Cloud Monitoring & Logging Cheat Sheet
Covers metrics, logs, traces, alerting, and the tooling used to observe cloud workloads across AWS CloudWatch and open standards.
2 PagesIntermediateFeb 5, 2026
Three Pillars of Observability
The core data types used to understand system behavior.
- Metrics- Numeric time-series data (CPU%, request count, latency) aggregated over time
- Logs- Discrete timestamped event records, structured or unstructured
- Traces- End-to-end records of a request's path across distributed services, with spans
- Alerting- Automated notification triggered when a metric crosses a defined threshold
- Dashboards- Visual aggregation of metrics/logs for at-a-glance system health
Create a CloudWatch Alarm
Trigger SNS notification when average CPU exceeds 80% for 3 periods.
bash
aws cloudwatch put-metric-alarm \ --alarm-name high-cpu \ --metric-name CPUUtilization \ --namespace AWS/EC2 \ --statistic Average \ --period 300 \ --threshold 80 \ --comparison-operator GreaterThanThreshold \ --evaluation-periods 3 \ --alarm-actions arn:aws:sns:us-east-1:123456789012:alerts
CloudWatch Logs Insights Query
Query structured logs for slow requests.
sql
fields @timestamp, @message| filter duration_ms > 1000| sort @timestamp desc| limit 20
Common Tooling
Popular monitoring stacks and services.
- AWS CloudWatch- Native AWS metrics, logs, alarms, and dashboards
- Prometheus- Open-source pull-based metrics collection with its own query language (PromQL)
- Grafana- Open-source dashboarding tool, commonly paired with Prometheus or CloudWatch
- OpenTelemetry- Vendor-neutral standard/SDK for collecting metrics, logs, and traces
- Datadog- Commercial SaaS observability platform covering metrics, logs, APM, and RUM
Pro Tip
Alert on symptoms (elevated error rate, latency) rather than causes (high CPU) — cause-based alerts create noisy pages for conditions that don't actually affect users.
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