【原創 Spark動手實踐 1】Hadoop2.7.3安裝部署實際動手

阿新 • • 發佈：2017-05-08

dmi 遠程 nag proc host 一個 error img 連接

目錄：

第一部分：操作系統準備工作：

　　1. 安裝部署CentOS7.3 1611

　　2. CentOS7軟件安裝（net-tools, wget, vim等）

　　3. 更新CentOS7的Yum源，更新軟件速度更快

　　4. CentOS 用戶配置，Sudo授權

第二部分：Java環境準備

　　1. JDK1.8 安裝與配置

第三部分：Hadoop配置,啟動與驗證

　　1. 解壓Hadoop2.7.3更新全局變量

　　2. 更新Hadoop配置文件

　　3. 啟動Hadoop

　　4. 驗證Hadoop

=============================================================================================

第一部分：操作系統準備工作：

　　1. 安裝部署CentOS7.3 1611

　　2. CentOS7軟件安裝（net-tools, wget, vim等）

　　3. 更新CentOS7的Yum源，更新軟件速度更快

　　4. CentOS 用戶配置，Sudo授權

1. 安裝部署CentOS7.3 1611

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2. CentOS7軟件安裝（net-tools, wget, vim等）

sudo yum install -y net-tools

sudo yum install -y wget

sudo yum install -y vim

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3. 更新CentOS7的Yum源(更新為阿裏雲的CentOS7的源)，更新軟件速度更快

http://mirrors.aliyun.com/help/centos

1、備份

mv /etc/yum.repos.d/CentOS-Base.repo /etc/yum.repos.d/CentOS-Base.repo.backup

2、下載新的CentOS-Base.repo 到/etc/yum.repos.d/

CentOS 5

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-5.repo

CentOS 6

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-6.repo

CentOS 7

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-7.repo

3、之後運行yum makecache生成緩存

4、sudo yum -y update 對系統進行升級

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sudo vim /etc/hosts # 更新hosts文件，便於用spark02代表本機IP

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第二部分：Java環境準備

　　1. JDK1.8 安裝與配置

通過FileZilla 上傳實驗所需要用到的文件（JDK，Hadoop，Spark）

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對JDK和Hadoop進行解壓

tar -zxvf jdk-8u121-linux-x64.tar.gz

tar -zxvf hadoop-2.7.3.tar.gz

在 .bash_profile文件內增加環境便利，便於Java和Hadoop更容易操作

#Add JAVA_HOME and HADOOP_HOME
export JAVA_HOME=/home/spark/jdk1.8.0_121
export PATH=$PATH:$JAVA_HOME/bin
export HADOOP_HOME=/home/spark/hadoop-2.7.3
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

source .bash_profile #配置生效

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第三部分：Hadoop配置,啟動與驗證

　　1. 解壓Hadoop2.7.3更新全局變量

　　2. 更新Hadoop配置文件

　　3. 啟動Hadoop

　　4. 驗證Hadoop

參考：Hadoop2.7.3 官方文檔進行偽分布式的配置

http://hadoop.apache.org/docs/r2.7.3/hadoop-project-dist/hadoop-common/SingleCluster.html

Pseudo-Distributed Operation

Hadoop can also be run on a single-node in a pseudo-distributed mode where each Hadoop daemon runs in a separate Java process.

Configuration

Use the following:

etc/hadoop/core-site.xml:

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

etc/hadoop/hdfs-site.xml:

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

Setup passphraseless ssh

Now check that you can ssh to the localhost without a passphrase:

  $ ssh localhost

If you cannot ssh to localhost without a passphrase, execute the following commands:

  $ ssh-keygen -t rsa -P ‘‘ -f ~/.ssh/id_rsa
  $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys

Execution

The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see YARN on Single Node.

Format the filesystem:
```
  $ bin/hdfs namenode -format
```
Start NameNode daemon and DataNode daemon:
```
  $ sbin/start-dfs.sh
```
The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to $HADOOP_HOME/logs).
Browse the web interface for the NameNode; by default it is available at:
- NameNode - http://localhost:50070/

Make the HDFS directories required to execute MapReduce jobs:

  $ bin/hdfs dfs -mkdir /user
  $ bin/hdfs dfs -mkdir /user/<username>

Copy the input files into the distributed filesystem:
```
  $ bin/hdfs dfs -put etc/hadoop input
```

Run some of the examples provided:

  $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar grep input output ‘dfs[a-z.]+‘

Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:
```
  $ bin/hdfs dfs -get output output
  $ cat output/*
```
or

View the output files on the distributed filesystem:
```
  $ bin/hdfs dfs -cat output/*
```
When you’re done, stop the daemons with:
```
  $ sbin/stop-dfs.sh
```

YARN on a Single Node

You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.

