项目需求：
需要统计一下线上日志中某些信息每天出现的频率，举个简单的例子，统计线上每天的请求总数和异常请求数。线上大概几十台
服务器，每台服务器大概每天产生4到5G左右的日志，假设有30台，每台5G的，一天产生的日志总量为150G。
处理方案：
方案1：传统的处理方式，写个JAVA日志分析代码，部署到每台服务器进行处理，这种方式部署起来耗时费力，又不好维护。
方案2：采用Hadoop分布式处理，日志分析是Hadoop集群系统的拿手好戏。150G每天的日志也算是比较大的数据量了，搭个简
单的Hadoop集群来处理这些日志是再好不过的了。
Hadoop集群的搭建：
我们这里的集群就采用了两台机器，配置每台8核，32G内存，500G磁盘空间。
日志准备工作：
由于日志分散在各个服务器，所以我们先需要将所有的日志拷贝到我们的集群系统当中，这个可以通过linux服务器下rsync或者scp
服务来执行。这里我们通过scp服务来拷贝，由于都是内网的机器，所以拷贝几个G的日志可以很快就完成。下面是拷贝日志的脚本，脚本
还是有一些需要注意的地方，我们只需要拷贝前一天的数据，实际保存的数据可能是好几天的，所以我们只要把我们需要的这一天的数据
SCP过去就可以了。
#!/bin/sh
workdir=/home/myproj/bin/log/
files=`ls $workdir`
pre1date=`date + "%Y%m%d" -d "-1 days" `
pre1date1=`date + "%Y-%m-%d" -d "-1 days" `
curdate=`date + "%Y%m%d" `
hostname=`uname -n`
echo $pre1date $curdate
uploadpath= "/home/hadoop/hadoop/mytest/log/" $pre1date1 "/" $hostname
echo $uploadpath
cd $workdir
mintime= 240000
secondmintime= 0
for file in $files; do
filedate=`stat $file | grep Modify| awk '{print $2}' |sed -e 's/-//g' `
filetime=`stat $file | grep Modify| awk '{print $3}' |cut -d "." -f1 | sed -e 's/://g' | sed 's/^0\+//' `
if [ $filedate -eq $curdate ]; then
if [ $filetime -lt $mintime ]; then
secondmintime=$mintime
mintime=$filetime
fi
fi
done
echo "mintime:" $mintime
step= 1000
mintime=`expr $mintime + $step`
echo "mintime+1000:" $mintime
for file in $files; do
filedate=`stat $file | grep Modify| awk '{print $2}' |sed -e 's/-//g' `
filetime=`stat $file | grep Modify| awk '{print $3}' |cut -d "." -f1 | sed -e 's/://g' | sed 's/^0\+//' `
filename=`echo $file | cut -c 1 - 8 `
startchars= "info.log"
#echo $filename
if [ $filename == $startchars ]; then
if [ $filedate -eq $pre1date ]; then
scp -rp $file dir @antix2 :$uploadpath
#echo $file
elif [ $filedate -eq $curdate ]; then
if [ $filetime -lt $mintime ]; then
scp -rp $file dir @antix2 :$uploadpath
#echo $file
fi
fi
fi
#echo $filedate $filetime
done
MapReduce代码
接下来就是编写MapReduce的代码了。使用Eclipse环境来编写，需要安装hadoop插件，我们hadoop机器采用的是1.1.1版本，所以插
件使用hadoop-eclipse-plugin-1.1.1.jar，将插件拷贝到eclipse的plugins目录下就可以了。然后新建一个MapReduce项目：
工程新建好了然后我们就可以编写我们的MapReduce代码了。
import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Date;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class LogAnalysis {
public static class LogMapper
extends Mapper<LongWritable, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private Text hourWord = new Text();
public void map(LongWritable key, Text value, Context contex
) throws IOException, InterruptedException {
String line = value.toString();
SimpleDateFormat formatter2 = new SimpleDateFormat("yy-MM-dd");
java.util.Date d1 =new Date();
d1.setTime(System.currentTimeMillis()-1*24*3600*1000);
String strDate =formatter2.format(d1);
if(line.contains(strDate)){
String[] strArr = line.split(",");
int len = strArr[0].length();
String time = strArr[0].substring(1,len-1);
String[] timeArr = time.split(":");
String strHour = timeArr[0];
String hour = strHour.substring(strHour.length()-2,strHour.length());
String hourKey = "";
if(line.contains("StartASocket")){
word.set("SocketCount");
context.write(word, one);
hourKey = "SocketCount:" + hour;
hourWord.set(hourKey);
context.write(hourWord, one);
word.clear();
hourWord.clear();
}
if(line.contains("SocketException")){
word.set("SocketExceptionCount");
context.write(word, one);
hourKey = "SocketExceptionCount:" + hour;
hourWord.set(hourKey);
context.write(hourWord, one);
word.clear();
hourWord.clear();
}
}
}
public static class LogReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static int run(String[] args) throws Exception{
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: loganalysis <in> <out>");
System.exit(2);
}
FileSystem fileSys = FileSystem.get(conf);
String inputPath = "input/" + args[0];
fileSys.copyFromLocalFile(new Path(args[0]), new Path(inputPath));//将本地文件系统的文件拷贝到HDFS中
Job job = new Job(conf, "loganalysis");
job.setJarByClass(LogAnalysis.class);
job.setMapperClass(LogMapper.class);
job.setCombinerClass(LogReducer.class);
job.setReducerClass(LogReducer.class);
// 设置输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(inputPath));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
Date startTime = new Date();
System.out.println("Job started: " + startTime);
int ret = job.waitForCompletion(true)? 0 : 1;
fileSys.copyToLocalFile(new Path(otherArgs[1]), new Path(otherArgs[1]));
fileSys.delete(new Path(inputPath), true);
fileSys.delete(new Path(otherArgs[1]), true);
Date end_time = new Date();
System.out.println("Job ended: " + end_time);
System.out.println("The job took " + (end_time.getTime() - startTime.getTime()) /1000 + " seconds.");
return ret;
}
public static void main(String[] args)
{
try
{
int ret = run(args);
System.exit(ret);
} catch (Exception e)
{
e.printStackTrace();
System.out.println(e.getMessage());
}
}
}
部署到Hadoop集群：
代码完成后测试没有问题后，部署到集群当中去执行，我们有几十台服务器，所以每台的服务器的日志当成一个任务来执行。
workdir="/home/hadoop/hadoop/mytest"
cd $workdir
pre1date=`date +"%Y-%m-%d" -d "-1 days"`
servers=(mach1 mach2 mach3 )
for i in ${servers[@]};do
inputPath="log/"$pre1date"/"$i
outputPath="output/log/"$pre1date"/"$i
echo $inputPath $outputPath
echo "start job "$i" date:"`date`
hadoop jar LogAnalysis.jar loganalysis $inputPath $outputPath
echo "end job "$i" date:"`date`
done
