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><transformerimplementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"><resource>META-INF/spring.handlers</resource></transformer><transformerimplementation="org.springframework.boot.maven.PropertiesMergingResourceTransformer"><resource>META-INF/spring.factories</resource></transformer><transformerimplementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"><resource>META-INF/spring.schemas</resource></transformer><transformerimplementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /><transformerimplementation="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();}
}