The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.

Configure parameters as follows:etc/hadoop/mapred-site.xml:

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

etc/hadoop/yarn-site.xml:

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
</configuration>

Start ResourceManager daemon and NodeManager daemon:
```
  $ sbin/start-yarn.sh
```
Browse the web interface for the ResourceManager; by default it is available at:
- ResourceManager - http://localhost:8088/
Run a MapReduce job.
When you’re done, stop the daemons with:
```
  $ sbin/stop-yarn.sh
```
配置免密碼，否則運行的時候會報錯。

Setup passphraseless ssh

Now check that you can ssh to the localhost without a passphrase:
```
  $ ssh localhost
```
If you cannot ssh to localhost without a passphrase, execute the following commands:
```
  $ ssh-keygen -t rsa -P ‘‘ -f ~/.ssh/id_rsa
  $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys
```
Hadoop配置文件具體的配置信息如下:

1. vim etc/hadoop/hadoop-env.sh

#export JAVA_HOME=${JAVA_HOME}
export JAVA_HOME=/home/spark/jdk1.8.0_121

2. vim etc/hadoop/core-site.xml



<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://spark01:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/home/spark/hadoopdata</value>
</property>
</configuration>

3. vim etc/hadoop/hdfs-site.xml

<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>

4. vim etc/hadoop/mapred-site.xml

<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
5.vim etc/hadoop/yarn-site.xml
<configuration>


<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
</configuration>
1. 對HDFS進行格式化 hdfs namenode -format

2.啟動HDFS

start-dfs.sh

3. 啟動YARN

start-yarn.sh

關閉防火墻，其他服務器才能進行訪問和驗證

[[email protected] hadoop-2.7.3]$ systemctl stop firewalld.service
==== AUTHENTICATING FOR org.freedesktop.systemd1.manage-units ===
Authentication is required to manage system services or units.
Authenticating as: spark
Password:
==== AUTHENTICATION COMPLETE ===

