ubuntu下spark安装配置

一、安装vmware虚拟机

二、在虚拟机上安装ubuntu12.04操作系统

三、安装jdk1.8.0_25

http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html

注意：下载操作系统对应版本的jdk

解压：

tar -xzvf jdk-8u25-linux-i586.tar.gz

配置环境变量参数

sudo gedit /etc/profile

export JAVA_HOME=/home/yuanqin/Downloads/jdk1.8.0_25   (此地址为jdk安装路径，每个人根据自己jdk的安装地址进行配置)
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export PATH=${JAVA_HOME}/bin:$PATH

验证是否安装成功：java -version

手动设置系统默认jdk:

sudo update-alternatives --install /usr/bin/java java /home/yuanqin/Downloads/jdk1.8.0_25/bin/java 300

sudo update-alternatives --install /usr/bin/javac javac /home/yuanqin/Downloads/jdk1.8.0_25/bin/javac 300

sudo update-alternatives --config java

四、安装ssh并设置免密码登录

sudo apt-get install ssh

配置为可以免密码登录本机，首先查看yuanqin用户下是否有.ssh文件，没有的话自己创建一个

查看代码：ls -a /home/yuanqin

配置免密码登录的代码： ssh-keygen -t dsa

cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

验证是否安装成功：ssh -version    ; ssh localhost

五、安装hadoop-1.2.1

http://mirrors.cnnic.cn/apache/hadoop/common/hadoop-1.2.1/

解压：

tar -xzvf hadoop-1.2.1.tar.gz
配置jdk安装位置：
sudo gedit /home/yuanqin/Downloads/hadoop-1.2.1/conf/hadoop-env.sh
值：export JAVA_HOME=/home/yuanqin/Downloads/jdk1.8.0-25

配置core-site.xml文件：

sudo gedit /home/yuanqin/Downloads/hadoop-1.2.1/conf/core-site.xml

值：<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>

配置hdfs-site.xml文件：

sudo gedit /home/yuanqin/Downloads/hadoop-1.2.1/conf/hdfs-site.xml

值：<configuration>
<property>
<name>dfs.replication</name>
<value>3</value>
</property>
</configuration>

配置mapre-site.xml文件：

sudo gedit /home/yuanqin/Downloads/hadoop-1.2.1/conf/mapre-site.xml

值：<configuration>
<property>
<name>mapred.job.tracker</name>
<value>localhost:9001</value>
</property>
</configuration>

接下来先格式化文件系统hdfs,进入hadoop文件夹，输入：bin/hadoop namenode -format
启动hadoop: bin/start-all.sh(bin/start-dfs.sh 启动hdfs;    bin/start-mapred.sh 启动mapreduce)
验证hadoop是否安装成功；

在浏览器分别输入网址：http://localhost:50030  (mapreduce页面)

                   http://localhost:50030  (hdfs页面)

六、安装scala-2.10.3
参考：http://shiyanjun.cn/archives/696.html

• 下载安装配置Scala

1	wget http://www.scala-lang.org/files/archive/scala-2.10.3.tgz

2	tar xvzf scala-2.10.3.tgz

在~/.bashrc中增加环境变量SCALA_HOME，并使之生效：

1	export SCALA_HOME=/usr/scala/scala-2.10.3

2	export PATH=$PATH:$SCALA_HOME/bin

• 下载安装配置Spark

我们首先在主节点m1上配置Spark程序，然后将配置好的程序文件复制分发到集群的各个从结点上。下载解压缩：

1	wget http://d3kbcqa49mib13.cloudfront.net/spark-0.9.0-incubating-bin-hadoop1.tgz

2	tar xvzf spark-0.9.0-incubating-bin-hadoop1.tgz

在~/.bashrc中增加环境变量SPARK_HOME，并使之生效：

 

1	export SPARK_HOME=/home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1

2	export PATH=$PATH:$SPARK_HOME/bin

在m1上配置Spark，修改spark-env.sh配置文件：

1	cd /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/conf

2	cp spark-env.sh.template spark-env.sh

在该脚本文件中，同时将SCALA_HOME配置为Unix环境下实际指向路径，例如：

1	export SCALA_HOME=/usr/scala/scala-2.10.3

修改conf/slaves文件，将计算节点的主机名添加到该文件，一行一个，例如：

1

s1

2

s2

3

s3

最后，将Spark的程序文件和配置文件拷贝分发到从节点机器上：

 

1	scp -r ~/cloud/programs/spark-0.9.0-incubating-bin-hadoop1 shirdrn@s1:~/cloud/programs/

2	scp -r ~/cloud/programs/spark-0.9.0-incubating-bin-hadoop1 shirdrn@s2:~/cloud/programs/

3	scp -r ~/cloud/programs/spark-0.9.0-incubating-bin-hadoop1 shirdrn@s3:~/cloud/programs/

