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_aggFROM tpcds_sales_returns_obt AS obtWHERE ( 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_idORDER BY sold_date_d_year, sum_agg DESC, brand_idLIMIT 100Representative 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 resultGet 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 factorqueries = bench.get_queries(dialect="datafusion") # your platform's dialect; drop dialect= for native SQLprint(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