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 NameNodeSTARTUP_MSG: host = localhost/127.0.0.1STARTUP_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 ~]$ jps17254 Jps16473 NameNode16698 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）
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：
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）
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 = 2Samples per Map = 100Wrote input for Map #0Wrote input for Map #1Starting Job15/08/11 08:54:24 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:803215/08/11 08:54:25 INFO input.FileInputFormat: Total input paths to process : 215/08/11 08:54:25 INFO mapreduce.JobSubmitter: number of splits:215/08/11 08:54:25 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1439308289430_000115/08/11 08:54:26 INFO impl.YarnClientImpl: Submitted application application_1439308289430_000115/08/11 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_000115/08/11 08:54:41 INFO mapreduce.Job: Job job_1439308289430_0001 running in uber mode : false15/08/11 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 successfully15/08/11 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=97Job Finished in 42.318 secondsEstimated value of Pi is 3.12000000000000000000
最后记得把yarn关掉~~
[qiang@localhost hadoop-2.6.0]$ sbin/stop-yarn.sh