Hive(21) hive的综合案例实战--统计网站各种TopN指标

hive的综合案例实战

案例需求

统计youtube影音视频网站的常规指标，各种TopN指标：

--统计视频观看数Top10

--统计视频类别热度Top10

--统计视频观看数Top20所属类别

--统计视频观看数Top50所关联视频的所属类别Rank

--统计每个类别中的视频热度Top10

--统计每个类别中视频流量Top10

--统计上传视频最多的用户Top10以及他们上传的视频

--统计每个类别视频观看数Top10

项目表字段

1．视频表

#数据字段情况大致如下：
LKh7zAJ4nwo	TheReceptionist	653	Entertainment	424	13021	4.34	1305	744	DjdA-5oKYFQ	NxTDlnOuybo	c-8VuICzXtU	DH56yrIO5nI	W1Uo5DQTtzc	E-3zXq_r4w0	1TCeoRPg5dE	yAr26YhuYNY	2ZgXx72XmoE	-7ClGo-YgZ0	vmdPOOd6cxI	KRHfMQqSHpk	pIMpORZthYw	1tUDzOp10pk	heqocRij5P0	_XIuvoH6rUg	LGVU5DsezE0	uO2kj6_D8B4	xiDqywcDQRM	uX81lMev6_o

fQShwYqGqsw	lonelygirl15	736	People & Blogs	133	151763	3.01	666	765	fQShwYqGqsw	LfAaY1p_2Is	5LELNIVyMqo	vW6ZpqXjCE4	vPUAf43vc-Q	ZllfQZCc2_M	it2d7LaU_TA	KGRx8TgZEeU	aQWdqI1vd6o	kzwa8NBlUeo	X3ctuFCCF5k	Ble9N2kDiGc	R24FONE2CDs	IAY5q60CmYY	mUd0hcEnHiU	6OUcp6UJ2bA	dv0Y_uoHrLc	8YoxhsUMlgA	h59nXANN-oo	113yn3sv0eo
字段 备注 详细描述
video id 视频唯一id 11位字符串
uploader 视频上传者 上传视频的用户名String
age 视频年龄 视频在平台上的整数天
category 视频类别 上传视频指定的视频分类
length 视频长度 整形数字标识的视频长度
views 观看次数 视频被浏览的次数
rate 视频评分 满分5分
ratings 流量 视频的流量，整型数字
conments 评论数 一个视频的整数评论数
related ids 相关视频id 相关视频的id，最多20个

2．用户表

#数据字段情况大致如下：
barelypolitical	151	5106
bonk65	89	144
camelcars	26	674
cubskickass34	13	126
boydism08	32	50
字段 备注 字段类型
uploader 上传者用户名 string
videos 上传视频数 int
friends 朋友数量 int

ETL原始数据清洗

通过观察原始数据形式，可以发现：

  • 视频可以有多个所属分类category,如People & Blogs，每个所属分类用&符号分割，且分割的两边有空格字符
  • 同时相关视频也是可以有多个元素，多个相关视频又用“\t”进行分割。
  • 为了分析数据时方便对存在多个子元素的数据进行操作，我们首先进行数据重组清洗操作。即：将所有的类别用“&”分割，同时去掉两边空格，多个相关视频id也使用“&”进行分割。
  • 视频的原始数据里，一行应该有至少9个字段。三件事情

因为我们的数据清洗工作主要有以下三件事情：

  • 长度不够9的删掉。（视频数据里，一行数据不够9个字段的证明改行数据损坏掉，要删掉）
  • 视频类别删掉空格
  • 相关视频的分割符用&

第一步：创建Maven工程

	<repositories>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
    </repositories>
    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0-mr1-cdh5.14.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.6.0-cdh5.14.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.6.0-cdh5.14.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>2.6.0-cdh5.14.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/junit/junit -->
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.testng</groupId>
            <artifactId>testng</artifactId>
            <version>RELEASE</version>
            <scope>test</scope>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
            <scope>compile</scope>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                    <!--    <verbal>true</verbal>-->
                </configuration>
            </plugin>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <minimizeJar>true</minimizeJar>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <!--  <plugin>
                  <artifactId>maven-assembly-plugin </artifactId>
                  <configuration>
                      <descriptorRefs>
                          <descriptorRef>jar-with-dependencies</descriptorRef>
                      </descriptorRefs>
                      <archive>
                          <manifest>
                              <mainClass></mainClass>
                          </manifest>
                      </archive>
                  </configuration>
                  <executions>
                      <execution>
                          <id>make-assembly</id>
                          <phase>package</phase>
                          <goals>
                              <goal>single</goal>
                          </goals>
                      </execution>
                  </executions>
              </plugin>-->
        </plugins>
    </build>

