# Real-Time Processing

## Stream Processing Architecture

### Kafka Edge Configuration

```yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: edge-kafka
spec:
  kafka:
    version: 3.5.1
    replicas: 3
    resources:
      requests:
        memory: 2Gi
        cpu: "500m"
      limits:
        memory: 4Gi
        cpu: "2"
    config:
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      default.replication.factor: 3
      min.insync.replicas: 2
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
```

## Event Processing

### Flink Edge Job

```yaml
apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: edge-processing
spec:
  image: flink:1.17
  flinkVersion: v1_17
  flinkConfiguration:
    taskmanager.numberOfTaskSlots: "2"
    parallelism.default: "2"
  serviceAccount: flink
  jobManager:
    resource:
      memory: "2048m"
      cpu: 1
  taskManager:
    resource:
      memory: "2048m"
      cpu: 1
  job:
    jarURI: local:///opt/flink/edge-processor.jar
    parallelism: 2
    upgradeMode: stateless
```

## Edge Analytics

### Vector Configuration

```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: vector-config
data:
  vector.toml: |
    [sources.edge_metrics]
      type = "prometheus_scrape"
      endpoints = ["http://localhost:9090/metrics"]
      scrape_interval_secs = 15
      
    [transforms.edge_processing]
      type = "remap"
      inputs = ["edge_metrics"]
      source = '''
        . = parse_json!(.message)
        .timestamp = parse_timestamp!(.timestamp, format: "%Y-%m-%d %H:%M:%S")
      '''
      
    [sinks.edge_storage]
      type = "aws_s3"
      inputs = ["edge_processing"]
      bucket = "edge-metrics"
      compression = "gzip"
      encoding.codec = "json"
      batch.timeout_secs = 300
```

## Best Practices

1. **Data Processing**
   * Stream windowing
   * State management
   * Backpressure handling
   * Error recovery
2. **Performance Optimization**
   * Resource allocation
   * Data locality
   * Caching strategy
   * Network optimization
3. **Monitoring**
   * Processing latency
   * Throughput metrics
   * Error rates
   * Resource utilization
4. **Reliability**
   * Data persistence
   * Failover handling
   * Message guarantees
   * Recovery procedures
