TPC-DS-OBT Q6 — obt_avg_ticket_by_channel

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

Average ticket and sales count by channel

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  bill_addr_ca_state AS state,
  COUNT(*) AS cnt
FROM tpcds_sales_returns_obt AS obt
WHERE
  (
    bill_addr_ca_address_sk = bill_customer_c_current_addr_sk
    AND bill_customer_c_customer_sk = obt.ship_customer_sk
    AND obt.sold_date_sk = sold_date_d_date_sk
    AND obt.item_sk = item_i_item_sk
    AND sold_date_d_month_seq = (
      SELECT DISTINCT
        (
          obt.sold_date_d_month_seq
        )
      FROM tpcds_sales_returns_obt AS obt
      WHERE
        (
          obt.sold_date_d_year = 1998 AND obt.sold_date_d_moy = 1
        )
        AND channel = 'store'
    )
    AND item_i_current_price > 1.2 * (
      SELECT
        AVG(item_i_current_price)
      FROM tpcds_sales_returns_obt AS obt
      WHERE
        item_i_category = obt.item_i_category AND channel = 'store'
    )
  )
  AND channel = 'store'
GROUP BY
  bill_addr_ca_state
HAVING
  COUNT(*) >= 10
ORDER BY
  cnt,
  bill_addr_ca_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['6'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('tpcds_obt', '6', family="expression").source)  # or family="pandas"

Source

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