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"""
TPC-H Problem Statement Query 17:

The Small-Quantity-Order Revenue Query considers parts of a given brand and with a given container
type and determines the average lineitem quantity of such parts ordered for all orders (past and
pending) in the 7-year database. What would be the average yearly gross (undiscounted) loss in
revenue if orders for these parts with a quantity of less than 20% of this average were no longer
taken?

The above problem statement text is copyrighted by the Transaction Processing Performance Council
as part of their TPC Benchmark H Specification revision 2.18.0.

Reference SQL (from TPC-H specification, used by the benchmark suite)::

    select
        sum(l_extendedprice) / 7.0 as avg_yearly
    from
        lineitem,
        part
    where
        p_partkey = l_partkey
        and p_brand = 'Brand#23'
        and p_container = 'MED BOX'
        and l_quantity < (
                select
                        0.2 * avg(l_quantity)
                from
                        lineitem
                where
                        l_partkey = p_partkey
        );
"""

from datafusion import SessionContext, WindowFrame, col, lit
from datafusion import functions as F
from datafusion.expr import Window
from util import get_data_path

BRAND = "Brand#23"
CONTAINER = "MED BOX"

# Load the dataframes we need

ctx = SessionContext()

df_part = ctx.read_parquet(get_data_path("part.parquet")).select(
    "p_partkey", "p_brand", "p_container"
)
df_lineitem = ctx.read_parquet(get_data_path("lineitem.parquet")).select(
    "l_partkey", "l_quantity", "l_extendedprice"
)

# Limit to parts of the target brand/container, join their line items, and
# attach the per-part average quantity via a partitioned window function —
# the DataFrame form of the SQL's correlated ``avg(l_quantity)`` subquery.
whole_frame = WindowFrame("rows", None, None)

df = (
    df_part.filter(col("p_brand") == BRAND, col("p_container") == CONTAINER)
    .join(df_lineitem, left_on="p_partkey", right_on="l_partkey")
    .with_column(
        "avg_quantity",
        F.avg(col("l_quantity")).over(
            Window(partition_by=[col("l_partkey")], window_frame=whole_frame)
        ),
    )
    .filter(col("l_quantity") < lit(0.2) * col("avg_quantity"))
    .aggregate([], [F.sum(col("l_extendedprice")).alias("total")])
    .select((col("total") / lit(7.0)).alias("avg_yearly"))
)

df.show()
