本文尝试使用shell脚本来自动化安装配置hadoop。使用的操作系统为centos，hadoop版本为?1.x，jdk版本?1.7，其他版本未测试，可能有未知bug。 Hadoop安装脚本 Hadoop安装分为3步，首先安装jdk，然后安装Hadoop，接着配置ssh免密码登陆(非必须)。[1] #!/bin/ba
本文尝试使用shell脚本来自动化安装配置hadoop。使用的操作系统为centos，hadoop版本为?1.x，jdk版本?1.7，其他版本未测试，可能有未知bug。
Hadoop安装脚本
Hadoop安装分为3步，首先安装jdk，然后安装Hadoop，接着配置ssh免密码登陆(非必须)。[1]
#!/bin/bash
# Usage: Hadoop自动配置脚本
# History:
# 20140425 annhe 基本功能
#Hadoop版本
HADOOP_VERSION=1.2.1
#Jdk版本，Oracle官方无直链下载，请自备rpm包并设定版本号
JDK_VESION=7u51
#Hadoop下载镜像，默认为北理(bit)
MIRRORS=mirror.bit.edu.cn
#操作系统版本
OS=`uname -a |awk '{print $13}'`
# Check if user is root
if [ $(id -u) != "0" ]; then
printf "Error: You must be root to run this script!\n"
exit 1
fi
# 检查是否是Centos
cat /etc/issue|grep CentOS && r=0 || r=1
if [ $r -eq 1 ]; then
echo "This script can only run on CentOS!"
exit 1
fi
#软件包
HADOOP_FILE=hadoop-$HADOOP_VERSION-1.$OS.rpm
if [ "$OS"x = "x86_64"x ]; then
JDK_FILE=jdk-$JDK_VESION-linux-x64.rpm
else
JDK_FILE=jdk-$JDK_VESION-linux-i586.rpm
fi
function Install ()
{
#卸载已安装版本
rpm -qa |grep hadoop
rpm -e hadoop
rpm -qa | grep jdk
rpm -e jdk
#恢复/etc/profile备份文件
mv /etc/profile.bak /etc/profile
#准备软件包
if [ ! -f $HADOOP_FILE ]; then
wget "http://$MIRRORS/apache/hadoop/common/stable1/$HADOOP_FILE" && r=0 || r=1
[ $r -eq 1 ] && { echo "download error, please check your mirrors or check your network....exit"; exit 1; }
fi
[ ! -f $JDK_FILE ] && { echo "$JDK_FILE not found! Please download yourself....exit"; exit 1; }
#开始安装
rpm -ivh $JDK_FILE && r=0 || r=1
if [ $r -eq 1 ]; then
echo "$JDK_FILE install failed, please verify your rpm file....exit"
exit 1
fi
rpm -ivh $HADOOP_FILE && r=0 || r=1
if [ $r -eq 1 ]; then
echo "$HADOOP_FILE install failed, please verify your rpm file....exit"
exit 1
fi
#备份/etc/profile
cp /etc/profile /etc/profile.bak
#配置java环境变量
cat >> /etc/profile <<eof
#set java enviroment
JAVA_HOME=/usr/java/default
CLASSPATH=.:\$JAVA_HOME/lib
PATH=\$JAVA_HOME/bin:\$PATH
export JAVA_HOME CLASSPATH PATH
eof
bash /etc/profile
#配置Hadoop脚本权限
chmod u+x /usr/sbin/*.sh
#重置HADOOP_CLIENT_OPTS值
sed -i "s/export\ HADOOP_CLIENT_OPTS=\"-Xmx128m \$HADOOP_CLIENT_OPTS\"/export\ HADOOP_CLIENT_OPTS=\"-Xmx512m \$HADOOP_CLIENT_OPTS\"/g" /etc/hadoop/hadoop-env.sh
}
#配置ssh免密码登陆
function SSHlogin ()
{
#不重复生成私钥
[ ! -f ~/.ssh/id_dsa ] && ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
cat ~/.ssh/authorized_keys |grep "`cat ~/.ssh/id_dsa.pub`" && r=0 || r=1
#没有公钥的时候才添加
[ $r -eq 1 ] && cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
chmod 644 ~/.ssh/authorized_keys
}
Install 2>&1 | tee -a hadoop_install.log
SSHlogin 2>&1 | tee -a hadoop_install.log
#修改HADOOP_CLIENT_OPTS后需要重启
shutdown -r now单节点运行自带示例
默认情况下，Hadoop被配置成以非分布式模式运行的一个独立Java进程。这对调试非常有帮助。新建测试文本
[root@linux hadoop]# echo "hello world" >input/hello.txt [root@linux hadoop]# echo "hello hadoop" >input/hadoop.txt
运行Wordcount
[root@linux hadoop]# hadoop jar /usr/share/hadoop/hadoop-examples-1.2.1.jar wordcount input output 14/04/26 02:56:23 INFO util.NativeCodeLoader: Loaded the native-hadoop library 14/04/26 02:56:23 INFO input.FileInputFormat: Total input paths to process : 2 14/04/26 02:56:24 WARN snappy.LoadSnappy: Snappy native library not loaded 14/04/26 02:56:24 INFO mapred.JobClient: Running job: job_local275273933_0001 14/04/26 02:56:24 INFO mapred.LocalJobRunner: Waiting for map tasks 14/04/26 02:56:24 INFO mapred.LocalJobRunner: