Cloudera提供了一個(gè)可擴(kuò)展的菩浙，靈活的集成平臺(tái)锐墙，可以輕松管理企業(yè)中快速增長(zhǎng)的數(shù)據(jù)量和各種數(shù)據(jù)顶猜。 Cloudera產(chǎn)品和解決方案使您能夠部署和管理Apache Hadoop和相關(guān)項(xiàng)目汹碱，操縱和分析數(shù)據(jù)碧磅，并保持?jǐn)?shù)據(jù)的安全和受保護(hù)尊沸。
先決條件
Centos7.x主機(jī)一臺(tái)
Target
部署CDH偽分布式Hadoop集群應(yīng)用
部署好的版本
[root@localhost ~]# hadoop version
Hadoop 2.6.0-cdh5.13.1
Subversion http://github.com/cloudera/hadoop -r 0061e3eb8ab164e415630bca11d299a7c2ec74fd
Compiled by jenkins on 2017-11-09T16:34Z
Compiled with protoc 2.5.0
From source with checksum 16d5272b34af2d8a4b4b7ee8f7c4cbe
This command was run using /usr/lib/hadoop/hadoop-common-2.6.0-cdh5.13.1.jar
偶然間查到了cdh官網(wǎng)的偽分布式安裝教程
這里做下筆記和記錄.
開(kāi)始部署
(筆者以Centos7.x為例)
1.JAVA環(huán)境
#到oracle.com下載jdk1.8.161
$ wget http://download.oracle.com/otn-pub/java/jdk/8u161-b12/2f38c3b165be4555a1fa6e98c45e0808/jdk-8u161-linux-x64.rpm?AuthParam=1516458261_e7574995a6546eeecbe0e4e901bc61a8
#上面這個(gè)網(wǎng)址可能會(huì)由于session live失效
#到官網(wǎng)重新download 即可
$ rpm -ivh jdk-8u161-linux-x64.rpm
Set the Java_Home
$ vim ~/.bashrc
#Add the JAVA_HOME
export JAVA_HOME=/usr/java/jdk1.8.0_161
#保存退出
$ source ~/.bashrc
2.Download the CDH 5 Package
$ wget http://archive.cloudera.com/cdh5/one-click-install/redhat/6/x86_64/cloudera-cdh-5-0.x86_64.rpm
$ yum --nogpgcheck localinstall cloudera-cdh-5-0.x86_64.rpm
#For instructions on how to add a CDH 5 yum repository or build your own CDH 5 yum repository
3.Install CDH 5
#Add a repository key
$ rpm --import http://archive.cloudera.com/cdh5/redhat/7/x86_64/cdh/RPM-GPG-KEY-cloudera
#Install Hadoop in pseudo-distributed mode: To install Hadoop with YARN:
$ yum install hadoop-conf-pseudo -y
4.Starting Hadoop
查看安裝好的文件默認(rèn)存放位置
[root@localhost ~]# rpm -ql hadoop-conf-pseudo
/etc/hadoop/conf.pseudo
/etc/hadoop/conf.pseudo/README
/etc/hadoop/conf.pseudo/core-site.xml
/etc/hadoop/conf.pseudo/hadoop-env.sh
/etc/hadoop/conf.pseudo/hadoop-metrics.properties
/etc/hadoop/conf.pseudo/hdfs-site.xml
/etc/hadoop/conf.pseudo/log4j.properties
/etc/hadoop/conf.pseudo/mapred-site.xml
/etc/hadoop/conf.pseudo/yarn-site.xml
無(wú)需改動(dòng)，開(kāi)始部署
Step 1.格式化namenode hdfs namenode -format
[root@localhost ~]# hdfs namenode -format
18/01/21 00:13:39 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: user = root
..........................................
18/01/21 00:13:41 INFO common.Storage: Storage directory /var/lib/hadoop-hdfs/cache/root/dfs/name has been successfully formatted.
...........................................
18/01/21 00:13:41 INFO util.ExitUtil: Exiting with status 0
18/01/21 00:13:41 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at localhost/127.0.0.1
************************************************************/
Step 2: 啟動(dòng)HDFS集群
for x in `cd /etc/init.d ; ls hadoop-hdfs-*` ; do sudo service $x start ; done
[root@localhost ~]# for x in `cd /etc/init.d ; ls hadoop-hdfs-*` ; do sudo service $x start ; done
starting datanode, logging to /var/log/hadoop-hdfs/hadoop-hdfs-datanode-localhost.out
Started Hadoop datanode (hadoop-hdfs-datanode): [ OK ]
starting namenode, logging to /var/log/hadoop-hdfs/hadoop-hdfs-namenode-localhost.out
Started Hadoop namenode: [ OK ]
starting secondarynamenode, logging to /var/log/hadoop-hdfs/hadoop-hdfs-secondarynamenode-localhost.out
Started Hadoop secondarynamenode: [ OK ]
#為了確認(rèn)服務(wù)是否以及啟動(dòng)，可以使用jps命令或者查看webUI:http://localhost:50070
Step 3: Create the directories needed for Hadoop processes.
