1.flink集群搭建

不废话直接上代码，都是基于官网的，在此记录一下 Kubernetes | Apache Flink

flink-configuration-configmap.yaml

apiVersion: v1
kind: ConfigMap
metadata:name: flink-configlabels:app: flink
data:flink-conf.yaml: |+jobmanager.rpc.address: flink-jobmanagertaskmanager.numberOfTaskSlots: 2blob.server.port: 6124jobmanager.rpc.port: 6123taskmanager.rpc.port: 6122jobmanager.memory.process.size: 1600mtaskmanager.memory.process.size: 1728mparallelism.default: 2    log4j-console.properties: |+# This affects logging for both user code and FlinkrootLogger.level = INFOrootLogger.appenderRef.console.ref = ConsoleAppenderrootLogger.appenderRef.rolling.ref = RollingFileAppender# Uncomment this if you want to _only_ change Flink's logging#logger.flink.name = org.apache.flink#logger.flink.level = INFO# The following lines keep the log level of common libraries/connectors on# log level INFO. The root logger does not override this. You have to manually# change the log levels here.logger.pekko.name = org.apache.pekkologger.pekko.level = INFOlogger.kafka.name= org.apache.kafkalogger.kafka.level = INFOlogger.hadoop.name = org.apache.hadooplogger.hadoop.level = INFOlogger.zookeeper.name = org.apache.zookeeperlogger.zookeeper.level = INFO# Log all infos to the consoleappender.console.name = ConsoleAppenderappender.console.type = CONSOLEappender.console.layout.type = PatternLayoutappender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n# Log all infos in the given rolling fileappender.rolling.name = RollingFileAppenderappender.rolling.type = RollingFileappender.rolling.append = falseappender.rolling.fileName = ${sys:log.file}appender.rolling.filePattern = ${sys:log.file}.%iappender.rolling.layout.type = PatternLayoutappender.rolling.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%nappender.rolling.policies.type = Policiesappender.rolling.policies.size.type = SizeBasedTriggeringPolicyappender.rolling.policies.size.size=100MBappender.rolling.strategy.type = DefaultRolloverStrategyappender.rolling.strategy.max = 10# Suppress the irrelevant (wrong) warnings from the Netty channel handlerlogger.netty.name = org.jboss.netty.channel.DefaultChannelPipelinelogger.netty.level = OFF

jobmanager-service.yaml Optional service, which is only necessary for non-HA mode.

apiVersion: v1
kind: Service
metadata:name: flink-jobmanager
spec:type: ClusterIPports:- name: rpcport: 6123- name: blob-serverport: 6124- name: webuiport: 8081selector:app: flinkcomponent: jobmanager

Session cluster resource definitions #

jobmanager-session-deployment-non-ha.yaml

apiVersion: apps/v1
kind: Deployment
metadata:name: flink-jobmanager
spec:replicas: 1selector:matchLabels:app: flinkcomponent: jobmanagertemplate:metadata:labels:app: flinkcomponent: jobmanagerspec:containers:- name: jobmanagerimage: apache/flink:latestargs: ["jobmanager"]ports:- containerPort: 6123name: rpc- containerPort: 6124name: blob-server- containerPort: 8081name: webuilivenessProbe:tcpSocket:port: 6123initialDelaySeconds: 30periodSeconds: 60volumeMounts:- name: flink-config-volumemountPath: /opt/flink/confsecurityContext:runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessaryvolumes:- name: flink-config-volumeconfigMap:name: flink-configitems:- key: flink-conf.yamlpath: flink-conf.yaml- key: log4j-console.propertiespath: log4j-console.properties

taskmanager-session-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:name: flink-taskmanager
spec:replicas: 2selector:matchLabels:app: flinkcomponent: taskmanagertemplate:metadata:labels:app: flinkcomponent: taskmanagerspec:containers:- name: taskmanagerimage: apache/flink:latestargs: ["taskmanager"]ports:- containerPort: 6122name: rpclivenessProbe:tcpSocket:port: 6122initialDelaySeconds: 30periodSeconds: 60volumeMounts:- name: flink-config-volumemountPath: /opt/flink/conf/securityContext:runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessaryvolumes:- name: flink-config-volumeconfigMap:name: flink-configitems:- key: flink-conf.yamlpath: flink-conf.yaml- key: log4j-console.propertiespath: log4j-console.properties

kubectl apply -f xxx.yaml 或者 kubectl apply -f ./flink flink为文件夹，存放的是以上这几个.yaml文件

为flink的ui界面添加nodeport即可外部访问

2. demo代码测试

创建一个maven工程，pom.xml引入依赖：

<?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>test-platform</artifactId><groupId>com.test</groupId><version>2.0.0-SNAPSHOT</version></parent><modelVersion>4.0.0</modelVersion><artifactId>flink-demo</artifactId><properties><maven.compiler.source>11</maven.compiler.source><maven.compiler.target>11</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><flink.version>1.17.0</flink.version><log4j.version>2.20.0</log4j.version></properties><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-slf4j-impl</artifactId><scope>compile</scope><version>${log4j.version}</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-api</artifactId><scope>compile</scope><version>${log4j.version}</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-core</artifactId><scope>compile</scope><version>${log4j.version}</version></dependency></dependencies></project>

log4j2.xml:

<?xml version="1.0" encoding="UTF-8"?>
<configuration monitorInterval="5"><Properties><property name="LOG_PATTERN" value="%date{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n" /><!-- LOG_LEVEL 配置你需要的日志输出级别       --><property name="LOG_LEVEL" value="INFO" /></Properties><appenders><console name="Console" target="SYSTEM_OUT"><PatternLayout pattern="${LOG_PATTERN}"/><ThresholdFilter level="${LOG_LEVEL}" onMatch="ACCEPT" onMismatch="DENY"/></console></appenders><loggers><root level="${LOG_LEVEL}"><appender-ref ref="Console"/></root></loggers></configuration>

计数代码：

package com.test.flink;import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;public class WordCountUnboundStreamDemo {public static void main(String[] args) throws Exception {// TODO 1.创建执行环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(
//                3, // 尝试重启的次数
//                Time.of(10, TimeUnit.SECONDS) // 间隔
//        ));// TODO 2.读取数据DataStreamSource<String> lineDS = env.socketTextStream("192.168.0.28", 7777);// TODO 3.处理数据: 切分、转换、分组、聚合// TODO 3.1 切分、转换SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOneDS = lineDS //<输入类型, 输出类型>.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {@Overridepublic void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {// 按照 空格 切分String[] words = value.split(" ");for (String word : words) {// 转换成 二元组 （word，1）Tuple2<String, Integer> wordsAndOne = Tuple2.of(word, 1);// 通过 采集器 向下游发送数据out.collect(wordsAndOne);}}});// TODO 3.2 分组KeyedStream<Tuple2<String, Integer>, String> wordAndOneKS = wordAndOneDS.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> value) throws Exception {return value.f0;}});// TODO 3.3 聚合SingleOutputStreamOperator<Tuple2<String, Integer>> sumDS = wordAndOneKS.sum(1);// TODO 4.输出数据sumDS.print("接收到的数据=======").setParallelism(1);// TODO 5.执行：类似 sparkstreaming最后 ssc.start()env.execute(sumDS.getClass().getSimpleName());}}

打成jar包导入flink dashboard：