flink分流与合流案例

案例：

package kgc.kb11.transform;

import kgc.kb11.beans.SensorReading;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.collector.selector.OutputSelector;
import org.apache.flink.streaming.api.datastream.*;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.CoMapFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import org.apache.kafka.clients.consumer.ConsumerConfig;

import java.util.Collections;
import java.util.Properties;

/**
 * @author zhouhu
 * @Date
 * @Desription
 */

public class Transform3 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        Properties prop = new Properties();
        prop.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.119.125:9092");
        prop.setProperty(ConsumerConfig.GROUP_ID_CONFIG,"sensor_group1");
        prop.setProperty(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");
        prop.setProperty(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");

        DataStreamSource<String> inputStream = env.addSource(new FlinkKafkaConsumer011<String>(
                "sensor",
                new SimpleStringSchema(),
                prop
        ));

        SingleOutputStreamOperator<SensorReading> mapStream = inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] splits = s.split(",");
                return new SensorReading(
                        splits[0],
                        Long.parseLong(splits[1]),
                        Double.parseDouble(splits[2])
                );
            }
        });
        SplitStream<SensorReading> splitStream = mapStream.split(new OutputSelector<SensorReading>() {
            @Override
            public Iterable<String> select(SensorReading value) {
                if (value.getTemperature() > 38.0) {
                    return Collections.singletonList("high");
                } else {
                    return Collections.singletonList("normal");
                }
            }
        });
        DataStream<SensorReading> high = splitStream.select("high");
        DataStream<SensorReading> normal = splitStream.select("normal");
//        high.print("high");
//        normal.print("normal");

        //union 合流 合并的两个流，必须是相同类型
//        DataStream<SensorReading> union = high.union(normal);
//        union.print("union");

        //connect 合流 合并的两个流，是不同的类型

        //warningDataStream.union(normal) 合并的类型流不一致，报错
        SingleOutputStreamOperator<Tuple2<String, Double>> warningDataStream = high.map(new MapFunction<SensorReading, Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> map(SensorReading sensorReading) throws Exception {
                return new Tuple2<>(sensorReading.getId(), sensorReading.getTemperature());
            }
        });

        ConnectedStreams<Tuple2<String, Double>, SensorReading> connectStream = warningDataStream.connect(normal);

        SingleOutputStreamOperator<Object> result = connectStream.map(new CoMapFunction<Tuple2<String, Double>, SensorReading, Object>() {
            @Override
            public Object map1(Tuple2<String, Double> value) throws Exception {
                return new Tuple3<>(value.f0, value.f1, "shenbinle");
            }

            @Override
            public Object map2(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), "jiankang,meibin");
            }
        });
        result.print("connect");
        env.execute("e");
    }
}