Read Primitives — GROUP BY ALL Complex

Read Primitives benchmark · all Read Primitives queries

GROUP BY ALL with multiple non-aggregate expressions

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  DATE_TRUNC('MONTH', o_orderdate) AS order_month,
  o_orderpriority,
  COUNT(o_orderkey) AS order_count,
  SUM(o_totalprice) AS monthly_revenue
FROM orders
WHERE
  o_orderdate >= CAST('1995-01-01' AS DATE)
GROUP BY
  DATE_TRUNC('MONTH', o_orderdate),
  o_orderpriority
ORDER BY
  order_month,
  o_orderpriority

Representative DataFrame

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

def groupby_all_complex_expression_impl(ctx: DataFrameContext) -> Any:
    orders = ctx.get_table("orders")
    col = ctx.col
    lit = ctx.lit

    filtered = orders.filter(col("o_orderdate") >= lit(date(1995, 1, 1)))
    return (
        filtered.with_columns(col("o_orderdate").dt.truncate("1mo").alias("order_month"))
        .group_by("order_month", "o_orderpriority")
        .agg(
            col("o_orderkey").count().alias("order_count"),
            col("o_totalprice").sum().alias("monthly_revenue"),
        )
        .sort(["order_month", "o_orderpriority"])
    )

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['groupby_all_complex'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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