Notion Research Documentation

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query: "caching strategy architecture"
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teamspace_id: "engineering-teamspace-id"
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pages: [{
  properties: {
    "title": "Technical Summary: Caching Strategy - Oct 2025"
  },
  content: "[Structured technical summary using template]"
}]

# Technical Summary: Caching Strategy - Oct 2025

## Executive Summary

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.

## Architecture Overview

### Layer 1: API Response Caching (Redis)
**Technology**: Redis 7.0 cluster (3 nodes)
**Purpose**: Cache GET endpoint responses
**TTL Strategy**:
- Dynamic content: 5 minutes
- Static content: 1 hour
- User-specific: 15 minutes

**Source**: <mention-page url="...">System Architecture Overview</mention-page>

### Layer 2: Session Storage (Memcached)
**Technology**: Memcached 1.6
**Purpose**: User session data, temporary state
**TTL**: 24 hours (session lifetime)

## Implementation Details

### Cache Key Format

### Invalidation Strategy
- **Event-driven**: Critical data changes trigger immediate invalidation
- **Time-based**: TTL expiration for non-critical data
- **Manual**: Admin tools for emergency cache clear

**Source**: <mention-page url="...">Redis Implementation Guide</mention-page>

## Decision Rationale

### Why Redis for API Caching?

**Pros**:
- Advanced data structures (sorted sets, hashes)
- Built-in TTL with automatic eviction
- Pub/sub for cache invalidation events
- Persistence options for durability

**Cons**:
- Higher memory usage than Memcached
- More complex cluster management

**Decision**: Chosen for flexibility and rich feature set needed for API caching.

**Source**: <mention-page url="...">API Caching Decision Record</mention-page>

### Why Memcached for Sessions?

**Pros**:
- Simpler, lighter weight
- Excellent for key-value storage
- Lower memory footprint

**Cons**:
- No persistence
- Limited data structures

**Decision**: Perfect fit for ephemeral session data where simplicity is valued.

## Performance Impact

| Metric | Before Caching | After Caching | Improvement |
|--------|----------------|---------------|-------------|
| Avg Response Time | 200ms | 50ms | 75% faster |
| Database Load | 100% | 25% | 75% reduction |
| Cache Hit Rate | - | 85% | - |
| Peak RPS Handled | 1,000 | 4,000 | 4x increase |

**Source**: <mention-page url="...">Performance Optimization - Q3 2024</mention-page>

## Known Issues & Limitations

### Cache Stampede
**Issue**: When popular cache entries expire, multiple requests hit database simultaneously

**Mitigation**: Implemented probabilistic early expiration and request coalescing

**Status**: Reduced by 90% but not eliminated

### Stale Data Risk
**Issue**: Cached data can be up to TTL duration stale

**Mitigation**: Event-driven invalidation for critical data paths

**Status**: Acceptable trade-off for performance gains

## Monitoring & Observability

**Metrics tracked**:
- Cache hit/miss rates per endpoint
- Memory usage and eviction rates
- Response time distributions
- Invalidation event frequency

**Tools**: DataDog dashboards, CloudWatch alarms

## Future Considerations

1. **Edge Caching**: Evaluate CDN integration for static assets
2. **Cache Warming**: Pre-populate cache for predictable traffic spikes
3. **Adaptive TTLs**: Adjust TTLs based on data change frequency
4. **Regional Caching**: Multi-region cache replication for global performance

## Related Documentation

- <mention-page url="...">System Architecture Overview</mention-page>
- <mention-page url="...">Redis Implementation Guide</mention-page>
- <mention-page url="...">API Caching Decision Record</mention-page>
- <mention-page url="...">Performance Optimization - Q3 2024</mention-page>

## Appendix: Configuration Examples

### Redis Configuration
```yaml
maxmemory: 8gb
maxmemory-policy: allkeys-lru
tcp-keepalive: 60

# Set with TTL
cache.set(key, value, ttl=300)

# Get with fallback
value = cache.get(key) or fetch_from_db(key)

# Invalidate pattern
cache.delete_pattern("api:v1:users:*")

## Key Success Factors

1. **Multiple source integration**: Combined architecture docs, implementation guides, and decision records
2. **Technical depth**: Included configuration, code examples, metrics
3. **Decision context**: Explained why choices were made, not just what
4. **Practical focus**: Real performance numbers and known issues
5. **Future-looking**: Noted areas for improvement
6. **Well-cited**: Every major point links back to source material

## Workflow Pattern Demonstrated

This example shows the complete research workflow:
- **Scoped search** (teamspace filter for engineering)
- **Multi-page synthesis** (4 different sources)
- **Technical template** (architecture-focused format)
- **Proper placement** (under engineering docs)
- **Comprehensive citations** (links to all sources)