{"post":{"slug":"caching-strategies-2026","title":"Caching strategies in 2026: which pattern to use and when","excerpt":"Cache-aside, write-through, read-through, and CDN edge caching solve different problems. Here is the 2026 decision guide with concrete tradeoffs.","category":"code","date":"April 13, 2026","author":"ByteLedger Team","content":"<span class=\"dropcap\">C</span>aching is how you go from a database that handles 500 queries per second to a system that handles 50,000 without buying more hardware. Done wrong, it's also how you serve stale prices, wrong user data, and phantom inventory. The pattern you pick and the TTL rules you set determine which outcome you get.\n\n## What changed in 2026\n\n- **Redis 8 introduced a native tiered storage** (RAM + NVMe), making very large caches economic — datasets that previously needed a Memcached fleet now fit in a single Redis cluster with persistence.\n- **Valkey** (the Redis community fork) reached production parity and is now the default in several cloud providers' managed cache offerings, but the API is identical for application code.\n- **CDN providers (Cloudflare, Fastly, Vercel) added stale-while-revalidate at the edge** as a first-class policy, eliminating the origin spike at TTL expiry for public APIs.\n- **HTTP Cache-Control `no-store` vs `no-cache`** confusion remains the top cause of unintended CDN bypass — the HTTP spec hasn't changed, but awareness has improved.\n\n## The four main patterns\n\n| Pattern | Who populates the cache | Consistency | Write cost | Miss penalty |\n| --- | --- | --- | --- | --- |\n| Cache-aside | Application on read miss | Eventual (TTL) | Low | One extra read |\n| Read-through | Cache layer | Eventual (TTL) | Low | None (transparent) |\n| Write-through | Cache layer on write | Strong | Double write | None |\n| Write-behind (async) | Cache layer, async flush | Weak | Low | None |\n\n## Cache-aside (lazy loading)\n\nThe application checks the cache first. On a miss, it reads from the database, populates the cache, and returns the value. On a hit, it returns directly from cache.\n\n```python\ndef get_user(user_id: int) -> dict:\n    cache_key = f\"user:{user_id}\"\n    cached = redis.get(cache_key)\n    if cached:\n        return json.loads(cached)\n\n    user = db.query(\"SELECT * FROM users WHERE id = %s\", user_id)\n    redis.setex(cache_key, 300, json.dumps(user))  # TTL: 5 minutes\n    return user\n```\n\n**Pros:** Simple, resilient (cache failure doesn't break reads), only caches what's actually used.\n**Cons:** Cold start causes miss storms; stale for up to TTL after a write.\n\n**Invalidation:** on mutation, delete the key explicitly (`redis.delete(cache_key)`) rather than writing through — simpler and avoids race conditions.\n\n## Write-through\n\nEvery write goes to both the database and the cache atomically (or near-atomically). Reads always hit a warm cache.\n\n```python\ndef update_user(user_id: int, data: dict):\n    db.execute(\"UPDATE users SET ... WHERE id = %s\", user_id)\n    cache_key = f\"user:{user_id}\"\n    redis.setex(cache_key, 300, json.dumps(data))\n```\n\nUse write-through when cache misses are very expensive (e.g., expensive aggregation queries) and write volume is low. Avoid it for high-write tables — every write takes twice as long.\n\n## CDN and HTTP caching\n\nFor public read-heavy APIs, HTTP headers are your highest-leverage caching layer.\n\n```http\nCache-Control: public, max-age=60, stale-while-revalidate=30\nVary: Accept-Encoding\nETag: \"abc123\"\n```\n\n`stale-while-revalidate=30` tells the CDN to serve the stale cached response for 30 seconds while fetching a fresh copy in the background — zero latency penalty at TTL expiry.\n\nFor authenticated APIs: `Cache-Control: private, max-age=0, no-cache` — never cache user-specific responses at a shared CDN layer.\n\n## Cache eviction policies\n\n| Policy | Best for |\n| --- | --- |\n| LRU (Least Recently Used) | General purpose |\n| LFU (Least Frequently Used) | Skewed access patterns (hot keys) |\n| TTL-only | Time-sensitive data (prices, tokens) |\n| No eviction (Redis `noeviction`) | Session stores where overflow must fail loudly |\n\nRedis default is LRU. Set `maxmemory-policy` explicitly — never leave it at the default `noeviction` in a cache (vs session store) use case.\n\n## How to pick\n\n1. **Is the data public and mostly static?** → CDN/HTTP caching first, application cache second.\n2. **Is the data user-specific or dynamic?** → Cache-aside with a short TTL.\n3. **Are cache misses catastrophically slow (cold query > 500ms)?** → Write-through to keep it warm.