Tag: caching

Set Cache-Control, ETag, and Last-Modified headers to control browser and CDN caching for API responses and static assets

Implement a two-level cache combining in-memory L1 and Redis L2 for low-latency reads with cross-instance consistency

Configure Nginx as a caching reverse proxy to cache upstream HTTP responses with TTL zones, cache keys, and conditional purging.

Build a least-recently-used cache in Node.js with O(1) get and set operations using a Map-based doubly linked list

Invalidate Redis cache entries in Node.js with TTL expiry, explicit deletion, pattern-based clearing, and pub/sub-based distributed invalidation.

Build a Python decorator that caches function return values in Redis with configurable TTL, key generation, and cache invalidation

How to cache expensive computations and API responses using in-memory, LRU, and distributed caches across Python, JavaScript, and Java.

Cache API responses in Node.js with Redis using cache-aside and TTL patterns

Store data with TTL expiration in browser localStorage

How to keep caches consistent with databases using TTL, write-through, write-behind, and event-driven invalidation patterns.

Implement useful caching strategies for databases, APIs, and frontends using Redis, CDNs, and browser caches.

Cache LLM embeddings to reduce API calls and cost. Store embeddings with a content hash key and serve from cache on repeated inputs.

Load data into the cache on demand from the backing store. A caching pattern that gives the application full control over what and when to cache.

Strategies for keeping cached data fresh: TTL expiration, explicit invalidation, write-through, and event-driven cache eviction.

Prevent thundering herd cache misses with locks, single-flight, and early refresh strategies to protect the database from concurrent reloads.

Distribute static and live content through geographically dispersed edge servers to reduce latency, improve availability, and offload origin infrastructure.

Share objects to support large numbers of fine-grained objects efficiently. A structural design pattern for memory optimization.

Ensure each object is loaded only once per transaction by caching instances by their primary key, preventing duplicate in-memory representations of the same database row.

Precompute and store expensive query results in a read-optimized cache to avoid repeated costly aggregation or joins across large datasets.

Provide a surrogate or placeholder for another object to control access to it. A structural design pattern for access control, lazy loading, and logging.

How to implement a caching proxy that intercepts API calls and stores responses to reduce latency and avoid redundant network requests

A transparent cache layer that intercepts read requests, fetches from the data source on miss, and populates the cache automatically.

Proactively refresh cache entries before they expire to eliminate cache misses on hot keys and maintain consistent read latency.

Combine an L1 in-memory cache with an L2 distributed cache to reduce latency for hot keys while maintaining cache consistency across instances.

Write to cache synchronously and persist to the database asynchronously for high-throughput write workloads with eventual consistency.

Synchronously write to both cache and backing store so the cache always has the latest data without TTL-based invalidation.

Coalesce individual load requests into batched calls with per-request caching to prevent N+1 queries and redundant fetches.

Template for documenting cache eviction rules per cache layer: LRU, LFU, TTL, FIFO, random eviction. Includes policy selection matrix, per-layer configuration, memory limits, and monitoring rules with code examples.

Decision template for choosing cache strategies per use case: no-cache, cache-aside, read-through, write-through, write-back, and refresh-ahead. Includes decision matrix, TTL guidelines, invalidation rules, and code examples.

Runbook for warming caches after deployment, restart, or incident: identify hot keys, preload strategies, progressive warmup, health checks, and rollback procedures with code examples and automation scripts.

Template for defining CDN caching rules and edge behavior: cache keys, TTL by content type, query parameter handling, header forwarding, purge strategies, and origin shield configuration with code examples.

Optimize LLM costs in production. Covers model routing, prompt compression, caching, batch API, token management, semantic caching, prompt engineering for cost, monitoring, and budget control patterns for LLM applications.

Cache GraphQL responses at every layer: CDN, gateway, DataLoader, persisted queries, and client-side. Covers cache keys, invalidation, HTTP caching directives, and Apollo Client cache.

A decision guide comparing GraphQL and REST APIs: use cases, performance, caching, tooling, and migration strategies for engineering teams.

Implement in-memory, distributed, and hybrid caches at the application layer. Covers LRU caches, TTL caches, multi-tier strategies, cache sizing, thread safety, and production patterns for Python, Java, and Node.js.

Master cache invalidation strategies: TTL expiration, event-driven invalidation, versioned keys, tag-based purging, and write-through invalidation. Covers multi-tier invalidation, race conditions, and consistency patterns.

Design CDN caching for web applications and APIs. Covers edge caching, cache keys, cache headers, invalidation strategies, surrogate keys, and multi-CDN setups for global performance.

Master Redis caching with cache-aside, read-through, write-through, write-behind, and refresh-ahead patterns. Covers eviction policies, TTL tuning, serialization, and production operations.

A practical guide to caching strategies: browser caching, CDN edge caching, application caching with Redis, and database query caching. Learn when to use each and how to avoid cache invalidation nightmares.

Run Redis in production. Covers persistence (RDB, AOF), clustering, sentinel for HA, failover handling, memory management, eviction policies, pipelining, Lua scripting, monitoring, security hardening, and backup strategies with practical examples.

A thorough guide to optimizing web application performance for better Core Web Vitals and user experience.