Performance Engineering & Optimization Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Performance Trade-offs & Optimization Strategy
Deciding what to optimize, how far to take it, and at what cost to other qualities such as readability, reliability, engineering time, or a domain axis like accuracy, security, or freshness. Covers prioritizing the optimization with the highest payoff among competing candidates, judging when a system is fast enough to stop, sequencing optimization work with a safe rollout and rollback plan, and communicating performance-versus-other-quality trade-offs to stakeholders and executives. Cost is one input among several in this judgment, not the subject itself: techniques to actually reduce cloud or resource spend belong to performance-cost-optimization-and-resource-efficiency. This topic is distinct from profiling and root-cause diagnosis of a specific slow system (performance-optimization-and-bottleneck-analysis), the mechanics of a caching layer (caching-strategies-and-in-memory-storage), infrastructure capacity and scaling architecture (infrastructure-scaling-and-capacity-planning), tail-latency measurement and SLA-driven system design (latency-analysis-and-optimization), and time-pressured production incidents (performance-optimization-and-incident-response). Emphasizes optimization as a strategic engineering judgment call, not a reflex, and not a hands-on coding exercise.
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 across client, CDN, application, and in-memory store tiers, including platform-specific caching on mobile (in-memory and disk caches on iOS and Android, secure on-device token storage) and in the browser (localStorage, IndexedDB, Service Workers). Covers eviction policies, TTLs, write-through vs write-back, warming, and invalidation, plus cache stampede and thundering-herd mitigation when a hot key expires under load. Also covers using an in-memory store like Redis directly rather than only as a cache: distributed locks, counters, rate limiters, and leaderboards. Includes distributed and sharded cache architecture (replication, failover, sizing, migration) and the operational side of running one: health monitoring, alerting, and incident runbooks, plus the security and privacy risks caching introduces, such as cache poisoning and caching PII across shared tenants. Emphasizes hit-rate reasoning and the staleness/consistency trade-offs caching introduces.