DSB Q014

SF100 · F64 · Estimated plan · Read from bottom to top. Select a step for details.

Plan

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Query SQL

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with  cross_items as
 (select i_item_sk ss_item_sk
 from item,
 (select iss.i_brand_id brand_id
     ,iss.i_class_id class_id
     ,iss.i_category_id category_id
 from store_sales
     ,item iss
     ,date_dim d1
 where ss_item_sk = iss.i_item_sk
   and ss_sold_date_sk = d1.d_date_sk
   and d1.d_year between 2000 AND 2000 + 2
   and i_category IN ('Shoes', 'Sports', 'Women')
   and i_manager_id BETWEEN 91 and 100
   and ss_wholesale_cost BETWEEN 73 AND 93
intersect
 select ics.i_brand_id
     ,ics.i_class_id
     ,ics.i_category_id
 from catalog_sales
     ,item ics
     ,date_dim d2
 where cs_item_sk = ics.i_item_sk
   and cs_sold_date_sk = d2.d_date_sk
   and d2.d_year between 2000 AND 2000 + 2
   and i_category IN ('Shoes', 'Sports', 'Women')
   and i_manager_id BETWEEN 91 and 100
   and cs_wholesale_cost BETWEEN 73 AND 93
intersect
 select iws.i_brand_id
     ,iws.i_class_id
     ,iws.i_category_id
 from web_sales
     ,item iws
     ,date_dim d3
 where ws_item_sk = iws.i_item_sk
   and ws_sold_date_sk = d3.d_date_sk
   and ws_wholesale_cost BETWEEN 73 AND 93
   and d3.d_year between 2000 AND 2000 + 2) x
 where i_brand_id = brand_id
      and i_class_id = class_id
      and i_category_id = category_id
      and i_category IN ('Shoes', 'Sports', 'Women')
      and i_manager_id BETWEEN 91 and 100
),
 avg_sales as
(select avg(quantity*list_price) average_sales
  from (select ss_quantity quantity
             ,ss_list_price list_price
       from store_sales
           ,date_dim
       where ss_sold_date_sk = d_date_sk
         and d_year between 2000 and 2000 + 2
         and ss_wholesale_cost BETWEEN 73 AND 93
       union all
       select cs_quantity quantity
             ,cs_list_price list_price
       from catalog_sales
           ,date_dim
       where cs_sold_date_sk = d_date_sk
         and d_year between 2000 and 2000 + 2
         and cs_wholesale_cost BETWEEN 73 AND 93
       union all
       select ws_quantity quantity
             ,ws_list_price list_price
       from web_sales
           ,date_dim
       where ws_sold_date_sk = d_date_sk
        and ws_wholesale_cost BETWEEN 73 AND 93
         and d_year between 2000 and 2000 + 2) x)
  select top 100 this_year.channel ty_channel
                           ,this_year.i_brand_id ty_brand
                           ,this_year.i_class_id ty_class
                           ,this_year.i_category_id ty_category
                           ,this_year.sales ty_sales
                           ,this_year.number_sales ty_number_sales
                           ,last_year.channel ly_channel
                           ,last_year.i_brand_id ly_brand
                           ,last_year.i_class_id ly_class
                           ,last_year.i_category_id ly_category
                           ,last_year.sales ly_sales
                           ,last_year.number_sales ly_number_sales
 from
 (select 'store' channel, i_brand_id,i_class_id,i_category_id
        ,sum(ss_quantity*ss_list_price) sales, count(*) number_sales
 from store_sales
     ,item
     ,date_dim
 where ss_item_sk in (select ss_item_sk from cross_items)
   and ss_item_sk = i_item_sk
   and ss_sold_date_sk = d_date_sk
   and d_week_seq = (select d_week_seq
                     from date_dim
                     where d_year = 2000 + 1
                       and d_moy = 12
                       and d_dom = 11)
   and i_category IN ('Shoes', 'Sports', 'Women')
   and i_manager_id BETWEEN 91 and 100
   and ss_wholesale_cost BETWEEN 73 AND 93
 group by i_brand_id,i_class_id,i_category_id
 having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) this_year,
 (select 'store' channel, i_brand_id,i_class_id
        ,i_category_id, sum(ss_quantity*ss_list_price) sales, count(*) number_sales
 from store_sales
     ,item
     ,date_dim
 where ss_item_sk in (select ss_item_sk from cross_items)
   and ss_item_sk = i_item_sk
   and ss_sold_date_sk = d_date_sk
   and d_week_seq = (select d_week_seq
                     from date_dim
                     where d_year = 2000
                       and d_moy = 12
                       and d_dom = 11)
   and i_category IN ('Shoes', 'Sports', 'Women')
   and ss_wholesale_cost BETWEEN 73 AND 93
   and i_manager_id BETWEEN 91 and 100
group by i_brand_id,i_class_id,i_category_id
 having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)) last_year
 where this_year.i_brand_id= last_year.i_brand_id
   and this_year.i_class_id = last_year.i_class_id
   and this_year.i_category_id = last_year.i_category_id
 order by this_year.channel, this_year.i_brand_id, this_year.i_class_id, this_year.i_category_id
 ;


Step details

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Green: scan · yellow: join · purple: row movement. Rows and costs are optimiser estimates; costs use model units.