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Performance Guide

This guide outlines performance optimization strategies and best practices for AIDDDMAP deployments.

Performance Overview

1. Key Performance Indicators

  • Response Time
  • Throughput
  • Resource Utilization
  • Error Rates

2. Performance Goals

  • API Response Time < 200ms
  • 99.9% Uptime
  • < 0.1% Error Rate
  • < 70% Resource Utilization

Application Performance

1. Code Optimization

Memory Management

interface MemoryConfig {
  heap: {
    initial: "512M";
    max: "1G";
    newSize: "128M";
  };
  gc: {
    type: "G1GC";
    pauseTarget: "200ms";
  };
}

Async Operations

// Efficient async handling
async function processDataset(id: string): Promise<void> {
  const [metadata, permissions, stats] = await Promise.all([
    getMetadata(id),
    getPermissions(id),
    getStats(id),
  ]);

  await processInBackground({
    metadata,
    permissions,
    stats,
  });
}

2. Request Processing

Request Pipeline

interface RequestPipeline {
  stages: {
    validation: {
      timeout: number;
      retries: number;
    };
    authentication: {
      cache: boolean;
      timeout: number;
    };
    processing: {
      parallel: boolean;
      batchSize: number;
    };
    response: {
      compression: boolean;
      caching: boolean;
    };
  };
}

Rate Limiting

interface RateLimit {
  window: string;
  max: number;
  strategy: "token-bucket" | "leaky-bucket";
  headers: boolean;
  whitelist: string[];
}

Database Performance

1. Query Optimization

Index Strategy

-- Create optimized indexes
CREATE INDEX idx_datasets_user_timestamp ON datasets (user_id, created_at);
CREATE INDEX idx_agents_status ON agents (status) WHERE status = 'active';
CREATE INDEX idx_data_partial ON data (type, value) WHERE processed = false;

-- Monitor index usage
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read
FROM pg_stat_user_indexes
WHERE idx_scan = 0
ORDER BY idx_tup_read DESC;

Query Tuning

-- Example of an optimized query
EXPLAIN ANALYZE
SELECT d.*, COUNT(a.id) as agent_count
FROM datasets d
LEFT JOIN agents a ON d.id = a.dataset_id
WHERE d.status = 'active'
  AND d.created_at > NOW() - INTERVAL '7 days'
GROUP BY d.id
HAVING COUNT(a.id) > 0
ORDER BY d.created_at DESC
LIMIT 100;

2. Connection Management

Pool Configuration

interface ConnectionPool {
  min: number;
  max: number;
  idle: number;
  acquire: number;
  retry: {
    attempts: number;
    backoff: number;
  };
}

Query Monitoring

interface QueryMetrics {
  query: string;
  execution_time: number;
  rows_affected: number;
  cache_hit: boolean;
  explain_plan: string;
}

Caching Strategy

1. Multi-Level Caching

Cache Layers

interface CacheLayers {
  memory: {
    size: number;
    ttl: number;
  };
  redis: {
    clusters: string[];
    maxMemory: string;
  };
  cdn: {
    enabled: boolean;
    rules: CacheRule[];
  };
}

Cache Policies

interface CachePolicy {
  strategy: "write-through" | "write-behind";
  ttl: number;
  maxSize: number;
  eviction: "lru" | "lfu";
}

2. Cache Invalidation

Invalidation Strategy

interface InvalidationStrategy {
  mode: "immediate" | "delayed";
  pattern: string;
  cascade: boolean;
  notification: {
    enabled: boolean;
    channel: string;
  };
}

Cache Monitoring

cache_metrics:
  hit_rate:
    warning: 0.8
    critical: 0.6

  memory_usage:
    warning: 80
    critical: 90

  eviction_rate:
    warning: 100
    critical: 1000

Network Optimization

1. Content Delivery

CDN Configuration

interface CDNConfig {
  provider: string;
  regions: string[];
  caching: {
    default_ttl: number;
    rules: {
      pattern: string;
      ttl: number;
    }[];
  };
  optimization: {
    minify: boolean;
    compress: boolean;
    cache_everything: boolean;
  };
}

Response Compression

interface CompressionConfig {
  enabled: boolean;
  level: 1 | 2 | 3 | 4 | 5;
  threshold: number;
  types: string[];
}

2. Load Balancing

Backend Configuration

load_balancer:
  algorithm: least_connections
  health_check:
    path: /health
    interval: 10s
    timeout: 5s
    healthy_threshold: 2
    unhealthy_threshold: 3

  backends:
    - name: api-1
      weight: 100
      max_conns: 1000
    - name: api-2
      weight: 100
      max_conns: 1000

Resource Optimization

1. CPU Optimization

Worker Configuration

interface WorkerConfig {
  processes: number;
  threads: number;
  affinity: boolean;
  priority: number;
}

Task Scheduling

task_scheduler:
  queues:
    high_priority:
      concurrency: 10
      rate_limit: 1000

    background:
      concurrency: 5
      rate_limit: 100

2. Memory Management

Memory Limits

interface MemoryLimits {
  heap: {
    min: string;
    max: string;
  };
  cache: {
    size: string;
    items: number;
  };
  buffers: {
    size: string;
    pooling: boolean;
  };
}

Garbage Collection

gc_config:
  algorithm: G1GC
  pause_target: 200ms
  region_size: 1M
  heap_occupancy: 45%

  logging:
    enabled: true
    file: /var/log/gc.log
    rotation: daily

Performance Monitoring

1. Metrics Collection

System Metrics

metrics:
  system:
    cpu:
      - usage
      - load
      - context_switches

    memory:
      - usage
      - swap
      - page_faults

    disk:
      - iops
      - latency
      - throughput

    network:
      - bandwidth
      - packets
      - errors

Application Metrics

interface AppMetrics {
  requests: {
    total: number;
    active: number;
    queued: number;
  };
  response_time: {
    p50: number;
    p95: number;
    p99: number;
  };
  errors: {
    count: number;
    rate: number;
  };
}

2. Performance Alerts

Alert Rules

alerts:
  - name: high_response_time
    condition: p95_latency > 500ms
    duration: 5m
    severity: warning

  - name: error_spike
    condition: error_rate > 1%
    duration: 1m
    severity: critical

  - name: memory_pressure
    condition: memory_usage > 85%
    duration: 10m
    severity: warning

Best Practices

1. Development

  • Profile code regularly
  • Optimize database queries
  • Implement efficient caching
  • Use async operations

2. Database

  • Maintain indexes
  • Monitor query performance
  • Use connection pooling
  • Implement sharding

3. Caching

  • Implement multi-level caching
  • Use appropriate TTLs
  • Monitor hit rates
  • Plan invalidation

4. Monitoring

  • Track key metrics
  • Set up alerts
  • Monitor trends
  • Regular profiling

Next Steps

  1. Review current performance
  2. Implement caching strategy
  3. Optimize database queries
  4. Configure monitoring
  5. Set up alerts

Support

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Performance Alerts