Read Primitives — Predicate Ordering with Subquery

Read Primitives benchmark · all Read Primitives queries

Order filter predicates by selectivity with subquery predicate

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  o_orderkey,
  o_totalprice
FROM orders
WHERE
  o_totalprice > 100000
  AND o_orderdate >= CAST('1995-01-01' AS DATE)
  AND o_custkey IN (
    SELECT
      c_custkey
    FROM customer
    WHERE
      c_mktsegment = 'BUILDING' AND c_nationkey = 1
  )

Representative DataFrame

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

def predicate_ordering_subquery_expression_impl(ctx: DataFrameContext) -> Any:
    orders, customer = _tables(ctx, "orders", "customer")
    col = ctx.col
    lit = ctx.lit

    building_customers = customer.filter(
        (col("c_mktsegment") == lit("BUILDING")) & (col("c_nationkey") == lit(1))
    ).select("c_custkey")

    return (
        orders.filter((col("o_totalprice") > lit(100000)) & (col("o_orderdate") >= lit(date(1995, 1, 1))))
        .join(building_customers, left_on="o_custkey", right_on="c_custkey", how="semi")
        .select("o_orderkey", "o_totalprice")
    )

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 ReadPrimitives

bench = ReadPrimitives(scale_factor=0.01)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['predicate_ordering_subquery'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('read_primitives', 'predicate_ordering_subquery', family="expression").source)  # or family="pandas"

Source

Query text: benchbox/core/read_primitives/queries.py