Flink開(kāi)發(fā)IDEA環(huán)境搭建與測(cè)試的方法

更新時(shí)間：2020年03月31日 11:48:31 作者：Frankdeng

這篇文章主要介紹了Flink開(kāi)發(fā)IDEA環(huán)境搭建與測(cè)試的方法,文中通過(guò)示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)學(xué)習(xí)吧

一.IDEA開(kāi)發(fā)環(huán)境

1.pom文件設(shè)置

<properties>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <scala.version>2.11.12</scala.version>
    <scala.binary.version>2.11</scala.binary.version>
    <hadoop.version>2.7.6</hadoop.version>
    <flink.version>1.6.1</flink.version>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-java</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-scala_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-kafka-0.10_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.38</version>
    </dependency>
    <dependency>
      <groupId>com.alibaba</groupId>
      <artifactId>fastjson</artifactId>
      <version>1.2.22</version>
    </dependency>
  </dependencies>
  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>3.2.0</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
            <configuration>
              <args>
                <!-- <arg>-make:transitive</arg> -->
                <arg>-dependencyfile</arg>
                <arg>${project.build.directory}/.scala_dependencies</arg>
              </args>
            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>2.18.1</version>
        <configuration>
          <useFile>false</useFile>
          <disableXmlReport>true</disableXmlReport>
          <includes>
            <include>**/*Test.*</include>
            <include>**/*Suite.*</include>
          </includes>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>3.0.0</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <filters>
                <filter>
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
              <transformers>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                  <mainClass>org.apache.spark.WordCount</mainClass>
                </transformer>
              </transformers>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>

2.flink開(kāi)發(fā)流程

Flink具有特殊類DataSet并DataStream在程序中表示數(shù)據(jù)。您可以將它們視為可以包含重復(fù)項(xiàng)的不可變數(shù)據(jù)集合。在DataSet數(shù)據(jù)有限的情況下，對(duì)于一個(gè)DataStream元素的數(shù)量可以是無(wú)界的。

這些集合在某些關(guān)鍵方面與常規(guī)Java集合不同。首先，它們是不可變的，這意味著一旦創(chuàng)建它們就無(wú)法添加或刪除元素。你也不能簡(jiǎn)單地檢查里面的元素。

集合最初通過(guò)在弗林克程序添加源創(chuàng)建和新的集合從這些通過(guò)將它們使用API方法如衍生map，filter等等。

Flink程序看起來(lái)像是轉(zhuǎn)換數(shù)據(jù)集合的常規(guī)程序。每個(gè)程序包含相同的基本部分：

1.獲取execution environment,

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

2.加載/創(chuàng)建初始化數(shù)據(jù)

DataStream<String> text = env.readTextFile(file:///path/to/file);

3.指定此數(shù)據(jù)的轉(zhuǎn)換

val mapped = input.map { x => x.toInt }

4.指定放置計(jì)算結(jié)果的位置

writeAsText(String path)
print()

5.觸發(fā)程序執(zhí)行

在local模式下執(zhí)行程序

execute()

將程序達(dá)成jar運(yùn)行在線上

./bin/flink run \

-m node21:8081 \

./examples/batch/WordCount.jar \

--input hdfs:///user/admin/input/wc.txt\

--outputhdfs:///user/admin/output2\

二.Wordcount案例

1.Scala代碼

package com.xyg.streaming

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
/**
 * Author: Mr.Deng
 * Date: 2018/10/15
 * Desc:
 */
object SocketWindowWordCountScala {
 def main(args: Array[String]) : Unit = {
  // 定義一個(gè)數(shù)據(jù)類型保存單詞出現(xiàn)的次數(shù)
  case class WordWithCount(word: String, count: Long)
  // port 表示需要連接的端口
  val port: Int = try {
   ParameterTool.fromArgs(args).getInt("port")
  } catch {
   case e: Exception => {
    System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'")
    return
   }
  }
  // 獲取運(yùn)行環(huán)境
  val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
  // 連接此socket獲取輸入數(shù)據(jù)
  val text = env.socketTextStream("node21", port, '\n')
  //需要加上這一行隱式轉(zhuǎn)換 否則在調(diào)用flatmap方法的時(shí)候會(huì)報(bào)錯(cuò)
  import org.apache.flink.api.scala._
  // 解析數(shù)據(jù), 分組, 窗口化, 并且聚合求SUM
  val windowCounts = text
   .flatMap { w => w.split("\\s") }
   .map { w => WordWithCount(w, 1) }
   .keyBy("word")
   .timeWindow(Time.seconds(5), Time.seconds(1))
   .sum("count")
  // 打印輸出并設(shè)置使用一個(gè)并行度
  windowCounts.print().setParallelism(1)
  env.execute("Socket Window WordCount")
 }
}

