hadoop2.2.0 单机开发搭建。 环境： 系统 centos 6.3 64位 jdk版本 oracle jdk 1.7 hadoop版本 2.2.0 使用linux用户 hadoop 目录配置 /home/hadoop 用户目录 /app/hadoop/hadoop-2.2.0 软件home /app/hadoop/dfs/name 数据和编辑文件 /app/hadoop/dfs/data
hadoop2.2.0 单机开发搭建。
环境：
系统 centos 6.3 64位
jdk版本 oracle jdk 1.7
hadoop版本 2.2.0
使用linux用户 hadoop
目录配置
/home/hadoop 用户目录
/app/hadoop/hadoop-2.2.0 软件home
/app/hadoop/dfs/name 数据和编辑文件
/app/hadoop/dfs/data 数据和编辑文件
/app/hadoop/mapred/local 存放数据
/app/hadoop/mapred/system 存放数据
1. 安装jdk
sudo vim /etc/profile
export java_home=/usr/lib/jvm/jdk1.7.0_45
export jre_home=$java_home/jre
export classpath=$java_home/lib:$classpath
export path=$java_home/bin:$path
source /etc/profile
2. ssh无密码登录
?
hadoop用户操作：
ssh-keygen -t dsa -p '' -f ~/.ssh/id_dsacat ~/.ssh/id_dsa.pub>> ~/.ssh/authorized_keys
root用户操作：
chmod go-w ?/home/hadoop/.sshchmod 600 /home/hadoop/.ssh/authorized_keys
测试：
hadoop用户
[hadoop@hadoop01 ~]$ ssh localhost
3. 安装hadoop
可以自己下载源码包编译成适合本地native包
为了简单我直接下载编译好的hadoop包：
地址：http://apache.fayea.com/apache-mirror/hadoop/common/hadoop-2.2.0/
解压到目录;
移动解压软件到软件目录:
/app/hadoop/hadoop-2.2.0
4. 修改hadoop参数文件
vim ?core-site.xml
fs.default.name hdfs://hadoop-host:8020 the name of the defaultfile system. either the literal string local a host:port forndfs. true
修改hdfs-site.xml
dfs.namenode.name.dir file:/app/hadoop/dfs/name true dfs.datanode.data.dir file:/app/hadoop/dfs/data determineswhere on the local filesystem an dfs data node should store its blocks. if thisis a comma-delimited list of directories, then data will be stored in all nameddirectories, typically on different devices.directories that do not exist areignored. true dfs.replication 1 dfs.permissions false
修改mapred-site.xml mapreduce.framework.name yarn mapred.system.dir file:/app/hadoop/mapred/system true mapred.local.dir file:/app/hadoop/mapred/local true
修改yarn-site.xml yarn.nodemanager.aux-services mapreduce_shuffle
如果要配置成集群环境则yarn-site.xml的配置如下：
yarn.nodemanager.aux-services mapreduce_shuffle yarn.nodemanager.aux-services.mapreduce.shuffle.class org.apache.hadoop.mapred.shufflehandler yarn.resourcemanager.address master.hadoop:8032 yarn.resourcemanager.scheduler.address master.hadoop:8030 yarn.resourcemanager.resource-tracker.address master.hadoop:8031 yarn.resourcemanager.admin.address master.hadoop:8033 yarn.resourcemanager.webapp.address master.hadoop:8088
修改 hadoop-env.sh：
增加：
export java_home=/usr/java/jdk1.7.0_45
创建本地目录
mkdir –p ?/app/hadoop/dfs/namemkdir -p ?/app/hadoop/dfs/datamkdir –p ?/app/hadoop/mapred/localmkdir -p ?/app/hadoop/mapred/system
启动hadoop格式化namenode
[hadoop@hadoop01 ~]$ hdfs namenode –format
开启dfs守护进程
启动：start-all.sh
停止：stop-all.sh
开启yarn守护进程
启动：start-yarn.sh
停止：stop-yarn.sh
使用jps查看启动的进程：
[hadoop@ttpod sbin]$ jps
7621 namenode
11834 jps
7734 datanode
7881 secondarynamenode
10156 nodemanager
10053 resourcemanager
有以上内容说明已经启动
查看hadoop资源管理页面http://192.168.6.124:8088/
查看hdfs界面：http://192.168.6.124:50070
测试：
使用pi程序：
hadoop jar ?$hadoop_home/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 10 10
hadoop jar hadoop-mapreduce-examples-2.2.0.jar pi 10 10
number of maps? = 10samples per map = 1013/12/13 16:27:23 warn util.nativecodeloader: unable to load native-hadoop library for your platform… using builtin-java classes where applicablewrote input for map #0wrote input for map #1
wrote input for map #2
wrote input for map #3
wrote input for map #4
wrote input for map #5
wrote input for map #6
wrote input for map #7
wrote input for map #8
wrote input for map #9
starting job
13/12/13 16:27:24 info client.rmproxy: connecting to resourcemanager at /0.0.0.0:8032
13/12/13 16:27:25 info input.fileinputformat: total input paths to process : 10
13/12/13 16:27:25 info mapreduce.jobsubmitter: number of splits:10
13/12/13 16:27:25 info configuration.deprecation: user.name is deprecated. instead, use mapreduce.job.user.name
13/12/13 16:27:25 info configuration.deprecation: mapred.jar is deprecated. instead, use mapreduce.job.jar
