brainy/docs/operations/capacity-planning.md

Capacity Planning & Operations Guide

Brainy Enterprise Operations

This guide provides production-ready capacity planning formulas, deployment strategies, and operational guidelines for scaling Brainy from development (2GB) to enterprise (128GB+) deployments.

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📊 Quick Reference

Memory Allocation Formula

totalAvailable = systemMemory × utilizationFactor
modelReservation = 150MB (Q8) or 250MB (FP32)
availableForCache = totalAvailable - modelReservation
cacheSize = availableForCache × environmentRatio

Where:
- utilizationFactor = 0.80 (leave 20% for OS and other processes)
- environmentRatio = 0.25 (dev), 0.40 (container), 0.50 (production)

Adaptive Caching Strategy

estimatedVectorMemory = entityCount × 1536 bytes // 384 dims × 4 bytes per float
hnswCacheBudget = cacheSize × 0.80 // 80% threshold for preloading decision

if estimatedVectorMemory < hnswCacheBudget:
 cachingStrategy = 'preloaded' // All vectors loaded at init
else:
 cachingStrategy = 'on-demand' // Vectors loaded adaptively via UnifiedCache

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🎯 Deployment Scenarios

Scenario 1: Development (2GB System)

System Profile:

• Total RAM: 2GB
• Environment: Local development
• Expected scale: 10K-50K entities

Memory Breakdown:

System Memory: 2048 MB
OS Reserved (20%): -410 MB
Available: 1638 MB
Model Memory: -140 MB
 ├─ WASM + Weights: 90 MB
 └─ Workspace: 50 MB
───────────────────────────
Available for Cache: 1488 MB
Dev Allocation (25%): 372 MB UnifiedCache
 ├─ HNSW (30%): 112 MB
 ├─ Metadata (40%): 149 MB
 ├─ Search (20%): 74 MB
 └─ Shared (10%): 37 MB

Capacity:

• Standard Mode: Up to 70K entities (all vectors in memory)
• Lazy Mode: Up to 500K entities (on-demand vector loading)
• Search Latency: 5-15ms (standard), 8-20ms (lazy, cold)

Recommendations:

• ✅ Use Q8 model for smaller footprint
• ✅ System uses adaptive caching for datasets >70K entities
• ✅ Monitor cache hit rate with getCacheStats()
• ⚠️ Expect slower performance vs production systems

Configuration:

const brain = new Brainy({
 storage: { type: 'filesystem', path: './brainy-data' },
 model: { precision: 'q8' },
 cache: { /* auto-sized to 372MB */ }
})

---

Scenario 2: Small Production (8GB System)

System Profile:

• Total RAM: 8GB
• Environment: Single production server
• Expected scale: 100K-500K entities

Memory Breakdown:

System Memory: 8192 MB
OS Reserved (20%): -1638 MB
Available: 6554 MB
Model Memory (Q8): -150 MB
───────────────────────────
Available for Cache: 6404 MB
Prod Allocation (50%): 3202 MB UnifiedCache
 ├─ HNSW (30%): 961 MB
 ├─ Metadata (40%): 1281 MB
 ├─ Search (20%): 640 MB
 └─ Shared (10%): 320 MB

Capacity:

• Standard Mode: Up to 600K entities
• Lazy Mode: Up to 5M entities
• Search Latency: 3-8ms (standard), 5-12ms (lazy, 80% hit rate)

Recommendations:

• ✅ Q8 model balances performance and memory
• ✅ Adaptive on-demand caching activates automatically at ~620K entities
• ✅ Monitor memory pressure warnings
• ✅ Consider horizontal scaling beyond 3M entities

Configuration:

const brain = new Brainy({
 storage: { type: 'filesystem', path: '/var/lib/brainy' },
 model: { precision: 'q8' },
 // Auto-sized cache: 3202MB
})

// Monitor health
const stats = brain.hnsw.getCacheStats()
console.log(`Cache hit rate: ${stats.unifiedCache.hitRatePercent}%`)
console.log(`Caching strategy: ${stats.cachingStrategy}`)

---

Scenario 3: Medium Production (32GB System)

System Profile:

