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)!
精彩评论