App Hang & UI Freeze Monitoring Setup for iOS and Android

Imagine: a user opens an app, scrolls through the feed—and the screen freezes for half a second. Touches don't register, animation stutters. After a couple of seconds, everything returns to normal, but the impression lingers. The repeat session rate drops, while Crashlytics remains silent—no formal crash. This is a classic App Hang (iOS) or UI Freeze (Android) scenario. We help configure monitoring for such hangs, identify bottlenecks, and eliminate them. Budget savings from reduced freezes: up to 30% reduction in main thread load.

Why Standard Crash Reports Miss Hangs

App Hangs and UI Freezes don't throw exceptions—the app doesn't crash, but stops responding to user actions. The iOS Watchdog terminates the process only after 4–8 seconds of hanging, and Android ANR after 5 seconds. Short freezes of 200–500 ms go undetected but ruin the user experience. Standard crash reporters (Crashlytics, Firebase Crashlytics) only capture exceptions and signals, so separate tracking is needed to detect hangs. We use three approaches: watchdog thread, frame metrics, and task duration tracking.

What Hang Threshold to Set for Production

We recommend 250 ms for iOS and 200 ms for Android. A lower threshold (100 ms) will generate noise from random delays, while a higher threshold (500 ms) will miss noticeable freezes. The threshold can be adjusted per screen if needed—for example, complex lists can use 300 ms. Sentry and Datadog allow per-threshold configuration. The setup cost pays off through improved user experience.

Detection Tools

Sentry detects hangs in real-time, while MetricKit has a 24-hour delay. Sentry identifies freezes 100 times faster, which is critical for hotfixes. MetricKit Documentation confirms the up to 24-hour delay.

iOS — Sentry App Hang Detection

SentrySDK.start { options in
    options.dsn = "https://[email protected]/project"
    options.enableAppHangTracking = true
    options.appHangTimeoutInterval = 0.25 // 250ms
}

Sentry launches a watchdog thread that pings the main thread every 100 ms. If no response within appHangTimeoutInterval, it captures the stack via backtrace_thread and submits it as an Issue.

Android — FrameMetricsAggregator

val frameMetrics = FrameMetricsAggregator(FrameMetricsAggregator.JANK_DATA)
frameMetrics.add(activity)
// Later:
val metrics = frameMetrics.metrics
metrics?.get(FrameMetricsAggregator.JANK_INDEX)?.let { jankArray ->
    val jankyFrames = jankArray.size
}

FrameMetricsAggregator is suitable for production: lightweight, provides janky frame statistics. For deep local diagnostics, use Perfetto.

Configuring Monitoring in Datadog

RUM.enable(with: RUM.Configuration(
    applicationID: "your-rum-app-id",
    longTaskThreshold: 0.1 // 100ms
))

In the Datadog dashboard, build a widget:

count:rum.long_task{env:production,service:ios-app}
group_by: @view.name
visualize_as: top_list

This reveals which screens have the most hangs.

Where Hangs Come From

On iOS, the most common source of short freezes is synchronous calls on the main thread in response to a UI event:

func tableView(_ tableView: UITableView, cellForRowAt indexPath: IndexPath) -> UITableViewCell {
    let cell = tableView.dequeueReusableCell(withIdentifier: "ProductCell", for: indexPath) as! ProductCell
    cell.imageView?.image = UIImage(data: product.imageData)
    return cell
}

UIImage(data:) synchronously decodes JPEG/PNG. On an iPhone SE with a 1900x1200 image, this blocks the main thread for 30–80 ms.

On Android, a main culprit on Compose screens is unnecessary recomposition in LazyColumn:

@Composable
fun ProductList(products: List<Product>) {
    LazyColumn {
        items(products) { product ->
            ProductCard(product)
        }
    }
}

Replacing List<Product> with ImmutableList<Product> (from kotlinx.collections.immutable) or using the @Stable annotation eliminates unnecessary recompositions.

What's Included in Our Work

• Integration of Sentry with enableAppHangTracking and a 250 ms threshold
• Configuration of a MetricKit subscriber for daily diagnostics
• Enabling Datadog RUM Long Task tracking with a 100 ms threshold
• On Android, setting up FrameMetricsAggregator for key Activities
• Building a dashboard of screens with the highest number of Long Tasks
• Analysis of stack traces to identify specific culprits
• A written report with optimization recommendations

Our Experience

We have extensive experience with mobile projects. We've set up monitoring for 20+ apps with audiences of 100k+ users. We use modern approaches: MetricKit, Sentry, Datadog, Apple App Hang, Android ANR.

Timeline

Basic setup via Sentry and Datadog: 4–8 hours. Full diagnostics with MetricKit and custom per-screen metrics: 1–2 days. Pricing is determined after analysis.

Contact us to select the best tools for your project. Request monitoring setup—and get a performance guarantee.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.