TPC-DS-OBT Q1 — obt_row_count

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

Total row count for OBT table

Representative SQL

Rendered for DataFusion with default parameters.

WITH customer_total_return AS (
  SELECT
    obt.returning_customer_c_customer_sk AS ctr_customer_sk,
    obt.store_sk AS ctr_store_sk,
    SUM(return_amount) AS ctr_total_return
  FROM tpcds_sales_returns_obt AS obt
  WHERE
    (
      obt.returned_date_sk = obt.sold_date_d_date_sk AND obt.sold_date_d_year = 1998
    )
    AND channel = 'store'
  GROUP BY
    obt.returning_customer_c_customer_sk,
    obt.store_sk
)
SELECT
  bill_customer_c_customer_id
FROM tpcds_sales_returns_obt AS obt
CROSS JOIN customer_total_return AS ctr1
WHERE
  (
    ctr1.ctr_total_return > (
      SELECT
        AVG(ctr_total_return) * 1.2
      FROM customer_total_return AS ctr2
      WHERE
        ctr1.ctr_store_sk = ctr2.ctr_store_sk
    )
    AND store_s_store_sk = ctr1.ctr_store_sk
    AND store_s_state = '0'
    AND ctr1.ctr_customer_sk = bill_customer_c_customer_sk
  )
  AND channel = 'store'
ORDER BY
  bill_customer_c_customer_id
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['1'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('tpcds_obt', '1', family="expression").source)  # or family="pandas"

Source

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