尚硅谷大数据技术之Hive 第11章 Hive实战

11.1 需求描述

统计Youtube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

11.2 项目

11.2.1 数据结构

1)视频表

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

视频上传日期和2007年2月15日之间的整数天(Youtube的独特设定)

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

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

11.2.2 ETL原始数据

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,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。

1)ETL之ETLUtil

public class ETLUtil {

public static String oriString2ETLString(String ori){

StringBuilder etlString = new StringBuilder();

String[] splits = ori.split(“\t”);

if(splits.length < 9) return null;

splits[3] = splits[3].replace(” “, “”);

for(int i = 0; i < splits.length; i++){

if(i < 9){

if(i == splits.length – 1){

etlString.append(splits[i]);

}else{

etlString.append(splits[i] + “\t”);

}

}else{

if(i == splits.length – 1){

etlString.append(splits[i]);

}else{

etlString.append(splits[i] + “&”);

}

}

}

return etlString.toString();

}

}

2)ETL之Mapper

import java.io.IOException;

 

import org.apache.commons.lang.StringUtils;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

 

import com.z.youtube.util.ETLUtil;

 

public class VideoETLMapper extends Mapper<Object, Text, NullWritable, Text>{

Text text = new Text();

@Override

protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {

String etlString = ETLUtil.oriString2ETLString(value.toString());

if(StringUtils.isBlank(etlString)) return;

text.set(etlString);

context.write(NullWritable.get(), text);

}

}

3)ETL之Runner

import java.io.IOException;

 

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FileSystem;

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.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

 

public class VideoETLRunner implements Tool {

private Configuration conf = null;

 

@Override

public void setConf(Configuration conf) {

this.conf = conf;

}

 

@Override

public Configuration getConf() {

 

return this.conf;

}

 

@Override

public int run(String[] args) throws Exception {

conf = this.getConf();

conf.set(“inpath”, args[0]);

conf.set(“outpath”, args[1]);

 

Job job = Job.getInstance(conf, “youtube-video-etl”);

job.setJarByClass(VideoETLRunner.class);

job.setMapperClass(VideoETLMapper.class);

job.setMapOutputKeyClass(NullWritable.class);

job.setMapOutputValueClass(Text.class);

job.setNumReduceTasks(0);

this.initJobInputPath(job);

this.initJobOutputPath(job);

return job.waitForCompletion(true) ? 0 : 1;

}

 

private void initJobOutputPath(Job job) throws IOException {

Configuration conf = job.getConfiguration();

String outPathString = conf.get(“outpath”);

FileSystem fs = FileSystem.get(conf);

Path outPath = new Path(outPathString);

if(fs.exists(outPath)){

fs.delete(outPath, true);

}

FileOutputFormat.setOutputPath(job, outPath);

}

 

private void initJobInputPath(Job job) throws IOException {

Configuration conf = job.getConfiguration();

String inPathString = conf.get(“inpath”);

FileSystem fs = FileSystem.get(conf);

Path inPath = new Path(inPathString);

if(fs.exists(inPath)){

FileInputFormat.addInputPath(job, inPath);

}else{

throw new RuntimeException(“HDFS中该文件目录不存在:” + inPathString);

}

}

 

public static void main(String[] args) {

try {

int resultCode = ToolRunner.run(new VideoETLRunner(), args);

if(resultCode == 0){

System.out.println(“Success!”);

}else{

System.out.println(“Fail!”);

}

System.exit(resultCode);

} catch (Exception e) {

e.printStackTrace();

System.exit(1);

}

}

}

4)执行ETL

$ bin/yarn jar ~/softwares/jars/youtube-0.0.1-SNAPSHOT.jar \

com.z.youtube.etl.ETLYoutubeVideosRunner \

/youtube/video/2008/0222 \

/youtube/output/video/2008/0222

 

11.3 准备工作

11.3.1 创建表

创建表:youtube_ori,youtube_user_ori,

创建表:youtube_orc,youtube_user_orc

youtube_ori:

create table youtube_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;

youtube_user_ori:

create table youtube_user_ori(

    uploader string,

    videos int,

    friends int)

clustered by (uploader) into 24 buckets

row format delimited

fields terminated by “\t”

stored as textfile;

 

然后把原始数据插入到orc表中

youtube_orc:

create table youtube_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;

youtube_user_orc:

create table youtube_user_orc(

    uploader string,

    videos int,

    friends int)

clustered by (uploader) into 24 buckets

row format delimited

fields terminated by “\t”

stored as orc;

11.3.2 导入ETL后的数据

youtube_ori:

load data inpath “/youtube/output/video/2008/0222” into table youtube_ori;

youtube_user_ori:

load data inpath “/youtube/user/2008/0903” into table youtube_user_ori;

