How does monitoring work with CloudWatch?

Question

Accepted Answer

**Amazon CloudWatch** is AWS's monitoring and observability service — collecting **metrics**, **logs**, and **events** from AWS resources and applications, with **alarms** and dashboards. It's essential for understanding system health, diagnosing issues, and responding to problems.

## What CloudWatch provides

```text
METRICS → numerical data over time (CPU, memory, request count, latency, etc.)
  → AWS services publish metrics automatically; you can publish CUSTOM metrics too
LOGS → collect, store, and search application/system logs (CloudWatch Logs)
ALARMS → trigger actions when a metric crosses a threshold (alert, auto-scale, etc.)
DASHBOARDS → visualize metrics/logs in charts
EVENTS (EventBridge) → react to changes/events in your AWS environment
```

## Metrics and alarms

```text
→ Monitor key metrics: EC2 CPU, ALB request count/latency, RDS connections, Lambda
  errors/duration, custom application metrics
→ ALARMS act on thresholds:
  - "CPU > 80% for 5 min" → trigger auto scaling (add instances) OR notify the team
  - "error rate > 1%" → send an alert (SNS → email/Slack/PagerDuty)
  - "healthy hosts < 2" → page on-call
→ Alarms enable AUTOMATED responses and ALERTING (know about problems quickly)
```

## Logs and observability

```text
✓ Centralize LOGS from EC2, Lambda, containers → search and analyze them
✓ Logs Insights → query logs (find errors, patterns, debug issues)
✓ Combine metrics + logs + traces (X-Ray) for full OBSERVABILITY
✓ Set RETENTION; create metric filters (turn log patterns into metrics/alarms)
```

## Why it matters

Understanding CloudWatch is valuable because **monitoring and observability are essential for operating systems in production**, and CloudWatch is AWS's core service for this, so it's important practical knowledge.

Operating production systems requires knowing their health, detecting problems, and responding quickly — you can't manage what you can't see.

CloudWatch provides the foundation: **metrics** (numerical data like CPU, latency, and request counts, published automatically by AWS services and customizable for application-specific measurements) for understanding performance and health, **logs** (centralized collection and search of application and system logs) for diagnosing issues, **alarms** for acting on problems, and dashboards for visualization.

Understanding **alarms** is particularly important: they enable both **automated responses** (e.g. high CPU triggering auto scaling to add capacity) and **alerting** (notifying the team via SNS to email/Slack/PagerDuty when error rates spike or resources become unhealthy) — so problems trigger action quickly rather than going unnoticed, which is essential for reliability and incident response.

Understanding **log centralization and analysis** (collecting logs from EC2, Lambda, and containers, querying them with Logs Insights to find errors and debug) is necessary for diagnosing issues in distributed systems, and combining metrics, logs, and traces provides full **observability**.

Since operating production systems reliably requires monitoring (knowing health, detecting and responding to problems) and CloudWatch is AWS's central observability service (metrics, logs, alarms, dashboards), and since alarms enable critical automated responses and alerting, understanding CloudWatch is valuable, practically-important AWS knowledge for running systems in production — essential observability that makes AWS systems operable, debuggable, and reliable, and a key competency for anyone responsible for production AWS workloads.