这篇文章主要讲解了“1、如何用flink的table和sql构建pom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flink的table和sql构建pom文件”吧！
构建pom文件
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>flinksqldemo</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<!-- Encoding -->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<scala.binary.version>2.11</scala.binary.version>
<scala.version>2.11.8</scala.version>
<kafka.version>0.10.2.1</kafka.version>
<flink.version>1.12.0</flink.version>
<hadoop.version>2.7.3</hadoop.version>
<!-- scope 本地调试时注销 设定为默认的 compile 打包时设定为 provided -->
<setting.scope>compile</setting.scope>
</properties>
<build>
<plugins>
<plugin>
<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。这里是亿速云，小编将为大家推送更多相关知识点的文章，欢迎关注！
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