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# Example: Technical Investigation
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## User Request
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> "Research our current caching strategy and create a technical summary"
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## Workflow Execution
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### Step 1: Search for Caching Information
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```
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Notion:notion-search
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query: "caching strategy architecture"
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query_type: "internal"
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teamspace_id: "engineering-teamspace-id"
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```
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**Results Found:**
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- "System Architecture Overview" (Engineering)
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- "Redis Implementation Guide" (Backend Docs)
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- "Performance Optimization - Q3 2024" (Engineering)
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- "API Caching Decision Record" (Architecture)
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### Step 2: Fetch Relevant Pages
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```
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Notion:notion-fetch
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id: "system-architecture-page-url"
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```
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Extracted: Current caching architecture uses Redis for API responses, Memcached for session storage.
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```
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Notion:notion-fetch
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id: "redis-implementation-guide-url"
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```
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Extracted: Implementation details, TTL settings, invalidation strategies.
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```
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Notion:notion-fetch
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id: "api-caching-decision-record-url"
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```
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Extracted: Why Redis was chosen over alternatives, trade-offs considered.
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### Step 3: Synthesize Findings
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**Key findings identified:**
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- Two-tier caching: Redis (API responses) + Memcached (sessions)
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- TTL strategy: 5 minutes for dynamic data, 1 hour for static
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- Invalidation: Event-driven for critical updates
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- Performance impact: 75% reduction in database load
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- Known issues: Cache stampede on popular endpoints
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### Step 4: Create Technical Summary
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```
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Notion:notion-create-pages
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parent: { page_id: "engineering-docs-parent-id" }
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pages: [{
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properties: {
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"title": "Technical Summary: Caching Strategy - Oct 2025"
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},
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content: "[Structured technical summary using template]"
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}]
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```
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## Output Document
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```markdown
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# Technical Summary: Caching Strategy - Oct 2025
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## Executive Summary
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Our caching infrastructure uses a two-tier approach with Redis for API response caching and Memcached for session management. This strategy has reduced database load by 75% and improved API response times from 200ms to 50ms average.
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## Architecture Overview
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### Layer 1: API Response Caching (Redis)
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**Technology**: Redis 7.0 cluster (3 nodes)
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**Purpose**: Cache GET endpoint responses
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**TTL Strategy**:
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- Dynamic content: 5 minutes
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- Static content: 1 hour
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- User-specific: 15 minutes
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**Source**: <mention-page url="...">System Architecture Overview</mention-page>
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### Layer 2: Session Storage (Memcached)
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**Technology**: Memcached 1.6
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**Purpose**: User session data, temporary state
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**TTL**: 24 hours (session lifetime)
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## Implementation Details
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### Cache Key Format
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```
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api:v1:{endpoint}:{params_hash}
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session:{user_id}:{session_id}
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```
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### Invalidation Strategy
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- **Event-driven**: Critical data changes trigger immediate invalidation
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- **Time-based**: TTL expiration for non-critical data
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- **Manual**: Admin tools for emergency cache clear
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**Source**: <mention-page url="...">Redis Implementation Guide</mention-page>
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## Decision Rationale
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### Why Redis for API Caching?
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**Pros**:
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- Advanced data structures (sorted sets, hashes)
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- Built-in TTL with automatic eviction
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- Pub/sub for cache invalidation events
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- Persistence options for durability
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**Cons**:
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- Higher memory usage than Memcached
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- More complex cluster management
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**Decision**: Chosen for flexibility and rich feature set needed for API caching.
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**Source**: <mention-page url="...">API Caching Decision Record</mention-page>
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### Why Memcached for Sessions?
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**Pros**:
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- Simpler, lighter weight
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- Excellent for key-value storage
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- Lower memory footprint
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**Cons**:
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- No persistence
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- Limited data structures
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**Decision**: Perfect fit for ephemeral session data where simplicity is valued.
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## Performance Impact
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| Metric | Before Caching | After Caching | Improvement |
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|--------|----------------|---------------|-------------|
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| Avg Response Time | 200ms | 50ms | 75% faster |
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| Database Load | 100% | 25% | 75% reduction |
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| Cache Hit Rate | - | 85% | - |
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| Peak RPS Handled | 1,000 | 4,000 | 4x increase |
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**Source**: <mention-page url="...">Performance Optimization - Q3 2024</mention-page>
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## Known Issues & Limitations
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### Cache Stampede
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**Issue**: When popular cache entries expire, multiple requests hit database simultaneously
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**Mitigation**: Implemented probabilistic early expiration and request coalescing
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**Status**: Reduced by 90% but not eliminated
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### Stale Data Risk
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**Issue**: Cached data can be up to TTL duration stale
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**Mitigation**: Event-driven invalidation for critical data paths
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**Status**: Acceptable trade-off for performance gains
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## Monitoring & Observability
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**Metrics tracked**:
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- Cache hit/miss rates per endpoint
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- Memory usage and eviction rates
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- Response time distributions
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- Invalidation event frequency
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**Tools**: DataDog dashboards, CloudWatch alarms
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## Future Considerations
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1. **Edge Caching**: Evaluate CDN integration for static assets
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2. **Cache Warming**: Pre-populate cache for predictable traffic spikes
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3. **Adaptive TTLs**: Adjust TTLs based on data change frequency
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4. **Regional Caching**: Multi-region cache replication for global performance
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## Related Documentation
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- <mention-page url="...">System Architecture Overview</mention-page>
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- <mention-page url="...">Redis Implementation Guide</mention-page>
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- <mention-page url="...">API Caching Decision Record</mention-page>
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- <mention-page url="...">Performance Optimization - Q3 2024</mention-page>
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## Appendix: Configuration Examples
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### Redis Configuration
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```yaml
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maxmemory: 8gb
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maxmemory-policy: allkeys-lru
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tcp-keepalive: 60
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```
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### Common Cache Operations
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```python
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# Set with TTL
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cache.set(key, value, ttl=300)
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# Get with fallback
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value = cache.get(key) or fetch_from_db(key)
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# Invalidate pattern
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cache.delete_pattern("api:v1:users:*")
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```
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```
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## Key Success Factors
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1. **Multiple source integration**: Combined architecture docs, implementation guides, and decision records
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2. **Technical depth**: Included configuration, code examples, metrics
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3. **Decision context**: Explained why choices were made, not just what
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4. **Practical focus**: Real performance numbers and known issues
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5. **Future-looking**: Noted areas for improvement
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6. **Well-cited**: Every major point links back to source material
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## Workflow Pattern Demonstrated
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This example shows the complete research workflow:
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- **Scoped search** (teamspace filter for engineering)
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- **Multi-page synthesis** (4 different sources)
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- **Technical template** (architecture-focused format)
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- **Proper placement** (under engineering docs)
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- **Comprehensive citations** (links to all sources)
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