hadoop安装部署（伪分布及集群）

hadoop安装部署（伪分布及集群）

@(HADOOP)[hadoop]

hadoop安装部署伪分布及集群

第一部分伪分布式
一环境准备

二安装hdfs

三安装YARN

第二部分集群安装
一规划
一硬件资源

二基本资料

二环境配置
一统一用户名密码并为jediael赋予执行所有命令的权限

二创建目录mntjediael

三修改用户名及etchosts文件

第一部分：伪分布式

一、环境准备

1、安装linux、jdk

2、下载hadoop2.6.0，并解压

3、配置免密码ssh

（1）检查是否可以免密码：

$ ssh localhost

（2）若否：

$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

4、在/etc/profile中添加以下内容

#hadoop setting
export PATH=$PATH:/mnt/jediael/hadoop-2.6.0/bin:/mnt/jediael/hadoop-2.6.0/sbin
export HADOOP_HOME=/mnt/jediael/hadoop-2.6.0
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"

二、安装hdfs

1、配置etc/hadoop/core-site.xml:

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

2、配置etc/hadoop/hdfs-site.xml:

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

3、格式化namenode

$ bin/hdfs namenode -format

4、启动hdfs

$ sbin/start-dfs.sh

5、打开页面验证hdfs安装成功

http://localhost:50070/

6、运行自带示例

（1）创建目录

$ bin/hdfs dfs -mkdir /user
$ bin/hdfs dfs -mkdir /user/jediael

（2）复制文件

bin/hdfs dfs -put etc/hadoop input

（3）运行示例

$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar grep input output 'dfs[a-z.]+’

（4）检查输出结果

$ bin/hdfs dfs -cat 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

（5）关闭hdfs

$ sbin/stop-dfs.sh

三、安装YARN

1、配置etc/hadoop/mapred-site.xml

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

2、配置etc/hadoop/yarn-site.xml

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

3、启动yarn

$ sbin/start-yarn.sh

4、打开页面检查yarn

http://localhost:8088/

5、运行一个map-reduce job

$  bin/hadoop fs -mkdir /input
$ bin/hadoop fs -copyFromLocal /etc/profile /input
$ cd  /mnt/jediael/hadoop-2.6.0/share/hadoop/mapreduce
$ /mnt/jediael/hadoop-2.6.0/bin/hadoop jar hadoop-mapreduce-examples-2.6.0.jar wordcount /input /output

查看结果：

$/mnt/jediael/hadoop-2.6.0/bin/hadoop fs -cat /output/*

第二部分：集群安装

一、规划

（一）硬件资源

10.171.29.191 master

10.171.94.155 slave1

10.251.0.197 slave3

（二）基本资料

用户： jediael

目录：/mnt/jediael/

二、环境配置

（一）统一用户名密码，并为jediael赋予执行所有命令的权限

#passwd
# useradd jediael
# passwd jediael
# vi /etc/sudoers

增加以下一行：

jediael ALL=(ALL) ALL

（二）创建目录/mnt/jediael

$sudo chown jediael:jediael /opt
$ cd /opt
$ sudo mkdir jediael

注意:/opt必须是jediael的，否则会在format namenode时出错。

（三）修改用户名及/etc/hosts文件

1、修改/etc/sysconfig/network

NETWORKING=yes
HOSTNAME=*******

2、修改/etc/hosts

10.171.29.191 master

10.171.94.155 slave1

10.251.0.197 slave3

注 意hosts文件不能有127.0.0.1 *配置，否则会导致出现异常。org.apache.hadoop.ipc.Client: Retrying connect to server: master/10.171.29.191:9000. Already trie

3、hostname命令

hostname ****

（四）配置免密码登录

以上命令在master上使用jediael用户执行：

$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys然后，将authorized_keys复制到slave1,slave2
scp ~/.ssh/authorized_keys slave1:~/.ssh/
scp ~/.ssh/authorized_keys slave2:~/.ssh/

注意

（1）若提示.ssh目录不存在，则表示此机器从未运行过ssh，因此运行一次即可创建.ssh目录。

（2）.ssh/的权限为600,authorized_keys的权限为700，权限大了小了都不行。

（五）在3台机器上分别安装java，并设置相关环境变量

参考http://blog.csdn.net/jediael_lu/article/details/38925871

（六）下载hadoop-2.6.0.tar.gz，并将其解压到/mnt/jediael

wget http://mirror.bit.edu.cn/apache/hadoop/common/hadoop-2.6.0/hadoop-2.6.0.tar.gz

tar -zxvf hadoop-2.6.0.tar.gz

三、修改配置文件

【3台机器上均要执行，一般先在一台机器上配置完成，再用scp复制到其它机器】

（一）hadoop_env.sh

export JAVA_HOME=/usr/java/jdk1.7.0_51

（二）修改core-site.xml

<property>
<name>hadoop.tmp.dir</name>
<value>/mnt/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
<property>
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
</property>
<property>
<name>io.file.buffer.size</name>
<value>4096</value>
</property>

