Skip to content

TPC-DS-OBT Q3 — obt_returns_summary

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

Summarize returned sales and amount

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
sold_date_d_year,
item_i_brand_id AS brand_id,
item_i_brand AS brand,
SUM(ext_sales_price) AS sum_agg
FROM tpcds_sales_returns_obt AS obt
WHERE
(
sold_date_d_date_sk = obt.sold_date_sk
AND obt.item_sk = item_i_item_sk
AND item_i_manufact_id = 1
AND sold_date_d_moy = 11
)
AND channel = 'store'
GROUP BY
sold_date_d_year,
item_i_brand,
item_i_brand_id
ORDER BY
sold_date_d_year,
sum_agg DESC,
brand_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['3'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render
print(get_dataframe_render('tpcds_obt', '3', family="expression").source) # or family="pandas"

Source

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