Telemetry

Telemetry means collecting meaningful frontend data from production so teams can understand what users are doing, how the app performs, where failures happen, and how resources are being used.

It is the foundation for monitoring, debugging, and optimization.

Telemetry = Structured production signals from the frontend

One-line idea: Telemetry = Collect structured frontend signals about users, performance, errors, resources, and business events to monitor and improve production systems.

Core Concepts

Why It Matters

Without proper telemetry, monitoring becomes guesswork.

Frontend issues often happen only in production:

• failed API calls
• slow pages
• JavaScript exceptions
• broken user flows
• high resource usage
• poor mobile experience

Telemetry gives teams evidence instead of assumptions.

No telemetry -> guessing
Good telemetry -> observe, debug, and improve confidently

Core Flow

Telemetry follows a simple flow.

Collect
↓
Capture -> Classify -> Analyze

The goal is to convert raw frontend activity into useful information.

Step	Meaning
Collect	Gather useful production data
Capture	Record raw frontend signals
Classify	Organize signals into useful categories
Analyze	Use the data for monitoring, debugging, and improvement

Practical Examples

Capture

Capture means recording raw signals from the frontend application while real users are using it.

Common user interaction signals:

• clicks
• scrolls
• form submissions
• page navigation
• browser events

Capture = Record what users actually do inside the product

Why it matters:

• identifies behavior patterns
• reveals friction points
• shows feature adoption
• helps understand real usage

Classify

Raw data is noisy.

Classification organizes data into useful buckets so teams can analyze it properly.

Common categories:

• performance data
• errors
• resource usage
• business events
• security signals

Classify = Turn random events into structured telemetry

Without classification, collected data becomes difficult to search, monitor, and act on.

Performance Metrics

Performance telemetry tells how fast and smooth the app feels.

Core metrics:

• Web Vitals
• LCP
• FID / INP
• CLS
• API response time
• feature execution time
• resource timing

Additional signals:

• paint timings
• network latency
• frame rate / FPS
• script execution time

Performance telemetry = Speed + smoothness + responsiveness signals

Why it matters:

Performance directly impacts user retention and conversion.

Resource Errors

Resource error telemetry helps detect failures before they become large-scale problems.

Common error types:

• 5XX server errors
• 4XX client errors
• API failures
• network failures
• JavaScript exceptions
• asset load failures

Error telemetry = What broke, where it broke, and why it may have broken

The goal is to identify failures with enough context to debug them quickly.

Resource Utilization

Frontend apps also consume device resources.

Important resource signals:

• CPU usage
• memory usage
• battery-heavy operations
• excessive rendering

Poor resource usage can cause:

• lag
• crashes
• slow interactions
• bad mobile experiences

Resource telemetry = How much device power the frontend consumes

Custom Events

Custom events are business-specific signals connected to product goals.

Examples:

• purchase completed
• add to cart
• signup success
• feature usage
• subscription upgrade

Custom Events = Product and business actions tracked as telemetry

These events connect technical telemetry with business outcomes.

Practical Questions Telemetry Answers

Good telemetry should help answer:

What are users doing?
Is the app fast?
Where is it failing?
Are resources overloaded?
Which features matter most?

Key insight:

If you do not collect the right signals,
you cannot monitor effectively,
debug accurately,
or improve confidently.

Microsoft Clarity

Microsoft Clarity is a free website analytics tool that helps understand what people are doing on a site beyond numbers.

It shows user behavior such as:

• where people click
• how far they scroll
• where they get stuck
• what frustrates them

Google Analytics tells what happened.
Microsoft Clarity helps understand why it happened.

Clarity Features

Feature	Meaning
Session Recordings	Watch real user sessions
Heatmaps	See clicked areas, ignored sections, and scroll depth
Rage Clicks	Users repeatedly click something because it is not working
Dead Clicks	Users click something expecting action, but nothing happens
Quick Backs	Users open a page and immediately return
Dashboard Insights	Sessions, pages per session, active time, scroll percentage, frustration signals

Clarity is useful when the website looks fine to developers but users still struggle.

Clarity Best For

Team	Use
Developers	Broken buttons, UX issues, frontend friction
Designers	Layout improvement, CTA placement, confusing interfaces
Marketers	Landing page optimization, conversion improvement, funnel drop-off analysis

Biggest advantage:

Clarity is free and useful for behavior + UX understanding.

Limitation:

It does not replace Sentry, PostHog, or Google Analytics.

Google Analytics

Google Analytics helps understand website or app performance from a business and traffic perspective.

It answers:

• how many users are visiting
• where users are coming from
• which marketing channels are working
• which pages are converting
• whether users are completing business goals

Google Analytics = Traffic + Engagement + Conversion + Business insights

Google Analytics Flow

Traffic Acquisition -> User Engagement -> Conversion Tracking -> Business Insights

Google Analytics is not mainly about watching sessions.

It measures growth, traffic quality, and conversion performance at scale.

