Read Primitives — MAX_BY Complex

Read Primitives benchmark · all Read Primitives queries

Find the most expensive order for each customer segment

Representative SQL

Rendered for DataFusion with default parameters.

WITH ranked AS (
  SELECT
    c.c_mktsegment,
    o.o_orderkey,
    o.o_orderdate,
    o.o_totalprice,
    ROW_NUMBER() OVER (PARTITION BY c.c_mktsegment ORDER BY o.o_totalprice DESC, o.o_orderkey) AS rn
  FROM orders AS o
  JOIN customer AS c
    ON o.o_custkey = c.c_custkey
)
SELECT
  c_mktsegment,
  o_orderkey AS most_expensive_order_id,
  o_orderdate AS most_expensive_order_date,
  o_totalprice AS max_order_value
FROM ranked
WHERE
  rn = 1
ORDER BY
  max_order_value DESC,
  c_mktsegment

Representative DataFrame

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

def expression_impl(ctx: DataFrameContext) -> Any:
        table = _extreme_source(ctx, base, joins)
        extrema = table.group_by(*groups).agg(getattr(ctx.col(value), agg)().alias(alias))
        result = table.join(extrema, on=group_key).filter(ctx.col(value) == ctx.col(alias)).select(*expr_select)
        result = result.unique(subset=expr_unique[0] if len(expr_unique) == 1 else list(expr_unique)).sort(
            alias, descending=desc
        )
        return result.limit(limit) if limit is not None else result

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['max_by_complex'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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