第二部：代码开发：ETLUtil

public class VideoUtil {
    /**
     * 对我们的数据进行清洗的工作，
     * 数据切割，如果长度小于9 直接丢掉
     * 视频类别中间空格 去掉
     * 关联视频，使用 &  进行分割
     * @param line
     * @return
     * FM1KUDE3C3k  renetto	736	News & Politics	1063	9062	4.57	525	488	LnMvSxl0o0A&IKMtzNuKQso&Bq8ubu7WHkY&Su0VTfwia1w&0SNRfquDfZs&C72NVoPsRGw
     */
    public  static String washDatas(String line){
        if(null == line || "".equals(line)) {
            return null;
        }
        //判断数据的长度，如果小于9，直接丢掉
        String[] split = line.split("\t");
        if(split.length <9){
            return null;
        }
        //将视频类别空格进行去掉
        split[3] =  split[3].replace(" ","");
        StringBuilder builder = new StringBuilder();
        for(int i =0;i<split.length;i++){
            if(i <9){
                //这里面是前面八个字段
                builder.append(split[i]).append("\t");
            }else if(i >=9  && i < split.length -1){
                builder.append(split[i]).append("&");
            }else if( i == split.length -1){
                builder.append(split[i]);
            }
        }
        return  builder.toString();
    }
}

第三步：代码开发：ETLMapper

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;

public class VideoMapper extends Mapper<LongWritable,Text,Text,NullWritable> {
    private Text  key2 ;
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        key2 = new Text();
    }
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String s = VideoUtils.washDatas(value.toString());
        if(null != s ){
            key2.set(s);
            context.write(key2,NullWritable.get());
        }
    }
}

第四步：代码开发：ETLRunner

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class VideoMain extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        Job job = Job.getInstance(super.getConf(), "washDatas");
        job.setJarByClass(VideoMain.class);
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path(args[0]));

        job.setMapperClass(VideoMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path(args[1]));
        //注意，我们这里没有自定义reducer，会使用默认的reducer类
        //设置reduceTask为7，从而生成7个文件，便于后面表的数据加载
        job.setNumReduceTasks(7);
        boolean b = job.waitForCompletion(true);
        return b?0:1;
    }
    public static void main(String[] args) throws Exception {
        int run = ToolRunner.run(new Configuration(), new VideoMain(), args);
        System.exit(run);
    }
}

第五步：打包jar包到集群运行

#上传jar包到集群任意节点，这里以node03节点为例
[hadoop@node03 hivedatas]$ rz 
	original-MyHive-1.0-SNAPSHOT.jar
	
#上传视频数据到linux本地，再从本地到hdfs
[hadoop@node03 hivedatas]$ mkdir videoData
[hadoop@node03 hivedatas]$ cd videoData
[hadoop@node03 videoData]$ rz
	0.txt  1.txt  2.txt  3.txt  4.txt
hdfs dfs -put videoData /

#上传用户数据到linux本地，再从本地到hdfs
[hadoop@node03 hivedatas]$ mkdir userData;cd userData
[hadoop@node03 userData]$ rz
	user.txt
hdfs dfs -put userData /

#运行jar包
[hadoop@node03 hivedatas]$ hadoop jar original-MyHive-1.0-SNAPSHOT.jar com.jimmy.ETL.VideoMain /videoData/ /videoOut

项目建表并加载数据

创建普通表

接下来我们要建立分桶表，因为分桶表不能够直接通过load方式加载数据，因此我们首先要建立两个对应的普通表来为接下来分桶表加载数据做准备。普通表使用textfile格式进行存储。

youtubevideo_ori

set hive.enforce.bucketing=true;
set mapreduce.job.reduces=-1;

create database youtube;
use youtube;
create table youtubevideo_ori(
    videoId string, 
    uploader string, 
    age int, 
    category array<string>, 
    length int, 
    views int, 
    rate float, 
    ratings int, 
    comments int,
    relatedId array<string>)
row format delimited fields terminated by "\t"
collection items terminated by "&"
stored as textfile;

youtubevideo_user_ori

create table youtubevideo_user_ori(
    uploader string,
    videos int,
    friends int)
row format delimited fields terminated by "\t" 
stored as textfile;

