Amazon CloudWatch: Metrics, Logs & Alarms

CloudWatch turns workload signals into operational evidence

Amazon CloudWatch is the main AWS service for collecting and working with operational telemetry. AWS services publish service metrics into CloudWatch, and applications can publish custom metrics. A metric is a time-ordered set of data points identified by properties such as its namespace, metric name and dimensions. A dimension is a name/value label that distinguishes one time series from another—for example, the same EC2 metric observed for two different instance IDs represents two distinct metric identities.

Metrics answer questions about behavior over time: utilization, request volume, latency, errors or other measurable signals. The statistic and period used to view a metric matter. A one-minute average can tell a different story from a five-minute maximum, so dashboards and alarms should be built around the behavior the operator actually needs to detect. CloudWatch also supports custom and high-resolution metrics when sub-minute visibility is important, rather than forcing every workload into the cadence of AWS-vended standard-resolution metrics.

CloudWatch Logs preserves the detail behind the signal

CloudWatch Logs organizes log events into log streams and log groups. A log stream represents a sequence of events from the same source, while a log group collects streams that share settings such as retention, monitoring and access control. This hierarchy is important when designing application logging: streams can separate individual producers, while the group becomes the policy and analysis boundary for the related workload logs.

Log retention is configurable per log group, so teams should set it deliberately instead of treating logging as unlimited archival storage. CloudWatch Logs Insights can query stored log data, and metric filters can extract patterns from log events into CloudWatch metrics. That bridge is useful when a condition is naturally discovered in logs—such as a particular error pattern—but operators want a numeric time series and alarm on the rate of occurrences.

• Use metrics for numeric trends and thresholds; use logs when event-level context is required.
• A log group is the shared configuration boundary; its log streams represent individual event sources.
• Metric filters can turn matching log patterns into metrics that can then participate in dashboards and alarms.

Alarm behavior is controlled by period, evaluation and missing-data policy

A CloudWatch metric alarm evaluates a metric or supported expression against a configured condition and can be in OK, ALARM or INSUFFICIENT_DATA. The period determines the duration represented by each data point used by the alarm. Evaluation Periods defines how many recent periods are considered, while Datapoints to Alarm can implement an M-out-of-N rule—for example, requiring two breaching points among the last three rather than requiring every point to breach.

Missing data is a separate design choice. Depending on the signal, a missing point can mean normal inactivity, a failed telemetry path, or a real outage. CloudWatch lets an alarm treat missing data as breaching, not breaching, missing, or ignore it according to the documented behavior. Alarm actions can notify or trigger supported responses when state transitions occur; the important architectural step is to choose a signal and evaluation window that represent a real operational condition rather than creating noisy alerts for normal variation.