flink初体验-批处理与流处理

一、环境准备

本机环境：jdk11、scala2.12、maven3.6

新建一个maven项目，pom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.wzy</groupId>
    <artifactId>flink-project</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.12</artifactId>
            <version>1.10.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>1.10.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.10.2</version>
        </dependency>

    </dependencies>
    <build>
        <plugins>
            
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.4.6</version>
                <executions>
                    <execution>
                        <!-- 声明绑定到 maven 的 compile 阶段 -->
                        <goals>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

项目结构如下

1、添加scala源文件，新建一个scala的文件夹，并把它设置成源文件。设置方法 File -> Project Structure -> Modules
2、添加scala框架支持，右键项目 -> Add Framework Support -> scala(需要提前配置上scala的sdk)

 

二、wordcount批处理

新建一个txt文件，让程序读取文件里内容进行单词的统计

package wordcount

import org.apache.flink.api.scala._

/**
 * @description 批处理
 */
object WordCountBatch {

  def main(args: Array[String]): Unit = {
    // 创建执行环境
    val env = ExecutionEnvironment.getExecutionEnvironment

    // 从文件中读取数据
    val inputPath = "src/main/resources/wordcount.txt"
    val inputDS: DataSet[String] = env.readTextFile(inputPath)

    // 分词之后， 对单词进行 groupby 分组， 然后用 sum 进行聚合
    val wordCountDS: AggregateDataSet[(String, Int)] = inputDS
      .flatMap(_.split(" "))
      .map((_, 1))
      .groupBy(0)
      .sum(1)

    // 打印输出
    wordCountDS.print()
  }

}

打印结果如下

三、wordcount流处理

新建一个服务端口，让程序监听端口，收到一条处理一条，结果累加

开启临时服务命令：nc -lk 8088

手动输入单词回车就会发送出去

然后程序进行端口的监听

package wordcount

import org.apache.flink.streaming.api.scala._

/**
 * @description 流处理
 */
object WordCountStream {

  def main(args: Array[String]): Unit = {

    val host: String = "localhost"
    val port: Int = 8088

    // 创建流处理环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    // 接收 socket 文本流
    val textDstream: DataStream[String] = env.socketTextStream(host, port)

    // flatMap 和 Map 需要引用的隐式转换
    val dataStream: DataStream[(String, Int)] = textDstream
      .flatMap(_.split(" "))
      .filter(_.nonEmpty)
      .map((_, 1))
      .keyBy(0)
      .sum(1)

    dataStream.print()
    env.execute("wordcount job")
  }
}

打印结果如下，可以看到结果是累加的