Starting task: attempt_local275273933_0001_m_000000_0 14/04/26 02:56:25 INFO util.ProcessTree: setsid exited with exit code 0 14/04/26 02:56:25 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@7e86fe3a 14/04/26 02:56:25 INFO mapred.MapTask: Processing split: file:/root/hadoop/input/hadoop.txt:0+13 14/04/26 02:56:25 INFO mapred.MapTask: io.sort.mb = 100 14/04/26 02:56:25 INFO mapred.MapTask: data buffer = 79691776/99614720 14/04/26 02:56:25 INFO mapred.MapTask: record buffer = 262144/327680 14/04/26 02:56:25 INFO mapred.MapTask: Starting flush of map output 14/04/26 02:56:25 INFO mapred.MapTask: Finished spill 0 14/04/26 02:56:25 INFO mapred.Task: Task:attempt_local275273933_0001_m_000000_0 is done. And is in the process of commiting 14/04/26 02:56:25 INFO mapred.LocalJobRunner: 14/04/26 02:56:25 INFO mapred.Task: Task 'attempt_local275273933_0001_m_000000_0' done. 14/04/26 02:56:25 INFO mapred.LocalJobRunner: Finishing task: attempt_local275273933_0001_m_000000_0 14/04/26 02:56:25 INFO mapred.LocalJobRunner: Starting task: attempt_local275273933_0001_m_000001_0 14/04/26 02:56:25 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@16ed889d 14/04/26 02:56:25 INFO mapred.MapTask: Processing split: file:/root/hadoop/input/hello.txt:0+12 14/04/26 02:56:25 INFO mapred.MapTask: io.sort.mb = 100 14/04/26 02:56:25 INFO mapred.MapTask: data buffer = 79691776/99614720 14/04/26 02:56:25 INFO mapred.MapTask: record buffer = 262144/327680 14/04/26 02:56:25 INFO mapred.MapTask: Starting flush of map output 14/04/26 02:56:25 INFO mapred.MapTask: Finished spill 0 14/04/26 02:56:25 INFO mapred.Task: Task:attempt_local275273933_0001_m_000001_0 is done. And is in the process of commiting 14/04/26 02:56:25 INFO mapred.LocalJobRunner: 14/04/26 02:56:25 INFO mapred.Task: Task 'attempt_local275273933_0001_m_000001_0' done. 14/04/26 02:56:25 INFO mapred.LocalJobRunner: Finishing task: attempt_local275273933_0001_m_000001_0 14/04/26 02:56:25 INFO mapred.LocalJobRunner: Map task executor complete. 14/04/26 02:56:25 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@42701c57 14/04/26 02:56:25 INFO mapred.LocalJobRunner: 14/04/26 02:56:25 INFO mapred.Merger: Merging 2 sorted segments 14/04/26 02:56:25 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 53 bytes 14/04/26 02:56:25 INFO mapred.LocalJobRunner: 14/04/26 02:56:25 INFO mapred.Task: Task:attempt_local275273933_0001_r_000000_0 is done. And is in the process of commiting 14/04/26 02:56:25 INFO mapred.LocalJobRunner: 14/04/26 02:56:25 INFO mapred.Task: Task attempt_local275273933_0001_r_000000_0 is allowed to commit now 14/04/26 02:56:25 INFO output.FileOutputCommitter: Saved output of task 'attempt_local275273933_0001_r_000000_0' to output 14/04/26 02:56:25 INFO mapred.LocalJobRunner: reduce > reduce 14/04/26 02:56:25 INFO mapred.Task: Task 'attempt_local275273933_0001_r_000000_0' done. 14/04/26 02:56:25 INFO mapred.JobClient: map 100% reduce 100% 14/04/26 02:56:25 INFO mapred.JobClient: Job complete: job_local275273933_0001 14/04/26 02:56:25 INFO mapred.JobClient: Counters: 20 14/04/26 02:56:25 INFO mapred.JobClient: File Output Format Counters 14/04/26 02:56:25 INFO mapred.JobClient: Bytes Written=37 14/04/26 02:56:25 INFO mapred.JobClient: FileSystemCounters 14/04/26 02:56:25 INFO mapred.JobClient: FILE_BYTES_READ=429526 