H2ODB — Percentiles by Passenger Count

H2ODB benchmark · all H2ODB queries

PERCENTILE_CONT(0.5, 0.9) of fare_amount GROUP BY passenger_count

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  passenger_count,
  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY
    fare_amount) AS median_fare_amount,
  PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY
    fare_amount) AS p90_fare_amount
FROM trips
GROUP BY
  passenger_count
ORDER BY
  passenger_count

Representative DataFrame

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

def q9_expression_impl(ctx: DataFrameContext) -> Any:

    trips = ctx.get_table("trips")
    col = ctx.col
    return (
        trips.group_by("passenger_count")
        .agg(
            col("fare_amount").quantile(0.5, interpolation="linear").alias("median_fare_amount"),
            col("fare_amount").quantile(0.9, interpolation="linear").alias("p90_fare_amount"),
        )
        .sort("passenger_count")
    )

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 H2ODB

bench = H2ODB(scale_factor=0.01)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['Q9'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('h2odb', 'Q9', family="expression").source)  # or family="pandas"

Source

Query text: benchbox/core/h2odb/queries.py