13/12/13 16:27:25 info configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. instead, use mapreduce.map.speculative
13/12/13 16:27:25 info configuration.deprecation: mapred.reduce.tasks is deprecated. instead, use mapreduce.job.reduces
13/12/13 16:27:25 info configuration.deprecation: mapred.output.value.class is deprecated. instead, use mapreduce.job.output.value.class
13/12/13 16:27:25 info configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. instead, use mapreduce.reduce.speculative
13/12/13 16:27:25 info configuration.deprecation: mapreduce.map.class is deprecated. instead, use mapreduce.job.map.class
13/12/13 16:27:25 info configuration.deprecation: mapred.job.name is deprecated. instead, use mapreduce.job.name
13/12/13 16:27:25 info configuration.deprecation: mapreduce.reduce.class is deprecated. instead, use mapreduce.job.reduce.class
13/12/13 16:27:25 info configuration.deprecation: mapreduce.inputformat.class is deprecated. instead, use mapreduce.job.inputformat.class
13/12/13 16:27:25 info configuration.deprecation: mapred.input.dir is deprecated. instead, use mapreduce.input.fileinputformat.inputdir
13/12/13 16:27:25 info configuration.deprecation: mapred.output.dir is deprecated. instead, use mapreduce.output.fileoutputformat.outputdir
13/12/13 16:27:25 info configuration.deprecation: mapreduce.outputformat.class is deprecated. instead, use mapreduce.job.outputformat.class
13/12/13 16:27:25 info configuration.deprecation: mapred.map.tasks is deprecated. instead, use mapreduce.job.maps
13/12/13 16:27:25 info configuration.deprecation: mapred.output.key.class is deprecated. instead, use mapreduce.job.output.key.class
13/12/13 16:27:25 info configuration.deprecation: mapred.working.dir is deprecated. instead, use mapreduce.job.working.dir
13/12/13 16:27:25 info mapreduce.jobsubmitter: submitting tokens for job: job_1386923206015_0001
13/12/13 16:27:26 info impl.yarnclientimpl: submitted application application_1386923206015_0001 to resourcemanager at /0.0.0.0:8032
13/12/13 16:27:26 info mapreduce.job: the url to track the job: http://ttpod:8088/proxy/application_1386923206015_0001/
13/12/13 16:27:26 info mapreduce.job: running job: job_1386923206015_0001
13/12/13 16:27:34 info mapreduce.job: job job_1386923206015_0001 running in uber mode : false
13/12/13 16:27:34 info mapreduce.job:? map 0% reduce 0%
13/12/13 16:27:56 info mapreduce.job:? map 60% reduce 0%
13/12/13 16:28:13 info mapreduce.job:? map 100% reduce 0%
13/12/13 16:28:14 info mapreduce.job:? map 100% reduce 100%
13/12/13 16:28:15 info mapreduce.job: job job_1386923206015_0001 completed successfully
13/12/13 16:28:16 info mapreduce.job: counters: 43
file system counters
file: number of bytes read=226
file: number of bytes written=871752
file: number of read operations=0
file: number of large read operations=0
file: number of write operations=0
hdfs: number of bytes read=2610
hdfs: number of bytes written=215
hdfs: number of read operations=43
hdfs: number of large read operations=0
hdfs: number of write operations=3
job counters
launched map tasks=10
launched reduce tasks=1
data-local map tasks=10
total time spent by all maps in occupied slots (ms)=185391
total time spent by all reduces in occupied slots (ms)=15412
map-reduce framework
map input records=10
map output records=20
map output bytes=180
map output materialized bytes=280
input split bytes=1430
combine input records=0
combine output records=0
reduce input groups=2
reduce shuffle bytes=280
reduce input records=20
reduce output records=0
spilled records=40
shuffled maps =10
failed shuffles=0
merged map outputs=10
gc time elapsed (ms)=1841
cpu time spent (ms)=7600
physical memory (bytes) snapshot=2507419648
virtual memory (bytes) snapshot=9588948992
total committed heap usage (bytes)=1944584192
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=1180
file output format counters
bytes written=97
job finished in 51.367 seconds
estimated value of pi is 3.20000000000000000000
正常.
如出现什么异常请自行去查看运行日志。
原文地址：hadoop2.2.0 开发环境搭建测试, 感谢原作者分享。