| name | caching |
| description | This skill should be used when the user asks about a "caching strategy", "cache invalidation", "what to cache", "read-through vs write-through vs write-back", "cache eviction" (LRU/LFU/TTL), "Redis vs Memcached", "stale reads", or hits "thundering herd", "cache stampede", "cache penetration", or "hot key" problems. Use it whenever a design is read-heavy or a datastore is overloaded by reads, even if the user doesn't say "cache". |
Caching
Put a copy of hot data closer to the reader so most requests skip the slow path.
Caching is the highest-leverage move for read-heavy systems — and the easiest to
get subtly wrong, because a cache adds a second source of truth that can serve
stale or wrong data, and can amplify an outage when it misbehaves.
When to reach for this
Reads dominate (a high read:write ratio from back-of-the-envelope); the same
data is read repeatedly; the datastore is the read bottleneck; or recomputation
is expensive. A cache buys read latency and offloads the origin.
When NOT to
Write-heavy or read-once data (low hit rate — pure overhead). Data that must be
exactly current with zero staleness (a cache is a stale copy by nature; when
strict freshness is required, go to the source or use consistency-coordination). Don't
add a cache before a number shows reads are the problem (YAGNI) — it's a new
failure mode and a second thing to operate.
Clarify first
- Read:write ratio and hit rate — is the working set cacheable? (→
back-of-the-envelope, 80/20.)
- Staleness tolerance — seconds? minutes? must reads see their own writes?
- Working-set size — does the hot set fit in RAM across cache nodes?
- Consistency on write — can the cache briefly disagree with the store?
- Eviction trigger — what's the access pattern (recency? frequency? time-bound?).
The options
Where to cache (often layered): client/browser → CDN edge (→ content-delivery)
→ application/in-process → distributed cache (Redis/Memcached) → database buffer
pool. This skill focuses on the application and distributed layers.
Read strategy
- Cache-aside (lazy): app checks cache, on miss reads the store and populates.
Use when reads are unpredictable; the default for most systems.
- Read-through: the cache library fetches from the store on miss. Use to keep
app code simple and caching policy centralized.
Write strategy
- Write-through: write cache and store synchronously. Use when reads right
after writes must be fresh and slower writes are acceptable.
- Write-back (write-behind): write cache now, flush to store async. Use for
write-heavy/bursty paths that tolerate a small loss window.
- Write-around: write only the store; let the cache fill on read. Use when
written data is rarely re-read soon (avoids cache churn).
Eviction policy
- LRU for recency-skewed access (most common); LFU for stable popularity;
TTL to bound staleness; FIFO rarely. Match the policy to the pattern.
Trade-offs
| Option | What it solves | What it worsens | Change it when |
|---|
| Cache-aside | Simple, resilient (cache down ⇒ just slower) | First read per key is a miss; risk of stale after writes | Misses are too costly → read-through + warming |
| Read-through | Centralized, clean app code | Couples app to cache lib; cold-start misses | Custom per-key load logic is needed |
| Write-through | Fresh reads after write | Slower writes; writes cached data that may never be read | Writes dominate and aren't re-read → write-around/back |
| Write-back | Fast, absorbs write bursts | Data loss window on crash; complex | Durability of recent writes is required |
| Write-around | No churn from write-only data | Recently written keys miss on first read | That data IS read right after write → write-through |
| TTL eviction | Bounds staleness automatically | Mass expiry can stampede the origin | Add jitter / soft-TTL refresh |
Behavior under stress
A cache that misbehaves doesn't just stop helping — it can take down the origin.
- Thundering herd / stampede: a hot key expires (or the cache restarts) and
thousands of concurrent misses hit the store at once. Mitigate: per-key locks
/ request coalescing (single-flight), early/probabilistic refresh, TTL jitter.
- Cache penetration: requests for keys that don't exist bypass the cache every
time (often malicious). Mitigate: cache the negative result (short TTL), or a
Bloom filter in front.
- Hot key: one key (a celebrity, a viral item) exceeds a single node's
throughput. Mitigate: replicate the key across nodes, add a local/L1 tier, or
shard the value.
- Eviction storm / cold cache: after a flush or deploy, hit rate craters and
the origin sees full load. Mitigate: warm critical keys; ramp traffic.