http://spark02:8088

http://spark02:50070

使用HDFS創建目錄，拷貝文件和查看文件
hdfs dfs -mkdir hdfs://user/jonson/input hdfs dfs -cp etc/hadoop hdfs://user/jonson/input hdfs dfs -ls hdfs://user/jonson/input hdfs dfs -mkdir hdfs://user/jonson/output hdfs dfs -rmdir hdfs://user/jonson/output hdfs dfs -ls hdfs://user/jonson 嘗試使用MapReduce計算框架 [[email protected] hadoop-2.7.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar grep hdfs:///user/jonson/input/hadoop hdfs:///user/jonson/output ‘dfs[a-z.]+‘
[[email protected] hadoop-2.7.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar grep hdfs:///user/jonson/input/hadoop hdfs:///user/jonson/output ‘dfs[a-z.]+‘
17/05/07 23:36:17 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/05/07 23:36:18 INFO input.FileInputFormat: Total input paths to process : 30
17/05/07 23:36:18 INFO mapreduce.JobSubmitter: number of splits:30
17/05/07 23:36:19 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494169715431_0003
17/05/07 23:36:19 INFO impl.YarnClientImpl: Submitted application application_1494169715431_0003
17/05/07 23:36:19 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1494169715431_0003/
17/05/07 23:36:19 INFO mapreduce.Job: Running job: job_1494169715431_0003
17/05/07 23:36:28 INFO mapreduce.Job: Job job_1494169715431_0003 running in uber mode : false
17/05/07 23:36:28 INFO mapreduce.Job: map 0% reduce 0%
17/05/07 23:36:58 INFO mapreduce.Job: map 20% reduce 0%
17/05/07 23:37:25 INFO mapreduce.Job: map 37% reduce 0%
17/05/07 23:37:26 INFO mapreduce.Job: map 40% reduce 0%
17/05/07 23:37:50 INFO mapreduce.Job: map 47% reduce 0%
17/05/07 23:37:51 INFO mapreduce.Job: map 57% reduce 0%
17/05/07 23:37:54 INFO mapreduce.Job: map 57% reduce 19%
17/05/07 23:38:04 INFO mapreduce.Job: map 60% reduce 19%
17/05/07 23:38:06 INFO mapreduce.Job: map 60% reduce 20%
17/05/07 23:38:12 INFO mapreduce.Job: map 73% reduce 20%
17/05/07 23:38:15 INFO mapreduce.Job: map 73% reduce 24%
17/05/07 23:38:18 INFO mapreduce.Job: map 77% reduce 24%
17/05/07 23:38:21 INFO mapreduce.Job: map 77% reduce 26%
17/05/07 23:38:33 INFO mapreduce.Job: map 83% reduce 26%
17/05/07 23:38:34 INFO mapreduce.Job: map 90% reduce 26%
17/05/07 23:38:35 INFO mapreduce.Job: map 93% reduce 26%
17/05/07 23:38:36 INFO mapreduce.Job: map 93% reduce 31%
17/05/07 23:38:43 INFO mapreduce.Job: map 100% reduce 31%
17/05/07 23:38:44 INFO mapreduce.Job: map 100% reduce 100%
17/05/07 23:38:45 INFO mapreduce.Job: Job job_1494169715431_0003 completed successfully
17/05/07 23:38:45 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=345
FILE: Number of bytes written=3690573
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=81841
HDFS: Number of bytes written=437
HDFS: Number of read operations=93
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=30
Launched reduce tasks=1
Data-local map tasks=30
Total time spent by all maps in occupied slots (ms)=653035
Total time spent by all reduces in occupied slots (ms)=77840
Total time spent by all map tasks (ms)=653035
Total time spent by all reduce tasks (ms)=77840
Total vcore-milliseconds taken by all map tasks=653035
Total vcore-milliseconds taken by all reduce tasks=77840
Total megabyte-milliseconds taken by all map tasks=668707840
Total megabyte-milliseconds taken by all reduce tasks=79708160
Map-Reduce Framework
Map input records=2103
Map output records=24
Map output bytes=590
Map output materialized bytes=519
Input split bytes=3804
Combine input records=24
Combine output records=13
Reduce input groups=11
Reduce shuffle bytes=519
Reduce input records=13
Reduce output records=11
Spilled Records=26
Shuffled Maps =30
Failed Shuffles=0
Merged Map outputs=30
GC time elapsed (ms)=8250
CPU time spent (ms)=13990
Physical memory (bytes) snapshot=6025490432
Virtual memory (bytes) snapshot=64352063488
Total committed heap usage (bytes)=4090552320
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=78037
File Output Format Counters
Bytes Written=437
17/05/07 23:38:45 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/05/07 23:38:46 INFO input.FileInputFormat: Total input paths to process : 1
17/05/07 23:38:46 INFO mapreduce.JobSubmitter: number of splits:1
17/05/07 23:38:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494169715431_0004
17/05/07 23:38:46 INFO impl.YarnClientImpl: Submitted application application_1494169715431_0004
17/05/07 23:38:46 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1494169715431_0004/
17/05/07 23:38:46 INFO mapreduce.Job: Running job: job_1494169715431_0004
17/05/07 23:39:00 INFO mapreduce.Job: Job job_1494169715431_0004 running in uber mode : false
17/05/07 23:39:00 INFO mapreduce.Job: map 0% reduce 0%
17/05/07 23:39:06 INFO mapreduce.Job: map 100% reduce 0%
17/05/07 23:39:13 INFO mapreduce.Job: map 100% reduce 100%
17/05/07 23:39:14 INFO mapreduce.Job: Job job_1494169715431_0004 completed successfully
17/05/07 23:39:14 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=291
FILE: Number of bytes written=237535
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=566
HDFS: Number of bytes written=197
HDFS: Number of read operations=7
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=3838
Total time spent by all reduces in occupied slots (ms)=3849
Total time spent by all map tasks (ms)=3838
Total time spent by all reduce tasks (ms)=3849
Total vcore-milliseconds taken by all map tasks=3838
Total vcore-milliseconds taken by all reduce tasks=3849
Total megabyte-milliseconds taken by all map tasks=3930112
Total megabyte-milliseconds taken by all reduce tasks=3941376
Map-Reduce Framework
Map input records=11
Map output records=11
Map output bytes=263
Map output materialized bytes=291
Input split bytes=129
Combine input records=0
Combine output records=0
Reduce input groups=5
Reduce shuffle bytes=291
Reduce input records=11
Reduce output records=11
Spilled Records=22
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=143
CPU time spent (ms)=980
Physical memory (bytes) snapshot=306675712
Virtual memory (bytes) snapshot=4157272064
Total committed heap usage (bytes)=165810176
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=437
File Output Format Counters
Bytes Written=197

