之前看了很多理论上的知识，感觉云里雾里的，所以赶紧着手搭建个单机版的hadoop跑一跑，开启自学大数据技术的第一步~~

  1.在开源的世界里，我就是个土豪，要啥有啥，所以首先你得有个jdk，有钱所以用最新的java8，hadoop使用的是hadoop2.6.0。

  2.配置好java后，可以在/etc/profile里配置好环境变量，方便之后使用，紧接着解压hadoop2.6.0.tar.gz。

  3.接下来配置hadoop,所有的配置文件都在hadoop文件夹下的etc/hadoop中：

　（1）hadoop-env.sh ：这个脚本只需要修改最上面的JavaHome即可，修改为自己的java路径

　（2）core-site.xml，mapred-site.xml，hdfs-site.xml这几个配置完事再补上吧~~~，网上挺多的，不过要找自己对应的版本，不然会出很多奇怪的问题。

  4.配置好之后就要启动了

　　（1）启动之前首先要把namenode格式化一下，这是第一次启动hadoop需要做的动作，他会把hdfs中所有的东西全部清空掉的，所以要慎用~~



[qiang@localhost hadoop-2.6.0]$  bin/hadoop namenode -format



DEPRECATED: Use of this script to execute hdfs command is deprecated.
Instead use the hdfs command for it.

15/08/11 08:25:43 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = localhost/127.0.0.1
STARTUP_MSG:   args = [-format]
STARTUP_MSG:   version = 2.6.0
.....
.....
.....
15/08/11 08:25:46 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at localhost/127.0.0.1
************************************************************/



　　格式化会出现一大堆信息，如果没有报错，那么说明之前的配置应该是可以滴~~~

　　（2）启动的时候,可以直接使用sbin/start-all.sh，但是这种方式太low，如果集群启动出现错误，那么不会知道是那一部分的问题，不便于问题的排查，所以我们来一个一个启动它

启动namenode:



[qiang@localhost hadoop-2.6.0]$ sbin/hadoop-daemon.sh start namenode



starting namenode, logging to /home/qiang/hadoop-2.6.0/logs/hadoop-qiang-namenode-localhost.localdomain.out



启动datanode:



[qiang@localhost hadoop-2.6.0]$ sbin/hadoop-daemon.sh start datanode



starting datanode, logging to /home/qiang/hadoop-2.6.0/logs/hadoop-qiang-datanode-localhost.localdomain.out



可以用jps命令查看是否启动



[qiang@localhost ~]$ jps
17254 Jps
16473 NameNode
16698 DataNode



当然也可以使用开放的端口在web浏览器上查看：（hdfs开放的端口为50070）

deployment启动前先执行操作再启动_hdfs

开了当然要用用他了，看看是不是唬人的，所以我们向hdfs中上传点东西试试：



[qiang@localhost hadoop-2.6.0]$ bin/hadoop fs -mkdir /home
[qiang@localhost hadoop-2.6.0]$ bin/hadoop fs -mkdir /home/qiangweikang
[qiang@localhost hadoop-2.6.0]$ bin/hadoop fs -put README.txt /home/qiangweikang



点击uitilites中的system source会看到我们之前传进去的东东：

deployment启动前先执行操作再启动_大数据_02

 好开森~~

完事我们继续启动yarn



[qiang@localhost hadoop-2.6.0]$ sbin/start-yarn.sh



在web上就可以看到传说中的那只大象....  ，而且我们可以看到有一个活动的节点（yarn的ResourceManager的默认端口号是8088）

deployment启动前先执行操作再启动_java_03

 

接下来我们再跑一个demo，看看hadoop是怎么去运行的（在share下有自带的demo可供测试）这个pi的计算很有意思，是对一个圆做投掷飞镖的动作，第一个参数是map操作的次数

第二个参数是每次投掷多少个飞镖，好高大上啊，pi还可以这样算~~~，难道这就是传说中的概率统计？



bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar pi 2 100



Number of Maps  = 2
Samples per Map = 100
Wrote input for Map #0
Wrote input for Map #1
Starting Job
15/08/11 08:54:24 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/08/11 08:54:25 INFO input.FileInputFormat: Total input paths to process : 2
15/08/11 08:54:25 INFO mapreduce.JobSubmitter: number of splits:2
15/08/11 08:54:25 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1439308289430_0001
15/08/11 08:54:26 INFO impl.YarnClientImpl: Submitted application application_1439308289430_0001
15/08/11 08:54:26 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1439308289430_0001/
15/08/11 08:54:26 INFO mapreduce.Job: Running job: job_1439308289430_0001
15/08/11 08:54:41 INFO mapreduce.Job: Job job_1439308289430_0001 running in uber mode : false
15/08/11 08:54:41 INFO mapreduce.Job:  map 0% reduce 0%
15/08/11 08:54:51 INFO mapreduce.Job:  map 50% reduce 0%
15/08/11 08:54:52 INFO mapreduce.Job:  map 100% reduce 0%
15/08/11 08:55:04 INFO mapreduce.Job:  map 100% reduce 100%
15/08/11 08:55:05 INFO mapreduce.Job: Job job_1439308289430_0001 completed successfully
15/08/11 08:55:06 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=50
        FILE: Number of bytes written=317688
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=526
        HDFS: Number of bytes written=215
        HDFS: Number of read operations=11
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=3
    Job Counters 
        Launched map tasks=2
        Launched reduce tasks=1
        Data-local map tasks=2
        Total time spent by all maps in occupied slots (ms)=14463
        Total time spent by all reduces in occupied slots (ms)=10093
        Total time spent by all map tasks (ms)=14463
        Total time spent by all reduce tasks (ms)=10093
        Total vcore-seconds taken by all map tasks=14463
        Total vcore-seconds taken by all reduce tasks=10093
        Total megabyte-seconds taken by all map tasks=14810112
        Total megabyte-seconds taken by all reduce tasks=10335232
    Map-Reduce Framework
        Map input records=2
        Map output records=4
        Map output bytes=36
        Map output materialized bytes=56
        Input split bytes=290
        Combine input records=0
        Combine output records=0
        Reduce input groups=2
        Reduce shuffle bytes=56
        Reduce input records=4
        Reduce output records=0
        Spilled Records=8
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=412
        CPU time spent (ms)=4770
        Physical memory (bytes) snapshot=680353792
        Virtual memory (bytes) snapshot=6324887552
        Total committed heap usage (bytes)=501743616
    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=236
    File Output Format Counters 
        Bytes Written=97
Job Finished in 42.318 seconds
Estimated value of Pi is 3.12000000000000000000



 

最后记得把yarn关掉~~



[qiang@localhost hadoop-2.6.0]$ sbin/stop-yarn.sh