• Total RAM: 32GB
• Environment: Production server or container
• Expected scale: 1M-10M entities

Memory Breakdown:

System Memory: 32768 MB
OS Reserved (20%): -6554 MB
Available: 26214 MB
Model Memory (Q8): -150 MB
───────────────────────────
Available for Cache: 26064 MB
Prod Allocation (50%): 13032 MB UnifiedCache
 ├─ HNSW (30%): 3910 MB
 ├─ Metadata (40%): 5213 MB
 ├─ Search (20%): 2606 MB
 └─ Shared (10%): 1303 MB

Capacity:

• Standard Mode: Up to 2.5M entities
• Lazy Mode: Up to 20M entities
• Search Latency: 2-5ms (standard), 3-8ms (lazy, 85% hit rate)

Recommendations:

• ✅ Consider FP32 model if accuracy is critical (adds 100MB)
• ✅ Enable GCS/S3 storage for durability
• ✅ Adaptive on-demand caching handles 10M+ entities efficiently
• ✅ Monitor fairness metrics to prevent HNSW cache hogging

Configuration:

const brain = new Brainy({
 storage: {
 type: 'gcs-native',
 gcsNativeStorage: { bucketName: 'production-data' }
 },
 model: { precision: 'q8' } // or 'fp32' for +0.5% accuracy
})

// Verify allocation
const memoryInfo = brain.hnsw.unifiedCache.getMemoryInfo()
console.log(`Cache allocated: ${Math.round(memoryInfo.memoryInfo.available / 1024 / 1024 / 1024)}GB`)
console.log(`Environment: ${memoryInfo.memoryInfo.environment}`)

---

Scenario 4: Large Production (128GB System)

System Profile:

• Total RAM: 128GB
• Environment: Dedicated production server
• Expected scale: 10M-100M entities

Memory Breakdown:

System Memory: 131072 MB
OS Reserved (20%): -26214 MB
Available: 104858 MB
Model Memory (FP32): -250 MB
───────────────────────────────
Available for Cache: 104608 MB
Prod Allocation (50%): 52304 MB UnifiedCache (logarithmic scaling applies)
 ├─ HNSW (30%): 15691 MB
 ├─ Metadata (40%): 20922 MB
 ├─ Search (20%): 10461 MB
 └─ Shared (10%): 5230 MB

Logarithmic Scaling Applied: For systems >64GB, allocation uses logarithmic scaling to prevent over-allocation:

effectiveRatio = baseRatio × (1 + log10(systemGB / 64) × 0.15)
Actual cache size: ~40GB (prevents waste on 128GB systems)

Capacity:

• Standard Mode: Up to 10M entities
• Lazy Mode: Up to 100M+ entities
• Search Latency: 1-3ms (standard), 2-5ms (lazy, 90%+ hit rate)

Recommendations:

• ✅ Use FP32 model for maximum accuracy
• ✅ Monitor fairness violations (HNSW shouldn't dominate cache)
• ✅ Consider sharding beyond 50M entities
• ✅ Implement application-level caching for hot queries

Configuration:

const brain = new Brainy({
 storage: {
 type: 's3',
 s3Storage: {
 bucketName: 'enterprise-data',
 region: 'us-east-1'
 }
 },
 model: { precision: 'fp32' } // Maximum accuracy
})

// Enterprise monitoring
setInterval(() => {
 const stats = brain.hnsw.getCacheStats()

 if (stats.fairness.fairnessViolation) {
 console.warn('FAIRNESS VIOLATION: HNSW using too much cache')
 console.warn(`HNSW: ${stats.fairness.hnswAccessPercent}% access, ${stats.hnswCache.sizePercent}% size`)
 }

 if (stats.unifiedCache.hitRatePercent < 75) {
 console.warn(`Low cache hit rate: ${stats.unifiedCache.hitRatePercent}%`)
 console.warn('Recommendations:', stats.recommendations)
 }
}, 60000) // Check every minute

---

🐳 Container Deployments (Docker/Kubernetes)

Container Memory Detection

Brainy auto-detects container memory limits via cgroups v1/v2:

