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Mobile Performance and Data Structures Questions

Understanding how data structure and algorithm choices affect mobile applications and constrained environments. Candidates should discuss memory usage CPU and battery implications of different structures, garbage collection pressure and allocation patterns, and strategies to reduce memory and compute overhead on mobile devices. This includes preferring memory efficient structures avoiding unnecessary copies reducing allocation churn using pooling or compact representations and reasoning about algorithmic complexity in the context of limited RAM and CPU and thermal and battery constraints.

HardTechnical
29 practiced
Discuss the trade-offs of allocating large byte buffers in native memory (via JNI/NDK or direct ByteBuffers) to reduce Java heap pressure on Android. Cover performance advantages, the risk of manual memory leaks, interaction with GC and heap reporting, debugging challenges, and safety considerations (bounds, threading, lifecycle) for using native buffers to hold images or large payloads.
MediumTechnical
34 practiced
Coding (Swift): Implement a simple, thread-safe object pool for reusable Data/ByteBuffer objects used by network code. The pool should provide acquire() -> DataBuffer and release(buffer). Ensure the pool is bounded, prevents leaks (doesn't retain buffers when the app is low on memory), and minimizes contention. Provide Swift code and explain how you avoid retain cycles and over-retaining objects.
HardTechnical
50 practiced
Discuss the performance and concurrency trade-offs between using immutable value objects and using mutable, pooled objects in a mobile application. Address allocation overhead vs copying cost, thread-safety implications, cache locality, GC pressure, and provide practical guidelines and heuristics for when to prefer immutability versus mutation-and-reuse in a mobile codebase.
MediumSystem Design
29 practiced
Compare using SQLite (disk-backed, paged) versus an in-memory store (e.g., HashMap or in-memory Realm) for caching up to ~100MB user data on mobile devices. Analyze startup latency, memory pressure, query performance, battery/IO costs, durability, and recommended hybrid strategies for low-end devices.
EasyTechnical
29 practiced
Describe the differences between Automatic Reference Counting (ARC) used on iOS (Swift/Objective-C) and Garbage Collection designs (ART) used on Android. Focus on allocation costs, determinism of deallocation, pause characteristics, common leak types for each model, and how these differences should influence allocation patterns and memory-leak detection strategies in mobile apps.

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