Flink入门(二)(使用kafka作为sink和source)

Dear 丶 2024-04-18 17:13 0阅读 0赞

## 在Mac电脑上安装使用kafka ##

使用kafka需要先安装zookeeper作为注册中心，在Mac上可以先安装homebrew然后再使用homebrew作为工具安装kafka和zookeeper

> brew install kafka  
> brew install zookeeper

进入 /usr/local/Cellar下分别启动kafka和zookeeper  
启动命令为  
zookeeper命令为

> zkServer start

kafka的各种命令为

> Kafka 服务端启动启动  
> kafka-server-start /usr/local/etc/kafka/server.properties

> kafka生产者启动  
> kafka-console-producer --broker-list localhost:9092 --topic first

> kafka消费者启动  
> kafka-console-consumer --bootstrap-server localhost:9092 --topic pktest

> kafka创建topic  
> kafka-topics --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic pktest

> kafka展示topic列表  
> kafka-topics --list --zookeeper localhost:2181

## Scala 使用Kafka作为Source ##

import java.util.Properties
    
    import org.apache.flink.api.common.serialization.SimpleStringSchema
    import org.apache.flink.streaming.api.scala._
    import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
    object StreamingJob {
      def main(args: Array[String]) {
        // set up the streaming execution environment
        val env = StreamExecutionEnvironment.getExecutionEnvironment
    
        import org.apache.flink.api.scala._
    
        var topic ="pktest"
    
        var  properties = new Properties()
        properties.setProperty("bootstrap.servers","127.0.0.1:9092")
        properties.setProperty("group.id","test")
    
        val  data = env.addSource(new FlinkKafkaConsumer[String](topic,new SimpleStringSchema(),properties))
        data.print()
        env.execute("StreamingJob")
      }
    }

上述即为Kafka作为消费者的例子，我们在启动producer的窗口中进行输入，即可在控制台中看到输出结果

## 使用Kafka作为生产者 ##

import java.util.Properties
    
    import org.apache.flink.api.common.serialization.SimpleStringSchema
    import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
    import org.apache.flink.streaming.connectors.kafka.internals.KeyedSerializationSchemaWrapper
    import org.apache.flink.streaming.connectors.kafka.{FlinkKafkaConsumer, FlinkKafkaProducer}
    
    object KafkaConnectorProducer {
      def main(args: Array[String]): Unit = {
    
    
        // set up the streaming execution environment
        val env = StreamExecutionEnvironment.getExecutionEnvironment
    
        import org.apache.flink.api.scala._
    
    
        var data = env.socketTextStream("localhost",9999)
    
        var topic ="pktest"
    
        var  properties = new Properties()
        properties.setProperty("bootstrap.servers","127.0.0.1:9092")
        //properties.setProperty("group.id","test")
    
        //env.addSource(new FlinkKafkaConsumer[String](topic,new SimpleStringSchema(),properties))
        var kafkaSink = new FlinkKafkaProducer[String](topic,new KeyedSerializationSchemaWrapper[String](new SimpleStringSchema()),properties)
        data.addSink(kafkaSink)
        env.execute("KafkaConnectorProducer")
      }
    
    }

此例子使用nc -lk 9000 作为数据源。经过Flink包装后传输到对应的Topic在 nc -lk 9000的命令行窗口输入一些字符并回车。即可在对应的kafka消费者命令窗口看到消费的数据