TPC-DS Q20 — Catalog Sales Item Analysis

TPC-DS benchmark · all TPC-DS queries

Reports catalog sales by item category

Representative SQL

Rendered for DataFusion with default parameters.

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 ('Jewelry', 'Sports', 'Books')
  AND cs_sold_date_sk = d_date_sk
  AND d_date BETWEEN CAST('2001-01-12' AS DATE) AND (
    CAST('2001-01-12' AS DATE) + INTERVAL '30 DAY'
  )
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

Representative DataFrame

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

def impl(ctx: DataFrameContext) -> Any:
        return helper(ctx, query_id, *helper_args)

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['20'])

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

print(bench.get_query(20, 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', '20', family="expression").source)  # or family="pandas"

Source

Query text: _sources/tpc-ds/query_templates/query20.tpl