Oracle分析函数二(rank,lag等)

Oracle分析函数2(rank,lag等)
rank函数介绍：
   准备工作：对已有的基础数据做一些修改,将5763的数据改成与5761的数据相同.

  update latty.test_t t1 set local_fare = (       select local_fare from latty.test_t t2       where t1.bill_month = t2.bill_month       and t1.net_type = t2.net_type       and t2.area_code = '5761'   ) where area_code = '5763'

  我们先使用rank函数来计算各个地区的话费排名，SQL代码如下：
  

select area_code,sum(local_fare) local_fare,     rank() over (order by sum(local_fare) desc) fare_rank         from latty.test_t              group by area_code

   执行结果显示为:

   AREA_CODE      LOCAL_FARE  FARE_RANK---------- -------------- ----------5765            104548.72          15761             54225.41          25763             54225.41          2 5764             53156.77          4 5762             52039.62          5

注意查看结果发现这里没有出现排名3。
下面我们看下dense_rank排名结果，SQL如下：

   select area_code,sum(local_fare) local_fare,                     dense_rank()  over (order by sum(local_fare)     desc) fare_rank   from latty.test_t   group by area_code

  执行结果如下：
 

AREA_CODE      LOCAL_FARE  FARE_RANK---------- -------------- ----------5765            104548.72          15761             54225.41          25763             54225.41          25764             53156.77          3  这是这里出现了第三名5762             52039.62          4

再来看下row_number,查询SQL语句为:

 select area_code,sum(local_fare) local_fare,    row_number()  over (order by sum(local_fare) desc) fare_rank   from latty.test_t   group by area_code

执行结果如下:

         576577418080.181576154225413.042576354225413.043576252039619.64576445814632.65   

   rank如果出现两个相同的数据,那么后面的数据就会直接跳过这个排名,而dense_rank则不会,row_number哪怕是两个数据完全相同,排名也会不一样,这个特性在我们想找出对应没个条件的唯一记录的时候又很大用处。

   看看这个查询：取出各地区的话费收入在各个月份排名.SQL语句如下：

    select bill_month,area_code,sum(local_fare) local_fare,        rank() over (partition by bill_month order by sum   (local_fare)        desc) area_rank        from latty.test_t        group by bill_month,area_code  执行结果如下：1200405576525057737.4712200405576113060433.8923200405576313060433.8924200405576212643792.1145200405576412487791.9456200406576526058461.3117200406576113318931.0128200406576313318931.0129200406576413295187.67410200406576212795060.65511200407576526301881.4112200407576313710265.93213200407576113710265.93214200407576413444093.76415200407576213224298.12516200408576114135782.21117200408576314135782.21118200408576213376468.7231920040857646587559.234  

lag和lead函数介绍:取出每个月的上个月和下个月的话费总额
查询SQL如下：

    select area_code,bill_month, local_fare cur_local_fare,       lag(local_fare,2,0) over (partition by area_code order by bill_month ) pre_local_fare,       lag(local_fare,1,0) over (partition by area_code order by bill_month ) last_local_fare,       lead(local_fare,1,0) over (partition by area_code order by bill_month ) next_local_fare,       lead(local_fare,2,0) over (partition by area_code order by bill_month ) post_local_fare         from (           select area_code,bill_month,sum(local_fare) local_fare           from latty.test_t           group by area_code,bill_month         )