创建分桶表

youtubevideo_orc

create table youtubevideo_orc(
    videoId string, 
    uploader string, 
    age int, 
    category array<string>, 
    length int, 
    views int, 
    rate float, 
    ratings int, 
    comments int,
    relatedId array<string>)
clustered by (uploader) into 8 buckets 
row format delimited fields terminated by "\t" 
collection items terminated by "&" 
stored as orc;

youtubevideo_user_orc

create table youtubevideo_user_orc(
    uploader string,
    videos int,
    friends int)
clustered by (uploader) into 24 buckets 
row format delimited 
fields terminated by "\t" 
stored as orc;

2、导入ETL之后的数据

youtubevideo_ori

load data inpath "/videoOut" into table youtubevideo_ori;

youtubevideo_user_ori：

load data inpath "/userData" into table youtubevideo_user_ori;

3、向ORC表插入数据

youtubevideo_orc：

insert overwrite table youtubevideo_orc select * from youtubevideo_ori;

youtubevideo_user_orc：

insert into table youtubevideo_user_orc select * from youtubevideo_user_ori;

4、查看表结构

0: jdbc:hive2://node03:10000> desc youtubevideo_orc;
+------------+----------------+----------+--+
|  col_name  |   data_type    | comment  |
+------------+----------------+----------+--+
| videoid    | string         |          |
| uploader   | string         |          |
| age        | int            |          |
| category   | array<string>  |          |
| length     | int            |          |
| views      | int            |          |
| rate       | float          |          |
| ratings    | int            |          |
| comments   | int            |          |
| relatedid  | array<string>  |          |
+------------+----------------+----------+--+
10 rows selected (0.084 seconds)
0: jdbc:hive2://node03:10000> desc youtubevideo_user_orc ;
+-----------+------------+----------+--+
| col_name  | data_type  | comment  |
+-----------+------------+----------+--+
| uploader  | string     |          |
| videos    | int        |          |
| friends   | int        |          |
+-----------+------------+----------+--+

业务分析

1、统计视频观看数Top10的视频

思路：使用order by按照views字段做一个全局排序即可，同时我们设置只显示前10条。

select videoid,views
from youtubevideo_orc
order by views desc
limit 10;
+--------------+-----------+--+
|   videoid    |   views   |
+--------------+-----------+--+
| dMH0bHeiRNg  | 42513417  |
| 0XxI-hvPRRA  | 20282464  |
| 1dmVU08zVpA  | 16087899  |
| RB-wUgnyGv0  | 15712924  |
| QjA5faZF1A8  | 15256922  |
| -_CSo1gOd48  | 13199833  |
| 49IDp76kjPw  | 11970018  |
| tYnn51C3X_w  | 11823701  |
| pv5zWaTEVkI  | 11672017  |
| D2kJZOfq7zk  | 11184051  |
+--------------+-----------+--+

2、统计视频类别热度Top10的类别

思路：

  1. 即统计每个类别有多少个视频，显示出包含视频最多的前10个类别。

  2. 我们需要按照类别group by聚合，然后count组内的videoId个数即可。

  3. 因为当前表结构为：一个视频对应一个或多个类别。所以如果要group by类别，需要先将类别进行列转行(展开)，然后再进行count即可。

  4. 最后按照热度排序，显示前10条。

select t1.category_col as category,count(t1.videoid) as hot
from
(select videoid,category_col
from youtubevideo_orc lateral view explode(category) t_cate as category_col) t1
group by t1.category_col
order by hot desc
limit 10;
+----------------+---------+--+
|    category    |   hot   |
+----------------+---------+--+
| Music          | 179049  |
| Entertainment  | 127674  |
| Comedy         | 87818   |
| Animation      | 73293   |
| Film           | 73293   |
| Sports         | 67329   |
| Gadgets        | 59817   |
| Games          | 59817   |
| Blogs          | 48890   |
| People         | 48890   |
+----------------+---------+--+

3、统计出观看数最高的20个视频的所属类别以及类别包含Top20视频的个数

思路：

  1. 先找到观看数最高的20个视频所属条目的所有信息，降序排列

  2. 把这20条信息中的category分裂出来(列转行)

