Flink（一）上手案例

有不懂及不对的地方请大家提出，共同探讨与进步

1.搭建maven工程

这里就不多说了

2.导入依赖

<dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-scala -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

    </dependencies>
    <build>
        <plugins>
            <!-- 该插件用于将 Scala 代码编译成 class 文件 -->
            <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>testCompile</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>

3.添加scala框架和文件夹

标记为资源文件夹

4.批处理wordcount

package com.flink.study
import org.apache.flink.api.scala._
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment}

/**
  * @Author :star
  * @Date :2020/7/3 12:12
  * @Version :1.0
  */
object WordCount {

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

    //1.创建Flink运行环境
    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)
    //2.读取文件数据
    val inputPath = "C:\\Users\\DELL\\Desktop\\1.txt"
    val data: DataSet[String] = env.readTextFile(inputPath)
    //3.对数据进行切分及其处理

    val value: DataSet[(String, String)] = data.map(
      d => {
        val strings: Array[String] = d.split(",")
        (strings(0), strings(3))
      }
    )
    value.print()
    value.writeAsCsv("C:\\Users\\DELL\\Desktop\\1.csv")
  }
}

5.流处理wordcount

在这里我使用是网络测试工具 netcat 进数据的生成

package com.flink.study
import org.apache.flink.streaming.api.scala._
/**
  * @Author :star
  * @Date :2020/7/3 13:37
  * @Version :1.0
  */
object StreamWordCount {

  def main(args: Array[String]): Unit = {
    //1.创建Flink基本环境
    val streamEnv = StreamExecutionEnvironment.getExecutionEnvironment
    //2.获取实时数据
    val data: DataStream[String] = streamEnv.socketTextStream("Localhost",8888)
    //3.对数据进行处理
    val dataCount: DataStream[(String, Int)] = data.flatMap(_.split(" "))
      .map((_, 1))
      .keyBy(0)
      .sum(1)
    dataCount.print()
    //4.启动任务
    streamEnv.execute()

  }
}