Read Primitives — Window Growing Frame

Read Primitives benchmark · all Read Primitives queries

Running sum window aggregation with growing frame size

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  l_orderkey,
  l_linenumber,
  l_quantity,
  SUM(l_quantity) OVER (
    PARTITION BY l_orderkey
    ORDER BY l_linenumber
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS running_quantity
FROM lineitem
WHERE
  l_orderkey <= 1000

Representative DataFrame

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

def window_growing_frame_expression_impl(ctx: DataFrameContext) -> Any:
    col, lit = ctx.col, ctx.lit
    return (
        ctx.get_table("lineitem")
        .filter(col("l_orderkey") <= lit(1000))
        .sort(["l_orderkey", "l_linenumber"])
        .with_columns(
            ctx.window_sum("l_quantity", partition_by=["l_orderkey"], order_by=[("l_linenumber", True)]).alias(
                "running_quantity"
            )
        )
        .select("l_orderkey", "l_linenumber", "l_quantity", "running_quantity")
    )

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['window_growing_frame'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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