当前位置：首页 > article >正文

Kafka集成Flume/Spark/Flink（大数据）/SpringBoot

article 2025/9/14 4:09:47

Kafka集成Flume

在这里插入图片描述

Flume生产者

在这里插入图片描述
③、安装Flume，上传apache-flume的压缩包.tar.gz到Linux系统的software，并解压到/opt/module目录下，并修改其名称为flume

在这里插入图片描述

Flume消费者

在这里插入图片描述

Kafka集成Spark

在这里插入图片描述

生产者

在这里插入图片描述

object SparkKafkaProducer{def main(args:Array[String]):Unit = {//配置信息val properties  = new Properties()properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092,hadoop103:9092")properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,classOf[StringSerializer])properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,classOf[StringSerializer])//创建一个生产者var producer = new KafkaProducer[String,String](properties)//发送数据for(i <- 1 to 5){producer.send(new ProducerRecord[String,String]("first","atguigu"+i))}//关闭资源producer.close()}
}

在这里插入图片描述

消费者
在这里插入图片描述

Object SparkKafkaConsumer{def main(args:Array[String]):Unit = {//初始化上下文环境val conf = new SparkConf().setMaster("local[*]").setAppName("spark-kafka")val ssc = new StreamingContext(conf,Seconds(3))//消费数据val kafkapara = Map[String,Object](ConsumerConfig.BOOT_STRAP_SERVERS_CONFIG->"hadoop102:9092,hadoop103:9092",ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG->classOf[StringDeserializer],ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG->classOf[StringDeserializer],ConsumerConfig.GROUP_ID_CONFIG->"test")val kafkaDStream = KafkaUtils.createDirectStream(ssc,LocationStrategies.PreFerConsistent,ConsumerStrategies.Subscribe[String,String](Set("first"),kafkapara))val valueDStream = kafkaDStream.map(record=>record.value())valueDStream.print()//执行代码，并阻塞ssc.start()ssc.awaitTermination()}
}

Kafka集成Flink

在这里插入图片描述

创建maven项目，导入以下依赖
在这里插入图片描述
resources里面添加log4j.properties文件，可以更改打印日志的级别为error

Flink生产者

public class FlinkafkaProducer1{public static void main(String[] args){//获取环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(3);//准备数据源ArrayList<String> wordList = new ArrayList<>();wordList.add("hello");wordList.add("atguigu");DataStreamSource<String> stream = env.fromCollection();//创建一个kafka生产者Properties properteis = new Properties();properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092,hadoop103:9092");FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>("first",new SimpleStringSchema(),properties);//添加数据源Kafka生产者stream.addSink(kafkaProducer);//执行env.execute();}
}

在这里插入图片描述

Flink消费者

public class FlinkafkaConsumer1{public static void main(String[] args){//获取环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(3);//创建一个消费者Properties properties = new Properties();properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092,hadoop103:9092");properties.put(ConsumerConfig.GROUP_ID_CONFIG,"test");FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("first",new SimpleSStringSchema(),properties);//关联消费者和flink流env.addSource(kafkaConsumer).print();//执行env.execute();}
}