11.3.3 向ORC表插入数据

youtube_orc:

insert into table youtube_orc select * from youtube_ori;

 

youtube_user_orc:

insert into table youtube_user_orc select * from youtube_user_ori;

11.4 业务分析

11.4.1 统计视频观看数Top10

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。

最终代码:

select

    videoId,

    uploader,

    age,

    category,

    length,

    views,

    rate,

    ratings,

    comments

from

    youtube_orc

order by

    views

desc limit

    10;

11.4.2 统计视频类别热度Top10

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。

最终代码:

select

    category_name as category,

    count(t1.videoId) as hot

from (

    select

        videoId,

        category_name

    from

        youtube_orc lateral view explode(category) t_catetory as category_name) t1

group by

    t1.category_name

order by

    hot

desc limit

    10;

11.4.3 统计出视频观看数最高的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

最终代码:

select

    category_name as category,

    count(t2.videoId) as hot_with_views

from (

    select

        videoId,

        category_name

    from (

        select

            *

        from

            youtube_orc

        order by

            views

        desc limit

            20) t1 lateral view explode(category) t_catetory as category_name) t2

group by

    category_name

order by

    hot_with_views

desc;

11.4.4 统计视频观看数Top50所关联视频的所属类别Rank

思路:

1) 查询出观看数最多的前50个视频的所有信息(当然包含了每个视频对应的关联视频),记为临时表t1

t1:观看数前50的视频

select

    *

from

    youtube_orc

order by

    views

desc limit

    50;

2) 将找到的50条视频信息的相关视频relatedId列转行,记为临时表t2

t2:将相关视频的id进行列转行操作

select

    explode(relatedId) as videoId

from

t1;

3) 将相关视频的id和youtube_orc表进行inner join操作

t5:得到两列数据,一列是category,一列是之前查询出来的相关视频id

(select

    distinct(t2.videoId),

    t3.category

from

    t2

inner join

    youtube_orc t3 on t2.videoId = t3.videoId) t4 lateral view explode(category) t_catetory as category_name;

4) 按照视频类别进行分组,统计每组视频个数,然后排行

最终代码:

select

    category_name as category,

    count(t5.videoId) as hot

from (

    select

        videoId,

        category_name

    from (

        select

            distinct(t2.videoId),

            t3.category

        from (

            select

                explode(relatedId) as videoId

            from (

                select

                    *

                from

                    youtube_orc

                order by

                    views

                desc limit

                    50) t1) t2

        inner join

            youtube_orc t3 on t2.videoId = t3.videoId) t4 lateral view explode(category) t_catetory as category_name) t5

group by

    category_name

order by

    hot

desc;

 

11.4.5 统计每个类别中的视频热度Top10,以Music为例

思路:

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。

最终代码:

创建表类别表:

create table youtube_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 youtube_category  

    select

        videoId,

        uploader,

        age,

        categoryId,

        length,

        views,

        rate,

        ratings,

        comments,

        relatedId

    from

        youtube_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

    youtube_category

where

    categoryId = “Music”

order by

    views

desc limit

    10;

11.4.6 统计每个类别中视频流量Top10,以Music为例

思路:

1) 创建视频类别展开表(categoryId列转行后的表)

2) 按照ratings排序即可

最终代码:

select

    videoId,

    views,

    ratings

from

    youtube_category

where

    categoryId = “Music”

order by

    ratings

desc limit

    10;

 

11.4.7 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

思路:

1) 先找到上传视频最多的10个用户的用户信息

select

    *

from

    youtube_user_orc

order by

    videos

desc limit

    10;

 

2) 通过uploader字段与youtube_orc表进行join,得到的信息按照views观看次数进行排序即可。

最终代码:

select

    t2.videoId,

    t2.views,

    t2.ratings,

    t1.videos,

    t1.friends

from (

    select

        *

    from

        youtube_user_orc

    order by

        videos desc

    limit

        10) t1

join

    youtube_orc t2

on

    t1.uploader = t2.uploader

order by

    views desc

limit

    20;

11.4.8 统计每个类别视频观看数Top10

思路:

1) 先得到categoryId展开的表数据

2) 子查询按照categoryId进行分区,然后分区内排序,并生成递增数字,该递增数字这一列起名为rank列

3) 通过子查询产生的临时表,查询rank值小于等于10的数据行即可。

最终代码:

select

    t1.*

from (

    select

        videoId,

        categoryId,

        views,

        row_number() over(partition by categoryId order by views desc) rank from youtube_category) t1

where

    rank <= 10;


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