（三）修改hdfs-site.xml

<property>
<name>dfs.replication</name>
<value>2</value>
</property>

（四）修改mapred-site.xml

<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
<final>true</final>
</property>

<property>
<name>mapreduce.jobtracker.http.address</name>
<value>master:50030</value>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>master:10020</value>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>master:19888</value>
</property>
<property>
<name>mapred.job.tracker</name>
<value>http://master:9001</value>
</property>

（五）修改yarn.xml

<property>
<name>yarn.resourcemanager.hostname</name>
<value>master</value>
</property>

<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.resourcemanager.address</name>
<value>master:8032</value>
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>master:8030</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>master:8031</value>
</property>
<property>
<name>yarn.resourcemanager.admin.address</name>
<value>master:8033</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address</name>
<value>master:8088</value>
</property>

（六）修改slaves

slaves:

slave1
slave3

四、启动并验证

1、格式 化namenode

[jediael@master hadoop-1.2.1]$  bin/hadoop namenode -format

2、启动hadoop【此步骤只需要在master上执行】

[jediael@master hadoop-1.2.1]$ bin/start-all.sh

3、验证1：向hdfs中写入内容

[jediael@master hadoop-2.6.0]$ bin/hadoop fs -ls /
[jediael@master hadoop-2.6.0]$ bin/hadoop fs -mkdir /test
[jediael@master hadoop-2.6.0]$ bin/hadoop fs -ls /
Found 1 items
drwxr-xr-x   - jediael supergroup          0 2015-04-19 23:41 /test

4、验证：登录页面

NameNode http://ip:50070

5、查看各个主机的java进程

（1）master:

$ jps
3694 NameNode
3882 SecondaryNameNode
7216 Jps
4024 ResourceManager

（2）slave1:

$ jps
1913 NodeManager
2673 Jps
1801 DataNode

（3）slave3:

$ jps
1942 NodeManager
2252 Jps
1840 DataNode

五、运行一个完整的mapreduce程序:运行自带的wordcount程序

$ bin/hadoop fs -mkdir /input
$ bin/hadoop fs -ls /
Found 2 items
drwxr-xr-x   - jediael supergroup          0 2015-04-20 18:04 /input
drwxr-xr-x   - jediael supergroup          0 2015-04-19 23:41 /test