  3. 最后查询视频分类名称和该分类下有多少个Top20的视频

视频观看数最高的20个视频的所属类别

select videoid,views,category
from youtubevideo_orc
order by views desc
limit 20;
+--------------+-----------+---------------------+--+
|   videoid    |   views   |      category       |
+--------------+-----------+---------------------+--+
| dMH0bHeiRNg  | 42513417  | ["Comedy"]          |
| 0XxI-hvPRRA  | 20282464  | ["Comedy"]          |
| 1dmVU08zVpA  | 16087899  | ["Entertainment"]   |
| RB-wUgnyGv0  | 15712924  | ["Entertainment"]   |
| QjA5faZF1A8  | 15256922  | ["Music"]           |
| -_CSo1gOd48  | 13199833  | ["People","Blogs"]  |
| 49IDp76kjPw  | 11970018  | ["Comedy"]          |
| tYnn51C3X_w  | 11823701  | ["Music"]           |
| pv5zWaTEVkI  | 11672017  | ["Music"]           |
| D2kJZOfq7zk  | 11184051  | ["People","Blogs"]  |
| vr3x_RRJdd4  | 10786529  | ["Entertainment"]   |
| lsO6D1rwrKc  | 10334975  | ["Entertainment"]   |
| 5P6UU6m3cqk  | 10107491  | ["Comedy"]          |
| 8bbTtPL1jRs  | 9579911   | ["Music"]           |
| _BuRwH59oAo  | 9566609   | ["Comedy"]          |
| aRNzWyD7C9o  | 8825788   | ["UNA"]             |
| UMf40daefsI  | 7533070   | ["Music"]           |
| ixsZy2425eY  | 7456875   | ["Entertainment"]   |
| MNxwAU_xAMk  | 7066676   | ["Comedy"]          |
| RUCZJVJ_M8o  | 6952767   | ["Entertainment"]   |
+--------------+-----------+---------------------+--+

视频观看数最高的20个视频的所属类别包含Top20视频的个数

select t2.category_col as category,count(t2.videoid) as hot_videos
from
(select videoid,category_col
from
(select videoid,views,category
from youtubevideo_orc
order by views desc
limit 20) t1 
lateral view explode(category) t_category as category_col) t2
group by t2.category_col
order by hot_videos desc;
+----------------+-------------+--+
|    category    | hot_videos  |
+----------------+-------------+--+
| Entertainment  | 6           |
| Comedy         | 6           |
| Music          | 5           |
| People         | 2           |
| Blogs          | 2           |
| UNA            | 1           |
+----------------+-------------+--+

4、 统计观看数Top50的视频所关联的视频的所属类别Rank

#分析思路

#第一步，获取观看数前50的视频，select一定要有views，因为是根据views来排序的
select videoid,relatedid,views  
from youtubevideo_orc
order by views desc
limit 50

#第二步，获取每一个视频的关联视频
select explode(t1.relatedid) as videoid
from
(select videoid,relatedid,views  
from youtubevideo_orc
order by views desc
limit 50) t1


#第三步，对视频去重，再获取每一个视频的所属类型
select distinct(t2.videoid),t3.category
from
(select explode(t1.relatedid) as videoid
from
(select videoid,relatedid,views  
from youtubevideo_orc
order by views desc
limit 50) t1) t2 inner join youtubevideo_orc t3 on t3.videoid=t2.videoid

#第四步，展开视频所属类型
select videoid,category_col as category
from
(select distinct(t2.videoid),t3.category
from
(select explode(t1.relatedid) as videoid
from
(select videoid,relatedid,views  
from youtubevideo_orc
order by views desc
limit 50) t1) t2 inner join youtubevideo_orc t3 on t3.videoid=t2.videoid
) t4 lateral view explode(category)t_category as category_col
 
 
#第五步，进行分组，计数，排序
select count(t5.videoid) as hot,category
from
(select videoid,category_col as category
from
(select distinct(t2.videoid),t3.category
from
(select explode(t1.relatedid) as videoid
from
(select videoid,relatedid,views  
from youtubevideo_orc
order by views desc
limit 50) t1) t2 inner join youtubevideo_orc t3 on t3.videoid=t2.videoid
) t4 lateral view explode(category)t_category as category_col
) t5
group by category
order by hot desc;
+------+----------------+--+
| hot  |    category    |
+------+----------------+--+
| 232  | Comedy         |
| 216  | Entertainment  |
| 195  | Music          |
| 51   | Blogs          |
| 51   | People         |
| 47   | Film           |
| 47   | Animation      |
| 22   | News           |
| 22   | Politics       |
| 20   | Games          |
| 20   | Gadgets        |
| 19   | Sports         |
| 14   | Howto          |
| 14   | DIY            |
| 13   | UNA            |
| 12   | Places         |
| 12   | Travel         |
| 11   | Animals        |
| 11   | Pets           |
| 4    | Autos          |
| 4    | Vehicles       |
+------+----------------+--+

