Spark 1.6.2 + Hadoop 2.7.2 集群搭建

版本信息：Centos7 + Hadoop 2.7.2 + Spark 1.6.2 + Scala 2.11.8

Hadoop + Spark 集群搭建系列文章，建议按顺序参考：

Hadoop & Spark 集群搭建 理念思想

Hadoop 2.7.2 集群搭建－预备工作

Hadoop 2.7.2 集群搭建

Spark 1.6.2 + Hadoop 2.7.2 集群搭建 (不用点了，就是本文)

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预备工作

JDK 安装配置

请参考 Hadoop 2.7.2 集群搭建－预备工作中 JDK 的安装和配置。

Scala 安装配置

1.下载后解压至合适目录

[liuyao@master ~]$ cd 03Software/04BigData/
[liuyao@master 04BigData]$ tar -xzvf scala-2.11.8.tgz --directory=/home/liuyao/00Hadoop/
scala-2.11.8/
scala-2.11.8/man/
scala-2.11.8/man/man1/
……

2.配置环境变量，在 /etc/profile 中添加如下内容

export SCALA_HOME=/home/liuyao/00Hadoop/scala-2.11.8
export PATH=$PATH:${SCALA_HOME}/bin

3.使 /etc/profile 文件更新生效

[liuyao@master ~]$ source /etc/profile

4.在 master 主机上配置完毕后，把Scala整个文件夹分发到slave各节点，并配置环境变量

SSH配置

主要是配置 SSH 以实现集群各节点间免密码登录（公钥登录），请参考 Hadoop 2.7.2 集群搭建－预备工作中 SSH 的安装和配置。

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Hadoop安装配置

此部分所有步骤一一列出，但部分步骤省略操作细节，详情请参考 Hadoop 2.7.2 集群搭建 一文中的内容。

1.下载后解压至合适目录

2.配置 Hadoop 环境变量

3.编辑配置文件

(1) 配置hadoop-env.sh文件

(2) 配置core-site.xml文件

<configuration>
    <property>
        <name>fs.defaultFSname>
      <value>hdfs://master:9000value>
    property>
    <property> 
        <name>hadoop.tmp.dirname> 
        <value>/home/liuyao/00Hadoop/hadoop-2.7.2/tmpvalue> 
    property>
    <property>
        <name>io.file.buffer.sizename>
        <value>131072value>
    property>
    <property>
        <name>hadoop.proxyuser.hduser.hostsname> #Hadoop全分布模式里，此处是spark而非hduser?下同
        <value>*value>
    property>
    <property>
        <name>hadoop.proxyuser.hduser.groupsname>
        <value>*value>
    property>
configuration>

(3) 配置yarn-site.xml文件

<configuration>
    <property>
        <name>yarn.nodemanager.aux-servicesname>
        <value>mapreduce_shufflevalue>
    property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.classname>
        <value>org.apache.hadoop.mapred.ShuffleHandlervalue>
    property>
    <property>
        <name>yarn.resourcemanager.addressname>
        <value>master:8032value>
    property>
    <property>
        <name>yarn.resourcemanager.scheduler.addressname>
        <value>master:8030value>
    property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.addressname>
        <value>master:8031value> #Hadoop全分布模式里，此处为8035而非8031?
    property>
    <property>
        <name>yarn.resourcemanager.admin.addressname>
        <value>master:8033value>
    property>
    <property>
       <name>yarn.resourcemanager.webapp.addressname>
       <value>master:8088value>
    property>
configuration>

(4) 配置mapred-site.xml文件，完全同Hadoop全分布模式

<configuration>
    <property>
        <name>mapreduce.framework.namename>
        <value>yarnvalue>
    property>
    <property>
        <name>mapreduce.jobhistory.addressname>
        <value>master:10020value>
    property>
    <property>
        <name>mapreduce.jobhistory.webapp.addressname>
        <value>master:19888value>
    property>
configuration>

