A deep dive into architectural patterns using ActiveJob and Redis to solve LLM latency, moving from an 8-second wait to a sub-50ms cached response.
Read article →In this final part, we connect the dots. All frontend performance optimizations—caching, SSR, bundle splitting—are ultimately enabled or constrained by the backend. Learn how schema design, API ergonomics, and a full-stack mindset are the true foundation of a high-performance application.
Read article →Your app is only as fast as its JavaScript payload. This guide tackles bundle bloat from heavy SDKs and GraphQL clients, teaching practical patterns like dynamic imports and selective client loading to drastically cut down your JS size without hurting developer experience.
Read article →Go beyond client-side rendering. This guide demystifies Next.js data fetching: getStaticProps, getServerSideProps, and Incremental Static Regeneration (ISR). Learn how to balance blazing-fast loads and SEO with the hidden costs of server load and hydration.
Read article →Stop request waterfalls and N+1 problems at the source. This deep-dive covers advanced GraphQL data fetching patterns like fragments, query batching, cursor-based pagination, and real-time subscriptions to build truly performant clients.
Read article →Modern frontend performance bottlenecks often originate at the API layer. Instead of just identifying the problem, this post provides a blueprint for a solution, covering data fetching, caching, SSR, and more, kicking off a deep-dive series into building truly high-performance UIs.
Read article →Peak traffic was crushing our API—P95 latency at 450ms, dashboards taking 5 seconds to load, and users complaining of “slow mornings.” By introducing a Redis caching layer designed around real access patterns, not just generic key-value storage, we cut P95 latency in half, reduced DB CPU load from 85% to 45%, and eliminated the majority of timeouts. This is the story of how intentional caching turned a struggling system into one that scaled gracefully.
Read article →Effective caching strategies require intentional design to prevent memory waste and stale data issues. Implementation guidelines include evaluating query cost, frequency of access, data predictability, TTL settings, and scope management for optimal Redis performance.
Read article →Background job performance optimization requires systemic analysis beyond simple retries or thread allocation. Effective troubleshooting includes payload size reduction, external call isolation, internal profiling, batching strategies, and queue structure review.
Read article →SQL query optimization requires diagnostic thinking rather than checklist application. Effective performance tuning involves distinguishing between design issues and scaling problems through EXPLAIN analysis, indexing evaluation, filtering optimization, and concurrency assessment.
Read article →Scalable autocomplete functionality achieving sub-millisecond response times with millions of records employs trie data structures and advanced partitioning strategies in Ruby on Rails. Performance optimization techniques include memory management, database partitioning patterns, and efficient prefix-based search algorithms.
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