Hadoop单机部署方法

安装Java JDK：

到sun网站上下载jdk

chmod +x jdk-6u30-linux-x64.bin

./jdk-6u30-linux-x64.bin

 

下载Hadoop

wget http://labs.renren.com/apache-mirror/hadoop/common/hadoop-0.20.205.0/hadoop-0.20.205.0.tar.gz

tar zxvf hadoop-0.20.205.0.tar.gz

 

安装必要软件

yum install ssh rsync

 

修改配置文件

conf/core-site.xml:

<configuration>

<property>

<name>fs.default.name</name>

<value>hdfs://localhost:9000</value>

</property>

</configuration>

conf/hdfs-site.xml:

<configuration>

<property>

<name>dfs.replication</name>

<value>1</value>

</property>

</configuration>

conf/mapred-site.xml:

<configuration>

<property>

<name>mapred.job.tracker</name>

<value>localhost:9001</value>

</property>

</configuration>

 

conf/ hadoop-env.sh

export JAVA_HOME=/root/jdk1.6.0_30

 

bin/hadoop

以root用户运行会报-jvm参数不存在的错误，故将

if [[ $EUID -eq 0 ]]; then

HADOOP_OPTS="$HADOOP_OPTS -jvm server $HADOOP_DATANODE_OPTS"

else

HADOOP_OPTS="$HADOOP_OPTS -server $HADOOP_DATANODE_OPTS"

fi

修改为

elif [ "$COMMAND" = "datanode" ] ; then

CLASS='org.apache.hadoop.hdfs.server.datanode.DataNode'

HADOOP_OPTS="$HADOOP_OPTS -server $HADOOP_DATANODE_OPTS"

 

SSH设置

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

 

启动hadoop

$ bin/hadoop namenode -format
$ bin/start-all.sh

 

此时，通过本地的50070和50030端口就可以分别浏览到NameNode和JobTracker：

• NameNode - http://localhost:50070/
• JobTracker - http://localhost:50030/

   

   

  测试wordcount

  生成测试数据：

  [root hadoop-0.20.205.0]# mkdir input

  [root hadoop-0.20.205.0]# echo "hello world" >> input/a.txt

  [root hadoop-0.20.205.0]# echo "hello hadoop" >> input/b.txt

  将本地数据复制到HDFS中：

  [root hadoop-0.20.205.0]# bin/hadoop fs -put input in

  执行测试任务：

  [root hadoop-0.20.205.0]# bin/hadoop jar hadoop-examples-0.20.205.0.jar wordcount in out

  12/02/05 21:00:47 INFO input.FileInputFormat: Total input paths to process : 2

  12/02/05 21:00:48 INFO mapred.JobClient: Running job: job_201202052055_0001

  12/02/05 21:00:49 INFO mapred.JobClient: map 0% reduce 0%

  12/02/05 21:01:07 INFO mapred.JobClient: map 100% reduce 0%

  12/02/05 21:01:19 INFO mapred.JobClient: map 100% reduce 100%

  12/02/05 21:01:24 INFO mapred.JobClient: Job complete: job_201202052055_0001

  12/02/05 21:01:24 INFO mapred.JobClient: Counters: 29

  12/02/05 21:01:24 INFO mapred.JobClient: Job Counters

  12/02/05 21:01:24 INFO mapred.JobClient: Launched reduce tasks=1

  12/02/05 21:01:24 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=22356

  12/02/05 21:01:24 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0

  12/02/05 21:01:24 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0

  12/02/05 21:01:24 INFO mapred.JobClient: Launched map tasks=2

  12/02/05 21:01:24 INFO mapred.JobClient: Data-local map tasks=2

  12/02/05 21:01:24 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=10801

  12/02/05 21:01:24 INFO mapred.JobClient: File Output Format Counters

  12/02/05 21:01:24 INFO mapred.JobClient: Bytes Written=25

  12/02/05 21:01:24 INFO mapred.JobClient: FileSystemCounters

  12/02/05 21:01:24 INFO mapred.JobClient: FILE_BYTES_READ=55

  12/02/05 21:01:24 INFO mapred.JobClient: HDFS_BYTES_READ=235

  12/02/05 21:01:24 INFO mapred.JobClient: FILE_BYTES_WRITTEN=64345

  12/02/05 21:01:24 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=25

  12/02/05 21:01:24 INFO mapred.JobClient: File Input Format Counters

  12/02/05 21:01:24 INFO mapred.JobClient: Bytes Read=25

  12/02/05 21:01:24 INFO mapred.JobClient: Map-Reduce Framework

  12/02/05 21:01:24 INFO mapred.JobClient: Map output materialized bytes=61

  12/02/05 21:01:24 INFO mapred.JobClient: Map input records=2

  12/02/05 21:01:24 INFO mapred.JobClient: Reduce shuffle bytes=61

  12/02/05 21:01:24 INFO mapred.JobClient: Spilled Records=8

  12/02/05 21:01:24 INFO mapred.JobClient: Map output bytes=41

  12/02/05 21:01:24 INFO mapred.JobClient: CPU time spent (ms)=2900

  12/02/05 21:01:24 INFO mapred.JobClient: Total committed heap usage (bytes)=398852096

  12/02/05 21:01:24 INFO mapred.JobClient: Combine input records=4

  12/02/05 21:01:24 INFO mapred.JobClient: SPLIT_RAW_BYTES=210

  12/02/05 21:01:24 INFO mapred.JobClient: Reduce input records=4

  12/02/05 21:01:24 INFO mapred.JobClient: Reduce input groups=3

  12/02/05 21:01:24 INFO mapred.JobClient: Combine output records=4

  12/02/05 21:01:24 INFO mapred.JobClient: Physical memory (bytes) snapshot=422445056

  12/02/05 21:01:24 INFO mapred.JobClient: Reduce output records=3

  12/02/05 21:01:24 INFO mapred.JobClient: Virtual memory (bytes) snapshot=1544007680

  12/02/05 21:01:24 INFO mapred.JobClient: Map output records=4

  查看结果：

  [root hadoop-0.20.205.0]# bin/hadoop fs -cat out/*

  hadoop    1

  hello    2

  world    1

  cat: File does not exist: /user/root/out/_logs

  将结果从HDFS复制到本地并查看：

  [root hadoop-0.20.205.0]# bin/hadoop fs -get out output

  [root hadoop-0.20.205.0]# cat output/*

  cat: output/_logs: Is a directory

  hadoop    1

  hello    2

  world    1

   

  此时，从JobTracker网页中也可以看到任务的执行情况：