Help Center> Data Warehouse Service (DWS)> Performance White Paper> Appendixes> TPC-DS Test Sets
Updated on 2022-07-26 GMT+08:00
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TPC-DS Test Sets

You can use Using Commands to Generate TPC-DS Test Sets to generate TPC-DS test sets or directly use Using Scripts to Generate TPC-DS Test Sets to generate TPC-DS test sets. We provide the preceding 20 TPC-DS Test Sets for your reference.

Using Commands to Generate TPC-DS Test Sets

The SQL statements of the 99 TPC-DS SQL standard queries can be generated as follows:

  1. Preparations Modify the following files in the query_templates directory before generating TPC-DS query statements:

    1. Log in to the ECS and go to the /data1/script/tpcds-kit/DSGen-software-code-3.2.0rc1/query_templates directory. 
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      cd /data1/script/tpcds-kit/DSGen-software-code-3.2.0rc1/query_templates
    2. Create a file hwdws.tpl. 
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      define __LIMITA = "";
define __LIMITB = "";
define __LIMITC = "limit %d";
define _BEGIN = "-- begin query " + [_QUERY] + " in stream " + [_STREAM] + " using template " + [_TEMPLATE];
define _END = "-- end query " + [_QUERY] + " in stream " + [_STREAM] + " using template " + [_TEMPLATE];
    3. The syntax of the SQL statement generation template in the TPC-DS tool is incorrect. You need to change ', coalesce(returns, 0) returns' in line 135 to ', coalesce(returns, 0) as returns in query77.tpl.

  2. Run the following command to generate query statements:

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    ./dsqgen  -input ../query_templates/templates.lst -directory ../query_templates/ -scale 1000 -dialect hwdws

    After the command is executed, the query_0.sql file is generated, which contains 99 standard SQL statements. You need to manually split the file into 99 files.

  3. DWS does not support the following date function syntax in the generated standard queries. You need to manually modify the syntax.

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    Q5:  and (cast('2001-08-19' as date) +  14 days)
to
and (cast('2001-08-19' as date) +  14)

Q12:and (cast('1999-02-28' as date) + 30 days)
to
and (cast('1999-02-28' as date) + 30)

Q16:(cast('1999-4-01' as date) + 60 days)
to
(cast('1999-4-01' as date) + 60)

Q20:and (cast('1998-05-05' as date) + 30 days)
to
and (cast('1998-05-05' as date) + 30)

Q21:and d_date between (cast ('2000-05-19' as date) - 30 days)
to
and d_date between (cast ('2000-05-19' as date) - 30)

and (cast ('2000-05-19' as date) + 30 days)
to
and (cast ('2000-05-19' as date) + 30)

Q32:(cast('1999-02-22' as date) + 90 days)
to
(cast('1999-02-22' as date) + 90)

Q37:and d_date between cast('1998-04-29' as date) and (cast('1998-04-29' as date) +  60 days)
to
and d_date between cast('1998-04-29' as date) and (cast('1998-04-29' as date) +  60)

Q40:and d_date between (cast ('2002-05-10' as date) - 30 days)
to
and d_date between (cast ('2002-05-10' as date) - 30)

and (cast ('2002-05-10' as date) + 30 days)
to
and (cast ('2002-05-10' as date) + 30)

Q77:and (cast('1999-08-29' as date) +  30 days)
to
and (cast('1999-08-29' as date) +  30)

Q80:and (cast('2002-08-04' as date) +  30 days)
to
and (cast('2002-08-04' as date) +  30)

Q82:and d_date between cast('1998-01-18' as date) and (cast('1998-01-18' as date) +  60 days)
to
and d_date between cast('1998-01-18' as date) and (cast('1998-01-18' as date) +  60)

Q92:(cast('2001-01-26' as date) + 90 days)
to
(cast('2001-01-26' as date) + 90)

Q94:(cast('1999-5-01' as date) + 60 days)
to
(cast('1999-5-01' as date) + 60)

Q95:(cast('1999-4-01' as date) + 60 days)
to
(cast('1999-4-01' as date) + 60)

Q98:and (cast('2002-04-01' as date) + 30 days)
to
and (cast('2002-04-01' as date) + 30)

Using Scripts to Generate TPC-DS Test Sets

You are advised to use the following script to directly generate SQL statements for GaussDB(DWS):

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# -*- coding: utf-8 -*-
import os
import sys
import re


def gen_thp_seq(dsqgen_file, scale, query_dir):
    flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL
    modes = stat.S_IWUSR | stat.S_IRUSR
    if not os.path.exists(query_dir):
        os.mkdir(query_dir)

    cmd = dsqgen_file + ' -input ../query_templates/templates.lst -directory ../query_templates/ -scale ' + str(scale) + ' -dialect hwdws'
    os.system(cmd)
    with open('query_0.sql', 'r') as f1:
        line = f1.readline()
        queryname = ''
        while line:
            if '-- begin' in line.strip():
                #line:'-- begin query 1 in stream 0 using template query96.tpl\n'
                queryname = line.split(' ')[-1][5:-5]
                fquery = os.fdopen(os.open(query_dir + '/Q' + queryname, flags, modes), 'w+')
                line = f1.readline()
                continue

            if not queryname or line == '\n':
                line = f1.readline()
                continue

            if '-- end' in line.strip():
                fquery.close()
                line = f1.readline()
                continue

            if 'days)' in line:
                line = line.replace('days', '')
            fquery.write(line)
            line = f1.readline()

