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Monitor a web app in three connected layers: instrument the application, probe it from outside, and route meaningful failures to people who can act. Start with latency, traffic, errors, and saturation—the four golden signals—then add traces, logs, uptime checks, and synthetic user journeys. This tutorial shows how to design the measurements, dashboards, alerts, checks, and operating process without relying on arbitrary “industry” thresholds.
The monitoring model: inside, outside, and response
A useful monitoring system answers three questions:
- What is the application doing? Metrics, traces, and logs describe requests and dependencies from inside the system.
- Can a user reach and complete the service? Uptime checks and synthetic monitors test endpoints and journeys from outside.
- What should someone do now? Dashboards and alerts connect a failure to an owner, evidence, and a response procedure.
No single signal is sufficient. A healthy process can still serve an unusable page; an external check can fail while internal metrics look normal because of a DNS, routing, or regional problem.
Start with the four golden signals
Google SRE describes the baseline plainly: “The four golden signals of monitoring are latency, traffic, errors, and saturation.” If you can measure only four categories for a user-facing system, begin here.
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Latency
Latency is how long a request or user operation takes. Track a distribution rather than only an average. A p95 value means 95 percent of observations are at or below that duration; the slowest five percent can still represent many users. Separate server time from total browser journey time when possible, and break latency down by route, method, status, region, and dependency.
Traffic
Traffic is incoming request rate. Record requests per second (or another consistent interval), and segment by endpoint, status class, tenant, region, and authenticated versus anonymous traffic where those dimensions are safe and useful. A sudden drop can indicate an outage, routing error, or broken client release; a sudden increase can cause saturation before an error spike appears.
Errors
Measure both technical failures and user-visible failures. A common server-error rate is 5xx responses divided by incoming requests, but also count timeouts, dependency failures, rejected requests, failed background jobs, and unsuccessful business operations such as checkout or login. Keep numerator and denominator definitions explicit so a dashboard cannot make a small sample look catastrophic or hide failures in a large volume.
Saturation
Saturation indicates how close a resource is to its usable capacity. CPU utilization is one example; memory pressure, database connections, queue depth, disk space, worker concurrency, rate limits, and container throttling may matter more for a particular service. Capacity can be exhausted even when CPU is low, so choose resource measures that explain the actual bottleneck.
Instrument the application for diagnosis
Metrics summarize behavior over time, traces follow one request across services, and logs record detailed events. Use all three with shared identifiers and deployment context.
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Metrics design
- Define counters for requests, responses by status class, retries, queue jobs, and business outcomes.
- Define gauges for current queue depth, in-flight work, connections, and resource pressure.
- Define histograms or equivalent distributions for request and dependency latency.
- Attach dimensions such as service, route template, method, status class, region, and version. Avoid unbounded labels such as raw user IDs or full URLs with query strings.
OpenTelemetry is one documented route for application-generated metrics and traces. Whatever SDK you choose, standardize names and units across services, propagate trace context through HTTP and messaging, and record the deployment version so a regression can be correlated with a release.
Tracing
Create a trace for an incoming request and spans for database calls, HTTP dependencies, queues, and external APIs. Capture duration, status, retry count, and a sanitized error description. Sampling reduces volume, but preserve traces for errors and unusually slow operations so an alert can lead to a concrete dependency or code path.
Logging
Use structured logs with timestamps, severity, service, environment, route, request or trace ID, status, and a concise message. Redact credentials, access tokens, payment data, and personal information. Logs should explain an event; they should not be the only place an outage is visible. Emit a metric for conditions that require an alert and retain the detailed log for investigation.
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Put a compact service overview above deep diagnostic panels. The first row should show:
- Request rate (traffic).
- Error rate, including 5xx and important application failures.
- Latency percentiles such as p50, p95, and p99.
- Saturation for the resources that limit throughput.
- Uptime and synthetic-check status.
Add panels for dependency latency and errors, queue age, database health, deployment markers, and representative business transactions. Every panel needs a time range, unit, aggregation, and labels that a responder understands. Link from the dashboard to logs and traces filtered for the selected service, route, region, and time window.
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Uptime checks: the basic external test
An uptime check periodically queries an HTTP, HTTPS, or TCP endpoint and records reachability, response time, and (for HTTP) status or content conditions. Use checks for:
- A public health endpoint that verifies the process and essential dependencies.
- Critical API endpoints with the required method, headers, and response validation.
- DNS, TLS, and certificate expiry conditions where your provider supports them.
- Private endpoints when the monitoring service can reach the required network.
Keep a health endpoint intentionally small and define what “healthy” means. A process-only response may stay green while the database is unusable; a deep dependency check can create load or make every dependency outage look like an application outage. Document the choice and test it during failure drills.
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Synthetic monitoring for real user paths
Synthetic monitors issue scripted requests or simulate a browser journey and record success, failure, and latency. Use them for flows that a single endpoint cannot prove:
- Open the sign-in page.
- Submit a test account or a safe authentication flow.
- Navigate to a key page or API operation.
- Perform a non-destructive action, such as adding a test item.
- Assert the expected text, element, status, or business result.
Browser canaries can retain load-time data and screenshots, which helps distinguish a server response from a broken client render. Use dedicated test data, isolate destructive actions, and clean up state. Run from locations that represent your users, and treat a single probe failure as a signal to corroborate rather than automatically declaring a global outage.
Design alerts that lead to action
Alert on a meaningful user-facing failure or a service objective at risk—not every unusual measurement. Thresholds depend on your baseline, traffic pattern, architecture, and service-level objective, so there is no universal latency or error number.
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Alert ingredients
- Condition: the exact query, percentile, status, check, or objective being evaluated.
