Spark学习笔记-安装部署与运行实例

先解压scala，本次选用版本scala-2.11.1

[hadoop@centos software]$ tar -xzvf scala-2.11.1.tgz

[hadoop@centos software]$ su -

[root@centos ~]# vi /etc/profile

添加如下内容：

SCALA_HOME=/home/hadoop/software/scala-2.11.1

PATH=$SCALA_HOME/bin

EXPORT SCALA_HOME

[root@centos ~]# source /etc/profile

[root@centos ~]# scala -version

Scala code runner version 2.11.1 -- Copyright 2002-2013, LAMP/EPFL

然后解压spark，本次选用版本spark-1.0.0-bin-hadoop1.tgz，这次用的是hadoop 1.0.4

[hadoop@centos software]$ tar -xzvf spark-1.0.0-bin-hadoop1.tgz

进入到spark的conf目录下

[hadoop@centos conf]$ cp spark-env.sh.template spark-env.sh

[hadoop@centos conf]$ vi spark-env.sh

添加如下内容：

export SCALA_HOME=/home/hadoop/software/scala-2.11.1

export SPARK_MASTER_IP=centos.host1

export SPARK_WORKER_MEMORY=2G

export JAVA_HOME=/usr/software/jdk

如果是要集群安装部署的话，需要修改文件conf/slaves，添加要作为worker的主机

然后将spark-1.0.0-bin-hadoop1目录拷贝到相应主机上，注意目录要一致。

启动

[hadoop@centos spark-1.0.0-bin-hadoop1]$ sbin/start-master.sh

可以通过 http://centos.host1:8080/ 看到对应界面

[hadoop@centos spark-1.0.0-bin-hadoop1]$ sbin/start-slaves.sh spark://centos.host1:7077

可以通过 http://centos.host1:8081/ 看到对应界面

下面在spark上运行第一个例子：与Hadoop交互的WordCount

首先将word.txt文件上传到HDFS上，这里路径是 hdfs://centos.host1:9000/user/hadoop/data/wordcount/001/word.txt

进入交互模式

[hadoop@centos spark-1.0.0-bin-hadoop1]$ master=spark://centos.host1:7077 ./bin/spark-shell

scala>val file=sc.textFile("hdfs://centos.host1:9000/user/hadoop/data/wordcount/001/word.txt")

scala>val count=file.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey(_+_)

scala>count.collect()

可以看到控制台有如下结果：

res0: Array[(String, Int)] = Array((hive,2), (zookeeper,1), (pig,1), (spark,1), (hadoop,4), (hbase,2))

同时也可以将结果保存到HDFS上

scala>count.saveAsTextFile("hdfs://centos.host1:9000/user/hadoop/data/wordcount/001/result.txt")

接下来再来看下如何运行Java版本的WordCount

这里需要用到一个jar文件：spark-assembly-1.0.0-hadoop1.0.4.jar

WordCount代码如下：

[java] view
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public class WordCount {

private static final Pattern SPACE = Pattern.compile(" ");

@SuppressWarnings("serial")

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");

JavaSparkContext ctx = new JavaSparkContext(sparkConf);

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();

}

}

导出类文件生成jar包，这里生成为mining.jar。然后执行下面命令，其中--class 指定主类，--master 指定spark master地址，后面是执行的jar和需要的参数。

[hadoop@centos spark-1.0.0-bin-hadoop1]$ bin/spark-submit --class org.project.modules.spark.java.WordCount --master spark://centos.host1:7077 /home/hadoop/project/mining.jar hdfs://centos.host1:9000/user/hadoop/data/wordcount/001/word.txt

可以看到控制台有如下结果：

spark : 1

hive : 2

hadoop : 4

zookeeper : 1

pig : 1

hbase : 2

最后再来看下如何运行Python版本的WordCount
WordCount代码如下：

[python] view
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import sys

from operator import add

from pyspark import SparkContext

if __name__ == "__main__":

if len(sys.argv) != 2:

print >> sys.stderr, "Usage: wordcount <file>"

exit(-1)

sc = SparkContext(appName="PythonWordCount")

lines = sc.textFile(sys.argv[1], 1)

counts = lines.flatMap(lambda x: x.split(' ')) \

.map(lambda x: (x, 1)) \

.reduceByKey(add)

output = counts.collect()

for (word, count) in output:

print "%s: %i" % (word, count)

输入文件路径可以是本地也可以是HDFS上文件，命令如下：
[hadoop@centos spark-1.0.0-bin-hadoop1]$ bin/spark-submit --master spark://centos.host1:7077 /home/hadoop/project/WordCount.py /home/hadoop/temp/word.txt

[hadoop@centos spark-1.0.0-bin-hadoop1]$ bin/spark-submit --master spark://centos.host1:7077 /home/hadoop/project/WordCount.py hdfs://centos.host1:9000/user/hadoop/data/wordcount/001/word.txt

可以看到控制台有如下结果：
spark: 1
hbase: 2
hive: 2
zookeeper: 1
hadoop: 4
pig: 1

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