    print("TPCDS Q1~Q99 query store at " + query_dir)
    os.system('rm -rf query_0.sql')



if __name__ == '__main__':
    if len(sys.argv) != 4:
print('Wrong number of parameters! ')
print('Usage: python3 gen_tpcds_thpseq.py dsqgen_file_path scale query_dir')
        print("""Parameter:
qgen_file_path: tpcds dsqgen file path
scale: data scale corresponding to the generated query.
query_dir: path for storing the generated file""")
        print("""Example:
            python3 gen_tpcds_thpseq.py ./dsqgen 1000 tpcds_query1000x""")
        sys.exit(1)

    dsqgen_file_path = sys.argv[1]
    scale = sys.argv[2]
    query_dir = sys.argv[3]
    try:
        if not re.match(r'^\.?\/(\w+\/?)+$', dsqgen_file_path):
            print("error param qgenfilepath:", dsqgen_file_path)
        if not re.match(r'\d+', scale):
            print('error param scale:', scale)
        if not re.match(r'^\/?(\w+\/?)+$', query_dir):
            print('error param query_dir:', query_dir)
    except Exception as ex:
        print('exception: invalid param!')

    if not os.path.isfile(dsqgen_file_path):
        print('The file %s is not exist!' % dsqgen_file_path)
        sys.exit(1)

    gen_thp_seq(dsqgen_file_path, int(scale), query_dir)

Save the preceding script in gen_tpcds_thpseq.py. Run the python3 gen_tpcds_thpseq.py ./dsqgen 1000 tpcds_query1000x command to obtain 99 SQL statements. 1000 indicates the data scale, tpcds_query1000x indicates the location where the generated SQL statements are stored.

Test Sets

The TPC-DS test set contains 99 SQL queries. This section describes only the first 20 SQL queries. Use the preceding method to generate other files.

SQL1

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with customer_total_return as
(select sr_customer_sk as ctr_customer_sk
,sr_store_sk as ctr_store_sk
,sum(SR_RETURN_AMT_INC_TAX) as ctr_total_return
from store_returns
,date_dim
where sr_returned_date_sk = d_date_sk
and d_year =2001
group by sr_customer_sk
,sr_store_sk)
 select  c_customer_id
from customer_total_return ctr1
,store
,customer
where ctr1.ctr_total_return > (select avg(ctr_total_return)*1.2
from customer_total_return ctr2
where ctr1.ctr_store_sk = ctr2.ctr_store_sk)
and s_store_sk = ctr1.ctr_store_sk
and s_state = 'PA'
and ctr1.ctr_customer_sk = c_customer_sk
order by c_customer_id
limit 100;

SQL2

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with wscs as
 (select sold_date_sk
        ,sales_price
  from (select ws_sold_date_sk sold_date_sk
              ,ws_ext_sales_price sales_price
        from web_sales 
        union all
        select cs_sold_date_sk sold_date_sk
              ,cs_ext_sales_price sales_price
        from catalog_sales)),
 wswscs as 
 (select d_week_seq,
        sum(case when (d_day_name='Sunday') then sales_price else null end) sun_sales,
        sum(case when (d_day_name='Monday') then sales_price else null end) mon_sales,
        sum(case when (d_day_name='Tuesday') then sales_price else  null end) tue_sales,
        sum(case when (d_day_name='Wednesday') then sales_price else null end) wed_sales,
        sum(case when (d_day_name='Thursday') then sales_price else null end) thu_sales,
        sum(case when (d_day_name='Friday') then sales_price else null end) fri_sales,
        sum(case when (d_day_name='Saturday') then sales_price else null end) sat_sales
 from wscs
     ,date_dim
 where d_date_sk = sold_date_sk
 group by d_week_seq)
 select d_week_seq1
       ,round(sun_sales1/sun_sales2,2)
       ,round(mon_sales1/mon_sales2,2)
       ,round(tue_sales1/tue_sales2,2)
       ,round(wed_sales1/wed_sales2,2)
       ,round(thu_sales1/thu_sales2,2)
       ,round(fri_sales1/fri_sales2,2)
       ,round(sat_sales1/sat_sales2,2)
 from
 (select wswscs.d_week_seq d_week_seq1
        ,sun_sales sun_sales1
        ,mon_sales mon_sales1
        ,tue_sales tue_sales1
        ,wed_sales wed_sales1
        ,thu_sales thu_sales1
        ,fri_sales fri_sales1
        ,sat_sales sat_sales1
  from wswscs,date_dim 
  where date_dim.d_week_seq = wswscs.d_week_seq and
        d_year = 1999) y,
 (select wswscs.d_week_seq d_week_seq2
        ,sun_sales sun_sales2
        ,mon_sales mon_sales2
        ,tue_sales tue_sales2
        ,wed_sales wed_sales2
        ,thu_sales thu_sales2
        ,fri_sales fri_sales2
        ,sat_sales sat_sales2
  from wswscs
      ,date_dim 
  where date_dim.d_week_seq = wswscs.d_week_seq and
        d_year = 1999+1) z
 where d_week_seq1=d_week_seq2-53
 order by d_week_seq1;

SQL3

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select  dt.d_year 
       ,item.i_brand_id brand_id 
       ,item.i_brand brand
       ,sum(ss_ext_sales_price) sum_agg
 from  date_dim dt 
      ,store_sales
      ,item
 where dt.d_date_sk = store_sales.ss_sold_date_sk
   and store_sales.ss_item_sk = item.i_item_sk
   and item.i_manufact_id = 125
   and dt.d_moy=11
 group by dt.d_year
      ,item.i_brand
      ,item.i_brand_id
 order by dt.d_year
         ,sum_agg desc
         ,brand_id
 limit 100;

