hive中map相关函数总结

目录

hive官方函数解释

hive官网函数大全地址:hive官网函数大全地址

Return Type Name Description
map map(key1, value1, key2, value2, ...) Creates a map with the given key/value pairs.
array map_values(Map<K.V>) Returns an unordered array containing the values of the input map.
array map_keys(Map<K.V>) Returns an unordered array containing the keys of the input map.
map<string,string> str_to_map(text[, delimiter1, delimiter2]) Splits text into key-value pairs using two delimiters. Delimiter1 separates text into K-V pairs, and Delimiter2 splits each K-V pair. Default delimiters are ',' for delimiter1 and ':' for delimiter2.
Tkey,Tvalue explode(MAP<Tkey,Tvalue> m) Explodes a map to multiple rows. Returns a row-set with a two columns (key,value) , one row for each key-value pair from the input map. (As of Hive 0.8.0.).

示例

1、map(key1, value1, key2, value2, ...)

sql 复制代码
SELECT map('name', '张三', 'age', 20, 'gender', '男') AS student;
---结果:
student	
{"age":"20","gender":"男","name":"张三"}

2、map_values(Map<K.V>)

sql 复制代码
SELECT map_keys(map('name', '张三', 'age', 20, 'gender', '男')) AS keys;
---结果:
keys
["name","age","gender"]

3、map_values(Map<K.V>)

sql 复制代码
SELECT map_values(map('name', '张三', 'age', 20, 'gender', '男')) AS values;
---结果:
values	
["张三","20","男"]

4、str_to_map(str, delimiter1, delimiter2)

str_to_map 函数用于将一个字符串转换为 Map 对象。具体来说,str_to_map 函数会将一个由键值对组成的字符串解析成一个 Map 对象,其中键和值之间使用指定的分隔符进行分隔。其中,str 是要转换的字符串,delimiter1 是键值对之间的分隔符,delimiter2 是键和值之间的分隔符。默认情况下,delimiter1 的值是 ',',delimiter2 的值是 ':'。

sql 复制代码
SELECT str_to_map('name:张三,age:20,gender:男', ',', ':') AS student;
---结果:
student	
{"age":"20","gender":"男","name":"张三"}

SELECT str_to_map('name=张三,age=20,gender=男', ',', '=') AS student;
---结果:
student	
{"age":"20","gender":"男","name":"张三"}

5、explode (map)

sql 复制代码
select explode(map('A',10,'B',20,'C',30));
select explode(map('A',10,'B',20,'C',30)) as (key,value);
select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf;
select tf.* from (select 0) t lateral view explode(map('A',10,'B',20,'C',30)) tf as key,value;
---上述四个结果均为:
key     value
A       10	
B       20	
C       30

实战

给出一组学生数据,有名字,课程,等级,分数等字段,现在求每门课的情况,包含平均成绩,及这门课包含哪些学生及学生的等级

sql 复制代码
with stud as
( select  'zhang3' as name ,'优' as grade  ,'math' as course ,'88' as score  
  union all 
  select  'li4' as name ,'良' as grade  ,'math' as course ,'72' as score
    union all 
  select  'zhao6' as name ,'差' as grade  ,'math' as course ,'44' as score
    union all 
  select  'wang5' as name ,'优' as grade  ,'chinese' as course ,'80' as score
    union all 
  select  'zhao6' as name ,'优' as grade  ,'chinese' as course ,'55' as score
    union all 
  select  'tian7' as name ,'优' as grade  ,'chinese' as course ,'75' as score
)

--sql1
select course, collect_set(concat(name,':',grade)) as collect , avg(score) from stud group by course;
---结果:
course             collect                                             avg(score)	
math        ["li4:良","zhao6:差","zhang3:优"]                           68.0
chinese     ["wang5:优","tian7:优","zhao6:优"]                          70.0
----sql2
select course, concat_ws(',',collect_set(concat(name,':',grade))) as strings , avg(score) from stud group by course;
---结果:
course                      strings                                        avg(score)
math             li4:良,zhao6:差,zhang3:优                                  68.0
chinese          wang5:优,tian7:优,zhao6:优                                 70.0
----sql3
select course, str_to_map(concat_ws(',',collect_set(concat(name,':',grade))),',',':') as maps , avg(score) from stud group by course;
---结果:
course                               maps                              avg(score)	
math                 {"li4":"良","zhang3":"优","zhao6":"差"}             68.0
chinese              {"tian7":"优","wang5":"优","zhao6":"优"}            70.0

注意:

第一种sql,collect 字段的类型是array;第二种sql,strings字段的类型是string;第三种sql,maps字段的类型是map;

问题来了,能否在第二种的基础上,实现第一种和第三种的结果,且字段类型是string;

下面实现第二种转化为第三种,实际上就是map格式转换成json字符串;

sql 复制代码
with stud as
( select  'zhang3' as name ,'优' as grade  ,'math' as course ,'88' as score  
  union all 
  select  'li4' as name ,'良' as grade  ,'math' as course ,'72' as score
    union all 
  select  'zhao6' as name ,'差' as grade  ,'math' as course ,'44' as score
    union all 
  select  'wang5' as name ,'优' as grade  ,'chinese' as course ,'80' as score
    union all 
  select  'zhao6' as name ,'优' as grade  ,'chinese' as course ,'55' as score
    union all 
  select  'tian7' as name ,'优' as grade  ,'chinese' as course ,'75' as score
)

select 
course
,concat('{"',string2,'"}') as string3
from  
(select 
course
,regexp_replace(string1,'\\,','\\"\\,\\"') as string2
from  
(
select 
    course,
    concat_ws(',', collect_list(concat_ws('":"', k,v) ) ) as string1
from (
select course, str_to_map(concat_ws(',',collect_set(concat(name,':',grade))),',',':') as maps , avg(score) 
from stud group by course
)test_map_1
lateral view outer explode(maps) kv as k,v
group by course
) tt 
) tm 

---结果:
course                               string3                            	
math                 {"li4":"良","zhang3":"优","zhao6":"差"}           
chinese              {"tian7":"优","wang5":"优","zhao6":"优"}        
相关推荐
神奇侠202410 小时前
Hive SQL常见操作
hive·hadoop·sql
SelectDB技术团队13 小时前
从 ClickHouse、Druid、Kylin 到 Doris:网易云音乐 PB 级实时分析平台降本增效
大数据·数据仓库·clickhouse·kylin·实时分析
itachi-uchiha16 小时前
Docker部署Hive大数据组件
大数据·hive·docker
viperrrrrrrrrr716 小时前
大数据学习(131)-Hive数据分析函数总结
大数据·hive·学习
Leo.yuan19 小时前
API是什么意思?如何实现开放API?
大数据·运维·数据仓库·人工智能·信息可视化
qq_4084133920 小时前
spark 执行 hive sql数据丢失
hive·sql·spark
TDengine (老段)1 天前
TDengine 替换 Hadoop,彻底解决数据丢失问题 !
大数据·数据库·hadoop·物联网·时序数据库·tdengine·涛思数据
workflower1 天前
量子比特实现方式
数据仓库·服务发现·需求分析·量子计算·软件需求
yt948321 天前
如何在IDE中通过Spark操作Hive
ide·hive·spark
青春之我_XP1 天前
【基于阿里云搭建数据仓库(离线)】Data Studio创建资源与函数
大数据·数据仓库·sql·dataworks·maxcompute·data studio