\n4. **Is write volume high (>1k writes/sec per key)?** → Cache-aside with explicit invalidation; write-through doubles your write latency.\n5. **Can you tolerate brief staleness?** → Longer TTLs, stale-while-revalidate for public data.\n\n## Common mistakes\n\n**No TTL.** Cache entries without expiry are correct until they aren't, and you won't notice until users report stale data. Every `SET` in your application cache code must have a TTL.\n\n**Caching the absence of data.** If `db.get(id)` returns None (not found), don't skip caching — cache the null result with a short TTL (30–60s) to prevent a cache miss thundering herd on a hot missing key.\n\n**Stampede on expiry.** When a popular key expires, dozens of requests all miss simultaneously, hit the DB, and all try to write back. Use a probabilistic early expiration strategy or a distributed lock around the miss path.\n\n**Using the same cache for sessions and application data** with different eviction policies — separate Redis instances (or at least separate databases) for different concerns.\n\n**Over-caching.** Caching data that is already fast to compute or rarely accessed wastes memory and adds an invalidation surface. Profile before caching.\n\n## What to skip\n\n- **Caching in the ORM layer without knowing it** — many ORMs have optional first-level caches; make sure you understand their invalidation semantics before trusting them.\n- **Write-behind for financial or inventory data** — async flush means data can be lost on crash. Use write-through or skip caching for data that must be durable.\n- **Local in-process caches across multiple pods** — they diverge immediately. Use Redis/Valkey for any cache that must be consistent across instances.\n\n## FAQ\n\n**How do I handle cache invalidation across microservices?**\nPublish invalidation events (e.g., via a message queue or event stream) when data changes, and have each service subscribe and delete its own cache keys. See [Event-driven architecture in 2026](/blog/event-driven-architecture-2026).\n\n**What TTL should I start with?**\n60 seconds is a safe default for dynamic user-facing data. Tune up (5–30 minutes) for slow-changing reference data. Use TTL=0 (no cache) for transactional data that must always be fresh.\n\n**Should I use Redis or Memcached in 2026?**\nRedis (or Valkey) for almost everything — persistence, rich data types, and cluster mode make it far more capable. Memcached retains a niche only for teams that need pure ultra-low-latency key-value with no persistence.\n\n**How do I test cache behaviour in CI?**\nUse Fakeredis or an embedded Redis (Testcontainers) in tests. Write explicit tests for cache hits, misses, TTL expiry paths, and the miss stampede guard.\n\n## Where to go next\n\nSee [API rate limiting in 2026](/blog/api-rate-limiting-2026) for reducing load so fewer requests reach the origin, and [Database indexing explained in 2026](/blog/database-indexing-explained-2026) for making the underlying queries faster when cache misses do happen.","hue":322,"metaTitle":"Caching Strategies 2026: Cache-Aside, Write-Through","metaDescription":"Master caching strategies in 2026. Cache-aside vs write-through vs read-through, TTL rules, Redis patterns, CDN edge caching, and cache invalidation mistakes.","keywords":["caching strategies 2026","cache aside pattern","write through cache","read through cache","cache invalidation","redis caching patterns","cdn edge caching","ttl cache strategy","application layer caching","caching best practices 2026"],"tldr":["**Cache-aside (lazy loading) is the safest default** — the application owns the logic, stale reads are bounded by TTL, and cold-start misses are recoverable.","**Write-through keeps the cache warm but doubles every write** — use it only when cache misses on reads are unacceptably expensive.","**Set a TTL on every cache entry, no exceptions** — missing TTLs are the most common source of stale data bugs in production.","**Edge/CDN caching is orders of magnitude cheaper than origin requests** — cache public responses at the edge before optimising application-layer caching.","**Skip:** caching inside a transaction or before validating the underlying data — cache poisoning from a bad write is very hard to debug."],"readTime":"5 min read","wordCount":1105},"related":[{"slug":"how-to-cache-with-redis-2026","title":"How to cache with Redis in 2026: patterns, TTLs, and invalidation","excerpt":"Redis caching in 2026 is faster to set up than ever, but the same mistakes persist: missing TTLs, stale data, and cache stampedes. 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