开发者

处理Hive中的数据倾斜的方法

目录
  • 1 groupby(大表分组-局部聚合+全局聚合)
  • 2 join(大中表Join - 加salt + 小表膨胀)
  • 3 双大表Join - 抽样取倾斜key+BroadJoin
  • 4 小结

1 groupby(大表分组-局部聚合+全局聚合)

示例1:

select label,sum(cnt) as all from 
(
    select rd,label,sum(1) as cnt from 
    (
        select id,label,round(rand(),2) as rd,value from tmp1
    ) as tmp
    group by rd,label
) as tmp
group by label;

示例2:

select 
	split(new_source,'\\_')[0] as source 
	,sum(cnt) as cnt 
from  
(select  
	concat(source,'_', rand()*100) as  new_source
	,count(1) as cnt 
from  test_table 
where day ='2022-01-01'
group by 
	concat(source,'_', rand()*100)
)tt 
group by 
	split(new_source,'\\_')[0]

2 join(大中表Join - 加salt + 小表膨胀)

示例1:

select label,sum(value) as all from 
(
    select rd,label,sum(value) as cnt from
    (
        select tmp1.rd as rd,tmp1.label as label,tmp1.value*tmp2.value as value 
        from 
        (
            select id,round(rand(),1) as rd,label,value from tmp1
        ) as tmp1
        join
        (
            select id,rd,label,value from tmp2
            lateral view explode(split('0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9',',')) mytable as rd
        ) as tmp2
        on tmp1.rd = tmp2.rd and tmp1.label = tmp2.lab编程客栈el
    ) as tmp1
    group by rd,label
) as tmp1
group by label;

示例2:

select 
	source
	,source_name
	,sum(cnt) as cnt 
from  
(select 
	t1.source 
	,new_source
	,nvl(source_name,'未知') as source_name 
	,count(imei) as cnt 
from  
(select  
	编程imei
	,sphpource 
	,concat(cast(rand()*10 as int ),'_',source ) as new_source
from  test_table_1
where day ='2022-01-01'
) t1 
inner join 
(
select 
	source_name 
	,concat(preflix,'_',source) as new_source
from  test_table_1
where day ='2022-01-01'
lateral view explode(split('0,1,2,3,4,5,6,7,8,9,10',','))b as preflix 
) t2 
on t1.new_source =t2.new_source
group by 
t1.source 
,new_source
,nvl(soujsrce_name,'未知')
) tta  
group by 
	source
	,source_name

3 双大表Join - 抽样取倾斜key+BroadJoin

##优化前:
create table test.tmp_table_test_all as 
select  
imei 
,lable_id 
,nvl(label_name,'未知')
from tmp_table_1  t1  
left join 
(select  
lable_id
,label_name
from  tmp_table_2 
where day ='2024-01-01') t2 
on t1.lable_id =t2.lable_id
where t1.day ='2024-01-01'
;
 
## 优化后 :
create table test.tmp_table_test_all_new  as 
 
 
with tmp_table_test_1 as 
(select  
lable_id 
,count(1) as cnt 
from tmp_table_1  t1 
tablesample(5 percent) --抽样取5%的数据,减少table scan的量
group by lable_id
order by cnt desc 
limit 100
) 
 
 
select  
	imei 
	,lable_id 
	,nvl(label_name,'未知') as  label_name
from tmp_table_1  t1 
left join  tmp_table_test_1  t2
on tjavascript1.lable_id =t2.lable_id
left join 
(select  
	lable_id
	,label_name
from  tmp_table_2 
where day ='2024-01-01') t3
on t1.lable_id =t3.lable_id
where t1.day ='2024-01-01' and  t2.lable_id is null 
 
union all  
 
select  
	imei 
	,lable_id 
	,nvl(label_name,'未知') as  label_name 
from tmp_table_1  t1 
inner  join 
(select  
	lable_id
from  tmp_table_test_1  t1 
left   join   tmp_table_2  t2 
on t1.lable_id =t2.lable_id
where t2.day ='2024-01-01') t3
on t1.lable_id =t3.lable_id
where t1.day ='2024-01-01' 
;

4 小结

到此这篇关于处理Hive中的数据倾斜的方法的文章就介绍到这了,更多相关处理Hive数据倾斜内容请搜索编程客栈(www.devze.com)以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程客栈(www.devze.com)!

0

上一篇:

下一篇:

精彩评论

暂无评论...
验证码 换一张
取 消

最新数据库

数据库排行榜