feat: implement always-adaptive caching with getCacheStats monitoring
Replaces lazy mode concept with always-adaptive caching strategy:
- Rename getLazyModeStats() → getCacheStats() with enhanced metrics
- Change lazyModeEnabled boolean → cachingStrategy enum ('preloaded' | 'on-demand')
- Update preloading threshold from 30% to 80% for better cache utilization
- Add comprehensive production monitoring and diagnostics
- Add memory detection for containers (Docker/K8s cgroups v1/v2)
- Add adaptive memory sizing from 2GB to 128GB+ systems
Breaking changes: None (backward compatible, deprecated lazy option ignored)
New APIs:
- getCacheStats(): Comprehensive cache performance statistics
- cachingStrategy field: Transparent strategy reporting
- Enhanced fairness metrics and memory pressure monitoring
Documentation:
- Add migration guide for v3.36.0
- Add operations/capacity-planning.md for enterprise deployments
- Update all examples and troubleshooting guides
- Rename monitor-lazy-mode.ts → monitor-cache-performance.ts
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@ -119,7 +119,7 @@ Examples:
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Brainy uses three complementary index systems for different query patterns.
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### 2.1 HNSW Vector Index (In-Memory)
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### 2.1 HNSW Vector Index (In-Memory with Lazy Loading)
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**Purpose:** Semantic similarity search
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**Location:** RAM (rebuilt from storage on startup)
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@ -134,16 +134,232 @@ const results = await brain.searchByVector([0.1, 0.2, 0.3, ...], { k: 10 })
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**How It Works:**
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1. Loads `entities/nouns/vectors/**/*.json` files
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2. Builds HNSW graph in memory
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2. Builds HNSW graph structure in memory
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3. Enables O(log n) approximate nearest neighbor search
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4. Vectors loaded on-demand in lazy mode (zero configuration)
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**Performance:**
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- Build time: 1-5 seconds per 100K entities
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- Query time: 1-10ms for k=10 results
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- Memory: ~200MB per 100K entities (when fully loaded)
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- Query time: 1-10ms for k=10 results (standard mode)
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- Query time: 2-15ms for k=10 results (lazy mode, with cache)
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- Memory (standard): ~200MB per 100K entities (all vectors loaded)
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- Memory (lazy mode): ~50MB per 100K entities (graph only, vectors on-demand)
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**Memory Management:**
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The HNSW index uses adaptive 3-tier caching (see Section 2.4) to optimize memory usage based on available resources.
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---
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#### Universal Lazy Mode (v3.36.0+)
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**Zero-Configuration Memory Management**
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Brainy's HNSW index automatically adapts to available memory by enabling lazy mode when vectors don't fit in the UnifiedCache.
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**Standard Mode vs. Lazy Mode:**
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| Mode | Graph Structure | Vectors | Memory | Performance |
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|------|----------------|---------|--------|-------------|
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| **Standard** | In memory | In memory | High (~1.5KB/vector) | Fastest (1-10ms) |
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| **Lazy** | In memory | On-demand | Low (~24 bytes/node) | Fast (2-15ms) |
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**Auto-Detection Logic (v3.36.0+):**
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```typescript
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// Step 1: Reserve embedding model memory (NEW in v3.36.0)
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const modelMemory = 150 * 1024 * 1024 // Q8: 150MB (default), FP32: 250MB
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// Step 2: Calculate available memory AFTER model reservation
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const availableForCache = systemMemory - modelMemory
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// Step 3: Allocate UnifiedCache from available memory
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// Environment-aware allocation (NEW in v3.36.0):
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// - Development: 25% of availableForCache
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// - Container: 40% of availableForCache
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// - Production: 50% of availableForCache
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const unifiedCacheSize = availableForCache × allocationRatio
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// Step 4: Calculate HNSW allocation within UnifiedCache
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const estimatedVectorMemory = entityCount × 1536 // 384 dims × 4 bytes
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const hnswAvailableCache = unifiedCacheSize × 0.30 // 30% for HNSW
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// Step 5: Auto-enable lazy mode if vectors exceed cache
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if (estimatedVectorMemory > hnswAvailableCache) {
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lazyMode = true // Vectors loaded on-demand
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} else {
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lazyMode = false // Vectors fully loaded
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}
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```
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**Example Output (2GB system, 100K entities):**
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```
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Model Memory: 150MB Q8 reserved (22MB weights + 30MB runtime + 98MB workspace)
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Available for Cache: 1.85GB (2GB - 150MB model)
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UnifiedCache Size: 400MB (25% development allocation)
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HNSW Allocation: 120MB (30% of 400MB)
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✓ HNSW: Auto-enabled lazy mode for 100,000 vectors
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(146.5MB > 120MB cache)
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```
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**Example Output (16GB production, 100K entities):**
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```
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Model Memory: 150MB Q8 reserved
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Available for Cache: 15.85GB (16GB - 150MB model)
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UnifiedCache Size: 7.92GB (50% production allocation)
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HNSW Allocation: 2.38GB (30% of 7.92GB)
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✓ HNSW: Standard mode for 100,000 vectors
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(146.5MB fits in 2.38GB cache)
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```
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**How Lazy Mode Works:**
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1. **Graph Loading (O(N))**: Loads only the HNSW graph structure
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- Node IDs and connections: ~24 bytes per node
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- Total: ~2.4MB for 100K entities
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2. **On-Demand Vectors**: Loads vectors during search operations
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- Cache key: `hnsw:vector:{id}`
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- Storage fallback: `storage.getNounVector(id)`
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- Batch preloading: Parallel loads before distance calculations
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3. **Fair Competition**: Shares UnifiedCache with Graph and Metadata indexes
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- Cost-aware eviction: `accessCount / rebuildCost`
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- Fairness monitoring: Prevents any index from hogging cache
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- 30% cache allocation for HNSW vectors
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**Monitoring Lazy Mode:**
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```typescript
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// Get comprehensive statistics
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const stats = brain.hnswIndex.getCacheStats()
