregate, and visualize log data - extracting error patterns, calculating latency percentiles, and identifying trends across your application stack. The query language supports commands like fields, filter, stats, sort, and parse for structured analysis. A common pattern is to use parse to extract structured fields from unstructured log lines, then aggregate with stats to identify the most frequent error types or slowest API endpoints. Queries can scan gigabytes of log data in seconds, making it practical for real-time incident investigation. Azure Monitor's Log Analytics uses KQL (Kusto Query Language), which supports more advanced analytical queries including joins and time-series functions, but Logs Insights' simpler syntax has a lower learning curve for teams already working within the AWS ecosystem. For a comprehensive guide to Amazon CloudWatch best practices, refer to technical books (Amazon).
Dashboard Design and Three-Layer Metrics Strategy
CloudWatch monitoring design should be structured in three layers: business metrics, application metrics, and infrastructure metrics. At the business layer, use custom metrics to track indicators such as order counts, conversion rates, and error rates. At the application layer, monitor request latency, error rates, and throughput per service. At the infrastructure layer, track CPU utilization, memory usage, disk I/O, and network throughput. Custom metrics cost $0.30 per metric per month, so focus on metrics that directly inform operational decisions. Container Insights automatically collects container-level metrics for ECS/EKS, visualizing CPU/memory utilization per pod or task. Design separate dashboards for different audiences - operations teams need granular real-time metrics with alarm status, while executive stakeholders need high-level trend views of business KPIs and cost indicators. Enabling Anomaly Detection across key metrics provides machine learning-based detection of deviations from normal patterns, catching anomalies that fixed thresholds would miss.