Performance Engineering & Optimization Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Memory Management & Garbage Collection
Managing memory as a performance resource, in both managed-runtime and manual-allocation contexts. Covers allocation patterns, garbage-collection behavior and tuning, pauses and fragmentation, and detecting and fixing memory and resource leaks. Emphasizes the effect of memory pressure on throughput, latency, and stability.
Performance Profiling & Bottleneck Analysis
Techniques for measuring where time and resources go in a running system and isolating the dominant bottleneck. Covers CPU/memory/allocation profiling, flame graphs, sampling vs instrumentation, hotspot identification, and distinguishing symptom from root cause. Emphasizes forming a measurement-first hypothesis before optimizing rather than guessing.
Algorithmic Complexity & Code-Level Optimization
Reasoning about the time and space complexity of code and applying local optimizations that materially change performance. Covers Big-O analysis and performance modeling, data-structure selection, hot-loop and allocation reduction, and knowing when an algorithmic change beats micro-optimization. Emphasizes performance-aware coding grounded in complexity rather than premature tuning.
Performance Under Resource Constraints
Optimizing in environments with hard limits on compute, memory, battery, or bandwidth. Covers mobile and embedded performance, energy and power efficiency, working within tight memory and CPU envelopes, and platform-specific optimization and constraints. Emphasizes the trade-offs unique to constrained targets rather than server-class assumptions.