TPC-DS Q81 — Catalog Returns Customer Analysis

TPC-DS benchmark · all TPC-DS queries

Identifies customers with catalog return amounts above state average

Representative SQL

Rendered for DataFusion with default parameters.

WITH customer_total_return AS (
  SELECT
    cr_returning_customer_sk AS ctr_customer_sk,
    ca_state AS ctr_state,
    SUM(cr_return_amt_inc_tax) AS ctr_total_return
  FROM catalog_returns, date_dim, customer_address
  WHERE
    cr_returned_date_sk = d_date_sk
    AND d_year = 1998
    AND cr_returning_addr_sk = ca_address_sk
  GROUP BY
    cr_returning_customer_sk,
    ca_state
)
SELECT
  c_customer_id,
  c_salutation,
  c_first_name,
  c_last_name,
  ca_street_number,
  ca_street_name,
  ca_street_type,
  ca_suite_number,
  ca_city,
  ca_county,
  ca_state,
  ca_zip,
  ca_country,
  ca_gmt_offset,
  ca_location_type,
  ctr_total_return
FROM customer_total_return AS ctr1, customer_address, customer
WHERE
  ctr1.ctr_total_return > (
    SELECT
      AVG(ctr_total_return) * 1.2
    FROM customer_total_return AS ctr2
    WHERE
      ctr1.ctr_state = ctr2.ctr_state
  )
  AND ca_address_sk = c_current_addr_sk
  AND ca_state = 'IL'
  AND ctr1.ctr_customer_sk = c_customer_sk
ORDER BY
  c_customer_id,
  c_salutation,
  c_first_name,
  c_last_name,
  ca_street_number,
  ca_street_name,
  ca_street_type,
  ca_suite_number,
  ca_city,
  ca_county,
  ca_state,
  ca_zip,
  ca_country,
  ca_gmt_offset,
  ca_location_type,
  ctr_total_return
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['81'])

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

print(bench.get_query(81, 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', '81', family="expression").source)  # or family="pandas"

Source

Query text: _sources/tpc-ds/query_templates/query81.tpl