Read Primitives — Optimizer: Limit Pushdown

Read Primitives benchmark · all Read Primitives queries

Test limit pushdown through operations

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  c.c_name,
  c.c_mktsegment,
  o.o_orderdate,
  o.o_totalprice,
  o.o_orderpriority
FROM customer AS c
JOIN orders AS o
  ON c.c_custkey = o.o_custkey
WHERE
  c.c_mktsegment = 'BUILDING'
  AND o.o_orderdate >= CAST('1995-01-01' AS DATE)
  AND o.o_orderdate < CAST('1996-01-01' AS DATE)
ORDER BY
  o.o_totalprice DESC
LIMIT 100

Representative DataFrame

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

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

    return (
        customer.join(orders, col("c_custkey") == col("o_custkey"))
        .filter(col("c_mktsegment") == lit("BUILDING"))
        .filter(col("o_orderdate") >= lit(date(1995, 1, 1)))
        .filter(col("o_orderdate") < lit(date(1996, 1, 1)))
        .select("c_name", "c_mktsegment", "o_orderdate", "o_totalprice", "o_orderpriority")
        .sort(["o_totalprice", "c_name", "o_orderdate"], descending=[True, False, False])
        .limit(100)
    )

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['optimizer_limit_pushdown'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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