----原语句（运行18min）

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。

解决策略

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）

方案具体实施

----方案2:or 改成 union（运行35s）

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。