Hive-数据分析-分布统计

create table person_data(
name string comment '姓名',
sex  string comment '性别',
age  int comment'年龄',
year_income decimal(15,2) comment '年收入'
)comment '分布测试数据'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
stored as textfile;

张三,男,24,100000
丽丽,女,20,50000
王五,男,38,200000
阿姨,女,40,500000
王麻子,男,25,150000
李四,男,45,300000

load data local inpath '/home/bigdata/testdata/person.csv' overwrite into  table person_data;

性别分布,（男/女）
年龄段分布 (1-20],(20-30],(30,40],(40,60],60以上
年收入段分布 (0-10万],(10-30万],(30-50万],50万以上

create table distributed_data(
distributed_type                string  comment '分布项类型',
distributed_item                string  comment '分布项名称',
distributed_num                 int    comment '分布项数量',
distributed_num_rate            decimal(15,2)  comment '分布项所占比率',
distributed_amount              decimal(15,2)  comment '分布项内的年收入',
distributed_amount_rate         decimal(15,2)  comment '分布项内的年收入所占比率'
)comment '分布项结果数据'
stored as textfile;

select 
name,
sex,
age,
year_income,
count(name)over(partition by 1 ) as total_count,
sum(year_income)over(partition by 1 ) as total_amount
from person_data

with person_data_tmp  as (
select 
name,
sex,
age,
year_income,
--数据打标签
case 
when age >1 and age <=20 then '(1-20]'
when age >20 and age <=30 then '(20-30]'
when age >30 and age <=40 then '(30-40]'
when age >40 and age <=60 then '(40-60]'
else '60以上' end as   age_stage,
case 
when year_income >0 and year_income <=10*10000 then '(0-10万]'
when year_income >10*10000 and year_income <=30*10000 then '(10-30万]'
when year_income >30*10000 and year_income <=50*10000 then '(30-50万]'
else '50万以上' end as year_amount_stage,
count(name)over(partition by 1 ) as total_count,
sum(year_income)over(partition by 1 ) as total_amount
from person_data
)
--写入数据
insert overwrite table distributed_data
select 
"性别分布" as  distributed_type,
sex        as  distributed_item,
count(1)   as  distributed_num,
count(1)/max(total_count) as distributed_num_rate,
sum(year_income) as  distributed_amount,
sum(year_income)/max(total_amount) as  distributed_amount_rate
from person_data_tmp 
group by sex 
union all 
select 
"年龄段分布" as  distributed_type,
age_stage    as  distributed_item,
count(1)   as  distributed_num,
count(1)/max(total_count) as distributed_num_rate,
sum(year_income) as  distributed_amount,
sum(year_income)/max(total_amount) as  distributed_amount_rate
from person_data_tmp
group by age_stage 
union all 
select 
"年收入段分布" as  distributed_type,
year_amount_stage        as  distributed_item,
count(1)   as  distributed_num,
count(1)/max(total_count) as distributed_num_rate,
sum(year_income) as  distributed_amount,
sum(year_income)/max(total_amount) as  distributed_amount_rate
from person_data_tmp
group by year_amount_stage

select 
name,
year_income,
map (
"性别分布",sex,
"年龄段分布",case 
when age >1 and age <=20 then '(1-20]'
when age >20 and age <=30 then '(20-30]'
when age >30 and age <=40 then '(30-40]'
when age >40 and age <=60 then '(40-60]'
else '60以上' end ,
"年收入段分布",case 
when year_income >0 and year_income <=10*10000 then '(0-10万]'
when year_income >10*10000 and year_income <=30*10000 then '(10-30万]'
when year_income >30*10000 and year_income <=50*10000 then '(30-50万]'
else '50万以上' end 
)  as  map_data,
count(name)over(partition by 1 ) as total_count,
sum(year_income)over(partition by 1 ) as total_amount
from person_data;

insert overwrite table distributed_data
select
distributed_type,
distributed_item,
count(1)   as  distributed_num,
count(1)/max(total_count) as distributed_num_rate,
sum(year_income) as  distributed_amount,
sum(year_income)/max(total_amount) as  distributed_amount_rate
from 
(
    select 
    name,
    year_income,
    distributed_type,
    distributed_item,
    total_count,
    total_amount
    from 
    (
        select 
            name,
            year_income,
            map (
            "性别分布",sex,
            "年龄段分布",case 
            when age >1 and age <=20 then '(1-20]'
            when age >20 and age <=30 then '(20-30]'
            when age >30 and age <=40 then '(30-40]'
            when age >40 and age <=60 then '(40-60]'
            else '60以上' end ,
            "年收入段分布",case 
            when year_income >0 and year_income <=10*10000 then '(0-10万]'
            when year_income >10*10000 and year_income <=30*10000 then '(10-30万]'
            when year_income >30*10000 and year_income <=50*10000 then '(30-50万]'
            else '50万以上' end 
            )  as  map_data,
            count(name)over(partition by 1 ) as total_count,
            sum(year_income)over(partition by 1 ) as total_amount
        from person_data
    )tmp 
    lateral view explode(map_data) k  as distributed_type,distributed_item
)tmp2 
group by distributed_type,distributed_item;