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

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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,

 1 /* 2  * Licensed to the Apache Software Foundation (ASF) under one or more 3  * contributor license agreements.  See the NOTICE file distributed with 4  * this work for additional information regarding copyright ownership. 5  * The ASF licenses this file to You under the Apache License, Version 2.0 6  * (the "License"); you may not use this file except in compliance with 7  * the License.  You may obtain a copy of the License at 8  * 9  *    http://www.apache.org/licenses/LICENSE-2.010  *11  * Unless required by applicable law or agreed to in writing, software12  * distributed under the License is distributed on an "AS IS" BASIS,13  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14  * See the License for the specific language governing permissions and15  * limitations under the License.16  */17 18 package com.frank.spark;19 20 import scala.Tuple2;21 import org.apache.spark.SparkConf;22 import org.apache.spark.api.java.JavaPairRDD;23 import org.apache.spark.api.java.JavaRDD;24 import org.apache.spark.api.java.JavaSparkContext;25 import org.apache.spark.api.java.function.FlatMapFunction;26 import org.apache.spark.api.java.function.Function2;27 import org.apache.spark.api.java.function.PairFunction;28 29 import java.util.Arrays;30 import java.util.List;31 import java.util.regex.Pattern;32 33 public final class JavaWordCount {34   private static final Pattern SPACE = Pattern.compile(" ");35 36   public static void main(String[] args) throws Exception {37 38     if (args.length < 1) {39       System.err.println("Usage: JavaWordCount <file>");40       System.exit(1);41     }42 43     SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");44     sparkConf.setMaster("spark://192.168.0.1:7077");45     JavaSparkContext ctx = new JavaSparkContext(sparkConf);46     ctx.addJar("C:\\Users\\Frank\\sparkwordcount.jar");47     JavaRDD<String> lines = ctx.textFile(args[0], 1);48 49     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {50       @Override51       public Iterable<String> call(String s) {52         return Arrays.asList(SPACE.split(s));53       }54     });55 56     JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {57       @Override58       public Tuple2<String, Integer> call(String s) {59         return new Tuple2<String, Integer>(s, 1);60       }61     });62 63     JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {64       @Override65       public Integer call(Integer i1, Integer i2) {66         return i1 + i2;67       }68     });69 70     List<Tuple2<String, Integer>> output = counts.collect();71     for (Tuple2<?,?> tuple : output) {72       System.out.println(tuple._1() + ": " + tuple._2());73     }74     ctx.stop();75   }76 }

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

     1 /* 2  * Licensed to the Apache Software Foundation (ASF) under one or more 3  * contributor license agreements.  See the NOTICE file distributed with 4  * this work for additional information regarding copyright ownership. 5  * The ASF licenses this file to You under the Apache License, Version 2.0 6  * (the "License"); you may not use this file except in compliance with 7  * the License.  You may obtain a copy of the License at 8  * 9  *    http://www.apache.org/licenses/LICENSE-2.010  *11  * Unless required by applicable law or agreed to in writing, software12  * distributed under the License is distributed on an "AS IS" BASIS,13  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14  * See the License for the specific language governing permissions and15  * limitations under the License.16  */17 18 package com.frank.spark;19 20 import scala.Tuple2;21 import org.apache.spark.SparkConf;22 import org.apache.spark.api.java.JavaPairRDD;23 import org.apache.spark.api.java.JavaRDD;24 import org.apache.spark.api.java.JavaSparkContext;25 import org.apache.spark.api.java.function.FlatMapFunction;26 import org.apache.spark.api.java.function.Function2;27 import org.apache.spark.api.java.function.PairFunction;28 29 import java.util.Arrays;30 import java.util.List;31 import java.util.regex.Pattern;32 33 public final class JavaWordCount {34   private static final Pattern SPACE = Pattern.compile(" ");35 36   public static void main(String[] args) throws Exception {37       38     System.setProperty("HADOOP_USER_NAME", "hadoop");39 40     if (args.length < 1) {41       System.err.println("Usage: JavaWordCount <file>");42       System.exit(1);43     }44 45     SparkConf sparkConf = new SparkConf().setAppName("JavaWordCountByFrank01");46     sparkConf.setMaster("yarn-client");47     sparkConf.set("spark.yarn.dist.files", "C:\\software\\workspace\\sparkwordcount\\src\\yarn-site.xml");48     sparkConf.set("spark.yarn.jar", "hdfs://192.168.0.1:9000/user/bigdatagfts/spark-assembly-1.5.2-hadoop2.6.0.jar");49 50     JavaSparkContext ctx = new JavaSparkContext(sparkConf);51     ctx.addJar("C:\\Users\\Frank\\sparkwordcount.jar");52     JavaRDD<String> lines = ctx.textFile(args[0], 1);53 54     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {55       @Override56       public Iterable<String> call(String s) {57         return Arrays.asList(SPACE.split(s));58       }59     });60 61     JavaPairRDD<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {62       @Override63       public Tuple2<String, Integer> call(String s) {64         return new Tuple2<String, Integer>(s, 1);65       }66     });67 68     JavaPairRDD<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {69       @Override70       public Integer call(Integer i1, Integer i2) {71         return i1 + i2;72       }73     });74 75     List<Tuple2<String, Integer>> output = counts.collect();76     for (Tuple2<?,?> tuple : output) {77       System.out.println(tuple._1() + ": " + tuple._2());78     }79     ctx.stop();80   }81 }
  • 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。


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