(5) 配置hdfs-site.xml文件，完全同Hadoop全分布模式

<configuration>
    <property>
        <name>dfs.namenode.secondary.http-addressname>
        <value>master:9001value>
    property>
    <property>
        <name>dfs.replicationname>
        <value>2value>
    property>
    <property>
        <name>dfs.webhdfs.enabledname>
        <value>truevalue>
    property>
configuration>

(6) 配置slaves文件，完全同 Hadoop 全分布模式

101.X.XX.XX1
101.X.XX.XX2

(7) 将以上配置文件分发到各个节点，实际上应该直接把整个 Hadoop 目录分发过去（若从节点没安装Hadoop的话）

4.格式化namenode，启动并测试 Hadoop

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Spark 安装配置

下载安装并配置环境

1.从官网上下载后解压至合适目录

选择的版本是spark-1.6.2-bin-hadoop2.6.tgz，虽然Hadoop是2.7.2的，之前下载的是without-hadoop版本，运行出错。

[liuyao@master 04BigData]$ tar -xzvf spark-1.6.2-bin-hadoop2.6.tgz --directory=/home/liuyao/00Hadoop/
spark-1.6.2-bin-hadoop2.6/
spark-1.6.2-bin-hadoop2.6/NOTICE
spark-1.6.2-bin-hadoop2.6/CHANGES.txt
……

2.配置 Spark 环境变量，在 /etc/profie 里添加如下两行

export SPARK_HOME=/home/liuyao/00Hadoop/spark-1.6.2-bin-hadoop2.6
export PATH=$PATH:${SPARK_HOME}/bin

编辑配置文件

1.配置spark-env.sh文件，加入下面内容

export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.91-0.b14.el7_2.x86_64
export SCALA_HOME=/home/liuyao/00Hadoop/scala-2.11.8
export SPARK_WORKER_MEMORY=4g
export SPARK_MASTER_IP=101.X.XX.XX0
export MASTER=spark://101.X.XX.XX0:7077
export HADOOP_CONF_DIR=/home/liuyao/00Hadoop/hadoop-2.7.2/etc/hadoop

2.配置slaves文件，加入下面内容

101.X.XX.XX1
101.X.XX.XX2

3.将以上配置文件分发到各个节点，实际上应该直接把整个 Spark 目录分发过去（若从节点没安装 Spark 的话）

启动集群

1.启动 Hadoop

# master节点
[liuyao@master hadoop-2.7.2]$ start-dfs.sh
Starting namenodes on [master]
master: starting namenode, logging to ……
101.X.XX.XX2: starting datanode, logging to ……
101.X.XX.XX1: starting datanode, logging to ……
Starting secondary namenodes [master]
master: starting secondarynamenode, logging to ……

[liuyao@master hadoop-2.7.2]$ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to ……
101.X.XX.XX2: starting nodemanager, logging to ……
101.X.XX.XX1: starting nodemanager, logging to ……

[liuyao@master hadoop-2.7.2]$ jps
6441 Jps
5755 SecondaryNameNode
5373 NameNode
6126 ResourceManager

# slave1节点，slave2节点与之一样
[liuyao@slave1 hadoop-2.7.2]$ jps
5328 DataNode
5958 Jps
5661 NodeManager

2.启动 Spark

# master节点，可以看到，成功启动了Master进程
[liuyao@master spark-1.6.2-bin-hadoop2.6]$ ./sbin/start-all.sh 
starting org.apache.spark.deploy.master.Master, logging to ……
101.X.XX.XX2: starting org.apache.spark.deploy.worker.Worker, logging to ……
101.X.XX.XX1: starting org.apache.spark.deploy.worker.Worker, logging to ……

[liuyao@master spark-1.6.2-bin-hadoop2.6]$ jps
6485 Master
6584 Jps
5755 SecondaryNameNode
5373 NameNode
6126 ResourceManager

# slave1节点，可以看到，成功启动了Worker进程，slave2节点与之一样
[liuyao@slave1 hadoop-2.7.2]$ jps
5328 DataNode
6090 Worker
6282 Jps
5661 NodeManager

