缓存失效策略
学习在 Redis 中使用 TTL、写穿、写后和事件驱动的失效机制，让缓存数据保持新鲜和一致
缓存失效策略 是 CoddyKit 上的免费 Redis Caching & Messaging (Pub/Sub, Streams) 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Redis Caching & Messaging (Pub/Sub, Streams) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Redis Caching & Messaging (Pub/Sub, Streams) 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Why Invalidation Matters
Caching speeds up reads, but a cache that serves stale data can be worse than no cache at all. Cache invalidation is the discipline of removing or refreshing entries when the underlying source of truth changes.
It is famously hard: there are only two hard things in computer science, and one of them is cache invalidation.
TTL-Based Expiry
The simplest strategy is time-to-live. You accept that data may be stale for at most N seconds.
SET key value EX 60expires after 60 secondsEXPIRE key 60sets a TTL on an existing keyTTL keyshows remaining seconds
SET user:42:profile "...json..." EX 60
TTL user:42:profile
EXPIRE user:42:profile 120Write-Through Caching
In write-through caching, every write goes to both the database and the cache in the same operation. Reads are always fresh, at the cost of slower writes.
The application is responsible for keeping the two in lockstep.
def save_user(user):
db.update(user)
redis.set('user:' + user.id, serialize(user))
return userWrite-Behind Caching
Write-behind (write-back) writes to the cache first and flushes to the database asynchronously. Writes are fast, but you risk data loss if Redis goes down before the flush.
Use a queue or stream to buffer pending writes.
redis.set('order:' + id, data)
redis.lpush('pending_writes', id)Explicit Deletion on Update
The cache-aside + delete pattern: on every update to the database, delete the cached key. The next read repopulates it.
This avoids serving stale data and is simpler than keeping the cache value in sync.
def update_product(p):
db.update(p)
redis.delete('product:' + p.id)Why Delete Beats Update
Deleting the key (instead of overwriting it) avoids a race condition: two concurrent updates could otherwise write values in the wrong order. With deletion, the next read always pulls the latest from the source of truth.
Versioned Keys
Instead of invalidating, bump a version number embedded in the key. Old keys expire naturally via TTL while new reads use the new key.
INCR config:version
GET config:version
SET config:v7:settings "..."Event-Driven Invalidation
Publish an invalidation event whenever data changes. Other application nodes subscribe and clear their local caches. This pairs Redis Pub/Sub with caching.
redis.publish('invalidate', 'user:42')
# subscribers:
redis.subscribe('invalidate')Tag-Based Invalidation
Group related keys under a tag set. When a tag becomes invalid, delete all member keys in one sweep.
SADD tag:user:42 product:1 product:2- On change:
SMEMBERS tag:user:42thenDELeach
SADD tag:user:42 cart:42 wishlist:42
SMEMBERS tag:user:42Avoiding Stampedes
When a hot key expires, many requests may hit the database at once (a cache stampede). Mitigate with a short lock, probabilistic early expiry, or serving slightly stale data while one worker refreshes.
SET lock:user:42 1 NX EX 5Choosing a Strategy
There is no single best approach:
- TTL for tolerable staleness
- Delete-on-write for correctness
- Event-driven for multi-node consistency
- Versioning for bulk config changes
Quick Check
Test your understanding of invalidation strategies.
Recap
You learned the major cache invalidation strategies: TTL expiry, write-through, write-behind, delete-on-write, versioned keys, event-driven, and tag-based invalidation, plus how to avoid cache stampedes. Pick the strategy that matches your tolerance for staleness and your consistency needs.
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常见问题解答
「缓存失效策略」课时是免费的吗？
是的 — 「缓存失效策略」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Redis Caching & Messaging (Pub/Sub, Streams) 课程的其余内容，请升级到 CoddyKit PRO。 Redis Caching & Messaging (Pub/Sub, Streams) 课程共包含 4 节课。
「缓存失效策略」这节课中我会学到什么？
学习在 Redis 中使用 TTL、写穿、写后和事件驱动的失效机制，让缓存数据保持新鲜和一致 你通过在浏览器中直接运行的动手代码来练习 Redis Caching & Messaging (Pub/Sub, Streams)，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Redis Caching & Messaging (Pub/Sub, Streams) 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Redis Caching & Messaging (Pub/Sub, Streams) 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「缓存失效策略」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Redis Caching & Messaging (Pub/Sub, Streams) 课中编写并运行代码吗？
能。每节 Redis Caching & Messaging (Pub/Sub, Streams) 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。