启动Spark集群

我们会使用HDFS集群上存储的数据作为计算的输入，所以首先要把Hadoop集群安装配置好，并成功启动，我这里使用的是Hadoop 1.2.1版本。启动Spark计算集群非常简单，执行如下命令即可：

1	cd /home/shirdrn/cloud/programs/spark-0.9.0-incubating-bin-hadoop1/

2	sbin/start-all.sh

可以看到，在m1上启动了一个名称为Master的进程，在s1上启动了一个名称为Worker的进程，如下所示，我这里也启动了Hadoop集群：
主节点m1上：

1	54968 SecondaryNameNode

2	55651 Master

3	55087 JobTracker

4	54814 NameNode

5

6

从节点s1上：

7	33592 Worker

8	33442 TaskTracker

9	33336 DataNode

各个进程是否启动成功，也可以查看日志来诊断，例如：

1

主节点上：

2	tail -100f $SPARK_HOME/logs/spark-shirdrn-org.apache.spark.deploy.master.Master-1-m1.out

3

从节点上：

4	tail -100f $SPARK_HOME/logs/spark-shirdrn-org.apache.spark.deploy.worker.Worker-1-s1.out

Spark集群计算验证

我们使用我的网站的访问日志文件来演示，示例如下：

1	27.159.254.192 - - [21/Feb/2014:11:40:46 +0800] "GET /archives/526.html HTTP/1.1" 200 12080 "http://shiyanjun.cn/archives/526.html" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

2	120.43.4.206 - - [21/Feb/2014:10:37:37 +0800] "GET /archives/417.html HTTP/1.1" 200 11464 "http://shiyanjun.cn/archives/417.html/" "Mozilla/5.0 (Windows NT 5.1; rv:11.0) Gecko/20100101 Firefox/11.0"

统计该文件里面IP地址出现频率，来验证Spark集群能够正常计算。另外，我们需要从HDFS中读取这个日志文件，然后统计IP地址频率，最后将结果再保存到HDFS中的指定目录。
首先，需要启动用来提交计算任务的Spark Shell：

1	bin/spark-shell

在Spark Shell上只能使用Scala语言写代码来运行。
然后，执行统计IP地址频率，在Spark Shell中执行如下代码来实现：

1	val file = sc.textFile("hdfs://m1:9000/user/shirdrn/wwwlog20140222.log")

2	val result = file.flatMap(line => line.split("\\s+.*")).map(word => (word, 1)).reduceByKey((a, b) => a + b)

上述的文件hdfs://m1:9000/user/shirdrn/wwwlog20140222.log是输入日志文件。处理过程的日志信息，示例如下所示：

14/03/06 21:59:22 INFO MemoryStore: ensureFreeSpace(784) called with curMem=43296, maxMem=311387750

02	14/03/06 21:59:22 INFO MemoryStore: Block broadcast_11 stored as values to memory (estimated size 784.0 B, free 296.9 MB)

03	14/03/06 21:59:22 INFO FileInputFormat: Total input paths to process : 1

04	14/03/06 21:59:22 INFO SparkContext: Starting job: collect at <console>:13

05	14/03/06 21:59:22 INFO DAGScheduler: Registering RDD 84 (reduceByKey at <console>:13)

06	14/03/06 21:59:22 INFO DAGScheduler: Got job 10 (collect at <console>:13) with 1 output partitions (allowLocal=false)

07	14/03/06 21:59:22 INFO DAGScheduler: Final stage: Stage 20 (collect at <console>:13)

08	14/03/06 21:59:22 INFO DAGScheduler: Parents of final stage: List(Stage 21)

09	14/03/06 21:59:22 INFO DAGScheduler: Missing parents: List(Stage 21)

10	14/03/06 21:59:22 INFO DAGScheduler: Submitting Stage 21 (MapPartitionsRDD[84] at reduceByKey at <console>:13), which has no missing parents

11	14/03/06 21:59:22 INFO DAGScheduler: Submitting 1 missing tasks from Stage 21 (MapPartitionsRDD[84] at reduceByKey at <console>:13)

12	14/03/06 21:59:22 INFO TaskSchedulerImpl: Adding task set 21.0 with 1 tasks

13	14/03/06 21:59:22 INFO TaskSetManager: Starting task 21.0:0 as TID 19 on executor localhost: localhost (PROCESS_LOCAL)

14	14/03/06 21:59:22 INFO TaskSetManager: Serialized task 21.0:0 as 1941 bytes in 0 ms

15	14/03/06 21:59:22 INFO Executor: Running task ID 19

16	14/03/06 21:59:22 INFO BlockManager: Found block broadcast_11 locally

17	14/03/06 21:59:22 INFO HadoopRDD: Input split:hdfs://m1:9000/user/shirdrn/wwwlog20140222.log:0+4179514