集群测试

Spark支持两种方式运行样例： run-example 方式和 Spark Shell 方式

1.run-example 方式

(1) 输入代码

[liuyao@master spark-1.6.2-bin-hadoop2.6]$ ./bin/run-example org.apache.spark.examples.SparkPi
16/07/16 22:39:22 INFO spark.SparkContext: Running Spark version 1.6.2
16/07/16 22:39:22 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
……

(2) 通过Web UI 查看集群状态

浏览器中输入http://master:8080，可以观察集群整个状态，如下图所示

2.Spark Shell 方式

(1) 上传文件至HDFS

[liuyao@master hadoop-2.7.2]$ hdfs dfs -ls /
Found 1 items
drwx-wx-wx   - liuyao supergroup          0 2016-07-16 23:33 /tmp
[liuyao@master hadoop-2.7.2]$ hdfs dfs -put README.txt /tmp/
[liuyao@master hadoop-2.7.2]$ hdfs dfs -ls /tmp/
Found 2 items
-rw-r--r--   2 liuyao supergroup       1366 2016-07-16 23:45 /tmp/README.txt
drwx-wx-wx   - liuyao supergroup          0 2016-07-16 23:33 /tmp/hive

(2) 启动Spark-shell

[liuyao@master bin]$ spark-shell 
16/07/16 23:33:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
……
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.6.2
      /_/

Using Scala version 2.10.5 (OpenJDK 64-Bit Server VM, Java 1.8.0_91)
……
16/07/16 23:33:30 INFO repl.SparkILoop: Created spark context..
Spark context available as sc.
……
16/07/16 23:34:07 INFO repl.SparkILoop: Created sql context (with Hive support)..
SQL context available as sqlContext.

(3) 交互式操作

scala> val readmeFile = sc.textFile("hdfs://master:9000/tmp/README.txt")
16/07/16 23:46:21 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 62.4 KB, free 62.4 KB)
……
readmeFile: org.apache.spark.rdd.RDD[String] = hdfs://master:9000/tmp/README.txt MapPartitionsRDD[1] at textFile at :27

scala> var theCount = readmeFile.filter(line=>line.contains("The"))
theCount: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at :29

scala> theCount.count
16/07/16 23:47:19 INFO mapred.FileInputFormat: Total input paths to process : 1
…… 
16/07/16 23:47:21 INFO scheduler.DAGScheduler: Job 0 finished: count at :32, took 1.251228 s
res0: Long = 4

scala> val wordCount = readmeFile.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey(_+_)
wordCount: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[5] at reduceByKey at :29

scala> wordCount.collect
16/07/16 23:48:49 INFO spark.SparkContext: Starting job: collect at :32
…… 
16/07/16 23:48:49 INFO scheduler.DAGScheduler: Job 1 finished: collect at :32, took 0.222501 s
res1: Array[(String, Int)] = Array((under,1), (this,3), (distribution,2), (Technology,1), (country,1), (is,1), (Jetty,1), (currently,1), (permitted.,1), (check,1), (have,1), (Security,1), (U.S.,1), (with,1), (BIS,1), (This,1), (mortbay.org.,1), ((ECCN),1), (using,2), (security,1), (Department,1), (export,1), (reside,1), (any,1), (algorithms.,1), (from,1), (re-export,2), (has,1), (SSL,1), (Industry,1), (Administration,1), (details,1), (provides,1), (http://hadoop.apache.org/core/,1), (country's,1), (Unrestricted,1), (740.13),1), (policies,1), (country,,1), (concerning,1), (uses,1), (Apache,1), (possession,,2), (information,2), (our,2), (as,1), ("",18), (Bureau,1), (wiki,,1), (please,2), (form,1), (information.,1), (ENC,1), (Export,2), (included,1), (asymmetric,1), (Commodity,1), (Softwar...

(4) 通过Web UI 查看集群状态
Spark - Job 界面：http://master:4040/jobs/

Spark - Stage 界面：http://master:4040/stages/

Spark - Master & Workers 界面：http://master:8080/

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参考资料

http://www.cnblogs.com/tonylp/p/5233369.html

http://www.open-open.com/lib/view/open1419490748562.html

http://blog.csdn.net/stark_summer/article/details/42458081