[Flink]Flink三种运行模式安装部署以及实现WordCount

前言

Flink三种运行方式：Local、Standalone、On Yarn。成功部署后分别用Scala和Java实现wordcount

环境

版本：Flink 1.6.2
集群环境：Hadoop2.6
开发工具: IntelliJ IDEA

一.Local模式

解压：tar -zxvf flink-1.6.2-bin-hadoop26-scala_2.11.tgz
cd flink-1.6.2
启动：./bin/start-cluster.sh
停止：./bin/stop-cluster.sh

可以通过master：8081监控集群状态

二.Standalone模式

集群安装
1：修改conf/flink-conf.yaml
jobmanager.rpc.address: hadoop100
2：修改conf/slaves
hadoop101
hadoop102
3：拷贝到其他节点
scp -rq /usr/local/flink-1.6.2 hadoop101:/usr/local
scp -rq /usr/local/flink-1.6.2 hadoop102:/usr/local
4：在hadoop100(master)节点启动
bin/start-cluster.sh
5：访问http://hadoop100:8081

三.Flink On Yarn模式

On Yarn实现逻辑

第一种【yarn-session.sh(开辟资源)+flink run(提交任务)】

启动一个一直运行的flink集群
./bin/yarn-session.sh -n 2 -jm 1024 -tm 1024 [-d]
附着到一个已存在的flink yarn session
./bin/yarn-session.sh -id application_1463870264508_0029
执行任务
./bin/flink run ./examples/batch/WordCount.jar -input hdfs://hadoop100:9000/LICENSE -output hdfs://hadoop100:9000/wordcount-result.txt
停止任务【web界面或者命令行执行cancel命令】

第二种【flink run -m yarn-cluster(开辟资源+提交任务)】

启动集群，执行任务
./bin/flink run -m yarn-cluster -yn 2 -yjm 1024 -ytm 1024 ./examples/batch/WordCount.jar
注意：client端必须要设置YARN_CONF_DIR或者HADOOP_CONF_DIR或者HADOOP_HOME环境变量，通过这个环境变量来读取YARN和HDFS的配置信息，否则启动会失败

四.WordCount

代码

Scala实现代码

package com.skyell

import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time

/** * 滑动窗口计算 * * 每隔1秒统计最近2秒数据，打印到控制台 */ object SocketWindowWordCountScala { def main(args: Array[String]): Unit = { // 获取socket端口号 val port: Int = try{ ParameterTool.fromArgs(args).getInt("port") }catch { case e: Exception => { System.err.println("No port set use default port 9002--scala") } 9002 } // 获取运行环境 val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment // 连接socket获取数据 val text = env.socketTextStream("master", port, '\n') //添加隐式转换,否则会报错 import org.apache.flink.api.scala._ // 解析数据(把数据打平），分组,窗口计算，并且聚合求sum val windowCount = text.flatMap(line => line.split("\\s")) .map(w => WordWithCount(w, 1)) .keyBy("word") // 针对相同word进行分组 .timeWindow(Time.seconds(2), Time.seconds(1))// 窗口时间函数 .sum("count") windowCount.print().setParallelism(1) // 设置并行度为1 env.execute("Socket window count") } // case 定义的类可以直接调用，不用new case class WordWithCount(word:String,count: Long) }

Java实现代码

package com.skyell;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.util.Collector; public class BatchWordCountJava { public static void main(String[] args) throws Exception{ String inputPath = "D:\\DATA\\file"; String outPath = "D:\\DATA\\result"; // 获取运行环境 ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // 读取本地文件中内容 DataSource<String> text = env.readTextFile(inputPath); // groupBy(0):从0聚合 sum(1):以第二个字段加和计算 DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).groupBy(0).sum(1); counts.writeAsCsv(outPath, "\n", " ").setParallelism(1); env.execute("batch word count"); } public static class Tokenizer implements FlatMapFunction<String, Tuple2<String,Integer>>{ public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception { String[] tokens = value.toLowerCase().split("\\W+"); for (String token: tokens ) { if(token.length()>0){ out.collect(new Tuple2<String, Integer>(token, 1)); } } } } }

pom依赖配置

    <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.6.2</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.11</artifactId> <version>1.6.2</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-scala_2.11</artifactId> <version>1.6.2</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-scala_2.11</artifactId> <version>1.6.2</version> <scope>provided</scope> </dependency>

hadoop记录-[Flink]Flink三种运行模式安装部署以及实现WordCount（转载）