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Scaling Guide

This guide outlines strategies and best practices for scaling AIDDDMAP deployments.

Scaling Overview

1. Scaling Dimensions

  • Vertical Scaling (Up/Down)
  • Horizontal Scaling (Out/In)
  • Data Scaling
  • Geographic Distribution

2. Key Metrics

  • Request Latency
  • Resource Utilization
  • Error Rates
  • Throughput

Infrastructure Scaling

1. Application Scaling

Container Orchestration

# docker-compose.yml
version: "3.8"
services:
  api:
    image: aidddmap/api
    deploy:
      replicas: 3
      resources:
        limits:
          cpus: "0.5"
          memory: 512M
      update_config:
        parallelism: 1
        delay: 10s
      restart_policy:
        condition: on-failure

Load Balancing

# nginx.conf
upstream api_servers {
    least_conn;  # Load balancing method
    server api1.aidddmap.com:8000;
    server api2.aidddmap.com:8000;
    server api3.aidddmap.com:8000;
    keepalive 32;
}

server {
    listen 80;
    server_name api.aidddmap.com;

    location / {
        proxy_pass http://api_servers;
        proxy_http_version 1.1;
        proxy_set_header Connection "";
    }
}

2. Database Scaling

Connection Pooling

// database-config.ts
export const poolConfig = {
  min: 5,
  max: 20,
  idle: 10000,
  acquire: 30000,
  evict: 30000,
};

Read Replicas

database:
  primary:
    host: db-primary.aidddmap.com
    role: write

  replicas:
    - host: db-replica-1.aidddmap.com
      role: read
    - host: db-replica-2.aidddmap.com
      role: read

Application Optimization

1. Caching Strategy

Cache Configuration

interface CacheConfig {
  redis: {
    clusters: [
      {
        host: string;
        port: number;
        role: "primary" | "replica";
      },
    ];
    maxConnections: number;
    ttl: number;
  };
  local: {
    size: number;
    ttl: number;
  };
}

Cache Policies

interface CachePolicy {
  type: "write-through" | "write-behind" | "cache-aside";
  ttl: number;
  invalidation: {
    strategy: "time-based" | "event-based";
    events?: string[];
  };
}

2. Queue Management

Queue Configuration

interface QueueConfig {
  name: string;
  concurrency: number;
  attempts: number;
  backoff: {
    type: "exponential" | "fixed";
    delay: number;
  };
  priority: 1 | 2 | 3;
}

Job Processing

interface JobProcessor {
  process: (job: Job) => Promise<void>;
  onFailed: (job: Job, err: Error) => void;
  onCompleted: (job: Job, result: any) => void;
  rateLimiter: {
    max: number;
    duration: number;
  };
}

Data Management

1. Data Partitioning

Sharding Configuration

interface ShardConfig {
  strategy: "hash" | "range" | "directory";
  shardKey: string;
  numberOfShards: number;
  replicationFactor: number;
}

Partition Management

partitioning:
  datasets:
    strategy: hash
    key: dataset_id
    shards: 4

  users:
    strategy: range
    key: created_at
    ranges:
      - start: "2023-01-01"
        end: "2023-06-30"
      - start: "2023-07-01"
        end: "2023-12-31"

2. Data Migration

Migration Strategy

interface MigrationStrategy {
  type: "online" | "offline";
  batchSize: number;
  validation: boolean;
  rollback: {
    enabled: boolean;
    threshold: number;
  };
}

Data Verification

interface DataVerification {
  checksums: boolean;
  sampleSize: number;
  validations: {
    integrity: boolean;
    consistency: boolean;
    performance: boolean;
  };
}

Performance Monitoring

1. Metrics Collection

System Metrics

metrics:
  collection:
    interval: 10s
    retention: 30d

  system:
    - cpu_usage
    - memory_usage
    - disk_io
    - network_traffic

  application:
    - request_rate
    - error_rate
    - response_time
    - queue_length

Performance Alerts

alerts:
  - name: high_cpu_usage
    condition: cpu > 80%
    duration: 5m
    severity: warning

  - name: high_latency
    condition: p95_latency > 500ms
    duration: 10m
    severity: critical

2. Performance Testing

Load Test Configuration

interface LoadTest {
  duration: string;
  users: number;
  rampUp: string;
  scenarios: {
    name: string;
    weight: number;
    flow: string[];
  }[];
  thresholds: {
    http_req_duration: ["p95<500"];
    http_req_failed: ["rate<0.01"];
  };
}

Performance Benchmarks

benchmarks:
  api_endpoints:
    - path: /api/v1/datasets
      method: GET
      p95_latency: 200ms
      max_rps: 1000

    - path: /api/v1/agents
      method: POST
      p95_latency: 500ms
      max_rps: 100

Geographic Distribution

1. Multi-Region Deployment

Region Configuration

regions:
  us-east:
    primary: true
    services:
      - api
      - worker
      - database

  eu-west:
    primary: false
    services:
      - api
      - worker
      - database-replica

Traffic Routing

interface RoutingConfig {
  strategy: "latency" | "geolocation" | "weighted";
  fallback: string;
  healthChecks: {
    path: string;
    interval: number;
    timeout: number;
    unhealthyThreshold: number;
  };
}

2. Data Replication

Replication Strategy

interface ReplicationStrategy {
  mode: "sync" | "async";
  priority: number;
  conflicts: "primary-wins" | "last-write-wins";
  monitoring: {
    lag: number;
    alerts: boolean;
  };
}

Best Practices

1. Architecture

  • Design for horizontal scaling
  • Implement microservices
  • Use asynchronous processing
  • Cache effectively

2. Data Management

  • Partition data appropriately
  • Implement efficient indexes
  • Use connection pooling
  • Monitor query performance

3. Operations

  • Automate scaling operations
  • Monitor system metrics
  • Implement graceful degradation
  • Plan for failure

4. Testing

  • Conduct load testing
  • Verify scaling behavior
  • Test failure scenarios
  • Benchmark performance

Next Steps

  1. Review current metrics
  2. Implement caching strategy
  3. Configure load balancing
  4. Plan data partitioning
  5. Set up monitoring

Support

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