Read Primitives — Optimizer: Column Pruning

Read Primitives benchmark · all Read Primitives queries

Test column pruning at scan

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  c.c_name
FROM customer AS c
JOIN orders AS o
  ON c.c_custkey = o.o_custkey
JOIN lineitem AS l
  ON o.o_orderkey = l.l_orderkey
WHERE
  o.o_orderdate >= CAST('1995-01-01' AS DATE)
  AND o.o_orderdate < CAST('1996-01-01' AS DATE)
  AND l.l_quantity > 40
  AND c.c_nationkey = 15
ORDER BY
  c.c_name
LIMIT 500

Representative DataFrame

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

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

    return (
        customer.join(orders, col("c_custkey") == col("o_custkey"))
        .join(lineitem, col("o_orderkey") == col("l_orderkey"))
        .filter(col("o_orderdate") >= lit(date(1995, 1, 1)))
        .filter(col("o_orderdate") < lit(date(1996, 1, 1)))
        .filter(col("l_quantity") > lit(40))
        .filter(col("c_nationkey") == lit(15))
        .select("c_name")
        .sort("c_name")
        .limit(500)
    )

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_column_pruning'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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