TPC-DS-OBT Q12 — obt_ticket_extremes

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

Minimum, maximum, and average ticket

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  item_i_item_id,
  item_i_item_desc,
  item_i_category,
  item_i_class,
  item_i_current_price,
  SUM(ext_sales_price) AS itemrevenue,
  CAST(SUM(ext_sales_price) * 100 AS DOUBLE PRECISION) / SUM(SUM(ext_sales_price)) OVER (PARTITION BY item_i_class) AS revenueratio
FROM tpcds_sales_returns_obt AS obt
WHERE
  (
    item_sk = item_i_item_sk
    AND item_i_category IN ('1', '1', '1')
    AND sold_date_sk = sold_date_d_date_sk
    AND sold_date_d_date BETWEEN CAST('1998-01-01' AS DATE) AND (
      CAST('1998-01-01' AS DATE) + INTERVAL '30 DAY'
    )
  )
  AND channel = 'web'
GROUP BY
  item_i_item_id,
  item_i_item_desc,
  item_i_category,
  item_i_class,
  item_i_current_price
ORDER BY
  item_i_category,
  item_i_class,
  item_i_item_id,
  item_i_item_desc,
  revenueratio
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['12'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('tpcds_obt', '12', family="expression").source)  # or family="pandas"

Source

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