sparksql优化1（小表大表关联优化 & union替换or）

----原语句（运行18min）

INSERT into TABLE schema.dstable

SELECT bb.ip FROM

(SELECT ip, sum(click) click_num, round(sum(click)/sum(imp),4) user_click_rate FROM schema.srctable1

WHERE date = '20171020' AND ip IS NOT NULL AND imp>0 GROUP BY ip) bb

LEFT OUTER JOIN (SELECT round(sum(click)/sum(imp),4) avg_click_rate FROM schema.srctable1 WHERE date = '20171020') aa

LEFT OUTER JOIN schema.dstable cc on cc.ip = bb.ip

WHERE cc.ip is null AND

(bb.user_click_rate > aa.avg_click_rate * 3 AND click_num > 500) OR (click_num > 1000)

分析：

1、aa表存放的就是一个指标数据,1条记录,列为小表

2、bb表存放的是按ip聚合的明细数据,记录很多,列为大表

3、cc表用来过滤ip,数量也很小,列为过滤表,作用很小。

查看执行计划,发现bb与aa进行left outer join时，引发了shuffle过程,造成大量的磁盘及网络IO,影响性能。

优化方案1:调整大小表位置,将小表放在左边后，提升至29s (该方案一直不太明白为啥会提升，执行计划里显示的也就是大小表位置调换下而已，跟之前的没其他区别)

优化方案2: 将 or 改成 union,提升至35s(各种调整,一直怀疑跟or有关系,后面调整成union其他不变,果真效率不一样;但方案1只是调整了下大小表顺序,并未调整其他,其效率同样提升很大;不太明白sparksql内部到底走了什么优化机制,后面继续研究);

优化方案3： 采用cache+broadcast方式,提升至20s（该方案将小表缓存至内存，进行map侧关联）

----方案2:or 改成 union（运行35s）

INSERT into TABLE schema.dstable

select aa.ip

from

(

SELECT bb.ip ip FROM

(SELECT ip, sum(click) click_num, round(sum(click)/sum(imp),4) user_click_rate FROM schema.srctable1 WHERE date = '20171020' AND ip IS NOT NULL AND imp>0 GROUP BY ip) bb

LEFT OUTER JOIN(SELECT round(sum(click)/sum(imp),4) avg_click_rate FROM schema.srctable1 WHERE date = '20171020') aa

WHERE

(bb.user_click_rate > aa.avg_click_rate * 3 AND click_num > 20)

union

SELECT bb.ip ip FROM  

(SELECT ip, sum(click) click_num, round(sum(click)/sum(imp),4) user_click_rate FROM schema.srctable1 WHERE date = '20171020' AND ip IS NOT NULL AND imp>0 GROUP BY ip) bb

LEFT OUTER JOIN (SELECT round(sum(click)/sum(imp),4) avg_click_rate FROM schema.srctable1 WHERE date = '20171020') aa  

WHERE click_num > 40

) aa

LEFT OUTER JOIN schema.dstable cc on aa.ip=cc.ip

where cc.ip is null  

-----cache+broadcast方式（20s）

原理：使用broadcast将会把小表分发到每台执行节点上，因此，关联操作都在本地完成，基本就取消了shuffle的过程，运行效率大幅度提高。

cache table cta as SELECT round(sum(click)/sum(imp),4) avg_click_rate FROM schema.srctable1 WHERE date = '20171020';

INSERT into TABLE schema.dstable

SELECT bb.ip FROM

(SELECT ip, sum(click) click_num, round(sum(click)/sum(imp),4) user_click_rate FROM schema.srctable1

WHERE date = '20171020' AND ip IS NOT NULL AND imp>0 GROUP BY ip) bb

LEFT OUTER JOIN cta  aa

LEFT OUTER JOIN schema.dstable cc on cc.ip = bb.ip

WHERE cc.ip is null AND

(bb.user_click_rate > aa.avg_click_rate * 3 AND click_num > 500) OR (click_num > 1000)

 

注意：

cache 表不一定会被广播到Executor，执行map side join，还受另外一个参数：spark.sql.autoBroadcastJoinThreshold影响，该参数判断是否将该表广播；

spark.sql.autoBroadcastJoinThreshold参数默认值是10M，所以只有cache的表小于10M的才被广播到Executor上去执行map side join。