Android Performance

This topic turns performance optimization from intuition into a verifiable workflow: define the metric, use traces to locate the bottleneck, then confirm the gain through benchmarks and production monitoring.

Learning Path

1. Cold start: from Zygote fork to first frame.
2. Rendering: View, RenderThread, HWUI, and SurfaceFlinger.
3. Memory: Bitmap, leaks, native heap, and OOM.
4. Tools: Perfetto, Systrace, and Macrobenchmark.
5. Special topics: audio, lists, stability, and production governance.

Core Articles

• Android startup optimization: from Zygote fork to first frame with Perfetto
• Android app startup optimization: metrics, paths, tools, and governance
• RecyclerView cache internals: four cache levels, reuse, and Prefetch
• Android Bitmap memory model: Java heap, native heap, and Hardware Bitmap
• Android RenderThread and HWUI: rendering pipeline, DisplayList, and jank analysis
• Android rendering and graphics stack: View, HWUI, and SurfaceFlinger
• Android Perfetto and Systrace: system-level performance analysis
• Android Perfetto tracing: ftrace, TrackEvent, and production-grade performance monitoring
• Android audio system: AudioFlinger, mixing policy, and AAudio low latency
• Android Macrobenchmark in practice: startup, scrolling, and performance regression testing

Runtime, Memory, and Graphics Performance

• Android ART garbage collection internals
• Android ART dex2oat: from DEX bytecode to OAT machine code with AOT/JIT compilation
• Android native memory analysis: from malloc_debug to heapprofd
• Android Vulkan rendering: from OpenGL ES migration to GPU driver tuning
• Android power management: from WakeLock misuse to Doze-mode engineering
• Android AlarmManager scheduling: Binder calls, AlarmManagerService, and exact wakeups under Doze
• Android DEX bytecode and MultiDex loading
• Android Media3 playback architecture: from ExoPlayer to MediaSession
• Android networking stack: from DNS to HTTP/3 QUIC
• Android font rendering: Typeface loading and Skia glyph rasterization

Performance Debugging Framework

• Define the metric first: P50, P90, P99, first frame, dropped frames, memory peak.
• Collect evidence: Perfetto trace, logs, ANR trace, heap dump, benchmark results.
• Break down the path: main thread, Binder, I/O, rendering, GC, scheduling.
• Verify the fix: local benchmarks, staged monitoring, and regression gates.

Next Step

If the performance issue is tied to UI state updates, continue with Jetpack Compose Deep Dives. If the bottleneck involves model inference, NPU scheduling, power, or thermal behavior, go to Android On-device AI. If the issue comes from builds, tests, or release pipelines, go to Mobile Engineering.