原创

Hadoop学习笔记—20.网站日志分析项目案例(二)数据清洗

网站日志分析项目案例(一)项目介绍:http://www.cnblogs.com/edisonchou/p/4449082.html

网站日志分析项目案例(二)数据清洗:当前页面

网站日志分析项目案例(三)统计分析:http://www.cnblogs.com/edisonchou/p/4464349.html

一、数据情况分析

1.1 数据情况回顾

  该论坛数据有两部分:

  (1)历史数据约56GB,统计到2012-05-29。这也说明,在2012-05-29之前,日志文件都在一个文件里边,采用了追加写入的方式。

  (2)自2013-05-30起,每天生成一个数据文件,约150MB左右。这也说明,从2013-05-30之后,日志文件不再是在一个文件里边。

  图1展示了该日志数据的记录格式,其中每行记录有5部分组成:访问者IP、访问时间、访问资源、访问状态(HTTP状态码)、本次访问流量。

log

图1 日志记录数据格式

  本次使用数据来自于两个2013年的日志文件,分别为access_2013_05_30.log与access_2013_05_31.log,下载地址为:http://pan.baidu.com/s/1pJE7XR9

1.2 要清理的数据

  (1)根据前一篇的关键指标的分析,我们所要统计分析的均不涉及到访问状态(HTTP状态码)以及本次访问的流量,于是我们首先可以将这两项记录清理掉;

  (2)根据日志记录的数据格式,我们需要将日期格式转换为平常所见的普通格式如20150426这种,于是我们可以写一个类将日志记录的日期进行转换;

  (3)由于静态资源的访问请求对我们的数据分析没有意义,于是我们可以将"GET /staticsource/"开头的访问记录过滤掉,又因为GET和POST字符串对我们也没有意义,因此也可以将其省略掉;

二、数据清洗过程

2.1 定期上传日志至HDFS

  首先,把日志数据上传到HDFS中进行处理,可以分为以下几种情况:

  (1)如果是日志服务器数据较小、压力较小,可以直接使用shell命令把数据上传到HDFS中;

  (2)如果是日志服务器数据较大、压力较大,使用NFS在另一台服务器上上传数据;

  (3)如果日志服务器非常多、数据量大,使用flume进行数据处理;

  这里我们的实验数据文件较小,因此直接采用第一种Shell命令方式。又因为日志文件时每天产生的,因此需要设置一个定时任务,在第二天的1点钟自动将前一天产生的log文件上传到HDFS的指定目录中。所以,我们通过shell脚本结合crontab创建一个定时任务techbbs_core.sh,内容如下:

#!/bin/sh

#step1.get yesterday format string
yesterday=$(date --date='1 days ago' +%Y_%m_%d)
#step2.upload logs to hdfs
hadoop fs -put /usr/local/files/apache_logs/access_${yesterday}.log /project/techbbs/data

  结合crontab设置为每天1点钟自动执行的定期任务:crontab -e,内容如下(其中1代表每天1:00,techbbs_core.sh为要执行的脚本文件):

* 1 * * * techbbs_core.sh

  验证方式:通过命令 crontab -l 可以查看已经设置的定时任务

2.2 编写MapReduce程序清理日志

  (1)编写日志解析类对每行记录的五个组成部分进行单独的解析

    static class LogParser {
        public static final SimpleDateFormat FORMAT = new SimpleDateFormat(
                "d/MMM/yyyy:HH:mm:ss", Locale.ENGLISH);
        public static final SimpleDateFormat dateformat1 = new SimpleDateFormat(
                "yyyyMMddHHmmss");/**
         * 解析英文时间字符串
         * 
         * @param string
         * @return
         * @throws ParseException
         */
        private Date parseDateFormat(String string) {
            Date parse = null;
            try {
                parse = FORMAT.parse(string);
            } catch (ParseException e) {
                e.printStackTrace();
            }
            return parse;
        }

