【20笔记】Hive实战--影音案例

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

字段	备注	详细描述
uploader	上传者用户名	string
videos	上传视频数	int
friends	朋友数量	int

2、ETL原始数据

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

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

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);
		}
	}
}

4. 执行ETL

$ bin/yarn jar ~/softwares/jars/gulivideo-0.0.1-SNAPSHOT.jar \
com.atguigu.etl.ETLVideosRunner \
/gulivideo/video/2008/0222 \
/gulivideo/output/video/2008/0222

3、创建表

创建表：gulivideo_ori，gulivideo_user_ori，
创建表：gulivideo_orc，gulivideo_user_orc

gulivideo_ori：

create table gulivideo_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;

gulivideo_user_ori：

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

然后把原始数据插入到orc表中
gulivideo_orc：

create table gulivideo_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;

gulivideo_user_orc：

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

4、导入ETL后的数据

gulivideo_ori：

load data inpath "/gulivideo/output/video/2008/0222" into table gulivideo_ori;

gulivideo_user_ori：

load data inpath "/gulivideo/user/2008/0903" into table gulivideo_user_ori;

5、向ORC表插入数据

gulivideo_orc：

insert into table gulivideo_orc select * from gulivideo_ori;

gulivideo_user_ori：

insert into table gulivideo_user_orc select * from gulivideo_user_ori;

6、业务分析

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 
    gulivideo_orc 
order by 
    views 
desc limit 
    10;

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 
        gulivideo_orc lateral view explode(category) t_catetory as category_name) t1 
group by 
    t1.category_name 
order by 
    hot 
desc limit 
    10;

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 
            gulivideo_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;

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 
    gulivideo_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gulivideo_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 
    gulivideo_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 
                    gulivideo_orc 
                order by 
                    views 
                desc limit 
                    50) t1) t2 
        inner join 
            gulivideo_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;

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 gulivideo_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 gulivideo_category  
    select 
        videoId,
        uploader,
        age,
        categoryId,
        length,
        views,
        rate,
        ratings,
        comments,
        relatedId 
    from 
        gulivideo_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 
    gulivideo_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
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 
    gulivideo_category 
where 
    categoryId = "Music" 
order by 
    ratings 
desc limit 
    10;

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 
    gulivideo_user_orc 
order by 
    videos 
desc limit 
    10;

2. 通过uploader字段与gulivideo_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 
        gulivideo_user_orc 
    order by 
        videos desc 
    limit 
        10) t1 
join 
    gulivideo_orc t2
on 
    t1.uploader = t2.uploader 
order by 
    views desc 
limit 
    20;

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 gulivideo_category) t1 
where 
    rank <= 10;