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RULE: When using this prompt

you must create a file named `TODO_perf-tuning.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,wkaandem...

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**RULE:** When using this prompt

Write all proposed optimizations and any code snippets to `TODO_perf-tuning.md` only. Do not create any other files. If specific files should be created or edited

- **Over-caching without invalidation**: Adding caches without invalidation strategies

- **Missing database indexes**: Full table scans on frequently queried columns

- **Synchronous blocking in async code**: Blocking the event loop or thread pool with synchronous operations

- **N+1 query patterns**: Fetching related data in loops instead of using joins or batch queries

### Database (Query Analyzers

- Deploy Application Performance Monitoring (Datadog

- Use language-specific CPU and memory profilers (pprof for Go

- Use reproducible benchmarks with consistent hardware

- [ ] API response times meet SLA: <200ms (p95) for standard endpoints

- Correlate bottlenecks with specific code paths

- **API**: Payload size reduction

- **Frontend**: Code splitting

- Design cache-aside

- **Techniques**: Dynamic programming

- Query optimization: rewrite queries using execution plan analysis

- **Data Structures**: Map vs Object for lookups

- Define performance budgets for critical paths (API response times

- Apply specific optimizations based on profiling data: select optimal data structures

- Analyze database query patterns for N+1 problems

- Record reproducible benchmark conditions (hardware

- Collect database query execution plans to identify slow operations

- **Accelerate frontend rendering** via code splitting

RULE: When using this prompt

- Over-caching without invalidation: Adding caches without invalidation strategies

- Missing database indexes: Full table scans on frequently queried columns

- Synchronous blocking in async code: Blocking the event loop or thread pool with synchronous operations

- N+1 query patterns: Fetching related data in loops instead of using joins or batch queries

- API: Payload size reduction

- Frontend: Code splitting

- Techniques: Dynamic programming

- Data Structures: Map vs Object for lookups

- Accelerate frontend rendering via code splitting