在Eclipse上运行Spark(Standalone,Yarn-Client)

欢迎转载，且请注明出处，在文章页面明显位置给出原文连接。

原文链接：http://www.cnblogs.com/zdfjf/p/5175566.html

我们知道有eclipse的Hadoop插件，能够在eclipse上操作hdfs上的文件和新建mapreduce程序，以及以Run On Hadoop方式运行程序。那么我们可不可以直接在eclipse上运行Spark程序，提交到集群上以YARN-Client方式运行，或者以Standalone方式运行呢？

答案是可以的。下面我来介绍一下如何在eclipse上运行Spark的wordcount程序。我用的hadoop 版本为2.6.2,spark版本为1.5.2。

• 1.Standalone方式运行

• 1.1 新建一个普通的java工程即可，下面直接上代码，

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.frank.spark;

import scala.Tuple2;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;

import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;

public final class JavaWordCount {
  private static final Pattern SPACE = Pattern.compile(" ");

  public static void main(String[] args) throws Exception {

    if (args.length < 1) {
      System.err.println("Usage: JavaWordCount <file>");
      System.exit(1);
    }

    SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");
    sparkConf.setMaster("spark://192.168.0.1:7077");
    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
    ctx.addJar("C:\\Users\\Frank\\sparkwordcount.jar");
    JavaRDD<String> lines = ctx.textFile(args[0], 1);

    JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
      @Override
      public Iterable<String> call(String s) {
        return Arrays.asList(SPACE.split(s));
      }
    });

    JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {
      @Override
      public Tuple2<String, Integer> call(String s) {
        return new Tuple2<String, Integer>(s, 1);
      }
    });

    JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {
      @Override
      public Integer call(Integer i1, Integer i2) {
        return i1 + i2;
      }
    });

    List<Tuple2<String, Integer>> output = counts.collect();
    for (Tuple2<?,?> tuple : output) {
      System.out.println(tuple._1() + ": " + tuple._2());
    }
    ctx.stop();
  }
}

 

代码直接从spark安装包解压后在examples/src/main/java/org/apache/spark/examples/JavaWordCount.java拷贝出来，唯一不同的地方在增加了44行和46行，44行设置了Master,为hadoop的master 结点的IP,端口号为7077。46行设置了工程打包后放置在windows上的路径。

• 1.2 加入spark依赖包spark-assembly-1.5.2-hadoop2.6.0.jar，这个包可以从spark 安装包解压 后在lib目录下。

• 1.3 配置要统计的文件在hdfs上的路径

Run As->Run Configurations

点击Arguments,因为程序中47行要求输入被统计的文件路径，所以在这里配置以下，文件必须放在hdfs上，所以这里的ip也是你的hadoop的master机器的ip.

• 1.4 接下来就是Run程序了，统计的结果会显示在eclipse的控制台。你也可以通过spark的web页面查看刚才提交的程序。

• 2. 以YARN-Client方式运行

• 2.1 先上代码

  /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package com.frank.spark;
  
  import scala.Tuple2;
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaPairRDD;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.api.java.function.FlatMapFunction;
  import org.apache.spark.api.java.function.Function2;
  import org.apache.spark.api.java.function.PairFunction;
  
  import java.util.Arrays;
  import java.util.List;
  import java.util.regex.Pattern;
  
  public final class JavaWordCount {
    private static final Pattern SPACE = Pattern.compile(" ");
  
    public static void main(String[] args) throws Exception {
        
  38     System.setProperty("HADOOP_USER_NAME", "hadoop");
  
      if (args.length < 1) {
        System.err.println("Usage: JavaWordCount <file>");
        System.exit(1);
      }
  
      SparkConf sparkConf = new SparkConf().setAppName("JavaWordCountByFrank01");
      sparkConf.setMaster("yarn-client");
      sparkConf.set("spark.yarn.dist.files", "C:\\software\\workspace\\sparkwordcount\\src\\yarn-site.xml");
      sparkConf.set("spark.yarn.jar", "hdfs://192.168.0.1:9000/user/hadoop/spark-assembly-1.5.2-hadoop2.6.0.jar");
  
      JavaSparkContext ctx = new JavaSparkContext(sparkConf);
      ctx.addJar("C:\\Users\\Frank\\sparkwordcount.jar");
      JavaRDD<String> lines = ctx.textFile(args[0], 1);
  
      JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
        @Override
        public Iterable<String> call(String s) {
          return Arrays.asList(SPACE.split(s));
        }
      });
  
      JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {
        @Override
        public Tuple2<String, Integer> call(String s) {
          return new Tuple2<String, Integer>(s, 1);
        }
      });
  
      JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {
        @Override
        public Integer call(Integer i1, Integer i2) {
          return i1 + i2;
        }
      });
  
      List<Tuple2<String, Integer>> output = counts.collect();
      for (Tuple2<?,?> tuple : output) {
        System.out.println(tuple._1() + ": " + tuple._2());
      }
      ctx.stop();
    }
  }

   

• 2.2 程序解释

38行，如果你的windows用户名和集群上用户名不一样，这里就应该配置一下。比如我windows用户名为Frank,而装有hadoop的集群username为hadoop,这里我就以38行这样设置。

46行，这里配置以yarn-client方式

48行，以这种方式运行时候，每一次运行都会把spark-assembly-1.5.2-hadoop2.6.0.jar包上传到hdfs下这次生成的application-id文件夹下，会耗费几分钟时间，这里也可以配置spark.yarn.jar，先把spark-assembly-1.5.2-hadoop2.6.0.jar上传到hdfs一个目录下，这样就不用每次从windows上传到hdfs下了。参考https://spark.apache.org/docs/1.5.2/running-on-yarn.html

spark.yarn.jar :The location of the Spark jar file, in case overriding the default location is desired. By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, for example, set this configuration to "hdfs:///some/path".

51行，把项目打包后放在windows上的路径。

• 2.3 程序配置

 

 

把3个配置文件放在src下，配置文件从hadoop的linux机器上拷贝下来。

• 2.4 配置要统计的文件在hdfs上的路径

参考1.3,同样结果显示在eclipse控制台。