$ bin/hadoop fs -copyFromLocal etc/hadoop/mapred-site.xml.template /input

$ pwd
/mnt/jediael/hadoop-2.6.0/share/hadoop/mapreduce

$ /mnt/jediael/hadoop-2.6.0/bin/hadoop jar hadoop-mapreduce-examples-2.6.0.jar wordcount /input /output
15/04/20 18:15:47 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/04/20 18:15:48 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
15/04/20 18:15:48 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
15/04/20 18:15:49 INFO input.FileInputFormat: Total input paths to process : 1
15/04/20 18:15:49 INFO mapreduce.JobSubmitter: number of splits:1
15/04/20 18:15:49 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local657082309_0001
15/04/20 18:15:50 INFO mapreduce.Job: The url to track the job: http://localhost:8080/ 15/04/20 18:15:50 INFO mapreduce.Job: Running job: job_local657082309_0001
15/04/20 18:15:50 INFO mapred.LocalJobRunner: OutputCommitter set in config null
15/04/20 18:15:50 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
15/04/20 18:15:50 INFO mapred.LocalJobRunner: Waiting for map tasks
15/04/20 18:15:50 INFO mapred.LocalJobRunner: Starting task: attempt_local657082309_0001_m_000000_0
15/04/20 18:15:50 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
15/04/20 18:15:50 INFO mapred.MapTask: Processing split: hdfs://master:9000/input/mapred-site.xml.template:0+2268
15/04/20 18:15:51 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
15/04/20 18:15:51 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
15/04/20 18:15:51 INFO mapred.MapTask: soft limit at 83886080
15/04/20 18:15:51 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
15/04/20 18:15:51 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
15/04/20 18:15:51 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
15/04/20 18:15:51 INFO mapred.LocalJobRunner:
15/04/20 18:15:51 INFO mapred.MapTask: Starting flush of map output
15/04/20 18:15:51 INFO mapred.MapTask: Spilling map output
15/04/20 18:15:51 INFO mapred.MapTask: bufstart = 0; bufend = 1698; bufvoid = 104857600
15/04/20 18:15:51 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26213916(104855664); length = 481/6553600
15/04/20 18:15:51 INFO mapred.MapTask: Finished spill 0
15/04/20 18:15:51 INFO mapred.Task: Task:attempt_local657082309_0001_m_000000_0 is done. And is in the process of committing
15/04/20 18:15:51 INFO mapred.LocalJobRunner: map
15/04/20 18:15:51 INFO mapred.Task: Task 'attempt_local657082309_0001_m_000000_0' done.
15/04/20 18:15:51 INFO mapred.LocalJobRunner: Finishing task: attempt_local657082309_0001_m_000000_0
15/04/20 18:15:51 INFO mapred.LocalJobRunner: map task executor complete.
15/04/20 18:15:51 INFO mapred.LocalJobRunner: Waiting for reduce tasks
15/04/20 18:15:51 INFO mapred.LocalJobRunner: Starting task: attempt_local657082309_0001_r_000000_0
15/04/20 18:15:51 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
15/04/20 18:15:51 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@39be5e01
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=363285696, maxSingleShuffleLimit=90821424, mergeThreshold=239768576, ioSortFactor=10, memToMemMergeOutputsThreshold=10
15/04/20 18:15:51 INFO reduce.EventFetcher: attempt_local657082309_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
15/04/20 18:15:51 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local657082309_0001_m_000000_0 decomp: 1566 len: 1570 to MEMORY
15/04/20 18:15:51 INFO reduce.InMemoryMapOutput: Read 1566 bytes from map-output for attempt_local657082309_0001_m_000000_0
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 1566, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->1566
15/04/20 18:15:51 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
15/04/20 18:15:51 INFO mapred.LocalJobRunner: 1 / 1 copied.
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
15/04/20 18:15:51 INFO mapred.Merger: Merging 1 sorted segments
15/04/20 18:15:51 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 1560 bytes
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: Merged 1 segments, 1566 bytes to disk to satisfy reduce memory limit
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: Merging 1 files, 1570 bytes from disk
15/04/20 18:15:51 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
15/04/20 18:15:51 INFO mapred.Merger: Merging 1 sorted segments
15/04/20 18:15:51 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 1560 bytes
15/04/20 18:15:51 INFO mapred.LocalJobRunner: 1 / 1 copied.
15/04/20 18:15:51 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
15/04/20 18:15:51 INFO mapreduce.Job: Job job_local657082309_0001 running in uber mode : false
15/04/20 18:15:51 INFO mapreduce.Job:  map 100% reduce 0%
15/04/20 18:15:51 INFO mapred.Task: Task:attempt_local657082309_0001_r_000000_0 is done. And is in the process of committing
15/04/20 18:15:51 INFO mapred.LocalJobRunner: 1 / 1 copied.
15/04/20 18:15:51 INFO mapred.Task: Task attempt_local657082309_0001_r_000000_0 is allowed to commit now
15/04/20 18:15:51 INFO output.FileOutputCommitter: Saved output of task 'attempt_local657082309_0001_r_000000_0' to hdfs://master:9000/output/_temporary/0/task_local657082309_0001_r_000000
15/04/20 18:15:51 INFO mapred.LocalJobRunner: reduce > reduce
15/04/20 18:15:51 INFO mapred.Task: Task 'attempt_local657082309_0001_r_000000_0' done.
15/04/20 18:15:51 INFO mapred.LocalJobRunner: Finishing task: attempt_local657082309_0001_r_000000_0
15/04/20 18:15:51 INFO mapred.LocalJobRunner: reduce task executor complete.
15/04/20 18:15:52 INFO mapreduce.Job:  map 100% reduce 100%
15/04/20 18:15:52 INFO mapreduce.Job: Job job_local657082309_0001 completed successfully
15/04/20 18:15:52 INFO mapreduce.Job: Counters: 38
File System Counters
FILE: Number of bytes read=544164
FILE: Number of bytes written=1040966
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=4536
HDFS: Number of bytes written=1196
HDFS: Number of read operations=15
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Map-Reduce Framework
Map input records=43
Map output records=121
Map output bytes=1698
Map output materialized bytes=1570
Input split bytes=114
Combine input records=121
Combine output records=92
Reduce input groups=92
Reduce shuffle bytes=1570
Reduce input records=92
Reduce output records=92
Spilled Records=184
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=123
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=269361152
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=2268
File Output Format Counters

$ /mnt/jediael/hadoop-2.6.0/bin/hadoop fs -cat /output/*