建立Hadoop進(jìn)程所需的相關(guān)目錄
/usr/lib/hadoop/libexec/init-hdfs.sh
[root@localhost ~]# /usr/lib/hadoop/libexec/init-hdfs.sh
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /tmp'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chmod -R 1777 /tmp'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /var'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /var/log'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chmod -R 1775 /var/log'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chown yarn:mapred /var/log'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /tmp/hadoop-yarn'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chown -R mapred:mapred /tmp/hadoop-yarn'
....................................
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /user/oozie/share/lib/sqoop'
+ ls '/usr/lib/hive/lib/*.jar'
+ ls /usr/lib/hadoop-mapreduce/hadoop-streaming-2.6.0-cdh5.13.1.jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -put /usr/lib/hadoop-mapreduce/hadoop-streaming*.jar /user/oozie/share/lib/mapreduce-streaming'
+ ls /usr/lib/hadoop-mapreduce/hadoop-distcp-2.6.0-cdh5.13.1.jar /usr/lib/hadoop-mapreduce/hadoop-distcp.jar
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -put /usr/lib/hadoop-mapreduce/hadoop-distcp*.jar /user/oozie/share/lib/distcp'
+ ls '/usr/lib/pig/lib/*.jar' '/usr/lib/pig/*.jar'
+ ls '/usr/lib/sqoop/lib/*.jar' '/usr/lib/sqoop/*.jar'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chmod -R 777 /user/oozie'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chown -R oozie /user/oozie'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /var/lib/hadoop-hdfs/cache/mapred/mapred/staging'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chmod 1777 /var/lib/hadoop-hdfs/cache/mapred/mapred/staging'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chown -R mapred /var/lib/hadoop-hdfs/cache/mapred'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -mkdir -p /user/spark/applicationHistory'
+ su -s /bin/bash hdfs -c '/usr/bin/hadoop fs -chown spark /user/spark/applicationHistory'
Step 4: Verify the HDFS File Structure:
確認(rèn)HDFS的目錄結(jié)構(gòu)hadoop fs -ls -R /
[root@localhost ~]# sudo -u hdfs hadoop fs -ls -R /
drwxrwxrwx - hdfs supergroup 0 2018-01-20 16:42 /benchmarks
drwxr-xr-x - hbase supergroup 0 2018-01-20 16:42 /hbase
drwxrwxrwt - hdfs supergroup 0 2018-01-20 16:41 /tmp
drwxrwxrwt - mapred mapred 0 2018-01-20 16:42 /tmp/hadoop-yarn
drwxrwxrwt - mapred mapred 0 2018-01-20 16:42 /tmp/hadoop-yarn/staging
drwxrwxrwt - mapred mapred 0 2018-01-20 16:42 /tmp/hadoop-yarn/staging/history
drwxrwxrwt - mapred mapred 0 2018-01-20 16:42 /tmp/hadoop-yarn/staging/history/done_intermediate
drwxr-xr-x - hdfs supergroup 0 2018-01-20 16:44 /user
drwxr-xr-x - mapred supergroup 0 2018-01-20 16:42 /user/history
drwxrwxrwx - hive supergroup 0 2018-01-20 16:42 /user/hive
drwxrwxrwx - hue supergroup 0 2018-01-20 16:43 /user/hue
drwxrwxrwx - jenkins supergroup 0 2018-01-20 16:42 /user/jenkins
drwxrwxrwx - oozie supergroup 0 2018-01-20 16:43 /user/oozie
................
Step 5: Start YARN
啟動(dòng)Yarn管理器
service hadoop-yarn-resourcemanager startservice hadoop-yarn-nodemanager startservice hadoop-mapreduce-historyserver start
[root@localhost ~]# service hadoop-yarn-resourcemanager start
starting resourcemanager, logging to /var/log/hadoop-yarn/yarn-yarn-resourcemanager-localhost.out
Started Hadoop resourcemanager: [ OK ]
[root@localhost ~]# service hadoop-yarn-nodemanager start
starting nodemanager, logging to /var/log/hadoop-yarn/yarn-yarn-nodemanager-localhost.out
Started Hadoop nodemanager: [ OK ]
[root@localhost ~]# service hadoop-mapreduce-historyserver start
starting historyserver, logging to /var/log/hadoop-mapreduce/mapred-mapred-historyserver-localhost.out
STARTUP_MSG: java = 1.8.0_161
Started Hadoop historyserver: [ OK ]
通過(guò)jps查看相關(guān)服務(wù)是否啟動(dòng).
