Read Primitives — Basic Pivot

Read Primitives benchmark · all Read Primitives queries

Pivot ship modes into columns

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  *
FROM (
  SELECT
    l_returnflag,
    l_shipmode,
    l_quantity
  FROM lineitem
  WHERE
    l_shipdate >= CAST('1995-01-01' AS DATE)
    AND l_shipdate < CAST('1995-04-01' AS DATE)
)

Representative DataFrame

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

def pivot_basic_expression_impl(ctx: DataFrameContext) -> Any:
    lineitem = ctx.get_table("lineitem")
    col = ctx.col
    lit = ctx.lit

    filtered = lineitem.filter(
        (col("l_shipdate") >= lit(date(1995, 1, 1))) & (col("l_shipdate") < lit(date(1995, 4, 1)))
    ).select("l_returnflag", "l_shipmode", "l_quantity")

    return filtered.group_by("l_returnflag").agg(
        col("l_quantity").filter(col("l_shipmode") == lit("AIR")).sum().alias("AIR"),
        col("l_quantity").filter(col("l_shipmode") == lit("RAIL")).sum().alias("RAIL"),
        col("l_quantity").filter(col("l_shipmode") == lit("SHIP")).sum().alias("SHIP"),
        col("l_quantity").filter(col("l_shipmode") == lit("TRUCK")).sum().alias("TRUCK"),
    )

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['pivot_basic'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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