利用命令行來查看運行結果：

[[email protected] hadoop-2.7.3]$ hadoop fs -cat hdfs:///user/jonson/output/*
6 dfs.audit.logger
4 dfs.class
3 dfs.server.namenode.
2 dfs.period
2 dfs.audit.log.maxfilesize
2 dfs.audit.log.maxbackupindex
1 dfsmetrics.log
1 dfsadmin
1 dfs.servers
1 dfs.replication
1 dfs.file
[[email protected] hadoop-2.7.3]$ hadoop fs -cat hdfs:///user/jonson/output/part-r-00000
6 dfs.audit.logger
4 dfs.class
3 dfs.server.namenode.
2 dfs.period
2 dfs.audit.log.maxfilesize
2 dfs.audit.log.maxbackupindex
1 dfsmetrics.log
1 dfsadmin
1 dfs.servers
1 dfs.replication
1 dfs.file

====================================

免密碼登錄原理和方法

背景：搭建Hadoop環境需要設置無密碼登陸，所謂無密碼登陸其實是指通過證書認證的方式登陸，使用一種被稱為"公私鑰"認證的方式來進行ssh登錄。

　　在linux系統中,ssh是遠程登錄的默認工具,因為該工具的協議使用了RSA/DSA的加密算法.該工具做linux系統的遠程管理是非常安全的。telnet,因為其不安全性,在linux系統中被擱置使用了。

　　" 公私鑰"認證方式簡單的解釋:首先在客戶端上創建一對公私鑰（公鑰文件：~/.ssh/id_rsa.pub；私鑰文件：~/.ssh/id_rsa）。然後把公鑰放到服務器上（~/.ssh/authorized_keys）, 自己保留好私鑰.在使用ssh登錄時,ssh程序會發送私鑰去和服務器上的公鑰做匹配.如果匹配成功就可以登錄了。

工具/原料
- linux系統
方法/步驟
1. 確認系統已經安裝了SSH。
  
  rpm –qa | grep openssh
  
  rpm –qa | grep rsync
  
  -->出現如下圖的信息表示已安裝
  
  假設沒有安裝ssh和rsync，可以通過下面命令進行安裝。
  
  yum install ssh -->安裝SSH協議
  
  yum install rsync -->rsync是一個遠程數據同步工具，可通過LAN/WAN快速同步多臺主機間的文件
  
  service sshd restart -->啟動服務
2. 生成秘鑰對
  
  ssh-keygen –t rsa –P ‘‘ -->直接回車生成的密鑰對：id_rsa和id_rsa.pub，默認存儲在"/home/hadoop/.ssh"目錄下。
3. 把id_rsa.pub追加到授權的key裏面去。
  
  cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
4. 修改授權key的權限
  
  chmod 600 ~/.ssh/authorized_keys
5. 修改SSH配置文件
  
  su - -->登陸root用戶修改配置文件
  
  vim /etc/ssh/sshd_config -->去掉下圖中三行的註釋
6. 測試連接
  
  service sshd restart -->重啟ssh服務,
  
  exit -->退出root用戶，回到普通用戶
  
  ssh localhost -->連接普通用戶測試
  
  這只是配置好了單機環境上的SSH服務，要遠程連接其它的服務器，接著看下面。
7. 現在秘鑰對已經生成好了，客戶端SSH服務也已經配置好了，現在就把我們的鑰匙（公鑰）送給服務器。
  
  scp ~/.ssh/id_rsa.pub [email protected]:~/ -->將公鑰復制到遠程服務器的~/目錄下
  
  如: scp ~/.ssh/id_rsa.pub [email protected]:~/
  
  可以看到我們復制的時候需要我們輸入服務器的密碼，等我們把SSH配置好之後這些步驟就可以不用輸入密碼了。
8. 8
  上一步把公鑰發送到192.168.1.134服務器上去了，我們去134機器上把公鑰追加到授權key中去。（註意：如果是第一次運行SSH，那麽.ssh目錄需要手動創建，或者使用命令ssh-keygen -t rsa生成秘鑰，它會自動在用戶目錄下生成.ssh目錄。特別註意的是.ssh目錄的權限問題，記得運行下chmod 700 .ssh命令）
  
  在134機器上使用命令：
  
  cat ~/id_rsa.pub >> ~/.ssh/authorized_keys -->追加公鑰到授權key中
  
  rm ~/id_rsa.pub -->保險起見，刪除公鑰
  
  同樣在134機器上重復第四步和第五步，
  
  service sshd restart -->重啟ssh服務
9. 9
  回到客戶機來，輸入:
  
  ssh 192.168.1.134 -->應該就能直接連接服務器咯。