当前位置：首页 > news >正文

HIVE 基础（四）

news 2025/8/24 0:10:57

分桶（Bucket）

设定属性

定义分桶

案例

建表语句

表数据

上传到数据

创建分桶语句

加载数据

分桶抽样（Sampling）

随机抽样---整行数据

随机抽样---指定列

随机抽样---百分比

随机抽样---抽取行数

Hive视图（View）

视图概述

应用场景

创建视图

查看视图定义

删除视图

更改视图属性

更改视图定义

Hive侧视图（Lateral View）

分桶（Bucket）

分桶对应于HDFS中的文件

更高的查询处理效率
使抽样（sampling）更高效
一般根据"桶列"的哈希函数将数据进行分桶

设定属性

SET hive.enforce.bucketing = true;

定义分桶

clustered by(employee) into 2 buckets

分桶只有动态分桶

必须使用INSERT方式加载数据

案例

建表语句

create table employee_id
(name         string,employee_id  int,work_place   array<string>,gender_age   struct<gender:string,age:int>,skills_score map<string,int>,depart_title map<string,array<string>>
)row format delimitedfields terminated by '|'collection items terminated by ','map keys terminated by ':'lines terminated by '\n';

表数据

Michael|100|Montreal,Toronto|Male,30|DB:80|Product:DeveloperLead
Will|101|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Steven|102|New York|Female,27|Python:80|Test:Lead,COE:Architect
Lucy|103|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Mike|104|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Shelley|105|New York|Female,27|Python:80|Test:Lead,COE:Architect
Luly|106|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Lily|107|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Shell|108|New York|Female,27|Python:80|Test:Lead,COE:Architect
Mich|109|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Dayong|110|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Sara|111|New York|Female,27|Python:80|Test:Lead,COE:Architect
Roman|112|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Christine|113|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Eman|114|New York|Female,27|Python:80|Test:Lead,COE:Architect
Alex|115|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Alan|116|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Andy|117|New York|Female,27|Python:80|Test:Lead,COE:Architect
Ryan|118|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Rome|119|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Lym|120|New York|Female,27|Python:80|Test:Lead,COE:Architect
Linm|121|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
Dach|122|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
Ilon|123|New York|Female,27|Python:80|Test:Lead,COE:Architect
Elaine|124|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead

上传到数据

load data local inpath '/opt/stufile/employee_id.txt' overwrite into table employee_id;

创建分桶语句

create table employee_id_buckets
(name         string,employee_id  int,work_place   array<string>,gender_age   struct<gender:string,age:int>,skills_score map<string,int>,depart_title map<string,array<string>>
) clustered by (employee_id) into 2 bucketsrow format delimitedfields terminated by '|'collection items terminated by ','map keys terminated by ':';

clustered by (employee_id) into 2 buckets 含义是：把employee_id设置两个分桶

加载数据

insert overwrite table employee_id_buckets select * from employee_id;

分桶抽样（Sampling）

随机抽样---整行数据

select *
from employee_id_buckets tablesample (bucket 1 out of 2 on rand());

随机抽样---指定列

（使用分桶列更高效）

select *
from employee_id_buckets tablesample (bucket 1 out of 2 on employee_id);

随机抽样---百分比

select *
from employee_id_buckets tablesample (10 percent);

随机抽样---抽取行数

select *
from employee_id_buckets tablesample (10 rows);

Hive视图（View）

视图概述

通过隐藏子查询、连接和函数来简化查询的逻辑结构
只保存定义，不存储数据
如果删除或更改基础表，则查询视图将失败
视图是只读的，不能插入或装载数据

应用场景

将特定的列提供给用户，保护数据隐私
用于查询语句复杂的场景

创建视图

支持 CTE, ORDER BY, LIMIT, JOIN等

create view view_name as select statement;

查看视图定义

show create table view_name;

删除视图

drop view_name;

更改视图属性

alter view view_name set tblproperties ('comment' = 'This is a view');

更改视图定义

alter view view_name as select statement;

Hive侧视图（Lateral View）

与表生成函数结合使用，将函数的输入和输出连接

OUTER关键字：即使output为空也会生成结果

select name, workplace, loc
from employee lateral view outer explode(split(null, ',')) a as loc;

支持多层级

select name,wps,gender_age.gender, gender_age.age,skill,score,depart,title
from employeelateral view explode(workplace) work_place as wpslateral view explode(skills_score) sks as skill, scorelateral view explode(depart_title) ga as depart, title;