1.如何用flink表和sql建立pom文件

这篇文章主要讲解了"1、如何用弗林克的桌子和结构化查询语言构建砰的一声文件",文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习"1、如何用弗林克的桌子和结构化查询语言构建砰的一声文件"吧！

构建砰的一声文件

？xmlversion='1.0 '编码='UTF-8 '？

项目xmlns=' http://aven。阿帕奇。org/POM/4。0 .0 '

xmlns : xsi=' http://www。w3。org/2001/XMLSchema-instance '

xsi :架构位置=' http://aven。阿帕奇。org/POM/4。0 .0http://aven。阿帕奇。org/xsd/maven-4。0 .0 .xsd '

模型版本4 .0 .0/模型版本

groupIdorg.example/groupId

artifactidflinksqldo/artifactId

1.0版-快照/版本

性能

！-编码-

项目。建造。sourceencodingutf-8/项目。建造。源编码

项目。报道。outputen coding TF-8/项目。报道。输出编码

斯卡拉。二进制。2.11版本/Scala。二进制。版本

斯卡拉。版本2。11 .8/Scala。版本

卡夫卡。版本0。10 .2 .1卡夫卡。版本

弗林克。版本1。12 .0/flink。版本

Hadoop。版本2。7 .3/Hadoop。版本

！表示"用来查看或检查的仪器”:显微镜|望远镜本地调试时注销设定为默认的编制打包时设定为假如

setting.scopecompile/setting.scope

/properties

建设

插件

插件

铌

sp;   <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

    <dependencies>
        <!--flink start-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.11</artifactId>
            <version>1.12.0</version>

        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.10_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-filesystem_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <!--<dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-statebackend-rocksdb_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>-->
        <!-- flink end-->

        <!-- kafka start -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_${scala.binary.version}</artifactId>
            <version>${kafka.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <!-- kafka end-->

        <!-- hadoop start -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <scope>${setting.scope}</scope>
        </dependency>
        <!-- hadoop end -->

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.25</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.72</version>
        </dependency>
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
            <version>29.0-jre</version>
        </dependency>

    </dependencies>

</project>

2、编写代码

package com.jd.data;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class test {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStreamSource<String> stream = env.readTextFile("/Users/liuhaijing/Desktop/flinktestword/aaa.txt");
//        DataStreamSource<String> stream = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<SensorReading> map = stream.map(new MapFunction<String, SensorReading>() {

            public SensorReading map(String s) throws Exception {
                String[] split = s.split(",");
                return new SensorReading(split[0], split[1], split[2]);
            }
        });



        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
//        使用 table api
//        Table table = tableEnv.fromDataStream(map);
//        table.printSchema();
//        Table select = table.select("a,b");

//        使用 sql api
        tableEnv.createTemporaryView("test", map);
        Table select = tableEnv.sqlQuery(" select a, b from test");


        DataStream<SensorReading2> sensorReading2DataStream = tableEnv.toAppendStream(select, SensorReading2.class);
        sensorReading2DataStream.map(new MapFunction<SensorReading2, Object>() {
            @Override
            public Object map(SensorReading2 value) throws Exception {
                System.out.println(value.a+"   "+ value.b);
                return null;
            }
        });
        env.execute();


    }
}

package com.jd.data;

public class SensorReading {
    public String a;
    public String b;
    public String c;

    public SensorReading(){

    }

    public SensorReading(String a, String b, String c) {
        this.a = a;
        this.b = b;
        this.c = c;
    }

    public String getA() {
        return a;
    }

    public void setA(String a) {
        this.a = a;
    }

    public String getB() {
        return b;
    }

    public void setB(String b) {
        this.b = b;
    }

    public String getC() {
        return c;
    }

    public void setC(String c) {
        this.c = c;
    }
}

package com.jd.data;

public class SensorReading2 {
    public String a;
    public String b;

    public SensorReading2(){

    }

    public SensorReading2(String a, String b) {
        this.a = a;
        this.b = b;
    }

    public String getA() {
        return a;
    }

    public void setA(String a) {
        this.a = a;
    }

    public String getB() {
        return b;
    }

    public void setB(String b) {
        this.b = b;
    }


}

注意：pojo 中属性必须是public的， 包含无参构造器

感谢各位的阅读，以上就是“1、如何用flink的table和sql构建pom文件”的内容了，经过本文的学习后，相信大家对1、如何用flink的table和sql构建pom文件这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是，小编将为大家推送更多相关知识点的文章，欢迎关注！