Frontend Observability: Telemetry, Real User Monitoring, and Dashboards

Introduction

Frontend observability is about understanding how users experience your application in the real world. By instrumenting telemetry, capturing Real User Monitoring (RUM) data, and building informative dashboards, you can diagnose performance bottlenecks, errors, and user friction faster. This guide lays out practical concepts and a clear path to implement frontend observability in modern web apps.

What is frontend observability?

Observability answers questions you can’t deduce from a single metric or log. For the frontend, this means collecting three core signal types:

• Telemetry (traces, metrics, and events) from user sessions and app code
• Real User Monitoring data (RUM) that reflects actual user experiences
• Dashboards and visualizations that turn raw signals into actionable insights

With these in place, you can spot slow interactions, failing UI paths, and degraded experiences, not just generic performance numbers.

Telemetry: instrumentation and signals

Telemetry is the backbone of observability. In the frontend, focus on these signals:

• Traces: capture end-to-end flows, such as page load, route changes, and critical user interactions. Use spans to correlate network calls, async work, and rendering.
• Metrics: collect performance metrics (LCP, FID, CLS, TTI, CLS stability), resource timing (load times for scripts, images), and custom business metrics (time-to-first-meaningful-paint for key actions).
• Events: record user interactions (clicks, form submissions), errors, and feature flags. These events enrich context for post hoc analysis.

Best practices:

• Instrument at critical boundaries: app startup, route transitions, key user journeys, and error boundaries.
• Correlate signals with identifiers (request IDs, session IDs) to tie traces, metrics, and events together.
• Use a standardized library or SDK (e.g., OpenTelemetry) to ensure consistency across apps.

Real User Monitoring (RUM)

RUM captures data from real users, complementing synthetic tests. Key considerations:

• Page views and session data: capture timestamped page views, dwell time, and navigation paths.
• Interaction timing: measure how long users wait before meaningful interactions and how long actions take to complete.
• Errors and failures: automatically capture frontend exceptions, unhandled rejections, and failed network requests with context (URL, user agent, app state).
• Privacy and consent: respect user privacy, minimize personally identifiable information, and implement consent controls where required.
• Sampling: balance coverage with cost by sampling a subset of sessions; ensure critical user journeys are represented.

RUM feeds your dashboards with real-world latency distributions, error rates, and journey insights. Pair it with synthetic monitors to cover known scenarios and regressions.

Dashboards: turning signals into insights

Dashboards should answer practical questions:

• How is overall user experience across cohorts? Track latency distributions (percentiles), error rate, and successful interaction rates.
• Where do users encounter issues? Identify top routes, components, or network calls contributing to delays or failures.
• Are performance improvements sticking? Compare pre/post changes with consistent baselines and SLOs.
• How do user journeys perform end-to-end? Build funnels from landing to conversion to reveal drop-offs and slow steps.

Recommended dashboard layouts:

• Global latency and error overview: percentiles (p50, p75, p95), error rate, and slowest routes.
• Route and page performance: breakdown by page, route, or component, with drill-down capability.
• Resource and network health: slow assets, failed requests, and cache effectiveness.
• RUM journey maps: visualize common user paths and where they slow down.
• SLOs and health: track targets for key user journeys and performance promises.

Architecture and implementation patterns

A practical frontend observability stack typically includes:

• Instrumentation layer:
  • OpenTelemetry in the browser to collect traces, metrics, and events
  • Custom instrumentation for business events and UI interactions
• Exporters and backend:
  • OTLP exporters to a collector or backend (Jaeger, Tempo, Grafana Cloud, or a custom backend)
  • Logs and metrics shipping to a centralized backend
• Data consolidation:
  • A backend capable of merging traces, metrics, and events
  • A data warehouse or time-series store for dashboards
• Visualization:
  • Grafana dashboards or other BI tools to present metrics and traces
  • Alerting rules tied to SLOs and thresholds

Implementation tips:

• Start small: instrument critical screens and route transitions, then broaden scope.
• Use a single source of truth for identifiers (trace IDs, session IDs) to connect telemetry, RUM, and events.
• Plan for privacy: redact or mask sensitive data and implement data retention controls.

Getting started: a practical plan

• Step 1: Choose instrumentation
  • Adopt OpenTelemetry JS for traces, metrics, and events
  • Define a minimal set of business-critical metrics and user interactions
• Step 2: Instrument your app
  • Add automatic page-load and route-change traces
  • Instrument key user actions and error boundaries
  • Emit custom events for business milestones (e.g., checkout started, feature flag toggled)
• Step 3: Set up exporters
  • Send data to a central collector or backend
  • Ensure network policies and privacy considerations are in place
• Step 4: Enable RUM
  • Integrate RUM collection for real user data
  • Implement sampling and consent flows
• Step 5: Build dashboards
  • Create global health dashboards with latency and error metrics
  • Build journey dashboards to visualize user flows and bottlenecks
  • Establish SLO-based dashboards and alerting
• Step 6: Iterate
  • Review dashboards with teams, identify gaps, and expand instrumentation accordingly

Tooling options

• OpenTelemetry (browser): core instrumentation for traces, metrics, and events
• Tempo or Jaeger: trace backends
• Grafana or Grafana Cloud: dashboards and visualization
• Sentry, Rollbar, or similar: error monitoring and aggregation
• Synthetic monitoring: synthetic tests to cover critical journeys beyond RUM

Practical example: a lightweight frontend observability setup

• Instrumentation: add OpenTelemetry in the browser to capture page loads, route changes, click events, and API call timings
• Data flow: browser sends OTLP data to a local collector, which forwards traces and metrics to Tempo (traces) and a metrics backend
• Dashboards: Grafana dashboards show page-load latency distributions, top error-causing routes, and user journey funnels
• RUM: collect session-level data to monitor real user experiences and correlate with synthetic tests for regression checks

Best practices and pitfalls

• Avoid over-instrumentation: prioritize meaningful signals to control noise and cost
• Ensure privacy and compliance: minimize PII, implement consent, and respect retention policies
• Use sampling thoughtfully: ensure critical journeys and error signals are represented in the data
• Align with product goals: tie observability to user outcomes and business KPIs
• Plan for evolution: architecture and dashboards should adapt as the app grows

Conclusion

Frontend observability combines telemetry, Real User Monitoring, and dashboards to give you a clear, actionable view of how users experience your app. By instrumenting thoughtfully, collecting real-world data, and visualizing it effectively, you can diagnose issues faster, optimize performance, and deliver smoother experiences to your users.