performance

Understanding query execution plans is essential for optimizing slow queries. Rails provides explain method on ActiveRecord relations to show PostgreSQL's query planner output. I look for sequential scans on large tables (indicating missing indexes),

I use Django's cache framework to avoid expensive queries and computations. The cache_page decorator caches entire view responses by URL. For more control, I use the low-level cache API to store query results or computed values. I set reasonable timeo

Django Silk profiles SQL queries, HTTP requests, and Python code. I install it in development to identify bottlenecks. It shows query counts, execution times, and duplicate queries per request. The web UI visualizes performance data. I use @silk_profi

Loading everything on initial page render slows perceived performance. Lazy-loaded Turbo Frames defer expensive content until it's needed, keeping initial page loads fast. Setting loading='lazy' with a src attribute tells Turbo to fetch content when t

I’ve had endpoints where the same lookups get repeated across helpers and partials, and I didn’t want to pay for query caching on every request. In Scoped query cache, I wrap only the expensive report build in ActiveRecord::Base.cache, so the cache li

Materialized views store query results physically for fast access. I use them for expensive aggregations, complex joins, reporting queries. Unlike views, materialized views cache data—need manual refresh. REFRESH MATERIALIZED VIEW updates cached data.

If you have to hit multiple APIs, you can cut tail latency by running calls concurrently. Keep it bounded and use timeouts. Rails itself is thread-safe for reads; be careful with DB connections and use with_connection for threaded work.

For read-heavy endpoints, clients often fetch the same resource repeatedly (profile data, settings, a snip page) even when it hasn’t changed. ETags let the client send If-None-Match and the server respond with 304 Not Modified, saving bandwidth and CP

Observability provides insight into database health and performance. I monitor key metrics—queries per second, connection count, cache hit ratio. Slow query logs identify performance problems. Query latency percentiles show user experience. Lock wait

React Suspense handles async operations declaratively, showing fallback UI while data loads. With frameworks like Relay or React Query's experimental Suspense mode, components throw promises when data isn't ready, triggering the nearest Suspense bound

Performance optimization reduces load times and improves user experience. I use code splitting to break bundles into smaller chunks loaded on demand. React's lazy() and Suspense enable component-level code splitting. Dynamic import() loads modules asy

EXPLAIN reveals database execution plans. I use EXPLAIN ANALYZE for actual runtime statistics. Understanding plan nodes—Seq Scan, Index Scan, Nested Loop, Hash Join—guides optimization. Cost estimates predict query expense. Rows estimates show expecte