5、统计每个类别中的视频热度Top10，以Music为例

第一种做法：直接查询

select t1.category,t1.videoid,t1.views
from
(select videoid, category_col as category,views
from youtubevideo_orc lateral view explode (category) t_category as category_col
) t1 where t1.category="Music" 
order by views desc
limit 10;
+--------------+--------------+-----------+--+
| t1.category  |  t1.videoid  | t1.views  |
+--------------+--------------+-----------+--+
| Music        | QjA5faZF1A8  | 15256922  |
| Music        | tYnn51C3X_w  | 11823701  |
| Music        | pv5zWaTEVkI  | 11672017  |
| Music        | 8bbTtPL1jRs  | 9579911   |
| Music        | UMf40daefsI  | 7533070   |
| Music        | -xEzGIuY7kw  | 6946033   |
| Music        | d6C0bNDqf3Y  | 6935578   |
| Music        | HSoVKUVOnfQ  | 6193057   |
| Music        | 3URfWTEPmtE  | 5581171   |
| Music        | thtmaZnxk_0  | 5142238   |
+--------------+--------------+-----------+--+

第二种做法：创建表

  1. 要想统计Music类别中的视频热度Top10，需要先找到Music类别，那么就需要将category展开，所以可以创建一张表用于存放categoryId展开的数据。

  2. 向category展开的表中插入数据。

  3. 统计对应类别（Music）中的视频热度。

创建表类别表：

create table youtubevideo_category(
    videoId string, 
    uploader string, 
    age int, 
    categoryId string, 
    length int, 
    views int, 
    rate float, 
    ratings int, 
    comments int, 
    relatedId array<string>)
row format delimited 
fields terminated by "\t" 
collection items terminated by "&" 
stored as orc;

向类别表中插入数据：

insert into table youtubevideo_category  
    select 
        videoId,
        uploader,
        age,
        categoryId,
        length,
        views,
        rate,
        ratings,
        comments,
        relatedId 
    from 
        youtubevideo_orc lateral view explode(category) catetory as categoryId;

统计Music类别的Top10（也可以统计其他）

select 
    videoId, 
    views
from 
    youtubevideo_category 
where 
    categoryId = "Music" 
order by 
    views 
desc limit
    10;

6、 统计每个类别中视频流量Top10，以Music为例

最终代码：

select t1.category,t1.videoid,t1.ratings
from
(select videoid, category_col as category,ratings
from youtubevideo_orc lateral view explode (category) t_category as category_col
) t1 where t1.category="Music" 
order by ratings desc
limit 10;
+--------------+--------------+-------------+--+
| t1.category  |  t1.videoid  | t1.ratings  |
+--------------+--------------+-------------+--+
| Music        | QjA5faZF1A8  | 120506      |
| Music        | pv5zWaTEVkI  | 42386       |
| Music        | UMf40daefsI  | 31886       |
| Music        | tYnn51C3X_w  | 29479       |
| Music        | 59ZX5qdIEB0  | 21481       |
| Music        | FLn45-7Pn2Y  | 21249       |
| Music        | -xEzGIuY7kw  | 20828       |
| Music        | HSoVKUVOnfQ  | 19803       |
| Music        | ARHyRI9_NB4  | 19243       |
| Music        | gg5_mlQOsUQ  | 19190       |
+--------------+--------------+-------------+--+

7、 统计上传视频最多的用户Top10以及他们上传的观看次数在前20的视频

#第一步，找到上传视频最多的用户Top10
select uploader,videos
from youtubevideo_user_orc
order by videos desc
limit 10

#第二步，找到Top10用户上传的视频，并按照views排序、截取
select t2.videoid,t2.views,t2.ratings,t1.uploader,t1.videos
from
(select uploader,videos
from youtubevideo_user_orc
order by videos desc
limit 10) t1 join youtubevideo_orc t2 on t2.uploader=t1.uploader
order by views desc
limit 20;