SQL4

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with year_total as (
 select c_customer_id customer_id
       ,c_first_name customer_first_name
       ,c_last_name customer_last_name
       ,c_preferred_cust_flag customer_preferred_cust_flag
       ,c_birth_country customer_birth_country
       ,c_login customer_login
       ,c_email_address customer_email_address
       ,d_year dyear
       ,sum(((ss_ext_list_price-ss_ext_wholesale_cost-ss_ext_discount_amt)+ss_ext_sales_price)/2) year_total
       ,'s' sale_type
 from customer
     ,store_sales
     ,date_dim
 where c_customer_sk = ss_customer_sk
   and ss_sold_date_sk = d_date_sk
 group by c_customer_id
         ,c_first_name
         ,c_last_name
         ,c_preferred_cust_flag
         ,c_birth_country
         ,c_login
         ,c_email_address
         ,d_year
 union all
 select c_customer_id customer_id
       ,c_first_name customer_first_name
       ,c_last_name customer_last_name
       ,c_preferred_cust_flag customer_preferred_cust_flag
       ,c_birth_country customer_birth_country
       ,c_login customer_login
       ,c_email_address customer_email_address
       ,d_year dyear
       ,sum((((cs_ext_list_price-cs_ext_wholesale_cost-cs_ext_discount_amt)+cs_ext_sales_price)/2) ) year_total
       ,'c' sale_type
 from customer
     ,catalog_sales
     ,date_dim
 where c_customer_sk = cs_bill_customer_sk
   and cs_sold_date_sk = d_date_sk
 group by c_customer_id
         ,c_first_name
         ,c_last_name
         ,c_preferred_cust_flag
         ,c_birth_country
         ,c_login
         ,c_email_address
         ,d_year
union all
 select c_customer_id customer_id
       ,c_first_name customer_first_name
       ,c_last_name customer_last_name
       ,c_preferred_cust_flag customer_preferred_cust_flag
       ,c_birth_country customer_birth_country
       ,c_login customer_login
       ,c_email_address customer_email_address
       ,d_year dyear
       ,sum((((ws_ext_list_price-ws_ext_wholesale_cost-ws_ext_discount_amt)+ws_ext_sales_price)/2) ) year_total
       ,'w' sale_type
 from customer
     ,web_sales
     ,date_dim
 where c_customer_sk = ws_bill_customer_sk
   and ws_sold_date_sk = d_date_sk
 group by c_customer_id
         ,c_first_name
         ,c_last_name
         ,c_preferred_cust_flag
         ,c_birth_country
         ,c_login
         ,c_email_address
         ,d_year
         )
  select  
                  t_s_secyear.customer_id
                 ,t_s_secyear.customer_first_name
                 ,t_s_secyear.customer_last_name
                 ,t_s_secyear.customer_preferred_cust_flag
 from year_total t_s_firstyear
     ,year_total t_s_secyear
     ,year_total t_c_firstyear
     ,year_total t_c_secyear
     ,year_total t_w_firstyear
     ,year_total t_w_secyear
 where t_s_secyear.customer_id = t_s_firstyear.customer_id
   and t_s_firstyear.customer_id = t_c_secyear.customer_id
   and t_s_firstyear.customer_id = t_c_firstyear.customer_id
   and t_s_firstyear.customer_id = t_w_firstyear.customer_id
   and t_s_firstyear.customer_id = t_w_secyear.customer_id
   and t_s_firstyear.sale_type = 's'
   and t_c_firstyear.sale_type = 'c'
   and t_w_firstyear.sale_type = 'w'
   and t_s_secyear.sale_type = 's'
   and t_c_secyear.sale_type = 'c'
   and t_w_secyear.sale_type = 'w'
   and t_s_firstyear.dyear =  2000
   and t_s_secyear.dyear = 2000+1
   and t_c_firstyear.dyear =  2000
   and t_c_secyear.dyear =  2000+1
   and t_w_firstyear.dyear = 2000
   and t_w_secyear.dyear = 2000+1
   and t_s_firstyear.year_total > 0
   and t_c_firstyear.year_total > 0
   and t_w_firstyear.year_total > 0
   and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end
           > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else null end
   and case when t_c_firstyear.year_total > 0 then t_c_secyear.year_total / t_c_firstyear.year_total else null end
           > case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else null end
 order by t_s_secyear.customer_id
         ,t_s_secyear.customer_first_name
         ,t_s_secyear.customer_last_name
         ,t_s_secyear.customer_preferred_cust_flag
limit 100;

