| 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 item, store_sales, date_dim, 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.i_category, v1.i_brand | |
| ,v1.d_year, v1.d_moy | |
| ,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, nsum | |
| limit 100; | |