TPC-H Skew Q22 — Global Sales Opportunity

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

Identify customers likely to make purchases

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  cntrycode,
  COUNT(*) AS numcust,
  SUM(c_acctbal) AS totacctbal
FROM (
  SELECT
    SUBSTRING(c_phone FROM 1 FOR 2) AS cntrycode,
    c_acctbal
  FROM customer
  WHERE
    SUBSTRING(c_phone FROM 1 FOR 2) IN ('13', '31', '23', '29', '30', '18', '17')
    AND c_acctbal > (
      SELECT
        AVG(c_acctbal)
      FROM customer
      WHERE
        c_acctbal > 0.00
        AND SUBSTRING(c_phone FROM 1 FOR 2) IN ('13', '31', '23', '29', '30', '18', '17')
    )
    AND NOT EXISTS(
      SELECT
        *
      FROM orders
      WHERE
        o_custkey = c_custkey
    )
) AS custsale
GROUP BY
  cntrycode
ORDER BY
  cntrycode

Representative DataFrame

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

def q22_expression_impl(ctx: DataFrameContext) -> Any:
    customer = ctx.get_table("customer")
    orders = ctx.get_table("orders")
    col = ctx.col
    lit = ctx.lit

    params = get_tpch_parameters(22)
    country_codes = params["country_codes"]

    customer_with_code = customer.with_columns(col("c_phone").str.slice(0, 2).alias("cntrycode"))

    avg_balance = ctx.scalar(
        customer_with_code.filter((col("c_acctbal") > lit(0)) & col("cntrycode").is_in(country_codes)).select(
            col("c_acctbal").mean().alias("avg_bal")
        )
    )

    customers_with_orders = orders.select("o_custkey").unique()

    return (
        customer_with_code.filter(col("cntrycode").is_in(country_codes) & (col("c_acctbal") > lit(avg_balance)))
        .join(customers_with_orders, left_on="c_custkey", right_on="o_custkey", how="anti")
        .group_by("cntrycode")
        .agg(col("c_custkey").count().alias("numcust"), col("c_acctbal").sum().alias("totacctbal"))
        .sort("cntrycode")
    )

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['22'])

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

print(bench.get_query(22, 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', '22', family="expression").source)  # or family="pandas"

Source

Query text: benchbox/_binaries/tpc-h/templates/queries/22.sql