SQL5

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with ssr as
 (select s_store_id,
        sum(sales_price) as sales,
        sum(profit) as profit,
        sum(return_amt) as returns,
        sum(net_loss) as profit_loss
 from
  ( select  ss_store_sk as store_sk,
            ss_sold_date_sk  as date_sk,
            ss_ext_sales_price as sales_price,
            ss_net_profit as profit,
            cast(0 as decimal(7,2)) as return_amt,
            cast(0 as decimal(7,2)) as net_loss
    from store_sales
    union all
    select sr_store_sk as store_sk,
           sr_returned_date_sk as date_sk,
           cast(0 as decimal(7,2)) as sales_price,
           cast(0 as decimal(7,2)) as profit,
           sr_return_amt as return_amt,
           sr_net_loss as net_loss
    from store_returns
   ) salesreturns,
     date_dim,
     store
 where date_sk = d_date_sk
       and d_date between cast('2002-08-05' as date) 
                  and (cast('2002-08-05' as date) +  14 )
       and store_sk = s_store_sk
 group by s_store_id)
 ,
 csr as
 (select cp_catalog_page_id,
        sum(sales_price) as sales,
        sum(profit) as profit,
        sum(return_amt) as returns,
        sum(net_loss) as profit_loss
 from
  ( select  cs_catalog_page_sk as page_sk,
            cs_sold_date_sk  as date_sk,
            cs_ext_sales_price as sales_price,
            cs_net_profit as profit,
            cast(0 as decimal(7,2)) as return_amt,
            cast(0 as decimal(7,2)) as net_loss
    from catalog_sales
    union all
    select cr_catalog_page_sk as page_sk,
           cr_returned_date_sk as date_sk,
           cast(0 as decimal(7,2)) as sales_price,
           cast(0 as decimal(7,2)) as profit,
           cr_return_amount as return_amt,
           cr_net_loss as net_loss
    from catalog_returns
   ) salesreturns,
     date_dim,
     catalog_page
 where date_sk = d_date_sk
       and d_date between cast('2002-08-05' as date)
                  and (cast('2002-08-05' as date) +  14 )
       and page_sk = cp_catalog_page_sk
 group by cp_catalog_page_id)
 ,
 wsr as
 (select web_site_id,
        sum(sales_price) as sales,
        sum(profit) as profit,
        sum(return_amt) as returns,
        sum(net_loss) as profit_loss
 from
  ( select  ws_web_site_sk as wsr_web_site_sk,
            ws_sold_date_sk  as date_sk,
            ws_ext_sales_price as sales_price,
            ws_net_profit as profit,
            cast(0 as decimal(7,2)) as return_amt,
            cast(0 as decimal(7,2)) as net_loss
    from web_sales
    union all
    select ws_web_site_sk as wsr_web_site_sk,
           wr_returned_date_sk as date_sk,
           cast(0 as decimal(7,2)) as sales_price,
           cast(0 as decimal(7,2)) as profit,
           wr_return_amt as return_amt,
           wr_net_loss as net_loss
    from web_returns left outer join web_sales on
         ( wr_item_sk = ws_item_sk
           and wr_order_number = ws_order_number)
   ) salesreturns,
     date_dim,
     web_site
 where date_sk = d_date_sk
       and d_date between cast('2002-08-05' as date)
                  and (cast('2002-08-05' as date) +  14 )
       and wsr_web_site_sk = web_site_sk
 group by web_site_id)
  select  channel
        , id
        , sum(sales) as sales
        , sum(returns) as returns
        , sum(profit) as profit
 from 
 (select 'store channel' as channel
        , 'store' || s_store_id as id
        , sales
        , returns
        , (profit - profit_loss) as profit
 from   ssr
 union all
 select 'catalog channel' as channel
        , 'catalog_page' || cp_catalog_page_id as id
        , sales
        , returns
        , (profit - profit_loss) as profit
 from  csr
 union all
 select 'web channel' as channel
        , 'web_site' || web_site_id as id
        , sales
        , returns
        , (profit - profit_loss) as profit
 from   wsr
 ) x
 group by rollup (channel, id)
 order by channel
         ,id
 limit 100;

SQL6

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select  a.ca_state state, count(*) cnt
 from customer_address a
     ,customer c
     ,store_sales s
     ,date_dim d
     ,item i
 where       a.ca_address_sk = c.c_current_addr_sk
	and c.c_customer_sk = s.ss_customer_sk
	and s.ss_sold_date_sk = d.d_date_sk
	and s.ss_item_sk = i.i_item_sk
	and d.d_month_seq = 
	     (select distinct (d_month_seq)
	      from date_dim
               where d_year = 1998
	        and d_moy = 7 )
	and i.i_current_price > 1.2 * 
             (select avg(j.i_current_price) 
	     from item j 
	     where j.i_category = i.i_category)
 group by a.ca_state
 having count(*) >= 10
 order by cnt, a.ca_state 
 limit 100;

SQL7

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select  i_item_id, 
        avg(ss_quantity) agg1,
        avg(ss_list_price) agg2,
        avg(ss_coupon_amt) agg3,
        avg(ss_sales_price) agg4 
 from store_sales, customer_demographics, date_dim, item, promotion
 where ss_sold_date_sk = d_date_sk and
       ss_item_sk = i_item_sk and
       ss_cdemo_sk = cd_demo_sk and
       ss_promo_sk = p_promo_sk and
       cd_gender = 'M' and 
       cd_marital_status = 'U' and
       cd_education_status = 'College' and
       (p_channel_email = 'N' or p_channel_event = 'N') and
       d_year = 1999 
 group by i_item_id
 order by i_item_id
 limit 100;