8、统计每个类别视频观看数Top10

select category_col,videoid,views,rankcol 
from
(select videoid,views,category_col,
rank() over(partition by category_col order by views desc) as rankcol
from youtubevideo_orc lateral view explode(category)t_category as category_col
) t1 where rankcol<=10;

+----------------+--------------+-----------+----------+--+
|  category_col  |   videoid    |   views   | rankcol  |
+----------------+--------------+-----------+----------+--+
| Animals        | 2GWPOPSXGYI  | 3660009   | 1        |
| Animals        | xmsV9R8FsDA  | 3164582   | 2        |
| Animals        | 12PsUW-8ge4  | 3133523   | 3        |
| Animals        | OeNggIGSKH8  | 2457750   | 4        |
| Animals        | WofFb_eOxxA  | 2075728   | 5        |
| Animals        | AgEmZ39EtFk  | 1999469   | 6        |
| Animals        | a-gW3RbJd8U  | 1836870   | 7        |
| Animals        | 8CL2hetqpfg  | 1646808   | 8        |
| Animals        | QmroaYVD_so  | 1645984   | 9        |
| Animals        | Sg9x5mUjbH8  | 1527238   | 10       |
| Animation      | sdUUx5FdySs  | 5840839   | 1        |
| Animation      | 6B26asyGKDo  | 5147533   | 2        |
| Animation      | H20dhY01Xjk  | 3772116   | 3        |
| Animation      | 55YYaJIrmzo  | 3356163   | 4        |
| Animation      | JzqumbhfxRo  | 3230774   | 5        |
| Animation      | eAhfZUZiwSE  | 3114215   | 6        |
| Animation      | h7svw0m-wO0  | 2866490   | 7        |
| Animation      | tAq3hWBlalU  | 2830024   | 8        |
| Animation      | AJzU3NjDikY  | 2569611   | 9        |
| Animation      | ElrldD02if0  | 2337238   | 10       |
| Autos          | RjrEQaG5jPM  | 2803140   | 1        |
| Autos          | cv157ZIInUk  | 2773979   | 2        |
| Autos          | Gyg9U1YaVk8  | 1832224   | 3        |
| Autos          | 6GNB7xT3rNE  | 1412497   | 4        |
| Autos          | tth9krDtxII  | 1347317   | 5        |
| Autos          | 46LQd9dXFRU  | 1262173   | 6        |
| Autos          | pdiuDXwgrjQ  | 1013697   | 7        |
| Autos          | kY_cDpENQLE  | 956665    | 8        |
| Autos          | YtxfbxGz1u4  | 942604    | 9        |
| Autos          | aCamHfJwSGU  | 847442    | 10       |
| Blogs          | -_CSo1gOd48  | 13199833  | 1        |
| Blogs          | D2kJZOfq7zk  | 11184051  | 2        |
| Blogs          | pa_7P5AbUww  | 5705136   | 3        |
| Blogs          | f4B-r8KJhlE  | 4937616   | 4        |
| Blogs          | LB84A3zcmVo  | 4866739   | 5        |
| Blogs          | tXNquTYnyg0  | 3613323   | 6        |
| Blogs          | EYppbbbSxjc  | 2896562   | 7        |
| Blogs          | LH7vrLlDZ6U  | 2615359   | 8        |
| Blogs          | bTV85fQhj0E  | 2192656   | 9        |
| Blogs          | eVFF98kNg8Q  | 1813803   | 10       |
| Comedy         | dMH0bHeiRNg  | 42513417  | 1        |
| Comedy         | 0XxI-hvPRRA  | 20282464  | 2        |
| Comedy         | 49IDp76kjPw  | 11970018  | 3        |
| Comedy         | 5P6UU6m3cqk  | 10107491  | 4        |
| Comedy         | _BuRwH59oAo  | 9566609   | 5        |
| Comedy         | MNxwAU_xAMk  | 7066676   | 6        |
| Comedy         | pYak2F1hUYA  | 6322117   | 7        |
| Comedy         | h0zAlXr1UOs  | 5826923   | 8        |
| Comedy         | C8rjr4jmWd0  | 5587299   | 9        |
| omedy         | R4cQ3BoHFas  | 5508079   | 10       |
.....
posted @ 2020-08-24 00:56  Whatever_It_Takes  阅读(1856)  评论(0)  编辑  收藏  举报