执行结果为：

AREA_CODE BILL_MONTH CUR_LOCAL_FARE PRE_LOCAL_FARE LAST_LOCAL_FARE NEXT_LOCAL_FARE POST_LOCAL_FARE--------- ---------- -------------- -------------- --------------- --------------- ---------------5761      200405          13060.433              0               0        13318.93       13710.2655761      200406           13318.93              0       13060.433       13710.265       14135.7815761      200407          13710.265      13060.433        13318.93       14135.781               05761      200408          14135.781       13318.93       13710.265               0               05762      200405          12643.791              0               0        12795.06       13224.2975762      200406           12795.06              0       12643.791       13224.297       13376.4685762      200407          13224.297      12643.791        12795.06       13376.468               05762      200408          13376.468       12795.06       13224.297               0               05763      200405          13060.433              0               0        13318.93       13710.2655763      200406           13318.93              0       13060.433       13710.265       14135.7815763      200407          13710.265      13060.433        13318.93       14135.781               05763      200408          14135.781       13318.93       13710.265               0               05764      200405          12487.791              0               0       13295.187       13444.0935764      200406          13295.187              0       12487.791       13444.093       13929.6945764      200407          13444.093      12487.791       13295.187       13929.694               05764      200408          13929.694      13295.187       13444.093               0               05765      200405          25057.736              0               0        26058.46       26301.8815765      200406           26058.46              0       25057.736       26301.881       27130.6385765      200407          26301.881      25057.736        26058.46       27130.638               05765      200408          27130.638       26058.46       26301.881               0               0

sum,avg,max,min移动计算数据介绍：
查询为：计算出各个连续3个月的通话费用的平均数

SQL代码为：

select area_code,bill_month, local_fare cur_local_fare,       lag(local_fare,2,0) over (partition by area_code order by bill_month ) pre_local_fare,       lag(local_fare,1,0) over (partition by area_code order by bill_month ) last_local_fare,       lead(local_fare,1,0) over (partition by area_code order by bill_month ) next_local_fare,       lead(local_fare,2,0) over (partition by area_code order by bill_month ) post_local_fare    from (       select area_code,bill_month,sum(local_fare) local_fare       from latty.test_t       group by area_code,bill_month )

执行结果为:

AREA_CODE BILL_MONTH       LOCAL_FARE 3month_sum 3month_avg 3month_max 3month_min1576120040513060433.890013318931.0113710265.932576120040613318931.01013060433.8913710265.9314135782.213576120040713710265.9313060433.8913318931.0114135782.2104576120040814135782.2113318931.0113710265.93005576220040512643792.110012795060.6513224298.126576220040612795060.65012643792.1113224298.1213376468.727576220040713224298.1212643792.1112795060.6513376468.7208576220040813376468.7212795060.6513224298.12009576320040513060433.890013318931.0113710265.9310576320040613318931.01013060433.8913710265.9314135782.2111576320040713710265.9313060433.8913318931.0114135782.21012576320040814135782.2113318931.0113710265.930013576420040512487791.940013295187.6713444093.7614576420040613295187.67012487791.9413444093.766587559.2315576420040713444093.7612487791.9413295187.676587559.2301657642004086587559.2313295187.6713444093.760017576520040525057737.470026058461.3126301881.418576520040626058461.31025057737.4726301881.4019576520040726301881.425057737.4726058461.3100

first,last函数使用介绍：
查询为:取出每月通话费最高和最低的两个用户.
查询SQL为:

  select bill_month,area_code,sum(local_fare) local_fare,       first_value(area_code)               over (order by sum(local_fare) desc                       rows unbounded preceding) firstval,       first_value(area_code)               over (order by sum(local_fare) asc                       rows unbounded preceding) lastval    from latty.test_t    group by bill_month,area_code   order by bill_month

执行结果显示为:

   BILL_MONTH AREA_CODE       LOCAL_FARE FIRSTVAL        LASTVAL---------- --------- ---------------- --------------- ---------------200405     5764             12487.791 5765            5764200405     5762             12643.791 5765            5764200405     5761             13060.433 5765            5764200405     5765             25057.736 5765            5764200405     5763             13060.433 5765            5764200406     5762             12795.060 5765            5764200406     5763             13318.930 5765            5764200406     5764             13295.187 5765            5764200406     5765             26058.460 5765            5764200406     5761             13318.930 5765            5764200407     5762             13224.297 5765            5764200407     5765             26301.881 5765            5764200407     5761             13710.265 5765            5764200407     5763             13710.265 5765            5764200407     5764             13444.093 5765            5764200408     5762             13376.468 5765            5764200408     5764             13929.694 5765            5764200408     5761             14135.781 5765            5764200408     5765             27130.638 5765            5764200408     5763             14135.781 5765            5764

查看更多 下一篇