        /**
         * 解析日志的行记录
         * 
         * @param line
         * @return 数组含有5个元素,分别是ip、时间、url、状态、流量
         */
        public String[] parse(String line) {
            String ip = parseIP(line);
            String time = parseTime(line);
            String url = parseURL(line);
            String status = parseStatus(line);
            String traffic = parseTraffic(line);

            return new String[] { ip, time, url, status, traffic };
        }

        private String parseTraffic(String line) {
            final String trim = line.substring(line.lastIndexOf("\"") + 1)
                    .trim();
            String traffic = trim.split(" ")[1];
            return traffic;
        }

        private String parseStatus(String line) {
            final String trim = line.substring(line.lastIndexOf("\"") + 1)
                    .trim();
            String status = trim.split(" ")[0];
            return status;
        }

        private String parseURL(String line) {
            final int first = line.indexOf("\"");
            final int last = line.lastIndexOf("\"");
            String url = line.substring(first + 1, last);
            return url;
        }

        private String parseTime(String line) {
            final int first = line.indexOf("[");
            final int last = line.indexOf("+0800]");
            String time = line.substring(first + 1, last).trim();
            Date date = parseDateFormat(time);
            return dateformat1.format(date);
        }

        private String parseIP(String line) {
            String ip = line.split("- -")[0].trim();
            return ip;
        }
    }

  (2)编写MapReduce程序对指定日志文件的所有记录进行过滤

  Mapper类:

        static class MyMapper extends
            Mapper<LongWritable, Text, LongWritable, Text> {
        LogParser logParser = new LogParser();
        Text outputValue = new Text();

        protected void map(
                LongWritable key,
                Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, LongWritable, Text>.Context context)
                throws java.io.IOException, InterruptedException {
            final String[] parsed = logParser.parse(value.toString());

            // step1.过滤掉静态资源访问请求
            if (parsed[2].startsWith("GET /static/")
                    || parsed[2].startsWith("GET /uc_server")) {
                return;
            }
            // step2.过滤掉开头的指定字符串
            if (parsed[2].startsWith("GET /")) {
                parsed[2] = parsed[2].substring("GET /".length());
            } else if (parsed[2].startsWith("POST /")) {
                parsed[2] = parsed[2].substring("POST /".length());
            }
            // step3.过滤掉结尾的特定字符串
            if (parsed[2].endsWith(" HTTP/1.1")) {
                parsed[2] = parsed[2].substring(0, parsed[2].length()
                        - " HTTP/1.1".length());
            }
            // step4.只写入前三个记录类型项
            outputValue.set(parsed[0] + "\t" + parsed[1] + "\t" + parsed[2]);
            context.write(key, outputValue);
        }
    }

  Reducer类:

    static class MyReducer extends
            Reducer<LongWritable, Text, Text, NullWritable> {
        protected void reduce(
                LongWritable k2,
                java.lang.Iterable<Text> v2s,
                org.apache.hadoop.mapreduce.Reducer<LongWritable, Text, Text, NullWritable>.Context context)
                throws java.io.IOException, InterruptedException {
            for (Text v2 : v2s) {
                context.write(v2, NullWritable.get());
            }
        };
    }

  (3)LogCleanJob.java的完整示例代码

package techbbs;

import java.net.URI;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Locale;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.Tool;
import org.apache.hadoop.util.ToolRunner;

public class LogCleanJob extends Configured implements Tool {

    public static void main(String[] args) {
        Configuration conf = new Configuration();
        try {
            int res = ToolRunner.run(conf, new LogCleanJob(), args);
            System.exit(res);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    @Override
    public int run(String[] args) throws Exception {
        final Job job = new Job(new Configuration(),
                LogCleanJob.class.getSimpleName());
        // 设置为可以打包运行
        job.setJarByClass(LogCleanJob.class);
        FileInputFormat.setInputPaths(job, args[0]);
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(Text.class);
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 清理已存在的输出文件
        FileSystem fs = FileSystem.get(new URI(args[0]), getConf());
        Path outPath = new Path(args[1]);
        if (fs.exists(outPath)) {
            fs.delete(outPath, true);
        }
        
        boolean success = job.waitForCompletion(true);
        if(success){
            System.out.println("Clean process success!");
        }
        else{
            System.out.println("Clean process failed!");
        }
        return 0;
    }

    static class MyMapper extends
            Mapper<LongWritable, Text, LongWritable, Text> {
        LogParser logParser = new LogParser();
        Text outputValue = new Text();

        protected void map(
                LongWritable key,
                Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, LongWritable, Text>.Context context)
                throws java.io.IOException, InterruptedException {
            final String[] parsed = logParser.parse(value.toString());

