admin管理员组文章数量:1570220
需求描述:
统计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所属类别以及类别包含的Top20的视频个数
- 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以及他们上传的观看次数在前20视频
- 统计每个类别视频观看数Top10
项目
数据表构成
- 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 |
- 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 |
源数据ETL
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,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。
ETLUtil.java
工具类
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();
}
}
VideoETLMapper.java
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.src.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);
}
}
ETLRunner.java
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);
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);
}
}
}
将数据打包,执行ETL操作
bin/yarn jar ~/softwares/jars/ETL-0.0.1-SNAPSHOT.jar com.src.etl.ETLVideosRunner
/keven/video/2008/0222 /keven/output/video/2008/0222
创建表
创建表(原始表):
- video_ori
create table video_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;
- video_user_ori
create table video_user_ori(
uploader string,
videos int,
friends int)
row format delimited
fields terminated by "\t"
stored as textfile;
把原始数据插入到orc表中
- video_orc
create table video_orc(
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 orc;
- video_user_orc
create table video_user_orc(
uploader string,
videos int,
friends int)
row format delimited
fields terminated by "\t"
stored as orc;
导入ETL数据
- video_ori:
load data inpath "/video/output/video/2008/0222" into table video_ori;
- video_user_ori:
load data inpath "/video/user/2008/0903" into table video_user_ori;
ORC表插入数据
- video_orc:
insert into table video_orc select * from video_ori;
-
video_user_orc:
insert into table video_user_orc select * from video_user_ori;
业务分析
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。
select
videoId,
uploader,
age,
category,
length,
views,
rate,
ratings,
comments
from
video_orc
order by
views
desc limit
10;
统计视频类别热度Top10
思路:
-
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。
-
需要按照类别group by聚合,然后count组内的videoId个数即可。
-
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。
-
最后按照热度排序,显示前10条。
select
category_name as category,
count(t1.videoId) as hot
from (
select
videoId,
category_name
from
video_orc lateral view explode(category) t_catetory as category_name) t1
group by
t1.category_name
order by
hot
desc limit
10;
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:
-
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
-
把这20条信息中的category分裂出来(列转行)
-
最后查询视频分类名称和该分类下有多少个Top20的视频
select
category_name as category,
count(t2.videoId) as hot_with_views
from (
select
videoId,
category_name
from (
select
*
from
video_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;
统计视频观看数Top50所关联视频的所属类别Rank
思路:
- 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
video_orc
order by
views
desc limit
50;
- 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;
- 将相关视频的id和video_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
video_orc t3 on t2.videoId = t3.videoId) t4 lateral view explode(category) t_catetory as category_name;
- 按照视频类别进行分组,统计每组视频个数,然后排行
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
video_orc
order by
views
desc limit
50) t1) t2
inner join
video_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;
统计每个类别中的视频热度Top10,以Music为例
思路:
-
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。
-
向category展开的表中插入数据。
-
统计对应类别(Music)中的视频热度。
创建表类别表:
create table video_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 video_category
select
videoId,
uploader,
age,
categoryId,
length,
views,
rate,
ratings,
comments,
relatedId
from
video_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
video_category
where
categoryId = "Music"
order by
views
desc limit
10;
统计每个类别中视频流量Top10,以Music为例
思路:
-
创建视频类别展开表(categoryId列转行后的表)
-
按照ratings排序即可
select
videoId,
views,
ratings
from video_category
where
categoryId = "Music"
order by
ratings
desc limit
10;
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:
- 先找到上传视频最多的10个用户的用户信息
select
*
from
video_user_orc
order by
videos
desc limit
10;
- 通过uploader字段与video_orc表进行join,得到的信息按照views观看次数进行排序即可。
select
t2.videoId,
t2.views,
t2.ratings,
t1.videos,
t1.friends
from (
select * from video_user_orc order by videos desc limit 10) t1
join
video_orc t2
on
t1.uploader = t2.uploader
order by
views desc
limit
20;
统计每个类别视频观看数Top10
思路:
-
先得到categoryId展开的表数据
-
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
-
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。
select
t1.*
from (
select
videoId,
categoryId,
views,
row_number() over(partition by categoryId order by views desc) rank from video_category) t1
where
rank <= 10;
版权声明:本文标题:Hive实战之视频网站数据分析 内容由热心网友自发贡献,该文观点仅代表作者本人, 转载请联系作者并注明出处:https://m.elefans.com/dongtai/1727664819a1124543.html, 本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容,一经查实,本站将立刻删除。
发表评论