[root@localhost ~]# jps
5232 ResourceManager
3425 SecondaryNameNode
5906 Jps
5827 JobHistoryServer
3286 NameNode
5574 NodeManager
3162 DataNode
Step 6: 創(chuàng)建用戶(hù)目錄
[root@localhost ~]# sudo -u hdfs hadoop fs -mkdir /taroballs/
[root@localhost ~]# hadoop fs -ls /
Found 6 items
drwxrwxrwx - hdfs supergroup 0 2018-01-20 16:42 /benchmarks
drwxr-xr-x - hbase supergroup 0 2018-01-20 16:42 /hbase
drwxr-xr-x - hdfs supergroup 0 2018-01-20 16:48 /taroballs
drwxrwxrwt - hdfs supergroup 0 2018-01-20 16:41 /tmp
drwxr-xr-x - hdfs supergroup 0 2018-01-20 16:44 /user
drwxr-xr-x - hdfs supergroup 0 2018-01-20 16:44 /var
在Yarn上運(yùn)行一個(gè)簡(jiǎn)單的例子
#首先在root用戶(hù)下建立個(gè)Input文件夾
[root@localhost ~]# hadoop fs -mkdir input
[root@localhost ~]# hadoop fs -ls /user/root/
Found 1 items
drwxr-xr-x - root supergroup 0 2018-01-20 17:51 /user/root/input
#然后put一些東西上去
[root@localhost ~]# hadoop fs -put /etc/hadoop/conf/*.xml input/
[root@localhost ~]# hadoop fs -ls input/
Found 4 items
-rw-r--r-- 1 root supergroup 2133 2018-01-20 17:54 input/core-site.xml
-rw-r--r-- 1 root supergroup 2324 2018-01-20 17:54 input/hdfs-site.xml
-rw-r--r-- 1 root supergroup 1549 2018-01-20 17:54 input/mapred-site.xml
-rw-r--r-- 1 root supergroup 2375 2018-01-20 17:54 input/yarn-site.xml
Set HADOOP_MAPRED_HOME
#Set HADOOP_MAPRED_HOME
[root@localhost ~]# vim ~/.bashrc
#Add the HADOOP_MAPRED_HOME
export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce
#保存退出
[root@localhost ~]# source ~/.bashrc
運(yùn)行Hadoop MR實(shí)例
hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar grep input outputroot23 'dfs[a-z.]+'
#運(yùn)行Hadoop simple
[root@localhost ~]# hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar grep input outputroot23 'dfs[a-z.]+'
18/01/20 17:55:54 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/01/20 17:55:55 WARN mapreduce.JobResourceUploader: No job jar file set. User classes may not be found. See Job or Job#setJar(String).
18/01/20 17:55:55 INFO input.FileInputFormat: Total input paths to process : 4
18/01/20 17:56:44 INFO mapreduce.Job: Job job_1516438047064_0004 running in uber mode : false
18/01/20 17:56:44 INFO mapreduce.Job: map 0% reduce 0%
18/01/20 17:56:51 INFO mapreduce.Job: map 100% reduce 0%
18/01/20 17:56:59 INFO mapreduce.Job: map 100% reduce 100%
18/01/20 17:56:59 INFO mapreduce.Job: Job job_1516438047064_0004 completed successfully
18/01/20 17:56:59 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=330
FILE: Number of bytes written=287357
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=599
HDFS: Number of bytes written=244
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)=4358
Total time spent by all reduces in occupied slots (ms)=4738
Total time spent by all map tasks (ms)=4358
Total time spent by all reduce tasks (ms)=4738
Total vcore-milliseconds taken by all map tasks=4358
Total vcore-milliseconds taken by all reduce tasks=4738
Total megabyte-milliseconds taken by all map tasks=4462592
Total megabyte-milliseconds taken by all reduce tasks=4851712
Map-Reduce Framework
Map input records=10
Map output records=10
Map output bytes=304
Map output materialized bytes=330
Input split bytes=129
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=330
Reduce input records=10
Reduce output records=10
Spilled Records=20
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=161
CPU time spent (ms)=1320
Physical memory (bytes) snapshot=328933376
Virtual memory (bytes) snapshot=5055086592
Total committed heap usage (bytes)=170004480
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=470
File Output Format Counters
Bytes Written=244
Result
[root@localhost ~]# hadoop fs -ls
Found 2 items
drwxr-xr-x - root supergroup 0 2018-01-20 17:54 input
drwxr-xr-x - root supergroup 0 2018-01-20 17:56 outputroot23
[root@localhost ~]# hadoop fs -ls outputroot23
Found 2 items
-rw-r--r-- 1 root supergroup 0 2018-01-20 17:56 outputroot23/_SUCCESS
-rw-r--r-- 1 root supergroup 244 2018-01-20 17:56 outputroot23/part-r-00000
[root@localhost ~]# hadoop fs -cat outputroot23/part-r-00000
1 dfs.safemode.min.datanodes
1 dfs.safemode.extension
1 dfs.replication
1 dfs.namenode.name.dir
1 dfs.namenode.checkpoint.dir
1 dfs.domain.socket.path
1 dfs.datanode.hdfs
1 dfs.datanode.data.dir
1 dfs.client.read.shortcircuit
1 dfs.client.file
[root@localhost ~]#
大功告成～CDH偽分布式Hadoop集群搭建成功～如有勘誤敬拓，歡迎斧正～
