Performance & Profiling: Recomposition Optimization with Live Metrics | Kotlin Desktop #19 скачать в хорошем качестве

Performance & Profiling: Recomposition Optimization with Live Metrics | Kotlin Desktop #19

Learn Compose recomposition optimization in Kotlin Desktop! We build a Performance Lab — a product catalog with 200 items, search, sorting, and a real-time ticker. A toggle switches between Naive (5 anti-patterns) and Optimized (5 fixes) implementations. A MetricsPanel shows live recomposition counts, making the performance difference dramatically visible. What You'll Learn: @Immutable annotation to mark data classes as stable for Compose skip optimization derivedStateOf to wrap computed values that only recompute when their specific inputs change key() parameter in LazyColumn for identity-based diffing instead of positional matching remember for lambda callbacks to avoid creating new instances every recomposition remember(key) for caching formatted strings and computed values Isolating volatile state (tickers) into dedicated composables to prevent cascading recomposition SideEffect for tracking recomposition counts after every successful composition ConcurrentHashMap with AtomicInteger for thread-safe recomposition counting SegmentedButtonRow for clean mode toggling between Naive and Optimized implementations Real-time MetricsPanel with color-coded severity (green/orange/red) Timestamps: 0:00 - Introduction 0:15 - Preview: Performance Lab App 0:45 - Project Setup & Product Models 4:31 - Recomposition Tracker & Metrics Panel 8:57 - Naive Implementation (5 Anti-Patterns) 17:23 - Optimized Implementation (5 Fixes) 23:39 - AppContent + Main & Compile 28:34 - Live Demo: Naive vs Optimized 29:39 - Recap & Key Takeaways 30:39 - Thanks for Watching Key Takeaways: 1. @Immutable / @Stable — Annotate data classes so Compose can safely skip recomposition checks 2. derivedStateOf — Wrap computed values so they only recompute when their specific inputs change 3. key() in LazyColumn — Pass unique IDs for identity-based diffing instead of positional matching 4. Remember Lambdas — Extract callbacks with remember to avoid new instances every recomposition 5. Isolate Volatile State — Put tickers and counters in their own composable to prevent cascading recomposition Previous Lesson: Lesson 18: System Integration — System Toolbox with Clipboard, Commands & Notifications    / .  . Next Lesson: Lesson 20: (coming soon) Tech Stack: Kotlin 2.1, Compose Multiplatform 1.7.3, Material3 Material Icons Extended, Compose Runtime (@Immutable, derivedStateOf, SideEffect) Gradle 8.10, JVM 17+ Source Code: https://github.com/GoCelesteAI/kotlin... Taught by CelesteAI If you found this helpful, please like and subscribe for more Kotlin Desktop tutorials! #kotlin #compose #desktop #material3 #performance #recomposition #optimization #immutable #derivedstateof #lazycolumn #profiling #tutorial #programming Tags kotlin, compose multiplatform, compose desktop, material3, performance, recomposition, optimization, immutable, stable, derivedstateof, lazycolumn, key, remember, jetbrains compose, kotlin tutorial, desktop app, jvm, composable, performance lab, profiling Category: Science & Technology | Visibility: Public