SQL8

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select  s_store_name
      ,sum(ss_net_profit)
 from store_sales
     ,date_dim
     ,store,
     (select ca_zip
     from (
      SELECT substr(ca_zip,1,5) ca_zip
      FROM customer_address
      WHERE substr(ca_zip,1,5) IN (
                          '74804','87276','13428','49436','56281','79805',
                          '46826','68570','20368','28846','41886',
                          '68164','68097','16113','18727','96789',
                          '63317','57937','19554','69911','83554',
                          '84246','61336','46999','25229','15960',
                          '61657','28058','64558','39712','74928',
                          '34018','87826','69733','26479','73630',
                          '88683','61704','81441','42706','54175',
                          '45152','49049','30850','63980','40484',
                          '71665','63755','23769','79855','24308',
                          '28241','16343','25663','85999','46359',
                          '93691','34706','99973','74947','60316',
                          '58637','48063','81363','19268','66228',
                          '78136','16368','99907','58139','17043',
                          '89764','14834','25152','70158','76080',
                          '81251','83972','48635','54671','35602',
                          '10788','57325','46354','92707','41103',
                          '89761','31840','69225','76139','18826',
                          '12556','51692','20579','50965','32136',
                          '71357','16309','82922','59273','40999',
                          '73273','93217','65679','12653','16978',
                          '27319','41973','65580','56237','17799',
                          '53192','63632','37089','65994','58048',
                          '14388','58085','80614','40042','79194',
                          '42268','61913','97332','37349','72146',
                          '52681','18176','39332','89283','69023',
                          '84175','11520','33483','60169','93562',
                          '10097','14536','70276','64042','22822',
                          '87229','51528','70269','44519','48044',
                          '78170','81440','60315','14543','30719',
                          '13240','62325','35517','51529','98085',
                          '79007','16582','95187','15625','88780',
                          '38656','74607','75117','62819','31929',
                          '27665','88890','98611','53527','48652',
                          '10324','62273','17726','36232','38526',
                          '50705','61179','30363','54408','58631',
                          '23622','50319','33299','78829','91267',
                          '25571','60347','44750','62797','10713',
                          '46494','91163','20973','42007','54724',
                          '89203','12561','71116','60404','70589',
                          '66744','46074','69138','34737','25092',
                          '59246','74778','40140','89476','71030',
                          '89861','93207','44996','34850','48752',
                          '79574','29570','76507','79728','43195',
                          '47596','46415','42514','68144','14169',
                          '17041','75747','33630','19378','32618',
                          '78704','75807','76800','80916','87272',
                          '37109','21714','14867','83806','33895',
                          '80637','20658','75224','92772','55791',
                          '58603','31681','38788','91922','42465',
                          '74371','72854','75746','80383','75909',
                          '37151','82077','80604','66771','46075',
                          '58723','15380','83174','53615','50347',
                          '98340','68957','63361','18705','47629',
                          '76013','68572','50588','31168','41563',
                          '38936','88746','19052','75648','46403',
                          '24332','54711','28218','80432','53870',
                          '25049','32562','94211','99803','49133',
                          '21202','50005','17953','14324','85525',
                          '51984','37304','69870','64321','66962',
                          '66453','40619','91199','54400','28804',
                          '65544','27059','31143','20303','52429',
                          '24476','91458','52514','55145','99015',
                          '51657','10001','96434','38325','39628',
                          '83338','62381','67697','61542','86076',
                          '89833','32657','56881','93983','85031',
                          '57530','56318','46934','34740','79458',
                          '88443','15861','56034','24808','32336',
                          '34312','96450','12923','91876','53509',
                          '30241','35816','52377','23946','23644',
                          '16413','35796','59100','21689','49199',
                          '40062','82510','14072','78823','49158',
                          '99933','75399','11365','44799','77549',
                          '19569','39186','78909','68143','70468',
                          '14944','33047','98329','42262','68647',
                          '65754','27357','56372','18073','12363',
                          '64467','26221','32914','70431','42436',
                          '55316','33335','27701','44687','22360',
                          '76124','44007','59525','51574','71555',
                          '43130','64199','19616','94285')
     intersect
      select ca_zip
      from (SELECT substr(ca_zip,1,5) ca_zip,count(*) cnt
            FROM customer_address, customer
            WHERE ca_address_sk = c_current_addr_sk and
                  c_preferred_cust_flag='Y'
            group by ca_zip
            having count(*) > 10)A1)A2) V1
 where ss_store_sk = s_store_sk
  and ss_sold_date_sk = d_date_sk
  and d_qoy = 1 and d_year = 1999
  and (substr(s_zip,1,2) = substr(V1.ca_zip,1,2))
 group by s_store_name
 order by s_store_name
 limit 100;

SQL9

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select case when (select count(*) 
                  from store_sales 
                  where ss_quantity between 1 and 20) > 40845849
            then (select avg(ss_ext_tax) 
                  from store_sales 
                  where ss_quantity between 1 and 20) 
            else (select avg(ss_net_paid)
                  from store_sales
                  where ss_quantity between 1 and 20) end bucket1 ,
       case when (select count(*)
                  from store_sales
                  where ss_quantity between 21 and 40) > 5712087
            then (select avg(ss_ext_tax)
                  from store_sales
                  where ss_quantity between 21 and 40) 
            else (select avg(ss_net_paid)
                  from store_sales
                  where ss_quantity between 21 and 40) end bucket2,
       case when (select count(*)
                  from store_sales
                  where ss_quantity between 41 and 60) > 30393328
            then (select avg(ss_ext_tax)
                  from store_sales
                  where ss_quantity between 41 and 60)
            else (select avg(ss_net_paid)
                  from store_sales
                  where ss_quantity between 41 and 60) end bucket3,
       case when (select count(*)
                  from store_sales
                  where ss_quantity between 61 and 80) > 46385791
            then (select avg(ss_ext_tax)
                  from store_sales
                  where ss_quantity between 61 and 80)
            else (select avg(ss_net_paid)
                  from store_sales
                  where ss_quantity between 61 and 80) end bucket4,
       case when (select count(*)
                  from store_sales
                  where ss_quantity between 81 and 100) > 29981928
            then (select avg(ss_ext_tax)
                  from store_sales
                  where ss_quantity between 81 and 100)
            else (select avg(ss_net_paid)
                  from store_sales
                  where ss_quantity between 81 and 100) end bucket5
from reason
where r_reason_sk = 1
;

