Live Data Handler¶

Overview¶

The Live Data Handler is a core component of AIDDDMAP that manages real-time data processing, streaming analytics, and event-driven operations. It provides robust capabilities for handling high-throughput data streams while ensuring data integrity and low latency.

Core Features¶

1. Stream Processing¶

Real-time Data Ingestion
Stream Analytics
Event Processing
Data Transformation

2. Data Management¶

Buffer Management
Flow Control
Data Persistence
State Management

3. Performance Features¶

Low Latency Processing
High Throughput
Scalable Architecture
Load Balancing

4. Integration Support¶

Multiple Data Sources
Protocol Support
Custom Handlers
Event Broadcasting

Implementation¶

1. Handler Configuration¶

interface HandlerConfig {
  streams: StreamConfig[];
  processing: ProcessingConfig;
  persistence: PersistenceConfig;
  monitoring: MonitoringConfig;
}

interface StreamConfig {
  source: DataSource;
  format: DataFormat;
  schema: SchemaConfig;
  validation: ValidationConfig;
}

enum DataFormat {
  JSON = "json",
  BINARY = "binary",
  PROTOBUF = "protobuf",
  CUSTOM = "custom",
}

2. Stream Processing¶

interface StreamProcessor {
  pipeline: PipelineStage[];
  analytics: AnalyticsConfig;
  output: OutputConfig;
}

interface PipelineStage {
  type: StageType;
  config: StageConfig;
  transformation: TransformConfig;
}

interface AnalyticsConfig {
  windows: WindowConfig[];
  metrics: MetricConfig[];
  alerts: AlertConfig[];
}

3. State Management¶

interface StateManager {
  storage: StorageConfig;
  replication: ReplicationConfig;
  recovery: RecoveryConfig;
}

interface StorageConfig {
  type: StorageType;
  persistence: PersistenceConfig;
  optimization: OptimizationConfig;
}

interface ReplicationConfig {
  strategy: ReplicationStrategy;
  nodes: number;
  consistency: ConsistencyLevel;
}

Integration Examples¶

1. Stream Processing Setup¶

// Configure stream processor
const streamProcessor = new StreamProcessor({
  input: {
    sources: [
      {
        type: "kafka",
        topic: "sensor-data",
        config: {
          brokers: ["localhost:9092"],
          group: "processor-group",
          autoCommit: false,
        },
      },
      {
        type: "websocket",
        endpoint: "ws://data-source/feed",
        protocol: "v1",
      },
    ],
    format: {
      type: "json",
      schema: sensorSchema,
      validation: true,
    },
  },
  processing: {
    pipeline: [
      {
        name: "validation",
        type: "filter",
        config: {
          rules: [
            {
              field: "temperature",
              operator: "range",
              value: [-50, 150],
            },
            {
              field: "humidity",
              operator: "range",
              value: [0, 100],
            },
          ],
        },
      },
      {
        name: "enrichment",
        type: "transform",
        config: {
          operations: [
            {
              type: "add-field",
              field: "timestamp",
              value: "now()",
            },
            {
              type: "calculate",
              field: "heat_index",
              formula: "heat_index(temperature, humidity)",
            },
          ],
        },
      },
    ],
    windows: [
      {
        type: "tumbling",
        size: "1m",
        metrics: ["avg", "max", "min"],
      },
      {
        type: "sliding",
        size: "5m",
        slide: "1m",
        metrics: ["trend"],
      },
    ],
  },
  output: {
    destinations: [
      {
        type: "elasticsearch",
        index: "sensor-metrics",
        batch: {
          size: 1000,
          interval: "10s",
        },
      },
      {
        type: "websocket",
        endpoint: "/metrics",
        format: "json",
      },
    ],
    alerts: {
      conditions: [
        {
          metric: "temperature",
          operator: "gt",
          value: 100,
          window: "5m",
        },
      ],
      notifications: ["email", "webhook"],
    },
  },
});

// Start processing
const stream = await streamProcessor.start({
  autoCommit: true,
  recovery: {
    enabled: true,
    strategy: "latest",
  },
});

2. Real-time Analytics¶

// Configure analytics engine
const analyticsEngine = new RealTimeAnalytics({
  processing: {
    windows: {
      types: ["tumbling", "sliding", "session"],
      default: {
        size: "1m",
        slide: "30s",
      },
    },
    metrics: {
      basic: ["count", "sum", "avg", "min", "max"],
      advanced: ["percentile", "stddev", "trend"],
    },
    aggregations: {
      dimensions: ["sensor_id", "location", "type"],
      measures: ["temperature", "humidity", "pressure"],
    },
  },
  state: {
    storage: {
      type: "memory",
      backup: {
        enabled: true,
        interval: "1m",
      },
    },
    recovery: {
      enabled: true,
      maxAge: "24h",
    },
  },
  output: {
    format: "json",
    compression: true,
    batching: {
      size: 1000,
      timeout: "5s",
    },
  },
});

// Process analytics
const analysis = await analyticsEngine.analyze({
  stream: dataStream,
  windows: [
    {
      type: "sliding",
      size: "5m",
      metrics: ["avg", "trend"],
    },
  ],
  groupBy: ["sensor_id", "location"],
});

3. Event Processing¶

// Configure event processor
const eventProcessor = new EventProcessor({
  events: {
    types: [
      {
        name: "temperature_alert",
        priority: "high",
        schema: temperatureSchema,
      },
      {
        name: "system_status",
        priority: "medium",
        schema: statusSchema,
      },
    ],
    routing: {
      rules: [
        {
          event: "temperature_alert",
          condition: "temperature > 100",
          action: "notify",
        },
        {
          event: "system_status",
          condition: 'status == "error"',
          action: "log",
        },
      ],
    },
  },
  processing: {
    parallel: true,
    maxWorkers: 4,
    queueSize: 1000,
  },
  handlers: {
    temperature_alert: async (event) => {
      await notificationService.send({
        type: "alert",
        severity: "high",
        message: `High temperature detected: ${event.temperature}°C`,
      });
    },
    system_status: async (event) => {
      await logger.log({
        level: "error",
        message: `System error: ${event.details}`,
        timestamp: event.timestamp,
      });
    },
  },
});

// Start event processing
const processor = await eventProcessor.start({
  autoAck: true,
  retry: {
    enabled: true,
    maxAttempts: 3,
  },
});

Performance Optimization¶

1. Buffer Management¶

interface BufferManager {
  policy: BufferPolicy;
  limits: BufferLimits;
  optimization: OptimizationConfig;
}

interface BufferPolicy {
  overflow: "drop" | "block" | "resize";
  underflow: "wait" | "flush";
}

2. Flow Control¶

interface FlowController {
  backpressure: BackpressureConfig;
  throttling: ThrottlingConfig;
  monitoring: MonitoringConfig;
}

Best Practices¶

1. Data Handling¶

Validate input data
Implement error handling
Manage backpressure
Monitor performance

2. Processing¶

Optimize batch sizes
Handle state properly
Implement recovery
Monitor latency

3. Integration¶

Use standard protocols
Implement retry logic
Handle disconnections
Monitor health

Future Enhancements¶

Planned Features
Advanced analytics
ML integration
Custom processors
Enhanced monitoring
Research Areas
Stream processing
State management
Distributed processing
Real-time ML