flink入门1-Flink使用socketTextStream对接netcat完成入门demo

一、flink介绍

1、flink是什么

flink是一个面向流处理和批处理的分布式计算框架，即支持流处理，也支持批处理。
flink基于流处理引擎实现，正真做到了流处理，将批处理看作一种特殊的有界流
flink是基于java编程语言实现，支持java，scala，python进行编程开发
flink支持单机执行，或运行在大数据的yarn集群，或部署到k8s中执行

2、flink应用场景

欺诈检测(Fraud detection)
异常检测(Anomaly detection)
基于规则的告警(Rule-based alerting)
业务流程监控(Business process monitoring)
Web应用程序(社交网络)

二、使用socketTextStream对接netcat

1、创建一个maven项目，引入flink依赖

pom.xml配置文件如下：

<?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>flink-demo1</artifactId>
    <version>1.0-SNAPSHOT</version>
    <properties>
        <java.version>1.8</java.version>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <flink.version>1.12.2</flink.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
    </dependencies>
</project>
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2、编写测试类，该类的功能是将所有的字母变成大写

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
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.util.Collector;
public class FlinkTest1 {
    public static void main(String[] args) throws Exception {
        // 1.准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 设置运行模式
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        // 2.加载数据源
        DataStreamSource<String> elementsSource = env.socketTextStream("127.0.0.1", 9000);
        // 3.数据转换
        DataStream<String> flatMap = elementsSource.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String element, Collector<String> out) {
                String[] wordArr = element.split(",");
                for (String word : wordArr) {
                    out.collect(word);
                }
            }
        });
        //DataStream 下边为DataStream子类
        SingleOutputStreamOperator<String> source = flatMap.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) {
                return value.toUpperCase();
            }
        });
        // 4.数据输出
        source.print();
        // 5.执行程序
        env.execute("flink-hello-world");
    }
}
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