# ⚡ SFERA Security System - Performance Optimization Plan

## 🎯 Optimization Goals

| Metric           | Current     | Target      | Improvement |
| ---------------- | ----------- | ----------- | ----------- |
| Filter Latency   | 45ms        | 20ms        | -55%        |
| Throughput       | 1,200 req/s | 3,000 req/s | +150%       |
| Memory Usage     | 85MB        | 60MB        | -30%        |
| CPU Usage        | 65%         | 40%         | -38%        |
| Concurrent Users | 500         | 2,000       | +300%       |

## 🔧 Optimization Strategies

### 1. **Caching Layer Implementation**

#### Redis Integration

```typescript
// src/graphql/security/cache/security-cache.ts
import Redis from 'ioredis'

export class SecurityCache {
  private redis: Redis
  private readonly TTL = {
    PARTNERSHIP: 3600, // 1 hour
    USER_PROFILE: 1800, // 30 minutes
    FILTER_RESULT: 300, // 5 minutes
    THREAT_PROFILE: 900, // 15 minutes
  }

  constructor() {
    this.redis = new Redis({
      host: process.env.REDIS_HOST,
      port: parseInt(process.env.REDIS_PORT || '6379'),
      maxRetriesPerRequest: 3,
      enableReadyCheck: true,
      lazyConnect: true,
    })
  }

  async getPartnership(key: string): Promise<Partnership | null> {
    const cached = await this.redis.get(`partnership:${key}`)
    return cached ? JSON.parse(cached) : null
  }

  async setPartnership(key: string, value: Partnership): Promise<void> {
    await this.redis.setex(`partnership:${key}`, this.TTL.PARTNERSHIP, JSON.stringify(value))
  }

  async getFilterResult(orderId: string, userId: string): Promise<FilteredData | null> {
    const key = `filter:${orderId}:${userId}`
    const cached = await this.redis.get(key)
    return cached ? JSON.parse(cached) : null
  }

  async invalidateUserCache(userId: string): Promise<void> {
    const pattern = `*:${userId}*`
    const keys = await this.redis.keys(pattern)
    if (keys.length > 0) {
      await this.redis.del(...keys)
    }
  }
}
```

#### Optimized SupplyDataFilter

```typescript
// Optimized version with caching
export class OptimizedSupplyDataFilter {
  private static cache = new SecurityCache()

  static async filterSupplyOrder(order: any, context: SecurityContext): Promise<FilteredData> {
    // Check cache first
    const cacheKey = `${order.id}:${context.user.id}`
    const cached = await this.cache.getFilterResult(order.id, context.user.id)

    if (cached) {
      return cached
    }

    // Perform filtering
    const result = this.performFiltering(order, context)

    // Cache result
    await this.cache.setFilterResult(cacheKey, result)

    return result
  }

  private static performFiltering(order: any, context: SecurityContext): FilteredData {
    // Use object pooling for better memory management
    const filteredOrder = ObjectPool.acquire()

    try {
      // Optimized filtering logic
      this.applyRoleBasedFiltering(order, filteredOrder, context)

      return {
        data: filteredOrder,
        accessLevel: this.determineAccessLevel(context),
        removedFields: this.getRemovedFields(context),
      }
    } finally {
      ObjectPool.release(filteredOrder)
    }
  }
}
```

### 2. **Database Query Optimization**

#### Indexed Queries

```sql
-- Performance indexes
CREATE INDEX CONCURRENTLY idx_supply_orders_lookup
  ON supply_orders(organization_id, status, created_at DESC);

CREATE INDEX CONCURRENTLY idx_partnerships_active
  ON partnerships(organization_id, partner_id, active)
  WHERE active = true;

CREATE INDEX CONCURRENTLY idx_audit_logs_user_time
  ON commercial_data_audits(user_id, created_at DESC)
  INCLUDE (action, resource_type);

CREATE INDEX CONCURRENTLY idx_supply_items_product
  ON supply_order_items(supply_order_id, product_id);

-- Materialized view for common queries
CREATE MATERIALIZED VIEW mv_user_access_summary AS
SELECT
  user_id,
  organization_id,
  COUNT(*) as access_count,
  MAX(created_at) as last_access,
  array_agg(DISTINCT resource_type) as accessed_resources
FROM commercial_data_audits
WHERE created_at > NOW() - INTERVAL '7 days'
GROUP BY user_id, organization_id;

CREATE UNIQUE INDEX ON mv_user_access_summary(user_id, organization_id);
```

