TPC-DS-OBT Q17 — obt_overall_totals

TPC-DS-OBT benchmark · all TPC-DS-OBT queries

Overall sales, revenue, and profit totals

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  item_i_item_id,
  item_i_item_desc,
  store_s_state,
  COUNT(quantity) AS store_sales_quantitycount,
  AVG(quantity) AS store_sales_quantityave,
  STDDEV_SAMP(quantity) AS store_sales_quantitystdev,
  CAST(STDDEV_SAMP(quantity) AS DOUBLE PRECISION) / AVG(quantity) AS store_sales_quantitycov,
  COUNT(return_quantity) AS store_returns_quantitycount,
  AVG(return_quantity) AS store_returns_quantityave,
  STDDEV_SAMP(return_quantity) AS store_returns_quantitystdev,
  CAST(STDDEV_SAMP(return_quantity) AS DOUBLE PRECISION) / AVG(return_quantity) AS store_returns_quantitycov,
  COUNT(quantity) AS catalog_sales_quantitycount,
  AVG(quantity) AS catalog_sales_quantityave,
  STDDEV_SAMP(quantity) AS catalog_sales_quantitystdev,
  CAST(STDDEV_SAMP(quantity) AS DOUBLE PRECISION) / AVG(quantity) AS catalog_sales_quantitycov
FROM tpcds_sales_returns_obt AS obt
WHERE
  (
    sold_date_d_quarter_name = '1998Q1'
    AND sold_date_d_date_sk = sold_date_sk
    AND item_i_item_sk = item_sk
    AND store_s_store_sk = store_sk
    AND ship_customer_sk = returning_customer_c_customer_sk
    AND item_sk = item_sk
    AND sale_id = sale_id
    AND returned_date_sk = return_date_d_date_sk
    AND return_date_d_quarter_name IN ('1998Q1', '1998Q2', '1998Q3')
    AND returning_customer_c_customer_sk = bill_customer_sk
    AND item_sk = item_sk
    AND sold_date_sk = sold_date_d_date_sk
    AND sold_date_d_quarter_name IN ('1998Q1', '1998Q2', '1998Q3')
  )
  AND channel IN ('catalog', 'store')
GROUP BY
  item_i_item_id,
  item_i_item_desc,
  store_s_state
ORDER BY
  item_i_item_id,
  item_i_item_desc,
  store_s_state
LIMIT 100

Representative DataFrame

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

def impl(ctx: DataFrameContext) -> Any:
        table = ctx.get_table(OBT_TABLE)
        for column, op, value in spec.filters:
            table = table.filter(_FILTER_OPS[op](ctx.col(column), ctx.lit(value)))
        aggregates = [getattr(ctx.col(column), func)().alias(alias) for alias, column, func in spec.aggregations]
        if spec.group_keys:
            result = table.group_by(*spec.group_keys).agg(*aggregates)
        else:
            result = table.select(*aggregates)
        if spec.sort_keys:
            descending = list(spec.descending) if spec.descending else [False] * len(spec.sort_keys)
            result = result.sort(list(spec.sort_keys), descending=descending)
        if spec.limit:
            result = result.limit(spec.limit)
        return result

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 TPCDSOBT

bench = TPCDSOBT(scale_factor=1.0)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['17'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('tpcds_obt', '17', family="expression").source)  # or family="pandas"

Source

Query text: benchbox/core/tpcds_obt/queries.py