SQL10

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select  
  cd_gender,
  cd_marital_status,
  cd_education_status,
  count(*) cnt1,
  cd_purchase_estimate,
  count(*) cnt2,
  cd_credit_rating,
  count(*) cnt3,
  cd_dep_count,
  count(*) cnt4,
  cd_dep_employed_count,
  count(*) cnt5,
  cd_dep_college_count,
  count(*) cnt6
 from
  customer c,customer_address ca,customer_demographics
 where
  c.c_current_addr_sk = ca.ca_address_sk and
  ca_county in ('Clark County','Richardson County','Tom Green County','Sullivan County','Cass County') and
  cd_demo_sk = c.c_current_cdemo_sk and 
  exists (select *
          from store_sales,date_dim
          where c.c_customer_sk = ss_customer_sk and
                ss_sold_date_sk = d_date_sk and
                d_year = 2000 and
                d_moy between 1 and 1+3) and
   (exists (select *
            from web_sales,date_dim
            where c.c_customer_sk = ws_bill_customer_sk and
                  ws_sold_date_sk = d_date_sk and
                  d_year = 2000 and
                  d_moy between 1 ANd 1+3) or 
    exists (select * 
            from catalog_sales,date_dim
            where c.c_customer_sk = cs_ship_customer_sk and
                  cs_sold_date_sk = d_date_sk and
                  d_year = 2000 and
                  d_moy between 1 and 1+3))
 group by cd_gender,
          cd_marital_status,
          cd_education_status,
          cd_purchase_estimate,
          cd_credit_rating,
          cd_dep_count,
          cd_dep_employed_count,
          cd_dep_college_count
 order by cd_gender,
          cd_marital_status,
          cd_education_status,
          cd_purchase_estimate,
          cd_credit_rating,
          cd_dep_count,
          cd_dep_employed_count,
          cd_dep_college_count
limit 100;

SQL11

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with year_total as (
 select c_customer_id customer_id
       ,c_first_name customer_first_name
       ,c_last_name customer_last_name
       ,c_preferred_cust_flag customer_preferred_cust_flag
       ,c_birth_country customer_birth_country
       ,c_login customer_login
       ,c_email_address customer_email_address
       ,d_year dyear
       ,sum(ss_ext_list_price-ss_ext_discount_amt) year_total
       ,'s' sale_type
 from customer
     ,store_sales
     ,date_dim
 where c_customer_sk = ss_customer_sk
   and ss_sold_date_sk = d_date_sk
 group by c_customer_id
         ,c_first_name
         ,c_last_name
         ,c_preferred_cust_flag 
         ,c_birth_country
         ,c_login
         ,c_email_address
         ,d_year 
 union all
 select c_customer_id customer_id
       ,c_first_name customer_first_name
       ,c_last_name customer_last_name
       ,c_preferred_cust_flag customer_preferred_cust_flag
       ,c_birth_country customer_birth_country
       ,c_login customer_login
       ,c_email_address customer_email_address
       ,d_year dyear
       ,sum(ws_ext_list_price-ws_ext_discount_amt) year_total
       ,'w' sale_type
 from customer
     ,web_sales
     ,date_dim
 where c_customer_sk = ws_bill_customer_sk
   and ws_sold_date_sk = d_date_sk
 group by c_customer_id
         ,c_first_name
         ,c_last_name
         ,c_preferred_cust_flag 
         ,c_birth_country
         ,c_login
         ,c_email_address
         ,d_year
         )
  select  
                  t_s_secyear.customer_id
                 ,t_s_secyear.customer_first_name
                 ,t_s_secyear.customer_last_name
                 ,t_s_secyear.customer_birth_country
 from year_total t_s_firstyear
     ,year_total t_s_secyear
     ,year_total t_w_firstyear
     ,year_total t_w_secyear
 where t_s_secyear.customer_id = t_s_firstyear.customer_id
         and t_s_firstyear.customer_id = t_w_secyear.customer_id
         and t_s_firstyear.customer_id = t_w_firstyear.customer_id
         and t_s_firstyear.sale_type = 's'
         and t_w_firstyear.sale_type = 'w'
         and t_s_secyear.sale_type = 's'
         and t_w_secyear.sale_type = 'w'
         and t_s_firstyear.dyear = 2001
         and t_s_secyear.dyear = 2001+1
         and t_w_firstyear.dyear = 2001
         and t_w_secyear.dyear = 2001+1
         and t_s_firstyear.year_total > 0
         and t_w_firstyear.year_total > 0
         and case when t_w_firstyear.year_total > 0 then t_w_secyear.year_total / t_w_firstyear.year_total else 0.0 end
             > case when t_s_firstyear.year_total > 0 then t_s_secyear.year_total / t_s_firstyear.year_total else 0.0 end
 order by t_s_secyear.customer_id
         ,t_s_secyear.customer_first_name
         ,t_s_secyear.customer_last_name
         ,t_s_secyear.customer_birth_country
limit 100;