// Automatic detection
const brain = new Brainy() // Detects cgroup limits automatically

// Verify detection
const memoryInfo = brain.hnsw.unifiedCache.getMemoryInfo()
console.log(`Container: ${memoryInfo.memoryInfo.isContainer}`)
console.log(`Source: ${memoryInfo.memoryInfo.source}`) // 'cgroup-v2' or 'cgroup-v1'
console.log(`Limit: ${Math.round(memoryInfo.memoryInfo.available / 1024 / 1024)}MB`)

Docker Resource Limits

Small Container (2GB)

FROM node:22-alpine

WORKDIR /app
COPY package*.json ./
RUN npm ci --production

COPY . .

# Download models at build time
RUN npm run download-models

ENV NODE_OPTIONS="--max-old-space-size=1536"

CMD ["node", "dist/index.js"]

docker run \
 --memory="2g" \
 --memory-reservation="1.5g" \
 --cpus="2" \
 my-brainy-app

Expected allocation:

Container Limit: 2048 MB
Available: 1638 MB (80% usable)
Model Memory: -150 MB
Available for Cache: 1488 MB
Container Ratio (40%): 595 MB UnifiedCache

Medium Container (8GB)

docker run \
 --memory="8g" \
 --memory-reservation="6g" \
 --cpus="4" \
 -e NODE_OPTIONS="--max-old-space-size=6144" \
 my-brainy-app

Expected allocation:

Container Limit: 8192 MB
Available: 6554 MB
Model Memory: -150 MB
Available for Cache: 6404 MB
Container Ratio (40%): 2562 MB UnifiedCache

Kubernetes Resource Requests/Limits

Small Pod (2GB)

apiVersion: apps/v1
kind: Deployment
metadata:
 name: brainy-api
spec:
 replicas: 3
 template:
 spec:
 containers:
 - name: brainy
 image: my-brainy-app:latest
 resources:
 requests:
 memory: "1.5Gi"
 cpu: "500m"
 limits:
 memory: "2Gi"
 cpu: "1000m"
 env:
 - name: NODE_OPTIONS
 value: "--max-old-space-size=1536"

Medium Pod (8GB)

apiVersion: apps/v1
kind: Deployment
metadata:
 name: brainy-api
spec:
 replicas: 2
 template:
 spec:
 containers:
 - name: brainy
 image: my-brainy-app:latest
 resources:
 requests:
 memory: "6Gi"
 cpu: "2000m"
 limits:
 memory: "8Gi"
 cpu: "4000m"
 env:
 - name: NODE_OPTIONS
 value: "--max-old-space-size=6144"

Best Practices:

• ✅ Set requests to 75% of limits for better scheduling
• ✅ Download models at Docker build time (not runtime)
• ✅ Use NODE_OPTIONS to match container memory limits
• ✅ Monitor actual usage and adjust based on workload

---

📈 Scaling Strategies

Adaptive Caching Behavior

The system automatically chooses the optimal caching strategy:

• ✅ Preloaded: Small datasets (<80% of cache) - all vectors loaded at init for zero-latency access
• ✅ On-demand: Large datasets (>80% of cache) - vectors loaded adaptively via UnifiedCache
• ✅ No configuration needed - system adapts automatically based on dataset size

Auto-detection logic:

const vectorMemoryNeeded = entityCount × 1536 // bytes
const hnswCacheAvailable = unifiedCache.maxSize × 0.80

if (vectorMemoryNeeded < hnswCacheAvailable) {
 // Preload strategy: all vectors loaded at init
 console.log('Caching strategy: preloaded (all vectors in memory)')
} else {
 // On-demand strategy: vectors loaded adaptively
 console.log('Caching strategy: on-demand (adaptive loading via UnifiedCache)')
}

When to Add More RAM

Consider increasing RAM when:

• ⚠️ Cache hit rate consistently < 70%
• ⚠️ Memory pressure warnings > 85% utilization
• ⚠️ Search latency > 20ms on hot paths
• ⚠️ On-demand caching active but working set is large

Decision tree:

If cache hit rate < 70%:
 └─> Is working set < 50% of total entities?
 ├─> YES: Increase cache size (add RAM)
 └─> NO: Working set too large, consider:
 ├─> Application-level caching
 ├─> Query optimization
 └─> Sharding dataset

When to Shard/Distribute

Consider sharding when:

• ⚠️ Entity count > 50M entities on single node
• ⚠️ Write throughput > 10K ops/sec
• ⚠️ Need geographic distribution
• ⚠️ Fault tolerance requirements

Sharding strategy:

// Example: Geographic sharding
const usEastBrain = new Brainy({
 storage: { type: 's3', s3Storage: { bucket: 'us-east-data' } }
})

const euWestBrain = new Brainy({
 storage: { type: 's3', s3Storage: { bucket: 'eu-west-data' } }
})

// Route queries based on user location
async function search(query, userRegion) {
 const brain = userRegion === 'US' ? usEastBrain : euWestBrain
 return await brain.search(query)
}

---

🔍 Monitoring & Diagnostics

Key Metrics to Track

1. Cache Performance

const stats = brain.hnsw.getCacheStats()

// Cache hit rate (target: >80%)
console.log(`Hit rate: ${stats.unifiedCache.hitRatePercent}%`)

// HNSW cache utilization
console.log(`HNSW memory: ${stats.hnswCache.estimatedMemoryMB}MB`)
console.log(`HNSW hit rate: ${stats.hnswCache.hitRatePercent}%`)

2. Memory Pressure

const memoryInfo = brain.hnsw.unifiedCache.getMemoryInfo()

console.log(`Pressure: ${memoryInfo.currentPressure.pressure}`)
// Values: 'low', 'moderate', 'high', 'critical'

if (memoryInfo.currentPressure.warnings.length > 0) {
 console.warn('Memory warnings:', memoryInfo.currentPressure.warnings)
}

3. Fairness Metrics

const stats = brain.hnsw.getCacheStats()

if (stats.fairness.fairnessViolation) {
 console.warn('Cache fairness violation detected')
 console.warn(`HNSW: ${stats.fairness.hnswAccessPercent}% access`)
 console.warn(`HNSW: ${stats.hnswCache.sizePercent}% of cache`)
}

4. Query Performance

// Track search latency
console.time('search')
const results = await brain.search('query')
console.timeEnd('search') // Target: <10ms for hot queries

Alerting Thresholds

Set up alerts for:

• ⚠️ Cache hit rate < 70% (sustained for 5+ minutes)
• 🚨 Memory utilization > 90%
• 🚨 Search latency > 50ms (p95)
• ⚠️ Fairness violations detected

Example monitoring script:

async function monitorHealth() {
 const stats = brain.hnsw.getCacheStats()

 // Alert on low cache hit rate
 if (stats.unifiedCache.hitRatePercent < 70) {
 await sendAlert({
 severity: 'warning',
 message: `Low cache hit rate: ${stats.unifiedCache.hitRatePercent}%`,
 recommendations: stats.recommendations
 })
 }

 // Alert on memory pressure
 const memoryInfo = brain.hnsw.unifiedCache.getMemoryInfo()
 if (memoryInfo.currentPressure.pressure === 'high') {
 await sendAlert({
 severity: 'critical',
 message: 'High memory pressure detected',
 warnings: memoryInfo.currentPressure.warnings
 })
 }
}

// Run every 60 seconds
setInterval(monitorHealth, 60000)

---

🎯 Real-World Examples

Example 1: E-Commerce Product Catalog (500K products)

System: 16GB production server

Sizing:

Products: 500,000
Vector memory needed: 500K × 1536 bytes = 768 MB
HNSW cache available: (16GB × 0.8 - 150MB) × 0.5 × 0.3 = 1,915 MB

Result: Standard mode (all vectors fit in HNSW cache)

Configuration:

const brain = new Brainy({
 storage: { type: 'filesystem', path: '/var/lib/brainy' },
 model: { precision: 'q8' }
})

await brain.init()

// Verify preloaded strategy (all vectors in memory)
const stats = brain.hnsw.getCacheStats()
console.log(`Caching strategy: ${stats.cachingStrategy}`) // 'preloaded'
console.log(`Search latency: ${stats.performance.avgSearchMs}ms`) // ~3ms