- Stale-after-write: the store changed but the cache didn't. Mitigate:
invalidate on write, or write-through, or short TTL — pick per staleness budget.
Monitor: hit rate, p99 latency, eviction rate, key distribution (hot spots),
and origin QPS during cache restarts.
How to apply
- Clarify the inputs — confirm the read:write ratio, staleness budget, and
hot-set size (see Clarify first). If no number yet shows reads are the
bottleneck, stop — a cache is not needed yet (→
back-of-the-envelope).
- Pick the strategies from the trade-off table — choose a read strategy
(cache-aside is the default), a write strategy keyed to the staleness budget,
and an eviction policy matched to the access pattern.
- Set the key knobs — define the key naming scheme, TTL (with jitter), and the
per-key-family invalidation event. Decide negative-caching and single-flight up
front, not after the first incident.
- Stress-test the choice — walk each failure in Behavior under stress
(stampede, penetration, hot key, cold cache, stale-after-write) and confirm a
mitigation is in place for the ones the traffic profile can trigger.
- Size it with numbers — fit the hot set in RAM across nodes, sanity-check the
target hit rate (90%+), and confirm the node count covers peak QPS (→ Numbers
that matter).
- Pick a provider — default to the generic recipe; only open a provider file
if the user named a cloud (see Choosing a provider).
Dos and don'ts
Do
- Default to cache-aside; it stays correct (just slower) when the cache is down.
- Set a TTL on every entry and add jitter so keys don't expire in lockstep.
- Add single-flight / request coalescing for hot keys before launch.
- Invalidate on write (or write-through, or short TTL) to a stated staleness budget.
- Size the cache to the hot set and alert on hit rate, evictions, and origin QPS.
Don't
- Don't add a cache before a number proves reads are the bottleneck (YAGNI).
- Don't cache data that must be exactly current — go to the source instead.
- Don't let a mass-expiry or cold start dump full load on the origin (warm, ramp).
- Don't ignore non-existent-key floods — cache negatives or front with a Bloom filter.
- Don't reuse a key across schema versions — bump the version (
...:v2) instead.
Numbers that matter
A cache node serves ~100k–1M QPS, far above an RDBMS (~1k). Memory access is
~100 ns vs ms-scale disk — the reason caching wins. Size the cache to the hot set
(~20% of data ≈ 80% of reads). Target hit rates are usually 90%+; below that,
question whether the data is cacheable. → back-of-the-envelope.
Interface sketch
A cache entry is a contract: a key (stable, namespaced, e.g.
user:123:profile), a value (serialized; watch size), and a TTL. Decide
the invalidation event per key family (on write? on TTL? on version bump?).
Versioned keys (...:v2) make invalidation a write of a new key instead of a
delete race.
Choosing a provider
Default to the generic recipe above (Redis or Memcached, self-hosted or managed).
If the user names a cloud, read references/providers/<provider>.md for the
managed-service mapping, limits, and provider-specific trade-offs. If no file
exists for that provider, the generic recipe is the answer.
Diagram
To visualize the cache-aside read path (app → cache → miss → origin → populate)
or the stampede/fallback flow, use the in-plugin architecture-diagram skill —
cache nodes use the cache color, the origin its store color, and the miss path a
dashed arrow.
Related building blocks
content-delivery — pairs with this as the edge layer above it; CDN/edge caching for static and media (that concept lives there).data-storage — depends on this as the origin a cache protects; its read replicas are an alternative to caching reads, and cache-node sharding mirrors its partitioning. (Consistent-hashing theory is owned by consistency-coordination.)consistency-coordination — pairs with this when staleness is unacceptable and read-your-writes or stronger guarantees are required.back-of-the-envelope — feeds into this skill: it supplies the read ratio and hot-set size that justify a cache.resilience-failure — pairs with this; rate limiting and circuit breakers (owned there) help contain the retry storms a misbehaving cache can trigger.system-design — owned-concept lives in the orchestrator: the reasoning loop, the trade-off method, and the ten failure modes.
References
references/deep-dive.md — invalidation patterns, single-flight/coalescing, consistent hashing for cache sharding, Redis vs Memcached internals, local (L1) + distributed (L2) tiers. Read when designing the cache layer in detail.references/providers/{generic,aws,azure,gcp}.md — service mappings, limits, and pitfalls per environment.