18	14/03/06 21:59:23 INFO Executor: Serialized size of result for 19 is 738

19	14/03/06 21:59:23 INFO Executor: Sending result for 19 directly to driver

20	14/03/06 21:59:23 INFO TaskSetManager: Finished TID 19 in 211 ms on localhost (progress: 0/1)

21	14/03/06 21:59:23 INFO TaskSchedulerImpl: Remove TaskSet 21.0 from pool

22	14/03/06 21:59:23 INFO DAGScheduler: Completed ShuffleMapTask(21, 0)

23	14/03/06 21:59:23 INFO DAGScheduler: Stage 21 (reduceByKey at <console>:13) finished in 0.211 s

24	14/03/06 21:59:23 INFO DAGScheduler: looking for newly runnable stages

25	14/03/06 21:59:23 INFO DAGScheduler: running: Set()

26	14/03/06 21:59:23 INFO DAGScheduler: waiting: Set(Stage 20)

27	14/03/06 21:59:23 INFO DAGScheduler: failed: Set()

28	14/03/06 21:59:23 INFO DAGScheduler: Missing parents for Stage 20: List()

29	14/03/06 21:59:23 INFO DAGScheduler: Submitting Stage 20 (MapPartitionsRDD[86] at reduceByKey at <console>:13), which is now runnable

30	14/03/06 21:59:23 INFO DAGScheduler: Submitting 1 missing tasks from Stage 20 (MapPartitionsRDD[86] at reduceByKey at <console>:13)

31	14/03/06 21:59:23 INFO TaskSchedulerImpl: Adding task set 20.0 with 1 tasks

14/03/06 21:59:23 INFO Executor: Finished task ID 19

33	14/03/06 21:59:23 INFO TaskSetManager: Starting task 20.0:0 as TID 20 on executor localhost: localhost (PROCESS_LOCAL)

34	14/03/06 21:59:23 INFO TaskSetManager: Serialized task 20.0:0 as 1803 bytes in 0 ms

35	14/03/06 21:59:23 INFO Executor: Running task ID 20

36	14/03/06 21:59:23 INFO BlockManager: Found block broadcast_11 locally

37	14/03/06 21:59:23 INFO BlockFetcherIterator$BasicBlockFetcherIterator: Getting 1 non-zero-bytes blocks out of 1 blocks

38	14/03/06 21:59:23 INFO BlockFetcherIterator$BasicBlockFetcherIterator: Started 0 remote gets in  1 ms

39	14/03/06 21:59:23 INFO Executor: Serialized size of result for 20 is 19423

40	14/03/06 21:59:23 INFO Executor: Sending result for 20 directly to driver

41	14/03/06 21:59:23 INFO TaskSetManager: Finished TID 20 in 17 ms on localhost (progress: 0/1)

42	14/03/06 21:59:23 INFO TaskSchedulerImpl: Remove TaskSet 20.0 from pool

43	14/03/06 21:59:23 INFO DAGScheduler: Completed ResultTask(20, 0)

44	14/03/06 21:59:23 INFO DAGScheduler: Stage 20 (collect at <console>:13) finished in 0.016 s

45	14/03/06 21:59:23 INFO SparkContext: Job finished: collect at <console>:13, took 0.242136929 s

46	14/03/06 21:59:23 INFO Executor: Finished task ID 20

47

res14: Array[(String, Int)] = Array((27.159.254.192,28), (120.43.9.81,40), (120.43.4.206,16), (120.37.242.176,56), (64.31.25.60,2), (27.153.161.9,32), (202.43.145.163,24), (61.187.102.6,1), (117.26.195.116,12), (27.153.186.194,64), (123.125.71.91,1), (110.85.106.105,64), (110.86.184.182,36), (27.150.247.36,52), (110.86.166.52,60), (175.98.162.2,20), (61.136.166.16,1), (46.105.105.217,1), (27.150.223.49,52), (112.5.252.6,20), (121.205.242.4,76), (183.61.174.211,3), (27.153.230.35,36), (112.111.172.96,40), (112.5.234.157,3), (144.76.95.232,7), (31.204.154.144,28), (123.125.71.22,1), (80.82.64.118,3), (27.153.248.188,160), (112.5.252.187,40), (221.219.105.71,4), (74.82.169.79,19), (117.26.253.195,32), (120.33.244.205,152), (110.86.165.8,84), (117.26.86.172,136), (27.153.233.101,8), (123.12...

可以看到，输出了经过map和reduce计算后的部分结果。
最后，我们想要将结果保存到HDFS中，只要输入如下代码：

1	result.saveAsTextFile("hdfs://m1:9000/user/shirdrn/wwwlog20140222.log.result")

查看HDFS上的结果数据：

 

1	[shirdrn@m1 ~]$ hadoop fs -cat /user/shirdrn/wwwlog20140222.log.result/part-00000 | head -5

2	(27.159.254.192,28)

3	(120.43.9.81,40)

4	(120.43.4.206,16)

5	(120.37.242.176,56)

6	(64.31.25.60,2)