2.Java代碼

package com.xyg.streaming;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

/**
 * Author: Mr.Deng
 * Date: 2018/10/15
 * Desc: 使用flink對(duì)指定窗口內(nèi)的數(shù)據(jù)進(jìn)行實(shí)時(shí)統(tǒng)計(jì)，最終把結(jié)果打印出來(lái)
 *    先在node21機(jī)器上執(zhí)行nc -l 9000
 */
public class StreamingWindowWordCountJava {
  public static void main(String[] args) throws Exception {
  //定義socket的端口號(hào)
  int port;
  try{
    ParameterTool parameterTool = ParameterTool.fromArgs(args);
    port = parameterTool.getInt("port");
  }catch (Exception e){
    System.err.println("沒(méi)有指定port參數(shù)，使用默認(rèn)值9000");
    port = 9000;
  }
  //獲取運(yùn)行環(huán)境
  StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
  //連接socket獲取輸入的數(shù)據(jù)
  DataStreamSource<String> text = env.socketTextStream("node21", port, "\n");
  //計(jì)算數(shù)據(jù)
  DataStream<WordWithCount> windowCount = text.flatMap(new FlatMapFunction<String, WordWithCount>() {
    public void flatMap(String value, Collector<WordWithCount> out) throws Exception {
      String[] splits = value.split("\\s");
      for (String word:splits) {
        out.collect(new WordWithCount(word,1L));
      }
    }
  })//打平操作，把每行的單詞轉(zhuǎn)為<word,count>類型的數(shù)據(jù)
      //針對(duì)相同的word數(shù)據(jù)進(jìn)行分組
      .keyBy("word")
      //指定計(jì)算數(shù)據(jù)的窗口大小和滑動(dòng)窗口大小
      .timeWindow(Time.seconds(2),Time.seconds(1))
      .sum("count");
  //把數(shù)據(jù)打印到控制臺(tái),使用一個(gè)并行度
  windowCount.print().setParallelism(1);
  //注意：因?yàn)閒link是懶加載的，所以必須調(diào)用execute方法，上面的代碼才會(huì)執(zhí)行
  env.execute("streaming word count");
}

  /**
   * 主要為了存儲(chǔ)單詞以及單詞出現(xiàn)的次數(shù)
   */
  public static class WordWithCount{
    public String word;
    public long count;
    public WordWithCount(){}
    public WordWithCount(String word, long count) {
      this.word = word;
      this.count = count;
    }

    @Override
    public String toString() {
      return "WordWithCount{" +
          "word='" + word + '\'' +
          ", count=" + count +
          '}';
    }
  }

}

3.運(yùn)行測(cè)試

首先，使用nc命令啟動(dòng)一個(gè)本地監(jiān)聽(tīng)，命令是：

[admin@node21 ~]$ nc -l 9000

通過(guò)netstat命令觀察9000端口。netstat -anlp | grep 9000，啟動(dòng)監(jiān)聽(tīng)如果報(bào)錯(cuò)：-bash: nc: command not found，請(qǐng)先安裝nc，在線安裝命令：yum -y install nc。

然后，IDEA上運(yùn)行flink官方案例程序

node21上輸入

IDEA控制臺(tái)輸出如下

4.集群測(cè)試

這里單機(jī)測(cè)試官方案例

[admin@node21 flink-1.6.1]$ pwd
/opt/flink-1.6.1
[admin@node21 flink-1.6.1]$ ./bin/start-cluster.sh 
Starting cluster.
Starting standalonesession daemon on host node21.
Starting taskexecutor daemon on host node21.
[admin@node21 flink-1.6.1]$ jps
StandaloneSessionClusterEntrypoint
TaskManagerRunner
Jps
[admin@node21 flink-1.6.1]$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000

程序連接到套接字并等待輸入。您可以檢查Web界面以驗(yàn)證作業(yè)是否按預(yù)期運(yùn)行：

單詞在5秒的時(shí)間窗口（處理時(shí)間，翻滾窗口）中計(jì)算并打印到stdout。監(jiān)視TaskManager的輸出文件并寫(xiě)入一些文本nc（輸入在點(diǎn)擊后逐行發(fā)送到Flink）：

三.使用IDEA開(kāi)發(fā)離線程序

Dataset是flink的常用程序，數(shù)據(jù)集通過(guò)source進(jìn)行初始化，例如讀取文件或者序列化集合，然后通過(guò)transformation（filtering、mapping、joining、grouping）將數(shù)據(jù)集轉(zhuǎn)成，然后通過(guò)sink進(jìn)行存儲(chǔ)，既可以寫(xiě)入hdfs這種分布式文件系統(tǒng)，也可以打印控制臺(tái)，flink可以有很多種運(yùn)行方式，如local、flink集群、yarn等.

1. scala程序

package com.xyg.batch

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

/**
 * Author: Mr.Deng
 * Date: 2018/10/19
 * Desc:
 */
object WordCountScala{
 def main(args: Array[String]) {
  //初始化環(huán)境
  val env = ExecutionEnvironment.getExecutionEnvironment
  //從字符串中加載數(shù)據(jù)
  val text = env.fromElements(
   "Who's there?",
   "I think I hear them. Stand, ho! Who's there?")
  //分割字符串、匯總tuple、按照key進(jìn)行分組、統(tǒng)計(jì)分組后word個(gè)數(shù)
  val counts = text.flatMap { _.toLowerCase.split("\\W+") filter { _.nonEmpty } }
   .map { (_, 1) }
   .groupBy(0)
   .sum(1)
  //打印
  counts.print()
 }
}

2. java程序

package com.xyg.batch;

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.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * Author: Mr.Deng
 * Date: 2018/10/19
 * Desc:
 */
public class WordCountJava {
  public static void main(String[] args) throws Exception {
    //構(gòu)建環(huán)境
    final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    //通過(guò)字符串構(gòu)建數(shù)據(jù)集
    DataSet<String> text = env.fromElements(
        "Who's there?",
        "I think I hear them. Stand, ho! Who's there?");
    //分割字符串、按照key進(jìn)行分組、統(tǒng)計(jì)相同的key個(gè)數(shù)
    DataSet<Tuple2<String, Integer>> wordCounts = text
        .flatMap(new LineSplitter())
        .groupBy(0)
        .sum(1);
    //打印
    wordCounts.print();
  }
  //分割字符串的方法
  public static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
    @Override
    public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
      for (String word : line.split(" ")) {
        out.collect(new Tuple2<String, Integer>(word, 1));
      }
    }
  }
}

3.運(yùn)行

到此這篇關(guān)于Flink開(kāi)發(fā)IDEA環(huán)境搭建與測(cè)試的方法的文章就介紹到這了,更多相關(guān)Flink IDEA環(huán)境搭建內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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