Read Primitives — Approximate Distinct Count

Read Primitives benchmark · all Read Primitives queries

HLL distinct count on a high-cardinality key (sketch on Polars/PySpark/DataFusion; exact on Pandas/Modin/cuDF/Dask)

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  APPROX_DISTINCT(o_custkey) AS unique_customers
FROM orders
WHERE
  o_orderdate >= CAST('1995-01-01' AS DATE)

Representative DataFrame

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

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

    return orders.filter(col("o_orderdate") >= lit(date(1995, 1, 1))).select(
        col("o_custkey").approx_n_unique().alias("unique_customers")
    )

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['approx_count_distinct_simple'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

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

Source

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