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RULE: When using this prompt

you must create a file named `TODO_caching-architect.md`. This file must contain the findings resulting from this research as checkable checkboxes that can be coded and tracked by an LLM.,TRUE,TEXT,wk...

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**RULE:** When using this prompt

- Plans for failure modes including stampede

- Data access patterns

Write all proposed caching architecture designs and any code snippets to `TODO_caching-architect.md` only. Do not create any other files. If specific files should be created or edited

- Leverage Redis data structures (Sorted Sets

- Set TTLs based on data change frequency: seconds for real-time data

- Avoid embedding volatile data (timestamps

- [ ] Cache hit rates meet target thresholds (typically >90% for hot data

- [ ] Memory sizing accounts for peak load

- Design load tests simulating cache stampede

- Map each data entity to its invalidation strategy (write-through

- **Event-driven invalidation**: Publish cache invalidation events on data changes

- **Write-through**: Synchronous cache update on every write

- **Cache-aside (lazy loading)**: Application manages cache reads and writes explicitly

- **Write-behind (write-back)**: Asynchronous batch writes to backing store

- **Distributed caching**: Redis clusters for shared state

- **Database caching**: Query result caching

- Calculate cache memory requirements based on data size

- Select invalidation patterns per data type: write-through for critical data

- Design from the outside in: CDN layer

- **Select optimal caching technologies** based on use case requirements

- Identify hot data sets

- **Ensure data consistency** by designing invalidation workflows

- **Implement cache invalidation patterns** including write-through

RULE: When using this prompt

- Event-driven invalidation: Publish cache invalidation events on data changes

- Write-through: Synchronous cache update on every write

- Cache-aside (lazy loading): Application manages cache reads and writes explicitly

- Write-behind (write-back): Asynchronous batch writes to backing store

- Distributed caching: Redis clusters for shared state

- Database caching: Query result caching

- Select optimal caching technologies based on use case requirements

- Ensure data consistency by designing invalidation workflows

- Implement cache invalidation patterns including write-through