#### Batch Database Operations

```typescript
// src/graphql/security/batch/batch-processor.ts
export class BatchProcessor {
  private queues = {
    audit: [] as AuditLog[],
    alerts: [] as SecurityAlert[],
    metrics: [] as SecurityMetric[],
  }

  private timers = {
    audit: null as NodeJS.Timeout | null,
    alerts: null as NodeJS.Timeout | null,
    metrics: null as NodeJS.Timeout | null,
  }

  async addAuditLog(log: AuditLog): Promise<void> {
    this.queues.audit.push(log)

    if (this.queues.audit.length >= 100) {
      await this.flushAuditLogs()
    } else if (!this.timers.audit) {
      this.timers.audit = setTimeout(() => this.flushAuditLogs(), 1000)
    }
  }

  private async flushAuditLogs(): Promise<void> {
    if (this.queues.audit.length === 0) return

    const logs = [...this.queues.audit]
    this.queues.audit = []

    if (this.timers.audit) {
      clearTimeout(this.timers.audit)
      this.timers.audit = null
    }

    await this.prisma.commercialDataAudit.createMany({
      data: logs,
      skipDuplicates: true,
    })
  }
}
```

### 3. **Memory Optimization**

#### Object Pooling

```typescript
// src/graphql/security/utils/object-pool.ts
export class ObjectPool<T> {
  private pool: T[] = []
  private factory: () => T
  private reset: (obj: T) => void
  private maxSize: number

  constructor(factory: () => T, reset: (obj: T) => void, maxSize: number = 100) {
    this.factory = factory
    this.reset = reset
    this.maxSize = maxSize
  }

  acquire(): T {
    return this.pool.pop() || this.factory()
  }

  release(obj: T): void {
    if (this.pool.length < this.maxSize) {
      this.reset(obj)
      this.pool.push(obj)
    }
  }
}

// Usage for filtered orders
const filteredOrderPool = new ObjectPool(
  () => ({}),
  (obj) => {
    // Clear all properties
    for (const key in obj) {
      delete obj[key]
    }
  },
  500,
)
```

#### Streaming for Large Data Sets

```typescript
// src/graphql/security/streaming/stream-filter.ts
import { Transform } from 'stream'

export class StreamingDataFilter extends Transform {
  constructor(private context: SecurityContext) {
    super({ objectMode: true })
  }

  _transform(order: any, encoding: string, callback: Function): void {
    try {
      const filtered = SupplyDataFilter.filterSupplyOrder(order, this.context)
      this.push(filtered)
      callback()
    } catch (error) {
      callback(error)
    }
  }
}

// Usage
async function* streamFilteredOrders(orders: AsyncIterable<Order>, context: SecurityContext) {
  const filter = new StreamingDataFilter(context)

  for await (const order of orders) {
    yield filter.transform(order)
  }
}
```

### 4. **Parallel Processing**

#### Worker Threads for CPU-Intensive Tasks

```typescript
// src/graphql/security/workers/filter-worker.ts
import { Worker } from 'worker_threads'
import os from 'os'

export class FilterWorkerPool {
  private workers: Worker[] = []
  private queue: Array<{
    data: any
    resolve: (value: any) => void
    reject: (error: any) => void
  }> = []

  constructor() {
    const numWorkers = os.cpus().length

    for (let i = 0; i < numWorkers; i++) {
      const worker = new Worker('./filter-worker-thread.js')

      worker.on('message', (result) => {
        const task = this.queue.shift()
        if (task) {
          task.resolve(result)
        }
      })

      worker.on('error', (error) => {
        const task = this.queue.shift()
        if (task) {
          task.reject(error)
        }
      })

      this.workers.push(worker)
    }
  }

  async filterOrder(order: any, context: SecurityContext): Promise<FilteredData> {
    return new Promise((resolve, reject) => {
      this.queue.push({ data: { order, context }, resolve, reject })

      const worker = this.getAvailableWorker()
      worker.postMessage({ order, context })
    })
  }

  private getAvailableWorker(): Worker {
    // Round-robin distribution
    return this.workers[this.queue.length % this.workers.length]
  }
}
```

### 5. **GraphQL Optimization**

#### DataLoader Integration

```typescript
// src/graphql/security/loaders/security-loaders.ts
import DataLoader from 'dataloader'

export class SecurityDataLoaders {
  partnershipLoader = new DataLoader<string, Partnership>(
    async (keys) => {
      const partnerships = await this.prisma.partnership.findMany({
        where: {
          id: { in: keys as string[] },
        },
      })

      const map = new Map(partnerships.map((p) => [p.id, p]))
      return keys.map((key) => map.get(key) || null)
    },
    { cache: true, maxBatchSize: 100 },
  )

  userProfileLoader = new DataLoader<string, UserProfile>(
    async (userIds) => {
      const profiles = await this.prisma.userProfile.findMany({
        where: {
          userId: { in: userIds as string[] },
        },
      })

      const map = new Map(profiles.map((p) => [p.userId, p]))
      return userIds.map((id) => map.get(id) || null)
    },
    { cache: true, maxBatchSize: 50 },
  )
}
```

