CoffeeShop — Product Mix

CoffeeShop benchmark · all CoffeeShop queries

Product mix by subcategory with date validity check on dim_products

Representative SQL

Rendered for DataFusion with default parameters.

SELECT
  dp.subcategory,
  COUNT(DISTINCT dp.product_id) AS active_products,
  SUM(ol.quantity) AS quantity_sold,
  SUM(ol.total_price) AS revenue
FROM order_lines AS ol
JOIN dim_products AS dp
  ON ol.product_record_id = dp.record_id
WHERE
  ol.order_date BETWEEN CAST('2023-01-01' AS DATE) AND CAST('2023-12-31' AS DATE)
  AND ol.order_date BETWEEN dp.from_date AND dp.to_date
GROUP BY
  dp.subcategory
ORDER BY
  revenue DESC

Representative DataFrame

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

def pr1_expression_impl(ctx: DataFrameContext) -> Any:
    params = get_parameters("PR1")
    start_date = params.get("start_date", "2023-01-01")
    end_date = params.get("end_date", "2023-12-31")

    ol = ctx.get_table("order_lines")
    dp = ctx.get_table("dim_products")
    col = ctx.col
    lit = ctx.lit

    return (
        ol.filter(
            (col("order_date") >= lit(_parse_date(start_date))) & (col("order_date") <= lit(_parse_date(end_date)))
        )
        .join(dp, left_on="product_record_id", right_on="record_id")
        .filter((col("order_date") >= col("from_date")) & (col("order_date") <= col("to_date")))
        .group_by("subcategory")
        .agg(
            col("product_id").n_unique().alias("active_products"),
            col("quantity").sum().alias("quantity_sold"),
            col("total_price").sum().alias("revenue"),
        )
        .sort("revenue", descending=True)
    )

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 CoffeeShop

bench = CoffeeShop(scale_factor=0.001)            # your scale factor
queries = bench.get_queries(dialect="datafusion")   # your platform's dialect; drop dialect= for native SQL
print(queries['PR1'])

For the DataFrame form:

from benchbox.core.query_catalog import get_dataframe_render

print(get_dataframe_render('coffeeshop', 'PR1', family="expression").source)  # or family="pandas"

Source

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