Read Primitives — QUALIFY DENSE_RANK

Read Primitives benchmark · all Read Primitives queries

Find top N parts by price per category using DENSE_RANK

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  p_type,
  p_name,
  p_retailprice,
  price_rank
FROM (
  SELECT
    p_type,
    p_name,
    p_retailprice,
    DENSE_RANK() OVER (PARTITION BY p_type ORDER BY p_retailprice DESC) AS price_rank,
    DENSE_RANK() OVER (PARTITION BY p_type ORDER BY p_retailprice DESC) AS _w
  FROM part
  ORDER BY
    p_type,
    price_rank,
    p_name
) AS _t
WHERE
  _w <= 2

Representative DataFrame

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

def qualify_dense_rank_expression_impl(ctx: DataFrameContext) -> Any:
    result = ctx.get_table("part").with_columns(
        ctx.window_dense_rank(order_by=[("p_retailprice", False)], partition_by=["p_type"]).alias("price_rank")
    )
    return (
        result.filter(ctx.col("price_rank") <= ctx.lit(2))
        .select("p_type", "p_name", "p_retailprice", "price_rank")
        .sort(["p_type", "price_rank", "p_name"])
    )

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['qualify_dense_rank'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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