            // step1.过滤掉静态资源访问请求
            if (parsed[2].startsWith("GET /static/")
                    || parsed[2].startsWith("GET /uc_server")) {
                return;
            }
            // step2.过滤掉开头的指定字符串
            if (parsed[2].startsWith("GET /")) {
                parsed[2] = parsed[2].substring("GET /".length());
            } else if (parsed[2].startsWith("POST /")) {
                parsed[2] = parsed[2].substring("POST /".length());
            }
            // step3.过滤掉结尾的特定字符串
            if (parsed[2].endsWith(" HTTP/1.1")) {
                parsed[2] = parsed[2].substring(0, parsed[2].length()
                        - " HTTP/1.1".length());
            }
            // step4.只写入前三个记录类型项
            outputValue.set(parsed[0] + "\t" + parsed[1] + "\t" + parsed[2]);
            context.write(key, outputValue);
        }
    }

    static class MyReducer extends
            Reducer<LongWritable, Text, Text, NullWritable> {
        protected void reduce(
                LongWritable k2,
                java.lang.Iterable<Text> v2s,
                org.apache.hadoop.mapreduce.Reducer<LongWritable, Text, Text, NullWritable>.Context context)
                throws java.io.IOException, InterruptedException {
            for (Text v2 : v2s) {
                context.write(v2, NullWritable.get());
            }
        };
    }

    /*
     * 日志解析类
     */
    static class LogParser {
        public static final SimpleDateFormat FORMAT = new SimpleDateFormat(
                "d/MMM/yyyy:HH:mm:ss", Locale.ENGLISH);
        public static final SimpleDateFormat dateformat1 = new SimpleDateFormat(
                "yyyyMMddHHmmss");

        public static void main(String[] args) throws ParseException {
            final String S1 = "27.19.74.143 - - [30/May/2013:17:38:20 +0800] \"GET /static/image/common/faq.gif HTTP/1.1\" 200 1127";
            LogParser parser = new LogParser();
            final String[] array = parser.parse(S1);
            System.out.println("样例数据: " + S1);
            System.out.format(
                    "解析结果:  ip=%s, time=%s, url=%s, status=%s, traffic=%s",
                    array[0], array[1], array[2], array[3], array[4]);
        }

        /**
         * 解析英文时间字符串
         * 
         * @param string
         * @return
         * @throws ParseException
         */
        private Date parseDateFormat(String string) {
            Date parse = null;
            try {
                parse = FORMAT.parse(string);
            } catch (ParseException e) {
                e.printStackTrace();
            }
            return parse;
        }

        /**
         * 解析日志的行记录
         * 
         * @param line
         * @return 数组含有5个元素,分别是ip、时间、url、状态、流量
         */
        public String[] parse(String line) {
            String ip = parseIP(line);
            String time = parseTime(line);
            String url = parseURL(line);
            String status = parseStatus(line);
            String traffic = parseTraffic(line);

            return new String[] { ip, time, url, status, traffic };
        }

        private String parseTraffic(String line) {
            final String trim = line.substring(line.lastIndexOf("\"") + 1)
                    .trim();
            String traffic = trim.split(" ")[1];
            return traffic;
        }

        private String parseStatus(String line) {
            final String trim = line.substring(line.lastIndexOf("\"") + 1)
                    .trim();
            String status = trim.split(" ")[0];
            return status;
        }

        private String parseURL(String line) {
            final int first = line.indexOf("\"");
            final int last = line.lastIndexOf("\"");
            String url = line.substring(first + 1, last);
            return url;
        }

        private String parseTime(String line) {
            final int first = line.indexOf("[");
            final int last = line.indexOf("+0800]");
            String time = line.substring(first + 1, last).trim();
            Date date = parseDateFormat(time);
            return dateformat1.format(date);
        }

        private String parseIP(String line) {
            String ip = line.split("- -")[0].trim();
            return ip;
        }
    }
}
View Code

  (4)导出jar包,并将其上传至Linux服务器指定目录中

2.3 定期清理日志至HDFS

  这里我们改写刚刚的定时任务脚本,将自动执行清理的MapReduce程序加入脚本中,内容如下:

#!/bin/sh

#step1.get yesterday format string
yesterday=$(date --date='1 days ago' +%Y_%m_%d)
#step2.upload logs to hdfs
hadoop fs -put /usr/local/files/apache_logs/access_${yesterday}.log /project/techbbs/data
#step3.clean log data
hadoop jar /usr/local/files/apache_logs/mycleaner.jar /project/techbbs/data/access_${yesterday}.log /project/techbbs/cleaned/${yesterday}

  这段脚本的意思就在于每天1点将日志文件上传到HDFS后,执行数据清理程序对已存入HDFS的日志文件进行过滤,并将过滤后的数据存入cleaned目录下。 

2.4 定时任务测试

  (1)因为两个日志文件是2013年的,因此这里将其名称改为2015年当天以及前一天的,以便这里能够测试通过。

  (2)执行命令:techbbs_core.sh 2014_04_26

  控制台的输出信息如下所示,可以看到过滤后的记录减少了很多:

15/04/26 04:27:20 INFO input.FileInputFormat: Total input paths to process : 1
15/04/26 04:27:20 INFO util.NativeCodeLoader: Loaded the native-hadoop library
15/04/26 04:27:20 WARN snappy.LoadSnappy: Snappy native library not loaded
15/04/26 04:27:22 INFO mapred.JobClient: Running job: job_201504260249_0002
15/04/26 04:27:23 INFO mapred.JobClient: map 0% reduce 0%
15/04/26 04:28:01 INFO mapred.JobClient: map 29% reduce 0%
15/04/26 04:28:07 INFO mapred.JobClient: map 42% reduce 0%
15/04/26 04:28:10 INFO mapred.JobClient: map 57% reduce 0%
15/04/26 04:28:13 INFO mapred.JobClient: map 74% reduce 0%
15/04/26 04:28:16 INFO mapred.JobClient: map 89% reduce 0%
15/04/26 04:28:19 INFO mapred.JobClient: map 100% reduce 0%
15/04/26 04:28:49 INFO mapred.JobClient: map 100% reduce 100%
15/04/26 04:28:50 INFO mapred.JobClient: Job complete: job_201504260249_0002
15/04/26 04:28:50 INFO mapred.JobClient: Counters: 29
15/04/26 04:28:50 INFO mapred.JobClient: Job Counters
15/04/26 04:28:50 INFO mapred.JobClient: Launched reduce tasks=1
15/04/26 04:28:50 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=58296
15/04/26 04:28:50 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
15/04/26 04:28:50 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
15/04/26 04:28:50 INFO mapred.JobClient: Launched map tasks=1
15/04/26 04:28:50 INFO mapred.JobClient: Data-local map tasks=1
15/04/26 04:28:50 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=25238
15/04/26 04:28:50 INFO mapred.JobClient: File Output Format Counters
15/04/26 04:28:50 INFO mapred.JobClient: Bytes Written=12794925
15/04/26 04:28:50 INFO mapred.JobClient: FileSystemCounters
15/04/26 04:28:50 INFO mapred.JobClient: FILE_BYTES_READ=14503530
15/04/26 04:28:50 INFO mapred.JobClient: HDFS_BYTES_READ=61084325
15/04/26 04:28:50 INFO mapred.JobClient: FILE_BYTES_WRITTEN=29111500
15/04/26 04:28:50 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=12794925
15/04/26 04:28:50 INFO mapred.JobClient: File Input Format Counters
15/04/26 04:28:50 INFO mapred.JobClient: Bytes Read=61084192
15/04/26 04:28:50 INFO mapred.JobClient: Map-Reduce Framework
15/04/26 04:28:50 INFO mapred.JobClient: Map output materialized bytes=14503530
15/04/26 04:28:50 INFO mapred.JobClient: Map input records=548160
15/04/26 04:28:50 INFO mapred.JobClient: Reduce shuffle bytes=14503530
15/04/26 04:28:50 INFO mapred.JobClient: Spilled Records=339714
15/04/26 04:28:50 INFO mapred.JobClient: Map output bytes=14158741
15/04/26 04:28:50 INFO mapred.JobClient: CPU time spent (ms)=21200
15/04/26 04:28:50 INFO mapred.JobClient: Total committed heap usage (bytes)=229003264
15/04/26 04:28:50 INFO mapred.JobClient: Combine input records=0
15/04/26 04:28:50 INFO mapred.JobClient: SPLIT_RAW_BYTES=133
15/04/26 04:28:50 INFO mapred.JobClient: Reduce input records=169857
15/04/26 04:28:50 INFO mapred.JobClient: Reduce input groups=169857
15/04/26 04:28:50 INFO mapred.JobClient: Combine output records=0
15/04/26 04:28:50 INFO mapred.JobClient: Physical memory (bytes) snapshot=154001408
15/04/26 04:28:50 INFO mapred.JobClient: Reduce output records=169857
15/04/26 04:28:50 INFO mapred.JobClient: Virtual memory (bytes) snapshot=689442816
15/04/26 04:28:50 INFO mapred.JobClient: Map output records=169857
Clean process success!

  (3)通过Web接口查看HDFS中的日志数据:

  存入的未过滤的日志数据:/project/techbbs/data/

  存入的已过滤的日志数据:/project/techbbs/cleaned/

 

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