Cache Strategy Planner
What is a cache strategy?
A cache strategy defines which reads may avoid the source of truth, how cached data stays acceptably fresh, and what happens when the cache or origin is under pressure.
Step 1
Model
Model the workload and freshness contract first. Then choose an invalidation policy and inject failures to test the protected fallback path.
Step 2
Observe
Use the recommendation, flow model, and pressure signals to define cache placement, key ownership, invalidation, stampede protection, and outage behavior.
Step 3
Challenge
Compare a read-heavy relaxed workload with a strict high-mutation workload, then inject stale reads, a stampede, write amplification, and a cache outage.
Design the path, then break it
Shape the workload and freshness contract, choose who owns invalidation, then inject pressure to see which dependency absorbs the consequence.
Current verdict
Resilient plan
The selected policy stays inside the modeled freshness and origin budgets.
Effective hit rate
85%
11K reads/s enter the path
Origin load
39%
1.6K origin reads/s
Modeled p99
48 ms
99.99% availability
Stale exposure
1.8%
3 invalidation ops/s
Bounded cache-aside with targeted invalidation
Keep the cache close to the owning service and use mutation events to bound stale exposure without coupling every write to every reader.
- Placement
- Edge cache for shared objects plus an application cache
- Fill policy
- Cache-aside with single-flight fills on misses
- Expiry
- 5-15 minute TTL with measured staleness
- Failure fallback
- Circuit-break slow origin reads and preserve a bounded stale option
Follow the consequence through the path
85% served before origin
Client requests
11K reads/s
Product catalog
Cache tier
85% hits
85% served before origin
Origin service
39% load
1.6K reads/s
Source of truth
180 writes/min
bounded freshness contract
Committed change
180 per min
The source remains authoritative
Event invalidation
3 invalidations/s
Freshness ownership is explicit
Key state updated
3 ops/s
Readers observe the next cache state
- The read-heavy shape can reuse enough values to justify cache complexity.
- The freshness budget allows reuse without turning every read into an origin query.
- The mutation rate leaves room for a bounded TTL as a recovery mechanism.
- Measure fill concurrency and protect hot keys with single-flight loading.
- Set an origin load budget and alarm on cache-miss amplification.
- Record cache age and invalidation lag as first-class telemetry.