Flink SQLJoin案例

package com.cnic.app;

import com.cnic.bean.Bean1;
import com.cnic.bean.Bean2;
import com.cnic.utils.MyKafkaUtil;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

import java.time.Duration;

public class FlinkSQLJoinTest {

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

        //获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        System.out.println(tableEnv.getConfig().getIdleStateRetention());
        tableEnv.getConfig().setIdleStateRetention(Duration.ofSeconds(10));

        FlinkKafkaConsumer kafkaConsumer = MyKafkaUtil.getKafkaConsumer("", "");

        //-host hadoop102 -port 8888
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String host = parameterTool.get("host");
        int port = parameterTool.getInt("port");

        ParameterTool propertiesFile = ParameterTool.fromPropertiesFile("./");
        String s = propertiesFile.get("");

        //读取数据创建流
        SingleOutputStreamOperator bean1DS = env.socketTextStream(host, port)
                .map(line -> {
                    String[] split = line.split(",");
                    return new Bean1(split[0],
                            split[1],
                            Long.parseLong(split[2]));
                });

        SingleOutputStreamOperator bean2DS = env.socketTextStream("hadoop102", 9999)
                .map(line -> {
                    String[] split = line.split(",");
                    return new Bean2(split[0],
                            split[1],
                            Long.parseLong(split[2]));
                });

        //将流转换为动态表
        tableEnv.createTemporaryView("t1", bean1DS);
        tableEnv.createTemporaryView("t2", bean2DS);

        //内连接       左表：OnCreateAndWrite   右表：OnCreateAndWrite
//        tableEnv.sqlQuery("select t1.id,t1.name,t2.sex from t1 join t2 on t1.id = t2.id")
//                .execute()
//                .print();

        //左外连接     左表：OnReadAndWrite     右表：OnCreateAndWrite
//        tableEnv.sqlQuery("select t1.id,t1.name,t2.sex from t1 left join t2 on t1.id = t2.id")
//                .execute()
//                .print();

        //右外连接     左表：OnCreateAndWrite   右表：OnReadAndWrite
//        tableEnv.sqlQuery("select t2.id,t1.name,t2.sex from t1 right join t2 on t1.id = t2.id")
//                .execute()
//                .print();

        //全外连接     左表：OnReadAndWrite     右表：OnReadAndWrite
//        tableEnv.sqlQuery("select t1.id,t2.id,t1.name,t2.sex from t1 full join t2 on t1.id = t2.id")
//                .execute()
//                .print();

        Table table = tableEnv.sqlQuery("select t1.id,t1.name,t2.sex from t1 left join t2 on t1.id = t2.id");
//        DataStream> retractStream = tableEnv.toRetractStream(table, Row.class);
//        retractStream.print(">>>>>>>>>");
        tableEnv.createTemporaryView("t", table);

        //创建Kafka表
        tableEnv.executeSql("" +
                "create table result_table(" +
                "    id string," +
                "    name string," +
                "    sex string," +
                "    PRIMARY KEY (id) NOT ENFORCED " +
                ") " + MyKafkaUtil.getUpsertKafkaDDL("test"));

        //将数据写入Kafka
        tableEnv.executeSql("insert into result_table select * from t")
                .print();

        env.execute();

    }

}