TPC-DS Q4 — Customer Year-over-Year Comparison
TPC-DS benchmark · all TPC-DS queries
Compares customer totals across store/catalog/web, filtering for catalog growth
Representative SQL
Rendered for DataFusion with default parameters.
WITH year_total AS ( SELECT c_customer_id AS customer_id, c_first_name AS customer_first_name, c_last_name AS customer_last_name, c_preferred_cust_flag AS customer_preferred_cust_flag, c_birth_country AS customer_birth_country, c_login AS customer_login, c_email_address AS customer_email_address, d_year AS dyear, SUM( ( ( ss_ext_list_price - ss_ext_wholesale_cost - ss_ext_discount_amt ) + ss_ext_sales_price ) / 2 ) AS year_total, 's' AS 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 AS customer_id, c_first_name AS customer_first_name, c_last_name AS customer_last_name, c_preferred_cust_flag AS customer_preferred_cust_flag, c_birth_country AS customer_birth_country, c_login AS customer_login, c_email_address AS customer_email_address, d_year AS dyear, SUM( ( ( ( cs_ext_list_price - cs_ext_wholesale_cost - cs_ext_discount_amt ) + cs_ext_sales_price ) / 2 ) ) AS year_total, 'c' AS 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 AS customer_id, c_first_name AS customer_first_name, c_last_name AS customer_last_name, c_preferred_cust_flag AS customer_preferred_cust_flag, c_birth_country AS customer_birth_country, c_login AS customer_login, c_email_address AS customer_email_address, d_year AS dyear, SUM( ( ( ( ws_ext_list_price - ws_ext_wholesale_cost - ws_ext_discount_amt ) + ws_ext_sales_price ) / 2 ) ) AS year_total, 'w' AS 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_email_addressFROM year_total AS t_s_firstyear, year_total AS t_s_secyear, year_total AS t_c_firstyear, year_total AS t_c_secyear, year_total AS t_w_firstyear, year_total AS t_w_secyearWHERE 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 = 2001 AND t_s_secyear.dyear = 2001 + 1 AND t_c_firstyear.dyear = 2001 AND t_c_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_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 ENDORDER BY t_s_secyear.customer_id, t_s_secyear.customer_first_name, t_s_secyear.customer_last_name, t_s_secyear.customer_email_addressLIMIT 100Representative DataFrame
Expression family (Polars, PySpark, DataFusion), default parameters.
def q4_expression_impl(ctx: DataFrameContext) -> Any: return _year_growth_expression(ctx, *_year_growth_args(4))Get the exact query for your run
The blocks above are representative. To get the precise statement a specific platform runs at a specific scale, substitute your own values:
from benchbox import TPCDS
bench = TPCDS(scale_factor=1.0) # your scale factorqueries = bench.get_queries(dialect="datafusion") # your platform's dialect; drop dialect= for native SQLprint(queries['4'])Parameter substitution for this benchmark is seed-driven; pass a seed for a specific stream’s parameters:
print(bench.get_query(4, scale_factor=1.0, seed=42, dialect="datafusion"))For the DataFrame form:
from benchbox.core.query_catalog import get_dataframe_render
print(get_dataframe_render('tpcds', '4', family="expression").source) # or family="pandas"Source
Query text: _sources/tpc-ds/query_templates/query4.tpl