TPC-DS Q19 — Store Sales Item/Customer Analysis

TPC-DS benchmark · all TPC-DS queries

Reports sales by item brand for customers, filtered by manager and zip codes

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  i_brand_id AS brand_id,
  i_brand AS brand,
  i_manufact_id,
  i_manufact,
  SUM(ss_ext_sales_price) AS 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 = 7
  AND d_moy = 11
  AND d_year = 1999
  AND ss_customer_sk = c_customer_sk
  AND c_current_addr_sk = ca_address_sk
  AND SUBSTRING(ca_zip FROM 1 FOR 5) <> SUBSTRING(s_zip FROM 1 FOR 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

Representative DataFrame

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

def impl(ctx: DataFrameContext) -> Any:
        return engine(ctx, spec)

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['19'])

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

print(bench.get_query(19, 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', '19', family="expression").source)  # or family="pandas"

Source

Query text: _sources/tpc-ds/query_templates/query19.tpl