TPC-H Q14 — Promotion Effect
TPC-H benchmark · all TPC-H queries
Effect of promotions on revenue
Representative SQL
Rendered for DataFusion with default parameters.
SELECT 100.00 * SUM( CASE WHEN p_type LIKE 'PROMO%' THEN l_extendedprice * ( 1 - l_discount ) ELSE 0 END ) / SUM(l_extendedprice * ( 1 - l_discount )) AS promo_revenueFROM lineitem, partWHERE l_partkey = p_partkey AND l_shipdate >= CAST('1995-09-01' AS DATE) AND l_shipdate < CAST('1995-09-01' AS DATE) + INTERVAL '1 MONTH'Representative DataFrame
Expression family (Polars, PySpark, DataFusion), default parameters.
def q14_expression_impl(ctx: DataFrameContext) -> Any: lineitem = ctx.get_table("lineitem") part = ctx.get_table("part") col = ctx.col lit = ctx.lit
params = get_tpch_parameters(14) start_date = params["start_date"] end_date = params["end_date"]
return ( lineitem.filter((col("l_shipdate") >= lit(start_date)) & (col("l_shipdate") < lit(end_date))) .join(part, left_on="l_partkey", right_on="p_partkey") .select( ( lit(100.0) * (col("l_extendedprice") * (lit(1) - col("l_discount"))) .filter(col("p_type").str.starts_with("PROMO")) .sum() / (col("l_extendedprice") * (lit(1) - col("l_discount"))).sum() ).alias("promo_revenue") ) )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 TPCH
bench = TPCH(scale_factor=0.01) # your scale factorqueries = bench.get_queries(dialect="datafusion") # your platform's dialect; drop dialect= for native SQLprint(queries['14'])Parameter substitution for this benchmark is seed-driven; pass a seed for a specific stream’s parameters:
print(bench.get_query(14, scale_factor=0.01, seed=42, dialect="datafusion"))For the DataFrame form:
from benchbox.core.query_catalog import get_dataframe_render
print(get_dataframe_render('tpch', '14', family="expression").source) # or family="pandas"Source
Query text: benchbox/_binaries/tpc-h/templates/queries/14.sql