H2ODB — Sum and Mean by Passenger and Vendor

H2ODB benchmark · all H2ODB queries

SUM and AVG of fare_amount GROUP BY passenger_count, vendor_id

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  passenger_count,
  vendor_id,
  SUM(fare_amount) AS sum_fare_amount,
  AVG(fare_amount) AS mean_fare_amount
FROM trips
GROUP BY
  passenger_count,
  vendor_id
ORDER BY
  passenger_count,
  vendor_id

Representative DataFrame

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

def q6_expression_impl(ctx: DataFrameContext) -> Any:

    trips = ctx.get_table("trips")
    col = ctx.col
    return (
        trips.group_by("passenger_count", "vendor_id")
        .agg(
            col("fare_amount").sum().alias("sum_fare_amount"),
            col("fare_amount").mean().alias("mean_fare_amount"),
        )
        .sort(["passenger_count", "vendor_id"])
    )

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['Q6'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('h2odb', 'Q6', family="expression").source)  # or family="pandas"

Source

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