Google Analytics Signals

Area	Examples
Traffic Acquisition	SEO, direct traffic, social media, paid ads, referrals, email
User Insights	New vs returning users, device, geography, browser, platform
Engagement Tracking	Sessions, active users, engagement rate, session duration, page views
Conversion Tracking	Purchases, signups, upgrades, form submissions, app installs
Event Tracking	Button clicks, scroll depth, purchases, video plays, feature usage

Modern GA4 is event-driven, so important product actions can be tracked as events.

Google Analytics vs Clarity

Feature	Google Analytics	Microsoft Clarity
Main focus	Traffic, growth, conversion	Behavior, UX, friction
Answers	What happened?	Why users struggled?
Best for	Business measurement	User behavior understanding
Examples	Channels, conversions, revenue	Clicks, scrolls, rage clicks

Together:

Google Analytics = Traffic + Conversion + Growth
Microsoft Clarity = Behavior + UX + Friction

Sentry

Sentry is used for error tracking, performance monitoring, and debugging.

It helps developers understand:

• what broke
• why it broke
• where it broke
• how to fix it faster

Sentry = Errors + Debugging + Performance

Core flow:

Error Occurs -> Capture Context -> Group Issues -> Alert Team -> Debug Faster

Sentry Captures

When the app crashes or throws an exception, Sentry can capture:

• error message
• stack trace
• file and line number
• browser and device info
• user context
• release version
• API/request details
• breadcrumbs

Sentry turns production failures into actionable debugging data.

Sentry Features

Feature	Purpose
Error Monitoring	Capture exceptions, crashes, and stack traces
Issue Grouping	Group similar errors into one root issue
Performance Monitoring	Track slow APIs, latency, rendering delays, and page slowness
Session Replay	See user actions, console logs, network calls, and error traces
Alerts	Notify teams through Slack, Email, Discord, Jira, or GitHub
Release Health	Detect crashes and performance regressions after deployments

Biggest strength:

Sentry reduces mean time to resolution.

Common limitation:

Sentry can become noisy if not configured properly.

Sentry vs Other Tools

Tool	Main Question
Google Analytics	Are users coming and converting?
Microsoft Clarity	Are users struggling?
Sentry	Is the product broken?

Sentry is a developer operations tool.

It helps teams move from reactive debugging to proactive reliability.

OpenTelemetry Collector Dashboard

The OpenTelemetry Collector dashboard shows the health of the telemetry pipeline.

It monitors the system responsible for receiving, processing, and exporting:

• logs
• metrics
• traces

OpenTelemetry Collector Dashboard = Control center for observability infrastructure

This is important because telemetry systems also need monitoring.

If the observability pipeline fails, teams lose visibility into production systems.

OpenTelemetry Dashboard Metrics

Area	Meaning
Spans Rate	Trace traffic
Metric Points Rate	Metrics ingestion
Log Records Rate	Log flow
Batch Metrics	Batch processing behavior
Batch Send Size Heatmap	Size of telemetry batches
Batch Metrics Processed	How efficiently telemetry is processed
Accepted / Refused Signals	Whether telemetry reaches observability backends

The dashboard helps answer:

Is telemetry entering correctly?
Is it being processed efficiently?
Is data being dropped or refused?
Is the observability pipeline healthy?

Locust Load Testing Dashboard

Locust is a performance testing tool used to simulate real users hitting a system at scale.

It helps test how APIs, websites, or distributed systems behave under traffic before real users expose bottlenecks.

Locust = Simulate traffic and observe system behavior under load

In the dashboard example, endpoints like cart, checkout, products, and recommendations are stress tested.

Locust Metrics

Important metrics include:

• total requests
• failures
• response times
• median response time
• 90th percentile response time
• 99th percentile response time
• requests per second
• current failure rate

Important insight:

Fast response time does not always mean a healthy system.
A system can fail instantly.

If failures are high even with low latency, possible causes include:

• backend or service misconfiguration
• broken routes
• dependency failures
• authentication issues
• proxy or infrastructure errors

Tool Comparison

Tool	Best For
Microsoft Clarity	UX behavior, heatmaps, rage clicks, session recordings
Google Analytics	Traffic, acquisition, engagement, conversions, growth
Sentry	Errors, crashes, stack traces, debugging, performance issues
OpenTelemetry Dashboard	Logs, metrics, traces, collector health, observability pipeline
Locust	Load testing, response times, request failures, traffic simulation

Telemetry Table

Area	Examples
User Interactions	Clicks, scrolls, forms, navigation, browser events
Performance Metrics	Web Vitals, API time, resource timing, FPS
Resource Errors	5XX, 4XX, API failures, network failures, JS exceptions
Resource Utilization	CPU, memory, battery-heavy work, excessive rendering
Custom Events	Purchases, add to cart, signup, feature usage, upgrades
Analysis Goal	Monitoring, debugging, optimization, business insight

Summary

Telemetry = Collect structured frontend signals about users, performance, errors, resources, and business events to monitor and improve production systems.

User behavior -> Clarity
Traffic and conversion -> Google Analytics
Errors and debugging -> Sentry
Logs, metrics, traces -> OpenTelemetry
Load and failures under traffic -> Locust

Collect the right signals first, then monitor and fix confidently.