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TPC-H Skew Q6 — Forecasting Revenue Change

TPC-H Skew benchmark · all TPC-H Skew queries

Revenue increase from eliminating discounts

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
SUM(l_extendedprice * l_discount) AS revenue
FROM lineitem
WHERE
l_shipdate >= CAST('1994-01-01' AS DATE)
AND l_shipdate < CAST('1994-01-01' AS DATE) + INTERVAL '1 YEAR'
AND l_discount BETWEEN 0.06 - 0.01 AND 0.06 + 0.01
AND l_quantity < 24

Representative DataFrame

Expression family (Polars, PySpark, DataFusion), default parameters.

def q6_expression_impl(ctx: DataFrameContext) -> Any:
lineitem = ctx.get_table("lineitem")
col = ctx.col
lit = ctx.lit
params = get_tpch_parameters(6)
start_date = params["start_date"]
end_date = params["end_date"]
discount_low = params["discount_low"]
discount_high = params["discount_high"]
quantity_limit = params["quantity_limit"]
return (
lineitem.filter(
(col("l_shipdate") >= lit(start_date))
& (col("l_shipdate") < lit(end_date))
& (col("l_discount") >= lit(discount_low))
& (col("l_discount") <= lit(discount_high))
& (col("l_quantity") < lit(quantity_limit))
)
.select((col("l_extendedprice") * col("l_discount")).alias("revenue"))
.sum()
)

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 factor
queries = bench.get_queries(dialect="datafusion") # your platform's dialect; drop dialect= for native SQL
print(queries['6'])

Parameter substitution for this benchmark is seed-driven; pass a seed for a specific stream’s parameters:

print(bench.get_query(6, 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', '6', family="expression").source) # or family="pandas"

Source

Query text: benchbox/_binaries/tpc-h/templates/queries/6.sql