SQL12

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select  i_item_id
      ,i_item_desc 
      ,i_category 
      ,i_class 
      ,i_current_price
      ,sum(ws_ext_sales_price) as itemrevenue 
      ,sum(ws_ext_sales_price)*100/sum(sum(ws_ext_sales_price)) over
          (partition by i_class) as revenueratio
from	
	web_sales
	,item 
	,date_dim
where 
	ws_item_sk = i_item_sk 
	and i_category in ('Music', 'Shoes', 'Children')
	and ws_sold_date_sk = d_date_sk
	and d_date between cast('2000-05-14' as date) 
				and (cast('2000-05-14' as date) + 30 )
group by 
	i_item_id
        ,i_item_desc 
        ,i_category
        ,i_class
        ,i_current_price
order by 
	i_category
        ,i_class
        ,i_item_id
        ,i_item_desc
        ,revenueratio
limit 100;

SQL13

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select avg(ss_quantity)
       ,avg(ss_ext_sales_price)
       ,avg(ss_ext_wholesale_cost)
       ,sum(ss_ext_wholesale_cost)
 from store_sales
     ,store
     ,customer_demographics
     ,household_demographics
     ,customer_address
     ,date_dim
 where s_store_sk = ss_store_sk
 and  ss_sold_date_sk = d_date_sk and d_year = 2001
 and((ss_hdemo_sk=hd_demo_sk
  and cd_demo_sk = ss_cdemo_sk
  and cd_marital_status = 'U'
  and cd_education_status = '4 yr Degree'
  and ss_sales_price between 100.00 and 150.00
  and hd_dep_count = 3   
     )or
     (ss_hdemo_sk=hd_demo_sk
  and cd_demo_sk = ss_cdemo_sk
  and cd_marital_status = 'D'
  and cd_education_status = '2 yr Degree'
  and ss_sales_price between 50.00 and 100.00   
  and hd_dep_count = 1
     ) or 
     (ss_hdemo_sk=hd_demo_sk
  and cd_demo_sk = ss_cdemo_sk
  and cd_marital_status = 'S'
  and cd_education_status = 'Advanced Degree'
  and ss_sales_price between 150.00 and 200.00 
  and hd_dep_count = 1  
     ))
 and((ss_addr_sk = ca_address_sk
  and ca_country = 'United States'
  and ca_state in ('IL', 'WI', 'TN')
  and ss_net_profit between 100 and 200  
     ) or
     (ss_addr_sk = ca_address_sk
  and ca_country = 'United States'
  and ca_state in ('MO', 'OK', 'WA')
  and ss_net_profit between 150 and 300  
     ) or
     (ss_addr_sk = ca_address_sk
  and ca_country = 'United States'
  and ca_state in ('NE', 'VA', 'GA')
  and ss_net_profit between 50 and 250  
     ))
;

SQL14

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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
 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
 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 d3.d_year between 2000 AND 2000 + 2)
 where i_brand_id = brand_id
      and i_class_id = class_id
      and i_category_id = category_id
),
 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
       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 
       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 d_year between 2000 and 2000 + 2) x)
  select  channel, i_brand_id,i_class_id,i_category_id,sum(sales), sum(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_year = 2000+2 
         and d_moy = 11
       group by i_brand_id,i_class_id,i_category_id
       having sum(ss_quantity*ss_list_price) > (select average_sales from avg_sales)
       union all
       select 'catalog' channel, i_brand_id,i_class_id,i_category_id, sum(cs_quantity*cs_list_price) sales, count(*) number_sales
       from catalog_sales
           ,item
           ,date_dim
       where cs_item_sk in (select ss_item_sk from cross_items)
         and cs_item_sk = i_item_sk
         and cs_sold_date_sk = d_date_sk
         and d_year = 2000+2 
         and d_moy = 11
       group by i_brand_id,i_class_id,i_category_id
       having sum(cs_quantity*cs_list_price) > (select average_sales from avg_sales)
       union all
       select 'web' channel, i_brand_id,i_class_id,i_category_id, sum(ws_quantity*ws_list_price) sales , count(*) number_sales
       from web_sales
           ,item
           ,date_dim
       where ws_item_sk in (select ss_item_sk from cross_items)
         and ws_item_sk = i_item_sk
         and ws_sold_date_sk = d_date_sk
         and d_year = 2000+2
         and d_moy = 11
       group by i_brand_id,i_class_id,i_category_id
       having sum(ws_quantity*ws_list_price) > (select average_sales from avg_sales)
 ) y
 group by rollup (channel, i_brand_id,i_class_id,i_category_id)
 order by channel,i_brand_id,i_class_id,i_category_id
 limit 100;
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
 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
 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 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
),
 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
       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
       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 d_year between 2000 and 2000 + 2) x)
  select  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 = 17)
 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 = 17)
 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
 limit 100;

SQL15

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select  ca_zip
       ,sum(cs_sales_price)
 from catalog_sales
     ,customer
     ,customer_address
     ,date_dim
 where cs_bill_customer_sk = c_customer_sk
	and c_current_addr_sk = ca_address_sk 
	and ( substr(ca_zip,1,5) in ('85669', '86197','88274','83405','86475',
                                   '85392', '85460', '80348', '81792')
	      or ca_state in ('CA','WA','GA')
	      or cs_sales_price > 500)
	and cs_sold_date_sk = d_date_sk
	and d_qoy = 2 and d_year = 1999
 group by ca_zip
 order by ca_zip
 limit 100;