Example 2: Document Search (5M documents)

System: 32GB production server with GCS storage

Sizing:

Documents: 5,000,000
Vector memory needed: 5M × 1536 bytes = 7,680 MB
HNSW cache available: (32GB × 0.8 - 150MB) × 0.5 × 0.3 = 3,910 MB

Result: On-demand caching (vectors loaded adaptively)

Configuration:

const brain = new Brainy({
 storage: {
 type: 'gcs-native',
 gcsNativeStorage: { bucketName: 'docs-production' }
 },
 model: { precision: 'q8' }
})

await brain.init()

// Monitor cache performance
const stats = brain.hnsw.getCacheStats()
console.log(`Caching strategy: ${stats.cachingStrategy}`) // 'on-demand'
console.log(`Cache hit rate: ${stats.unifiedCache.hitRatePercent}%`) // Target >80%
console.log(`Cold search latency: ${stats.performance.avgSearchMs}ms`) // ~12ms

// Recommendations
console.log('Recommendations:', stats.recommendations)
// Example: "Cache hit rate healthy at 84.2% - no action needed"

Example 3: Knowledge Graph (20M entities)

System: 128GB dedicated server with S3 storage

Sizing:

Entities: 20,000,000
Vector memory needed: 20M × 1536 bytes = 30,720 MB
HNSW cache available: ~15,691 MB (after logarithmic scaling)

Result: On-demand caching with high-performance adaptive loading

Configuration:

const brain = new Brainy({
 storage: {
 type: 's3',
 s3Storage: {
 bucketName: 'knowledge-graph-prod',
 region: 'us-east-1'
 }
 },
 model: { precision: 'fp32' } // Maximum accuracy
})

await brain.init()

// Enterprise monitoring
const stats = brain.hnsw.getCacheStats()
console.log(`Entities: ${stats.autoDetection.entityCount.toLocaleString()}`)
console.log(`Caching strategy: ${stats.cachingStrategy}`) // 'on-demand'
console.log(`Cache hit rate: ${stats.unifiedCache.hitRatePercent}%`) // Target >85%
console.log(`HNSW cache: ${stats.hnswCache.estimatedMemoryMB}MB`)

// Fairness check
if (stats.fairness.fairnessViolation) {
 console.warn('HNSW dominating cache - consider tuning eviction policies')
}

---

🛠️ Troubleshooting

Issue: Low Cache Hit Rate (<70%)

Diagnosis:

const stats = brain.hnsw.getCacheStats()
console.log(`Hit rate: ${stats.unifiedCache.hitRatePercent}%`)
console.log(`Working set: ${stats.hnswCache.estimatedMemoryMB}MB`)

Solutions:

1. Increase cache size (add RAM)
2. Optimize query patterns (reduce random access)
3. Implement application-level caching
4. Consider sharding if working set > available cache

Issue: High Memory Pressure (>85%)

Diagnosis:

const memoryInfo = brain.hnsw.unifiedCache.getMemoryInfo()
console.log(`Pressure: ${memoryInfo.currentPressure.pressure}`)
console.log(`Warnings:`, memoryInfo.currentPressure.warnings)

Solutions:

1. Reduce cache size manually (override auto-detection)
2. Reduce entity count (archive old data - system automatically uses on-demand caching for large datasets)
3. Increase system RAM

Issue: Fairness Violations

Diagnosis:

const stats = brain.hnsw.getCacheStats()
if (stats.fairness.fairnessViolation) {
 console.log(`HNSW access: ${stats.fairness.hnswAccessPercent}%`)
 console.log(`HNSW cache: ${stats.hnswCache.sizePercent}%`)
}

Solutions:

1. Contact support (fairness policies may need tuning)
2. Monitor over time (may self-correct as access patterns stabilize)
3. File GitHub issue with diagnostics

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📚 Additional Resources

• Migration Guide - Upgrading to Architecture Overview - Deep dive into storage and caching
• GitHub Issues - Report problems or ask questions

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Production-ready. Enterprise-scale. Zero-config. 🚀