### 6. **Monitoring & Metrics**

#### Performance Monitoring

```typescript
// src/graphql/security/monitoring/performance-monitor.ts
export class PerformanceMonitor {
  private metrics = {
    filterLatency: new Histogram({
      name: 'security_filter_latency_ms',
      help: 'Latency of security filtering operations',
      buckets: [10, 20, 30, 50, 100, 200, 500],
    }),

    cacheHitRate: new Gauge({
      name: 'security_cache_hit_rate',
      help: 'Cache hit rate for security operations',
    }),

    memoryUsage: new Gauge({
      name: 'security_memory_usage_mb',
      help: 'Memory usage of security components',
    }),
  }

  recordFilterLatency(duration: number): void {
    this.metrics.filterLatency.observe(duration)
  }

  updateCacheHitRate(hits: number, total: number): void {
    this.metrics.cacheHitRate.set(hits / total)
  }

  updateMemoryUsage(): void {
    const usage = process.memoryUsage()
    this.metrics.memoryUsage.set(usage.heapUsed / 1024 / 1024)
  }
}
```

## 📊 Implementation Timeline

### Week 1-2: Caching Layer

- [ ] Setup Redis infrastructure
- [ ] Implement SecurityCache class
- [ ] Integrate caching with filters
- [ ] Add cache invalidation logic
- [ ] Performance testing

### Week 3-4: Database Optimization

- [ ] Create performance indexes
- [ ] Implement batch processing
- [ ] Setup materialized views
- [ ] Optimize query patterns
- [ ] Load testing

### Week 5-6: Memory & CPU Optimization

- [ ] Implement object pooling
- [ ] Add streaming support
- [ ] Setup worker threads
- [ ] Memory profiling
- [ ] CPU usage optimization

### Week 7-8: Integration & Testing

- [ ] DataLoader integration
- [ ] Performance monitoring
- [ ] Load testing at scale
- [ ] Fine-tuning
- [ ] Documentation

## 🎯 Expected Results

### Performance Improvements

| Component              | Before | After | Improvement |
| ---------------------- | ------ | ----- | ----------- |
| Partnership Validation | 15ms   | 2ms   | -87%        |
| Supply Order Filtering | 45ms   | 18ms  | -60%        |
| Audit Log Writing      | 10ms   | 1ms   | -90%        |
| Threat Detection       | 200ms  | 50ms  | -75%        |
| Memory per Request     | 2MB    | 0.5MB | -75%        |

### Scalability Improvements

- **Concurrent Users**: 500 → 2,000+ (4x improvement)
- **Requests/Second**: 1,200 → 3,000+ (2.5x improvement)
- **Response Time (p99)**: 200ms → 50ms (4x improvement)
- **Memory Footprint**: 85MB → 60MB (-30%)
- **CPU Utilization**: 65% → 40% (-38%)

## 🚨 Risk Mitigation

### Potential Risks

1. **Cache Invalidation Complexity**
   - Mitigation: Implement smart TTLs and event-based invalidation

2. **Worker Thread Overhead**
   - Mitigation: Use worker pools only for CPU-intensive operations

3. **Database Connection Pool**
   - Mitigation: Monitor connection usage and implement circuit breakers

4. **Memory Leaks**
   - Mitigation: Regular profiling and automated memory monitoring

## 📈 Monitoring Dashboard

```yaml
# Grafana Dashboard for Optimization Metrics
dashboards:
  - name: 'Security Performance'
    panels:
      - title: 'Filter Latency Distribution'
        query: 'histogram_quantile(0.95, security_filter_latency_ms)'

      - title: 'Cache Hit Rate'
        query: 'security_cache_hit_rate'

      - title: 'Memory Usage Trend'
        query: 'security_memory_usage_mb'

      - title: 'Throughput'
        query: 'rate(security_requests_total[1m])'
```

## ✅ Success Criteria

- [ ] All performance targets met
- [ ] Zero security regressions
- [ ] Stable under 2x expected load
- [ ] Memory usage within limits
- [ ] Cache hit rate > 80%

---

_Document Version: 1.0_  
_Created: January 2024_  
_Implementation Start: February 2024_
