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Hadoop经典案例Spark实现(七)

时间:2018-01-30 10:22 点击:
Hadoop经典案例Spark实现(七)——日志分析:分析非结构化文件

Hadoop经典案例Spark实现(一)——通过采集的气象数据分析每年的最高温度
Hadoop经典案例Spark实现(二)——数据去重问题
Hadoop经典案例Spark实现(三)——数据排序
Hadoop经典案例Spark实现(四)——平均成绩
Hadoop经典案例Spark实现(五)——求最大最小值问题
Hadoop经典案例Spark实现(六)——求最大的K个值并排序
Hadoop经典案例Spark实现(七)——日志分析:分析非结构化文件


Hadoop经典案例Spark实现(七)——日志分析:分析非结构化文件

1、需求:根据tomcat日志计算url访问了情况,具体的url如下,
要求:区别统计GET和POST URL访问量
结果为:访问方式、URL、访问量

测试数据集:

196.168.2.1 - - [03/Jul/2014:23:36:38 +0800] "GET /course/detail/3.htm HTTP/1.0" 200 38435 0.038 182.131.89.195 - - [03/Jul/2014:23:37:43 +0800] "GET /html/notes/20140617/888.html HTTP/1.0" 301 - 0.000 196.168.2.1 - - [03/Jul/2014:23:38:27 +0800] "POST /service/notes/addViewTimes_23.htm HTTP/1.0" 200 2 0.003 196.168.2.1 - - [03/Jul/2014:23:39:03 +0800] "GET /html/notes/20140617/779.html HTTP/1.0" 200 69539 0.046 196.168.2.1 - - [03/Jul/2014:23:43:00 +0800] "GET /html/notes/20140318/24.html HTTP/1.0" 200 67171 0.049 196.168.2.1 - - [03/Jul/2014:23:43:59 +0800] "POST /service/notes/addViewTimes_779.htm HTTP/1.0" 200 1 0.003 196.168.2.1 - - [03/Jul/2014:23:45:51 +0800] "GET /html/notes/20140617/888.html HTTP/1.0" 200 70044 0.060 196.168.2.1 - - [03/Jul/2014:23:46:17 +0800] "GET /course/list/73.htm HTTP/1.0" 200 12125 0.010 196.168.2.1 - - [03/Jul/2014:23:46:58 +0800] "GET /html/notes/20140609/542.html HTTP/1.0" 200 94971 0.077 196.168.2.1 - - [03/Jul/2014:23:48:31 +0800] "POST /service/notes/addViewTimes_24.htm HTTP/1.0" 200 2 0.003 196.168.2.1 - - [03/Jul/2014:23:48:34 +0800] "POST /service/notes/addViewTimes_542.htm HTTP/1.0" 200 2 0.003 196.168.2.1 - - [03/Jul/2014:23:49:31 +0800] "GET /notes/index-top-3.htm HTTP/1.0" 200 53494 0.041 196.168.2.1 - - [03/Jul/2014:23:50:55 +0800] "GET /html/notes/20140609/544.html HTTP/1.0" 200 183694 0.076 196.168.2.1 - - [03/Jul/2014:23:53:32 +0800] "POST /service/notes/addViewTimes_544.htm HTTP/1.0" 200 2 0.004 196.168.2.1 - - [03/Jul/2014:23:54:53 +0800] "GET /service/notes/addViewTimes_900.htm HTTP/1.0" 200 151770 0.054 196.168.2.1 - - [03/Jul/2014:23:57:42 +0800] "GET /html/notes/20140620/872.html HTTP/1.0" 200 52373 0.034 196.168.2.1 - - [03/Jul/2014:23:58:17 +0800] "POST /service/notes/addViewTimes_900.htm HTTP/1.0" 200 2 0.003 196.168.2.1 - - [03/Jul/2014:23:58:51 +0800] "GET /html/notes/20140617/888.html HTTP/1.0" 200 70044 0.057 186.76.76.76 - - [03/Jul/2014:23:48:34 +0800] "POST /service/notes/addViewTimes_542.htm HTTP/1.0" 200 2 0.003 186.76.76.76 - - [03/Jul/2014:23:46:17 +0800] "GET /course/list/73.htm HTTP/1.0" 200 12125 0.010 8.8.8.8 - - [03/Jul/2014:23:46:58 +0800] "GET /html/notes/20140609/542.html HTTP/1.0" 200 94971 0.077


由于Tomcat日志是不规则的,需要先过滤清洗数据。


2、Hadoop之MapReduce实现:

Map类

import java.io.IOException; import javax.naming.spi.DirStateFactory.Result; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class LogMapper extends Mapper<LongWritable, Text, Text, IntWritable> { private IntWritable val = new IntWritable(1); @Override protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException { String line = value.toString().trim(); String tmp = handlerLog(line); if(tmp.length()>0){ context.write(new Text(tmp), val); } } //127.0.0.1 - - [03/Jul/2014:23:36:38 +0800] "GET /course/detail/3.htm HTTP/1.0" 200 38435 0.038 private String handlerLog(String line){ String result = ""; try{ if(line.length()>20){ if(line.indexOf("GET")>0){ result = line.substring(line.indexOf("GET"), line.indexOf("HTTP/1.0")).trim(); }else if(line.indexOf("POST")>0){ result = line.substring(line.indexOf("POST"), line.indexOf("HTTP/1.0")).trim(); } } }catch (Exception e) { System.out.println(line); } return result; } public static void main(String[] args){ String line = "127.0.0.1 - - [03/Jul/2014:23:36:38 +0800] \"GET /course/detail/3.htm HTTP/1.0\" 200 38435 0.038"; System.out.println(new LogMapper().handlerLog(line)); } }
Reduce类 import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class LogReducer extends Reducer<Text, IntWritable, Text, IntWritable> { @Override protected 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)); } }
启动类 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.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class JobMain { /** * @param args */ public static void main(String[] args)throws Exception { Configuration configuration = new Configuration(); Job job = new Job(configuration,"log_job"); job.setJarByClass(JobMain.class); job.setMapperClass(LogMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setReducerClass(LogReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); Path path = new Path(args[1]); FileSystem fs = FileSystem.get(configuration); if(fs.exists(path)){ fs.delete(path, true); } FileOutputFormat.setOutputPath(job, path); System.exit(job.waitForCompletion(true)?0:1); } }

数据统计中,请稍等!
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