Scala样例代码

//producer代码
object WriteIntoKafka {
  def main(args: Array[String]) {
    // 打印出执行flink run的参考命令
    System.out.println("use command as: ")
    System.out.println("./bin/flink run --class com.huawei.bigdata.flink.examples.WriteIntoKafka" +
      " /opt/test.jar --topic topic-test --bootstrap.servers 10.91.8.218:9092")
    System.out.println("******************************************************************************************")
    System.out.println("<topic> is the kafka topic name")
    System.out.println("<bootstrap.servers> is the ip:port list of brokers")
    System.out.println("******************************************************************************************")

    // 构造执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // 设置并发度
    env.setParallelism(1)
    // 解析运行参数
    val paraTool = ParameterTool.fromArgs(args)
    // 构造流图，将自定义Source生成的数据写入Kafka
    val messageStream: DataStream[String] = env.addSource(new SimpleStringGenerator)
    messageStream.addSink(new FlinkKafkaProducer(
      paraTool.get("topic"), new SimpleStringSchema, paraTool.getProperties))
    // 调用execute触发执行
    env.execute
  }
}

// 自定义Source，每隔1s持续产生消息
class SimpleStringGenerator extends SourceFunction[String] {
  var running = true
  var i = 0

  override def run(ctx: SourceContext[String]) {
    while (running) {
      ctx.collect("element-" + i)
      i += 1
      Thread.sleep(1000)
    }
  }

  override def cancel() {
    running = false
  }
}

//consumer代码
object ReadFromKafka {
  def main(args: Array[String]) {
    // 打印出执行flink run的参考命令
    System.out.println("use command as: ")
    System.out.println("./bin/flink run --class com.huawei.bigdata.flink.examples.ReadFromKafka" +
      " /opt/test.jar --topic topic-test -bootstrap.servers 10.91.8.218:9092")
    System.out.println("******************************************************************************************")
    System.out.println("<topic> is the kafka topic name")
    System.out.println("<bootstrap.servers> is the ip:port list of brokers")
    System.out.println("******************************************************************************************")

    // 构造执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // 设置并发度
    env.setParallelism(1)
    // 解析运行参数
    val paraTool = ParameterTool.fromArgs(args)
    // 构造流图，从Kafka读取数据并换行打印
    val messageStream = env.addSource(new FlinkKafkaConsumer(
      paraTool.get("topic"), new SimpleStringSchema, paraTool.getProperties))
    messageStream
      .map(s => "Flink says " + s + System.getProperty("line.separator")).print()
    // 调用execute触发执行
    env.execute()
  }
}