Result文件数据说明：
Ip：106.39.41.166,（城市）
Date：10/Nov/2016:00:01:02 +0800,（日期）
Day：10,（天数）
Traffic: 54 ,（流量）
Type: video,（类型：视频video或文章article）
Id: 8701（视频或者文章的id）
测试要求：
1、 数据清洗：按照进行数据清洗，并将清洗后的数据导入hive数据库中。
两阶段数据清洗：
（1）第一阶段：把需要的信息从原始日志中提取出来
ip: 199.30.25.88
time: 10/Nov/2016:00:01:03 +0800
traffic: 62
文章： article/11325
视频： video/3235
（2）第二阶段：根据提取出来的信息做精细化操作
ip--->城市 city（IP）
date--> time:2016-11-10 00:01:03
day: 10
traffic:62
type:article/video
id:11325
（3）hive数据库表结构:
create table data( ip string, time string , day string, traffic bigint,
type string, id string )
2、数据处理：
·统计最受欢迎的视频/文章的Top10访问次数 （video/article）
·按照地市统计最受欢迎的Top10课程 （ip）
·按照流量统计最受欢迎的Top10课程 （traffic）
3、数据可视化：将统计结果倒入MySql数据库中，通过图形化展示的方式展现出来。
实验代码：
package mapreduce;
import java.io.IOException;
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.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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class Filter {
public static class Map extends Mapper<Object, Text, Text, NullWritable> {
private static Text newKey = new Text();
/*public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
System.out.println(line);
String arr[] = line.split(" ");
newKey.set(arr[1]);
context.write(newKey, NullWritable.get());
System.out.println(newKey);
}
}*/
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
String S1 = value.toString ();
LogParser parser = new LogParser();
final String[] array = parser.parse(S1);
System.out.println(S1);
/*System.out.format(
"解析结果： ip=%s, time=%s,day=%s, traffic=%s, type=%s,id=%s",
array[0], array[1], array[2], array[3], array[4],array[5]);*/
String a=array[0];
String u=array[1];
String c=array[2];
String d=array[3];
String e=array[4];
String f=array[5];
String str = a +" "+u +" "+c+" "+d+" "+e+" "+f;
newKey.set(str);
context.write(newKey, NullWritable.get());
System.out.println(newKey);
}
}
public static class Reduce extends Reducer<Text, NullWritable, Text, NullWritable> {
public void reduce(Text key, Iterable<NullWritable> values, Context context)
throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
System.out.println("start");
Job job = new Job(conf, "filter");
job.setJarByClass(Filter.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
Path in = new Path("hdfs://localhost:9000/test/in/result");
Path out = new Path("hdfs://localhost:9000/test/out");
FileInputFormat.addInputPath(job, in);
FileOutputFormat.setOutputPath(job, out);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
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(
"yyyy-MM-dd HH:mm:ss");
/**
* 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、日期、状态、流量
*/
public String[] parse(String line) {
String ip = parseIP(line);
String time = parseTime(line);
String day = parseday(line);
String traffic = parseTraffic(line);
String type = parsertype(line);
String id = parseid( line);
return new String[] { ip, time, day,traffic , type, id };
}
private String parseIP(String line) {
String ip = line.split(",")[0].trim();
return ip;
}
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 parseday(String line) {
String riqi = line.split(",")[2].trim();
return riqi;
}
private String parseTraffic(String line) {
String riqi = line.split(",")[3].trim();
return riqi;
}
//private String parseTraffic(String line) {
// final String trim = line.substring(line.lastIndexOf(",") + 1)
// .trim();
//String traffic = trim.split(" ")[0];
//return traffic;
//}
//private String parsertype(String line) {
// final int first = line.indexOf(",");
// final int last = line.lastIndexOf(",");
// String url = line.substring(first + 1, last);
// return url;
//}
private String parsertype(String line) {
String riqi = line.split(",")[4].trim();
return riqi;
}
private String parseid(String line) {
final String trim = line.substring(line.lastIndexOf(",") + 1)
.trim();
String id = trim.split(" ")[0];
return id;
}
}
}
实验截图：
实验体会：
1.String str = a +"\t"+u +"\t"+c+"\t"+d+"t\"+e+"\t"+f;这行代码很重要，引号里的内容决定了输出内容的分割方式，hive中row format delimited fields terminated by '\t';的分割方式与输出结果保持一致可以省很多事。
2.对map和reduce的理解更深了。
3.String riqi = line.split(",")[2].trim();这句表示的是第3个逗号右边内容的格式。