-- start query 42 in stream 0 using template query47.tpl
with v1 as(
 select i_category, i_brand,
        s_store_name, s_company_name,
        d_year, d_moy,
        sum(ss_sales_price) sum_sales,
        avg(sum(ss_sales_price)) over
          (partition by i_category, i_brand,
                     s_store_name, s_company_name, d_year)
          avg_monthly_sales,
        rank() over
          (partition by i_category, i_brand,
                     s_store_name, s_company_name
           order by d_year, d_moy) rn
 from bigdata_public_dataset.tpcds_1t.item, bigdata_public_dataset.tpcds_1t.store_sales, bigdata_public_dataset.tpcds_1t.date_dim, bigdata_public_dataset.tpcds_1t.store
 where ss_item_sk = i_item_sk and
       ss_sold_date_sk = d_date_sk and
       ss_store_sk = s_store_sk and
       (
         d_year = 2000 or
         ( d_year = 2000-1 and d_moy =12) or
         ( d_year = 2000+1 and d_moy =1)
       )
 group by i_category, i_brand,
          s_store_name, s_company_name,
          d_year, d_moy),
 v2 as(
 select v1.s_company_name
        ,v1.d_year
        ,v1.avg_monthly_sales
        ,v1.sum_sales, v1_lag.sum_sales psum, v1_lead.sum_sales nsum
 from v1, v1 v1_lag, v1 v1_lead
 where v1.i_category = v1_lag.i_category and
       v1.i_category = v1_lead.i_category and
       v1.i_brand = v1_lag.i_brand and
       v1.i_brand = v1_lead.i_brand and
       v1.s_store_name = v1_lag.s_store_name and
       v1.s_store_name = v1_lead.s_store_name and
       v1.s_company_name = v1_lag.s_company_name and
       v1.s_company_name = v1_lead.s_company_name and
       v1.rn = v1_lag.rn + 1 and
       v1.rn = v1_lead.rn - 1)
  select  *
 from v2
 where  d_year = 2000 and    
        avg_monthly_sales > 0 and
        case when avg_monthly_sales > 0 then abs(sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1
 order by sum_sales - avg_monthly_sales, psum
 limit 100;

-- end query 42 in stream 0 using template query47.tpl
