Read Primitives — Window Dense Rank

Read Primitives benchmark · all Read Primitives queries

Window function DENSE_RANK() OVER (PARTITION BY … ORDER BY …)

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  l_orderkey,
  l_partkey,
  l_quantity,
  l_extendedprice,
  DENSE_RANK() OVER (PARTITION BY l_orderkey ORDER BY l_extendedprice DESC) AS price_rank,
  PERCENT_RANK() OVER (PARTITION BY l_orderkey ORDER BY l_quantity) AS quantity_percentile,
  CUME_DIST() OVER (PARTITION BY l_orderkey ORDER BY l_extendedprice) AS price_distribution
FROM lineitem
WHERE
  l_orderkey <= 10000
ORDER BY
  l_orderkey,
  price_rank

Representative DataFrame

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

def window_dense_rank_expression_impl(ctx: DataFrameContext) -> Any:
    import polars as pl

    n = pl.len().over("l_orderkey")
    return (
        ctx.get_table("lineitem")
        .native.filter(pl.col("l_orderkey") <= 10000)
        .with_columns(
            pl.col("l_extendedprice").rank("dense", descending=True).over("l_orderkey").alias("price_rank"),
            ((pl.col("l_quantity").rank("min").over("l_orderkey") - 1) / (n - 1))
            .fill_nan(0.0)
            .fill_null(0.0)
            .alias("quantity_percentile"),
            (pl.col("l_extendedprice").rank("max").over("l_orderkey") / n).alias("price_distribution"),
        )
        .sort(["l_orderkey", "price_rank"])
        .select(
            "l_orderkey",
            "l_partkey",
            "l_quantity",
            "l_extendedprice",
            "price_rank",
            "quantity_percentile",
            "price_distribution",
        )
    )

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_multiple_orderings'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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