TPC-H Skew Q10 — Returned Item Reporting
TPC-H Skew benchmark · all TPC-H Skew queries
Customers with returned parts and revenue impact
Representative SQL
Rendered for DataFusion with default parameters.
SELECT c_custkey, c_name, SUM(l_extendedprice * ( 1 - l_discount )) AS revenue, c_acctbal, n_name, c_address, c_phone, c_commentFROM customer, orders, lineitem, nationWHERE c_custkey = o_custkey AND l_orderkey = o_orderkey AND o_orderdate >= CAST('1993-10-01' AS DATE) AND o_orderdate < CAST('1993-10-01' AS DATE) + INTERVAL '3 MONTH' AND l_returnflag = 'R' AND c_nationkey = n_nationkeyGROUP BY c_custkey, c_name, c_acctbal, c_phone, n_name, c_address, c_commentORDER BY revenue DESCLIMIT 20Representative DataFrame
Expression family (Polars, PySpark, DataFusion), default parameters.
def q10_expression_impl(ctx: DataFrameContext) -> Any: customer = ctx.get_table("customer") orders = ctx.get_table("orders") lineitem = ctx.get_table("lineitem") nation = ctx.get_table("nation") col = ctx.col lit = ctx.lit
params = get_tpch_parameters(10) start_date = params["start_date"] end_date = params["end_date"]
return ( customer.join(orders, left_on="c_custkey", right_on="o_custkey") .filter((col("o_orderdate") >= lit(start_date)) & (col("o_orderdate") < lit(end_date))) .join(lineitem, left_on="o_orderkey", right_on="l_orderkey") .filter(col("l_returnflag") == lit("R")) .join(nation, left_on="c_nationkey", right_on="n_nationkey") .group_by( "c_custkey", "c_name", "c_acctbal", "c_phone", "n_name", "c_address", "c_comment", ) .agg((col("l_extendedprice") * (lit(1) - col("l_discount"))).sum().alias("revenue")) .select("c_custkey", "c_name", "revenue", "c_acctbal", "n_name", "c_address", "c_phone", "c_comment") .sort("revenue", descending=True) .limit(20) )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 TPCHSkew
bench = TPCHSkew(scale_factor=0.01) # your scale factorqueries = bench.get_queries(dialect="datafusion") # your platform's dialect; drop dialect= for native SQLprint(queries['10'])Parameter substitution for this benchmark is seed-driven; pass a seed for a specific stream’s parameters:
print(bench.get_query(10, 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_skew', '10', family="expression").source) # or family="pandas"Source
Query text: benchbox/_binaries/tpc-h/templates/queries/10.sql