Lesson 4.4 Video

1. Application health dashboards

We've built the individual pieces throughout this course: metrics, logs, alarms, traces, and service maps. This final lesson brings them together into application health dashboards for a unified view.

2. Dashboard design: hierarchy of information

Think of your dashboard like a hospital. Reception tells you in seconds whether someone is critical, stable, or fine: the top tier, overall system health with key business metrics, SLA compliance, and current incidents. If critical, the specialist on the ward is the mid-tier, service health, with error rates, latency percentiles, and throughput. For a full diagnosis, the consultant's test results are the bottom tier, detailed diagnostics, traces, logs, resource utilization, and dependency health. Most critical at the top, detail below.

3. The four golden signals

The four golden signals are like the gauges on a car dashboard that actually matter: speed, fuel level, the engine warning light for errors, and journey time for latency. You could have fifty other sensors, but these four tell you what counts. Cover them and you cover the vast majority of problems.

4. The RED method

The RED method identifies what needs action from three attributes. Rate, requests per second. Errors, error count and rate. Duration, response time. It's a focused subset of the golden signals for request-driven services. Add saturation to diagnose resource constraints.

5. Three data sources

A dashboard combines three sources. CloudWatch metrics for volume, error rate, and response time. X-Ray for the service map and per-service latency. CloudWatch Logs for recent errors and breakdowns. Each is a different lens; together they give the full picture.

6. CloudWatch metrics widgets

Each widget gets a metric query, and the three on screen cover the essentials. Request volume is a Sum of the load balancer's RequestCount. Error rate uses metric math: divide Lambda Errors by Invocations for the percentage of calls that fail. The third uses response-time percentiles, p50, p95, and p99, so a few slow requests don't hide behind the average. Together they cover traffic, errors, and latency, three golden signals.

7. X-Ray and logs widgets

For X-Ray widgets: the service map shows topology with health indicators, trace statistics give response time, error rate, and fault rate, and service latency compares services. For logs: a recent errors query, and an error count by type query for the most frequent errors.

8. Complete dashboard layout

Place numeric widgets across the top for total requests, error rate, p99 latency, and healthy hosts. The middle row has the X-Ray service map on the left, service response times on the right. The bottom row spans full width with recent errors. Most critical at the top, detail below.

9. Incident troubleshooting workflow

These seven steps work like triage in an emergency room: stabilize first, then scope, diagnose, treat, and verify recovery. Skipping steps causes mistakes. Identify indicators and error spikes, scope which services, correlate patterns, drill down to traces and logs, then diagnose, fix, and verify the service map turns green.

10. Scenario 1: sudden traffic spike

A common scenario is a sudden traffic spike. Volume jumps ten times, error rate hits twenty-five percent, latency climbs to three seconds, CPU at ninety-five percent. The dashboard tells the story: resources can't handle the load. Scale out, enable auto-scaling, add rate limiting and caching to reduce backend pressure.

11. Scenario 2: database bottleneck

Another scenario is a database bottleneck. Database CPU at ninety-five percent, connections nearly maxed, query latency at five seconds, app latency just above that. The database is the bottleneck, since app latency tracks query latency. Identify slow queries, add indexes, grow the connection pool, and scale the database.

12. Scenario 3: cascading failure

Finally, cascading failures. The service map tells the story: Service A is just starting to show errors, but faults deepen down the chain, B and C red, D completely failed. One small problem cascaded through the dependencies. Circuit breakers and timeouts stop the cascade, fallbacks limit the blast radius, then fix the root cause in Service A.

13. Dashboard best practices

Six recommended practices make a dashboard compelling. Business metrics first, then technical. Add context with links to runbooks. Use consistent time ranges and actionable links to detailed views. And maintain it: a stale dashboard is like a map from ten years ago, authoritative-looking, but the new motorway isn't on it, so it takes you somewhere you don't want to be.

14. Video summary and course completion

To recap: design using a hierarchy of information and the golden signals. Combine metrics, X-Ray traces, and logs into one view. Use the seven-step workflow to identify and resolve issues. You now have the full toolkit, CloudWatch, logs, alarms, X-Ray, and dashboards, to manage your AWS applications.

15. Let's practice!

Let's take a closer look at application health dashboards.

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