SQL16

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select  
   count(distinct cs_order_number) as "order count"
  ,sum(cs_ext_ship_cost) as "total shipping cost"
  ,sum(cs_net_profit) as "total net profit"
from
   catalog_sales cs1
  ,date_dim
  ,customer_address
  ,call_center
where
    d_date between '1999-2-01' and 
           (cast('1999-2-01' as date) + 60 )
and cs1.cs_ship_date_sk = d_date_sk
and cs1.cs_ship_addr_sk = ca_address_sk
and ca_state = 'TX'
and cs1.cs_call_center_sk = cc_call_center_sk
and cc_county in ('Barrow County','Luce County','Mobile County','Richland County',
                  'Wadena County'
)
and exists (select *
            from catalog_sales cs2
            where cs1.cs_order_number = cs2.cs_order_number
              and cs1.cs_warehouse_sk <> cs2.cs_warehouse_sk)
and not exists(select *
               from catalog_returns cr1
               where cs1.cs_order_number = cr1.cr_order_number)
order by count(distinct cs_order_number)
limit 100;

SQL17

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select  i_item_id
       ,i_item_desc
       ,s_state
       ,count(ss_quantity) as store_sales_quantitycount
       ,avg(ss_quantity) as store_sales_quantityave
       ,stddev_samp(ss_quantity) as store_sales_quantitystdev
       ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov
       ,count(sr_return_quantity) as store_returns_quantitycount
       ,avg(sr_return_quantity) as store_returns_quantityave
       ,stddev_samp(sr_return_quantity) as store_returns_quantitystdev
       ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as store_returns_quantitycov
       ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity) as catalog_sales_quantityave
       ,stddev_samp(cs_quantity) as catalog_sales_quantitystdev
       ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov
 from store_sales
     ,store_returns
     ,catalog_sales
     ,date_dim d1
     ,date_dim d2
     ,date_dim d3
     ,store
     ,item
 where d1.d_quarter_name = '2000Q1'
   and d1.d_date_sk = ss_sold_date_sk
   and i_item_sk = ss_item_sk
   and s_store_sk = ss_store_sk
   and ss_customer_sk = sr_customer_sk
   and ss_item_sk = sr_item_sk
   and ss_ticket_number = sr_ticket_number
   and sr_returned_date_sk = d2.d_date_sk
   and d2.d_quarter_name in ('2000Q1','2000Q2','2000Q3')
   and sr_customer_sk = cs_bill_customer_sk
   and sr_item_sk = cs_item_sk
   and cs_sold_date_sk = d3.d_date_sk
   and d3.d_quarter_name in ('2000Q1','2000Q2','2000Q3')
 group by i_item_id
         ,i_item_desc
         ,s_state
 order by i_item_id
         ,i_item_desc
         ,s_state
limit 100;

SQL18

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select  i_item_id,
        ca_country,
        ca_state, 
        ca_county,
        avg( cast(cs_quantity as decimal(12,2))) agg1,
        avg( cast(cs_list_price as decimal(12,2))) agg2,
        avg( cast(cs_coupon_amt as decimal(12,2))) agg3,
        avg( cast(cs_sales_price as decimal(12,2))) agg4,
        avg( cast(cs_net_profit as decimal(12,2))) agg5,
        avg( cast(c_birth_year as decimal(12,2))) agg6,
        avg( cast(cd1.cd_dep_count as decimal(12,2))) agg7
 from catalog_sales, customer_demographics cd1, 
      customer_demographics cd2, customer, customer_address, date_dim, item
 where cs_sold_date_sk = d_date_sk and
       cs_item_sk = i_item_sk and
       cs_bill_cdemo_sk = cd1.cd_demo_sk and
       cs_bill_customer_sk = c_customer_sk and
       cd1.cd_gender = 'M' and 
       cd1.cd_education_status = 'Primary' and
       c_current_cdemo_sk = cd2.cd_demo_sk and
       c_current_addr_sk = ca_address_sk and
       c_birth_month in (10,1,8,7,3,5) and
       d_year = 1998 and
       ca_state in ('NE','OK','NC'
                   ,'CO','ID','AR','MO')
 group by rollup (i_item_id, ca_country, ca_state, ca_county)
 order by ca_country,
        ca_state, 
        ca_county,
	i_item_id
 limit 100;

SQL19

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select  i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact,
	sum(ss_ext_sales_price) ext_price
 from date_dim, store_sales, item,customer,customer_address,store
 where d_date_sk = ss_sold_date_sk
   and ss_item_sk = i_item_sk
   and i_manager_id=62
   and d_moy=11
   and d_year=2000
   and ss_customer_sk = c_customer_sk 
   and c_current_addr_sk = ca_address_sk
   and substr(ca_zip,1,5) <> substr(s_zip,1,5) 
   and ss_store_sk = s_store_sk 
 group by i_brand
      ,i_brand_id
      ,i_manufact_id
      ,i_manufact
 order by ext_price desc
         ,i_brand
         ,i_brand_id
         ,i_manufact_id
         ,i_manufact
limit 100 ;

SQL20

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select  i_item_id
       ,i_item_desc 
       ,i_category 
       ,i_class 
       ,i_current_price
       ,sum(cs_ext_sales_price) as itemrevenue 
       ,sum(cs_ext_sales_price)*100/sum(sum(cs_ext_sales_price)) over
           (partition by i_class) as revenueratio
 from	catalog_sales
     ,item 
     ,date_dim
 where cs_item_sk = i_item_sk 
   and i_category in ('Sports', 'Shoes', 'Women')
   and cs_sold_date_sk = d_date_sk
 and d_date between cast('2001-03-21' as date) 
				and (cast('2001-03-21' as date) + 30)
 group by i_item_id
         ,i_item_desc 
         ,i_category
         ,i_class
         ,i_current_price
 order by i_category
         ,i_class
         ,i_item_id
         ,i_item_desc
         ,revenueratio
limit 100;
Parent topic: Appendixes