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。