14/04/26 02:56:25 INFO mapred.JobClient: FILE_BYTES_WRITTEN=586463 14/04/26 02:56:25 INFO mapred.JobClient: File Input Format Counters 14/04/26 02:56:25 INFO mapred.JobClient: Bytes Read=25 14/04/26 02:56:25 INFO mapred.JobClient: Map-Reduce Framework 14/04/26 02:56:25 INFO mapred.JobClient: Reduce input groups=3 14/04/26 02:56:25 INFO mapred.JobClient: Map output materialized bytes=61 14/04/26 02:56:25 INFO mapred.JobClient: Combine output records=4 14/04/26 02:56:25 INFO mapred.JobClient: Map input records=2 14/04/26 02:56:25 INFO mapred.JobClient: Reduce shuffle bytes=0 14/04/26 02:56:25 INFO mapred.JobClient: Physical memory (bytes) snapshot=0 14/04/26 02:56:25 INFO mapred.JobClient: Reduce output records=3 14/04/26 02:56:25 INFO mapred.JobClient: Spilled Records=8 14/04/26 02:56:25 INFO mapred.JobClient: Map output bytes=41 14/04/26 02:56:25 INFO mapred.JobClient: CPU time spent (ms)=0 14/04/26 02:56:25 INFO mapred.JobClient: Total committed heap usage (bytes)=480915456 14/04/26 02:56:25 INFO mapred.JobClient: Virtual memory (bytes) snapshot=0 14/04/26 02:56:25 INFO mapred.JobClient: Combine input records=4 14/04/26 02:56:25 INFO mapred.JobClient: Map output records=4 14/04/26 02:56:25 INFO mapred.JobClient: SPLIT_RAW_BYTES=197 14/04/26 02:56:25 INFO mapred.JobClient: Reduce input records=
结果
[root@linux hadoop]# cat output/* hadoop 1 hello 2 world 1
?运行自己编写的Wordcount
package net.annhe.wordcount;
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;
public class WordCount extends Configured implements Tool {
public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word,one);
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce (Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum=0;
for(IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}
public int run(String[] args) throws Exception {
Job job = new Job(getConf());
job.setJarByClass(WordCount.class);
job.setJobName("wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean success = job.waitForCompletion(true);
return success ? 0 : 1;
}
public static void main(String[] args) throws Exception {
int ret = ToolRunner.run(new WordCount(),args);
System.exit(ret);
}
}编译
javac -classpath /usr/share/hadoop/hadoop-core-1.2.1.jar -d . WordCount.java
打包
jar -vcf wordcount.jar -C demo/ .
运行
hadoop jar wordcount.jar net.annhe.wordcount.WordCount input/ out
结果
[root@linux hadoop]# cat out/* hadoop 1 hello 2 world 1
?遇到的问题
1. 内存不足
分给虚拟机的内存才180M，运行实例程序时报错：
java.lang.Exception: java.lang.OutOfMemoryError: Java heap space
解决方案：
增加虚拟机内存，并编辑/etc/hadoop/hadoop-env.sh，修改：
export HADOOP_CLIENT_OPTS="-Xmx512m $HADOOP_CLIENT_OPTS" #改成512m
原来启动JVM时配置的最大内存是128m，当运行hadoop的一些自带的实例会报内存溢出，其实这里是可以修改内存大小 如果不需要也不必修改。[2]
?2. 带有包名的类的引用
带有包名的类要按照包层次调用类。如上面的 net.annhe.wordcount.WordCount [3]
3. 带有包名的类的编译
需要打包编译，加-d选项。
java的类文件是应该放入包中的，如package abc;
public class ls {...} 那么这个abc就是就是类ls的包，那么编译的时候就应该创建相应的abc包，具体就是用javac的一个参数，就是这个-d来生成这个类文件的包，例如上面的类在编译时应该写javac -d . ls.java注意javac和-d,-d和后面的．,.和后面的ls.java中间都有空格[4]
参考资料
[1]. 陆嘉桓. Hadoop实战. 第二版. 机械工业出版社
[2]. OSchina博客:http://my.oschina.net/mynote/blog/93340
[3]. CSDN博客：http://blog.csdn.net/xw13106209/article/details/6861855
[4]. 百度知道:http://zhidao.baidu.com/link?url=ND1BWmyGb_5a05Jntd9vGZNWGtmJmcKF1V6dhVNM1eFNuHL6kbQyVrEWtCUmy7KYP5F66R2BumCifCnPQnYdD_
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