Performance Engineering & Optimization Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Scalability & Capacity Planning
Analyzing how a system's performance changes as load grows and planning the resources to keep it healthy. Covers horizontal vs vertical scaling, throughput vs latency under load, headroom and saturation, load modeling, and forecasting capacity for expected traffic. Includes identifying the scaling bottleneck that will bind first as demand increases.
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.
Performance Trade-offs & Optimization Strategy
Deciding what to optimize, how far, and at what cost to other qualities. Covers performance vs readability/reliability/cost trade-offs, prioritizing the optimization with the highest payoff, knowing when a system is fast enough, and sequencing optimization work. Emphasizes optimization as a strategic engineering judgment rather than a reflex.
Caching Strategies & In-Memory Optimization
Designing cache layers to cut redundant work and speed up reads, and the correctness costs that come with them. Covers cache placement (client/CDN/application/in-memory store), eviction policies, TTLs, write-through vs write-back, warming, and invalidation. Emphasizes hit-rate reasoning and the staleness/consistency trade-offs caching introduces.
Performance Troubleshooting & Incident Response
Diagnosing and resolving performance problems in production, often under time pressure. Covers latency and slowdown investigation, reproducing and narrowing performance regressions, operational readiness for performance incidents, and restoring healthy behavior while preserving reliability. Emphasizes systematic debugging of live systems over offline experimentation.
Performance Monitoring & Observability
Instrumenting systems so performance is continuously measured and regressions are visible. Covers performance metrics and SLIs, dashboards and time-series signals, tracing, alerting on latency and saturation, and using telemetry to guide tuning. Focuses on the ongoing measurement loop rather than one-off profiling.
Performance Cost Optimization & Resource Efficiency
Optimizing for the money and resources a given level of performance consumes, not just raw speed. Covers cost-per-request reasoning, right-sizing compute and memory, efficiency of resource utilization, and trading performance against spend. Emphasizes treating cost and resource efficiency as first-class performance objectives.
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.