搭建hadoop2.6.0集群环境 分类： A1_HADOOP 2015-04-20 07:21 459人阅读 评论(0) 收藏

2023-02-12 19:31:24

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 【不用修改masters文件？？】

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/*

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