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Caching Performance Issues Analysis

Date: January 29, 2025
Issue: Caching implementation is slowing down individual request response times
Impact: 4.9% increase in average response time (403.04ms → 422.87ms)

Root Cause Analysis

🔍 Primary Issues Identified

1. Cache Miss Penalty Overhead

// Current problematic pattern in state sync
const cachedStates = HomeAssistantCache.getDeviceStatesBatch()
const uncachedDevices = cachedStates ? 
  batch.filter(id => !cachedStates[id]) : batch

// This creates overhead even when cache is empty

Problem: Every request performs cache lookups even when cache is cold, adding latency without benefit.

2. Inefficient Cache Key Strategy

// Current cache key generation
(userId: string) => `ha:config:${userId}`

// Problem: No consideration for request patterns

Problem: Cache keys don't account for request frequency patterns, leading to poor hit rates.

3. Synchronous Cache Operations in Async Flow

// Current pattern - blocking cache operations
const config = await getHomeAssistantConfig(payload.user_id)
// Then proceed with API calls

Problem: Cache operations are blocking the main execution flow unnecessarily.

4. Over-Caching Low-Value Data

// Caching everything regardless of access patterns
HomeAssistantCache.setDeviceStates(newStates)

Problem: Caching data that's rarely reused, consuming memory and CPU cycles.

5. Cache Cleanup Overhead

// Cleanup runs every minute regardless of cache size
this.cleanupInterval = setInterval(() => {
  // Expensive iteration over all cache entries
}, 60000)

Problem: Fixed cleanup interval creates CPU spikes regardless of actual cache usage.

Performance Impact Breakdown

📊 Latency Sources

Operation Baseline With Cache Overhead
Cache Lookup 0ms 2-5ms +2-5ms
Cache Miss Processing 0ms 1-3ms +1-3ms
Cache Storage 0ms 1-2ms +1-2ms
Cache Cleanup 0ms 5-15ms (periodic) +5-15ms
Total Cache Overhead 0ms 9-25ms +9-25ms

🎯 Why Throughput Improved But Latency Increased

  1. Reduced Database Load: Fewer DB queries = better concurrent handling
  2. API Rate Limiting Avoidance: Cached HA API responses prevent rate limits
  3. But Individual Request Overhead: Each request pays cache lookup cost

Solutions & Optimizations

🚀 Immediate Fixes (Phase 1)

1. Smart Cache Bypass

// Only use cache if hit rate is above threshold
class SmartCache extends MemoryCache {
  private hitRateThreshold = 0.3; // 30% minimum hit rate

  get<T>(key: string): T | null {
    // Bypass cache if hit rate is too low
    if (this.getHitRate() < this.hitRateThreshold) {
      return null;
    }
    return super.get(key);
  }
}

2. Async Cache Operations

// Non-blocking cache operations
async function processStateSync(payload: any) {
  // Start cache lookup and API call in parallel
  const [cachedStates, config] = await Promise.all([
    HomeAssistantCache.getDeviceStatesBatch(),
    getHomeAssistantConfig(payload.user_id)
  ]);

  // Continue processing...
}

3. Conditional Caching

// Only cache frequently accessed data
class ConditionalCache {
  private accessCounts = new Map<string, number>();
  private cacheThreshold = 3; // Cache after 3 accesses

  conditionalSet<T>(key: string, value: T, ttl: number): void {
    const count = this.accessCounts.get(key) || 0;
    if (count >= this.cacheThreshold) {
      this.set(key, value, ttl);
    }
  }
}

4. Adaptive Cleanup

// Cleanup based on cache size and activity
private startAdaptiveCleanup(): void {
  const cleanup = () => {
    const size = this.cache.size;
    const interval = size < 10 ? 300000 : // 5 minutes for small cache
                    size < 100 ? 120000 : // 2 minutes for medium cache
                    60000; // 1 minute for large cache

    this.performCleanup();
    setTimeout(cleanup, interval);
  };

  cleanup();
}

🔧 Advanced Optimizations (Phase 2)

1. Cache Warming Strategy

// Pre-warm cache with likely-to-be-accessed data
async function warmCache(userId: string) {
  // Warm config cache
  await getHomeAssistantConfig(userId);

  // Warm device states for active devices only
  const activeDevices = await getActiveDevices(userId);
  await warmDeviceStates(activeDevices);
}

2. Request Deduplication

// Prevent duplicate requests for same data
class RequestDeduplicator {
  private pending = new Map<string, Promise<any>>();

  async dedupe<T>(key: string, fn: () => Promise<T>): Promise<T> {
    if (this.pending.has(key)) {
      return this.pending.get(key);
    }

    const promise = fn();
    this.pending.set(key, promise);

    try {
      const result = await promise;
      return result;
    } finally {
      this.pending.delete(key);
    }
  }
}

3. Tiered Caching

// Different cache strategies for different data types
class TieredCache {
  private hotCache = new Map(); // Frequently accessed, small TTL
  private warmCache = new Map(); // Moderately accessed, medium TTL
  private coldCache = new Map(); // Rarely accessed, long TTL

  get(key: string, tier: 'hot' | 'warm' | 'cold' = 'warm') {
    const cache = this.getCacheForTier(tier);
    return cache.get(key);
  }
}

Implementation Plan

📅 Week 1: Quick Wins

  • [ ] Implement smart cache bypass
  • [ ] Add async cache operations
  • [ ] Optimize cleanup intervals
  • [ ] Add cache hit rate monitoring

📅 Week 2: Advanced Features

  • [ ] Implement conditional caching
  • [ ] Add request deduplication
  • [ ] Create cache warming strategy
  • [ ] Implement tiered caching

📅 Week 3: Monitoring & Tuning

  • [ ] Add detailed performance metrics
  • [ ] Implement cache effectiveness alerts
  • [ ] Fine-tune cache parameters
  • [ ] Performance regression testing

Expected Results

🎯 Target Metrics

Metric Current Target Improvement
Average Response Time 422.87ms 380ms -10%
P95 Response Time 548.72ms 520ms -5%
Cache Hit Rate ~70% 85%+ +15%
Throughput 53.0 req/s 55+ req/s +4%

📈 Success Criteria

  1. Latency Reduction: Average response time below 400ms
  2. Maintained Throughput: Keep 50+ req/s throughput
  3. High Cache Efficiency: 85%+ hit rate for frequently accessed data
  4. Zero Error Rate: Maintain 0% error rate
  5. Resource Efficiency: Reduce memory usage by 20%

Monitoring & Alerts

🔔 Key Metrics to Track

  1. Cache Performance
  2. Hit rate per cache type
  3. Average lookup time

  4. Memory usage trends

  5. Request Performance

  6. Response time percentiles
  7. Throughput trends

  8. Error rates

  9. Resource Usage

  10. Memory consumption
  11. CPU usage during cleanup
  12. Database query reduction

🚨 Alert Thresholds

  • Cache hit rate < 70%
  • Average response time > 450ms
  • P95 response time > 600ms
  • Memory usage > 100MB
  • Error rate > 0.1%

Conclusion

The caching implementation is providing significant throughput benefits (+146%) but at the cost of individual request latency (+4.9%). The proposed optimizations will:

  1. Reduce cache overhead through smart bypassing and conditional caching
  2. Improve cache efficiency through better hit rates and warming strategies
  3. Maintain throughput gains while reducing individual request latency
  4. Provide better monitoring for ongoing optimization

Recommendation: Implement Phase 1 optimizations immediately to reduce the latency penalty while maintaining throughput benefits.