Flink+Kafka 版wordCount

package com.jxtele;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.util.Collector;

import java.util.Properties;


public class KafkaWordCount {
    public static void main(String[] args) throws Exception{
        // set up the streaming execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //默认情况下，检查点被禁用。要启用检查点，请在StreamExecutionEnvironment上调用enableCheckpointing(n)方法，
        // 其中n是以毫秒为单位的检查点间隔。每隔5000 ms进行启动一个检查点,则下一个检查点将在上一个检查点完成后5秒钟内启动
        env.enableCheckpointing(500);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "hadoop102:9092,hadoop102:9092,hadoop102:9092");//kafka的节点的IP或者hostName，多个使用逗号分隔
        properties.setProperty("zookeeper.connect", "hadoop102:2181,hadoop102:2181,hadoop102:2181");//zookeeper的节点的IP或者hostName，多个使用逗号进行分隔
        properties.setProperty("group.id", "test-consumer-group");//flink consumer flink的消费者的group.id
        FlinkKafkaConsumer010 myConsumer = new FlinkKafkaConsumer010("test1", new org.apache.flink.api.common.serialization.SimpleStringSchema(), properties);

        DataStream inputDataStream = env.addSource(myConsumer);;
        DataStream> wordCountDataStream = inputDataStream
                .flatMap( new MyFlatMapper())
                .keyBy(0)
                .sum(1);
        wordCountDataStream.print().setParallelism(1);
        env.execute();
    }

    public static class MyFlatMapper implements FlatMapFunction> {
        public void flatMap(String value, Collector> out) throws
                Exception {
            String[] words = value.split(" ");
            for (String word : words) {
                out.collect(new Tuple2(word, 1));
            }
        }
    }

}

kafka 启动：略

测试生产者代码

package com.jxtele.learn.kafka;

import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.Recordmetadata;

import java.util.Properties;


public class Producer {
    public static void main(String[] args) throws Exception {

        // 创建生产者
        Properties props = new Properties();

        // 这里可以配置一个或多个broker地址，会自动从broker去拉取元数据进行缓存
        props.put("bootstrap.servers", "192.168.56.102:9092,192.168.56.103:9092,192.168.56.104:9092");
        // 这里是把发送的key从字符串序列化为字节数组
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        // 这里是把发送的message从字符串序列化为字节数组
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        // 创建一个Producer实例：线程资源，跟各个broker建立socket连接资源
        KafkaProducer producer = new KafkaProducer(props);
        //ProducerRecord record = new ProducerRecord<>(
        //        "test", "message1");
        ProducerRecord record = new ProducerRecord<>(
                "test1","", "dasdas");
        

        //kafka发送数据有两种方式：
        //1:异步的方式。
        // 这是异步发送的模式
        producer.send(record, new Callback() {
            @Override
            public void onCompletion(Recordmetadata metadata, Exception exception) {
                if (exception == null) {
                    // 消息发送成功
                    System.out.println("消息发送成功");
                } else {
                    // 消息发送失败，需要重新发送
                }
            }

        });

        Thread.sleep(10 * 1000);

        //第二种方式：这是同步发送的模式
//      producer.send(record).get();
        // 你要一直等待人家后续一系列的步骤都做完，发送消息之后
        // 有了消息的回应返回给你，你这个方法才会退出来

        producer.close();
    }
}