- Duration: how long the condition must persist, with a deliberate treatment of missing data.
- Scope: service, route, region, version, and environment.
- Severity: what requires immediate response versus a ticket or review.
- Owner and runbook: the team, escalation path, and first diagnostic steps.
- Evidence: links to the relevant chart, logs, traces, deployment, and affected check.
Useful alert patterns
- High error rate for a critical route over a sustained window.
- p95 latency above the objective while traffic is non-trivial.
- A synthetic journey failing from one or more probe locations.
- Queue age, database connections, memory pressure, or another bottleneck approaching its safe capacity.
- An objective burn rate that indicates the service will miss its availability or latency target if the current condition continues.
Use separate warning and critical policies only when they produce different actions. Group related symptoms, deduplicate notifications, and provide a maintenance or silencing mechanism for planned work. After every alert, ask whether the recipient could decide what to do from the notification alone.
A practical setup sequence
- Map the service. List user journeys, public endpoints, dependencies, owners, regions, and failure consequences.
- Define objectives. Choose availability, latency, and important transaction goals for each critical journey. Record the measurement window and exclusions.
- Instrument. Add request, error, latency, traffic, and saturation telemetry; propagate trace IDs; structure logs.
- Create the overview dashboard. Put the four golden signals, check status, deployments, and dependency panels in one view.
- Add endpoint checks. Validate status, timing, TLS, and a safe response condition from appropriate locations.
- Add synthetic journeys. Script the smallest valuable flows, use test identities, and capture screenshots or traces for failures.
- Write alert policies. Tie each policy to an objective or explicit failure condition, duration, owner, severity, and runbook.
- Test failure handling. Trigger controlled errors, verify notification delivery, check that links work, and practice rollback or mitigation.
- Review noise and coverage. Remove alerts that never change an action, add missing journeys, and revise thresholds from observed behavior.
Choosing a monitoring approach
| Decision axis | Managed monitoring service | Self-operated metrics stack |
|---|---|---|
| Operations | Provider runs the monitoring control plane; you configure it. | Your team operates storage, upgrades, access, and availability. |
| Instrumentation | Check language support, OpenTelemetry integration, and existing exporters. | You control integrations but own compatibility and maintenance. |
| Checks | Often includes HTTP/TCP uptime checks and browser synthetics; verify private-network and probe-region support. | Endpoint probing is straightforward; browser journeys generally require additional workers and tooling. |
| Diagnosis | Look for linked dashboards, logs, traces, screenshots, and alert context. | Assemble and maintain correlation across components. |
| Scale and cost | Review telemetry volume, retention, check frequency, probe count, quotas, and current rates. | Budget compute, storage, retention, egress, and operator time. |
| Notifications | Confirm integrations, routing, deduplication, and silencing. | A Prometheus-style design commonly uses a separate Alertmanager component for notification routing and silencing. |
Google Cloud documents dashboards, SLO monitoring, synthetic monitors, and uptime checks; AWS CloudWatch Synthetics documents canaries for URLs, APIs, website content, and browser-based tests. Prometheus is a relevant self-operated metrics system. These are examples of operating models, not a universal ranking. Confirm current regional availability, quotas, supported runtimes, retention, and pricing before committing.
Performance, reliability, and cost controls
- Control cardinality: route templates are safer than raw paths; never label metrics with unbounded user or request values.
- Control sampling: retain error and slow traces while sampling routine traffic according to investigative needs.
- Control check load: use the least frequent interval that detects failures within your objective, and make synthetic actions idempotent.
- Separate probe failure from service failure: compare locations, inspect DNS and certificates, and corroborate with internal telemetry.
- Protect test accounts: restrict permissions, rotate credentials, and prevent synthetic transactions from reaching real customers or billing.
- Plan retention: keep high-volume metrics for trend analysis and shorter, searchable windows for detailed logs and traces unless compliance requires otherwise.
Troubleshooting common monitoring failures
The dashboard is green, but users report an outage
Check whether you monitor the affected region, route, authentication state, and browser path. Add a synthetic journey or business metric, and verify that the health endpoint tests the dependencies users need.
An alert fires during every traffic spike
Inspect whether the rule uses a fixed count instead of a rate or percentage, whether the denominator is too small, and whether saturation is the real leading indicator. Rework the condition against normal peak traffic and a documented objective.
A synthetic check fails intermittently
Compare probe locations and failure timestamps with DNS, TLS, dependency, and browser-console data. Add explicit waits for asynchronous content, stable test data, and assertions that distinguish a transient third-party widget from a failed user action.
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Latency looks normal while requests time out
Verify histogram aggregation, timeout accounting, and whether timed-out requests are omitted from the latency metric. Track client-observed duration and timeout counts separately from completed server responses.
Logs cannot be connected to traces
Ensure the trace context is propagated across every HTTP, queue, and worker boundary, and that the logger emits the trace and span identifiers in structured fields. Check sampling and clock synchronization.
Monitoring costs grow unexpectedly
Find high-cardinality labels, verbose logs, excessive trace retention, overly frequent checks, and unnecessary browser runs. Apply sampling and retention controls, then recalculate volume and current provider rates before changing plans.
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FAQ
Should every endpoint have an alert?
No. Prioritize user journeys and endpoints whose failure changes customer or operational outcomes. Use dashboards for lower-risk detail and alerts for conditions that require action.
How often should synthetic checks run?
Choose an interval that detects failure within the journey’s objective while keeping test load and cost acceptable. Reassess it after observing real incident detection and noise.
Can monitoring be private?
Sometimes. Private-endpoint support depends on the selected service, network integration, probe location, and regional availability. Verify those capabilities before designing around them.
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