TPC-DS Q46 — Store Sales Household Analysis

TPC-DS benchmark · all TPC-DS queries

Reports store sales by customer household across cities

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  c_last_name,
  c_first_name,
  ca_city,
  bought_city,
  ss_ticket_number,
  amt,
  profit
FROM (
  SELECT
    ss_ticket_number,
    ss_customer_sk,
    ca_city AS bought_city,
    SUM(ss_coupon_amt) AS amt,
    SUM(ss_net_profit) AS profit
  FROM store_sales, date_dim, store, household_demographics, customer_address
  WHERE
    store_sales.ss_sold_date_sk = date_dim.d_date_sk
    AND store_sales.ss_store_sk = store.s_store_sk
    AND store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk
    AND store_sales.ss_addr_sk = customer_address.ca_address_sk
    AND (
      household_demographics.hd_dep_count = 5
      OR household_demographics.hd_vehicle_count = 3
    )
    AND date_dim.d_dow IN (6, 0)
    AND date_dim.d_year IN (1999, 1999 + 1, 1999 + 2)
    AND store.s_city IN ('Midway', 'Fairview', 'Fairview', 'Midway', 'Fairview')
  GROUP BY
    ss_ticket_number,
    ss_customer_sk,
    ss_addr_sk,
    ca_city
) AS dn, customer, customer_address AS current_addr
WHERE
  ss_customer_sk = c_customer_sk
  AND customer.c_current_addr_sk = current_addr.ca_address_sk
  AND current_addr.ca_city <> bought_city
ORDER BY
  c_last_name,
  c_first_name,
  ca_city,
  bought_city,
  ss_ticket_number
LIMIT 100

Representative DataFrame

Expression family (Polars, PySpark, DataFusion), default parameters.

def q46_expression_impl(ctx: DataFrameContext) -> Any:
    inner = _ticket_household_inner_expression(
        ctx,
        46,
        1999,
        ["Midway", "Fairview", "Fairview", "Fairview", "Fairview"],
        5,
        (("amt", "ss_coupon_amt"), ("profit", "ss_net_profit")),
        dow_list=(6, 0),
    )
    return _ticket_household_outer_expression(
        ctx,
        inner,
        ["c_last_name", "c_first_name", "ca_city", "bought_city", "ss_ticket_number", "amt", "profit"],
        ["c_last_name", "c_first_name", "ca_city", "bought_city", "ss_ticket_number"],
    )

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 factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['46'])

Parameter substitution for this benchmark is seed-driven; pass a seed for a specific stream’s parameters:

print(bench.get_query(46, 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', '46', family="expression").source)  # or family="pandas"

Source

Query text: _sources/tpc-ds/query_templates/query46.tpl