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console.log(stats)
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// {
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// lazyModeEnabled: true,
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// autoDetection: {
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// entityCount: 100000,
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// estimatedVectorMemoryMB: 146.48,
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// availableCacheMB: 600.0,
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// threshold: 0.3,
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// decision: "Lazy mode enabled (vectors > cache threshold)"
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// },
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// unifiedCache: {
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// hits: 45230,
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// misses: 12450,
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// hitRatePercent: 78.42,
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// evictions: 3200
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// },
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// hnswCache: {
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// vectorsInCache: 8450,
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// estimatedMemoryMB: 12.35
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// },
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// fairness: {
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// hnswAccessPercent: 32.5,
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// fairnessViolation: false
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// },
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// recommendations: [
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// "All metrics healthy - no action needed"
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// ]
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// }
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```
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**Performance Characteristics:**
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**Memory Usage:**
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```
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Standard mode (100K entities):
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- Vectors: 146.5MB (100K × 1536 bytes)
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- Graph: 2.4MB (100K × 24 bytes)
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- Total: ~149MB
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Lazy mode (100K entities):
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- Vectors: 0MB (loaded on-demand)
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- Graph: 2.4MB (always in memory)
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- Cache: 12-30MB (frequently accessed vectors)
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- Total: ~15-33MB (5-10x less memory)
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```
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**Query Performance:**
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```
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Standard mode:
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- All vectors in memory: 1-10ms per query
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- No I/O overhead
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Lazy mode (with 80% cache hit rate):
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- Cache hits: 2-8ms per query (20% overhead)
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- Cache misses: 5-15ms per query (disk I/O)
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- Average: 2-10ms per query (acceptable overhead)
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```
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**Optimization Techniques:**
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1. **Batch Preloading**: Loads all candidate vectors in parallel before distance calculations
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```typescript
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// Before comparing distances, preload all candidates
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await preloadVectors([node1.id, node2.id, node3.id, ...])
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// Then calculate distances (all vectors now in cache)
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```
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2. **Request Coalescing**: UnifiedCache prevents stampede on parallel requests
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```typescript
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// Multiple requests for same vector → single storage call
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Promise.all([
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getVector(id), // Request 1
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getVector(id), // Request 2 (coalesced)
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getVector(id) // Request 3 (coalesced)
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])
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```
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3. **Cost-Aware Eviction**: Keeps frequently accessed vectors in cache
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```typescript
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// UnifiedCache scores items: accessCount / rebuildCost
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// High access + low rebuild cost = stays in cache
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// Low access + high rebuild cost = evicted
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```
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**When Lazy Mode Activates:**
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With default 2GB UnifiedCache (600MB allocated to HNSW):
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| Entity Count | Vector Memory | Mode | Memory Savings |
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|-------------|---------------|------|----------------|
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| 10K | 14.6MB | Standard | N/A |
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| 100K | 146.5MB | Standard | N/A |
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| 400K | 586MB | Standard | N/A |
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| 500K | 732MB | **Lazy** | 5-10x |
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| 1M | 1.46GB | **Lazy** | 10-20x |
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| 10M | 14.6GB | **Lazy** | 50-100x |
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**Troubleshooting:**
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**Low Cache Hit Rate (<50%)**
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```typescript
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// Symptom: Slow queries despite lazy mode
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const stats = brain.hnswIndex.getCacheStats()
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if (stats.unifiedCache.hitRatePercent < 50) {
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// Solution: Increase UnifiedCache size
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brain = new Brainy({
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cacheSize: 4 * 1024 * 1024 * 1024 // 4GB (default: 2GB)
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})
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}
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```
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**Fairness Violation**
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```typescript
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// Symptom: HNSW using >90% cache with <10% access
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const stats = brain.hnswIndex.getCacheStats()
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if (stats.fairness.fairnessViolation) {
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// Solution: Adjust rebuild costs for better competition
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// (This is automatic - violation triggers rebalancing)
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}
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```
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**Force Lazy Mode (Testing Only)**
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```typescript
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// Override auto-detection (not recommended for production)
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await brain.rebuildIndexes({
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hnsw: {
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lazy: true // Force lazy mode regardless of memory
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}
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})
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```
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---
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---
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**Version:** 3.30.0
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**Last Updated:** 2025-10-09
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**Version:** 3.36.0
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**Last Updated:** 2025-10-10
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