H2ODB — Sum by Year and Hour

H2ODB benchmark · all H2ODB queries

SUM(fare_amount) GROUP BY EXTRACT(YEAR), EXTRACT(HOUR)

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  EXTRACT(YEAR FROM pickup_datetime) AS year,
  EXTRACT(HOUR FROM pickup_datetime) AS hour,
  SUM(fare_amount) AS sum_fare_amount
FROM trips
GROUP BY
  EXTRACT(YEAR FROM pickup_datetime),
  EXTRACT(HOUR FROM pickup_datetime)
ORDER BY
  year,
  hour

Representative DataFrame

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

def q8_expression_impl(ctx: DataFrameContext) -> Any:

    trips = ctx.get_table("trips")
    col = ctx.col
    return (
        trips.with_columns(
            col("pickup_datetime").dt.year().alias("year"),
            col("pickup_datetime").dt.hour().alias("hour"),
        )
        .group_by("year", "hour")
        .agg(col("fare_amount").sum().alias("sum_fare_amount"))
        .sort(["year", "hour"])
    )

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['Q8'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('h2odb', 'Q8', family="expression").source)  # or family="pandas"

Source

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