Real-Time Processing

Stream Processing Architecture

Kafka Edge Configuration

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

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

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