SpringBoot集成flink

Flink是一个批处理和流处理结合的统一计算框架，其核心是一个提供了数据分发以及并行化计算的流数据处理引擎。
最大亮点是流处理，最适合的应用场景是低时延的数据处理。
场景：高并发pipeline处理数据，时延毫秒级，且兼具可靠性。

环境搭建：

①、安装flink

https://nightlies.apache.org/flink/flink-docs-master/zh/docs/try-flink/local_installation/

②、安装Netcat

Netcat（又称为NC）是一个计算机网络工具，它可以在两台计算机之间建立 TCP/IP 或 UDP 连接。
用于测试网络中的端口，发送文件等操作。
进行网络调试和探测，也可以进行加密连接和远程管理等高级网络操作

yum install -y nc # 安装nc命令

nc -lk 8888 # 启动socket端口

无界流之读取socket文本流

一、依赖


<?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">
    <parent>
        <artifactId>springboot-demo</artifactId>
        <groupId>com.et</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <artifactId>flink</artifactId>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>
    <dependencies>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-autoconfigure</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <!-- 添加 Flink 依赖 -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java</artifactId>
            <version>1.17.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.17.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients</artifactId>
            <version>1.17.0</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-base -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-base</artifactId>
            <version>1.17.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-files</artifactId>
            <version>1.17.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka</artifactId>
            <version>1.17.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web</artifactId>
            <version>1.17.0</version>
        </dependency>


    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <transformers>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                                    <resource>META-INF/spring.handlers</resource>
                                </transformer>
                                <transformer
                                        implementation="org.springframework.boot.maven.PropertiesMergingResourceTransformer">
                                    <resource>META-INF/spring.factories</resource>
                                </transformer>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                                    <resource>META-INF/spring.schemas</resource>
                                </transformer>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.et.flink.job.SocketJob</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

</project>

二、SoketJob

public class SocketJob{
	
	public static void main(String[] args)throws Exception{
		
		// 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 指定并行度,默认电脑线程数
        env.setParallelism(3);
        // 读取数据socket文本流 指定监听 IP 端口 只有在接收到数据才会执行任务
        DataStreamSource<String> socketDS = env.socketTextStream("172.24.4.193", 8888);

        // 处理数据: 切换、转换、分组、聚合 得到统计结果
        SingleOutputStreamOperator<Tuple2<String, Integer>> sum = socketDS
                .flatMap(
                        (String value, Collector<Tuple2<String, Integer>> out) -> {
                            String[] words = value.split(" ");
                            for (String word : words) {
                                out.collect(Tuple2.of(word, 1));
                            }
                        }
                )
                .setParallelism(2)
                // // 显式地提供类型信息:对于flatMap传入Lambda表达式，系统只能推断出返回的是Tuple2类型，而无法得到Tuple2<String, Long>。只有显式设置系统当前返回类型，才能正确解析出完整数据
                .returns(new TypeHint<Tuple2<String, Integer>>() {
                })
//                .returns(Types.TUPLE(Types.STRING,Types.INT))
                .keyBy(value -> value.f0)
                .sum(1);


        // 输出
        sum.print();

        // 执行
        env.execute();
	}
}