examples/tpch/q01_pricing_summary_report.py

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"""
TPC-H Problem Statement Query 1:

The Pricing Summary Report Query provides a summary pricing report for all lineitems shipped as of
a given date. The date is within 60 - 120 days of the greatest ship date contained in the database.
The query lists totals for extended price, discounted extended price, discounted extended price
plus tax, average quantity, average extended price, and average discount. These aggregates are
grouped by RETURNFLAG and LINESTATUS, and listed in ascending order of RETURNFLAG and LINESTATUS.
A count of the number of lineitems in each group is included.

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
        l_returnflag,
        l_linestatus,
        sum(l_quantity) as sum_qty,
        sum(l_extendedprice) as sum_base_price,
        sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
        sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
        avg(l_quantity) as avg_qty,
        avg(l_extendedprice) as avg_price,
        avg(l_discount) as avg_disc,
        count(*) as count_order
    from
        lineitem
    where
        l_shipdate <= date '1998-12-01' - interval '90 days'
    group by
        l_returnflag,
        l_linestatus
    order by
        l_returnflag,
        l_linestatus;
"""

import pyarrow as pa
from datafusion import SessionContext, col, lit
from datafusion import functions as F
from util import get_data_path

ctx = SessionContext()

df = ctx.read_parquet(get_data_path("lineitem.parquet"))

# It may be that the date can be hard coded, based on examples shown.
# This approach will work with any date range in the provided data set.

greatest_ship_date = df.aggregate(
    [], [F.max(col("l_shipdate")).alias("shipdate")]
).collect()[0]["shipdate"][0]

# From the given problem, this is how close to the last date in the database we
# want to report results for. It should be between 60-120 days before the end.
DAYS_BEFORE_FINAL = 90

interval = pa.scalar((0, DAYS_BEFORE_FINAL, 0), type=pa.month_day_nano_interval())

print("Final date in database:", greatest_ship_date)

# Filter data to the dates of interest
df = df.filter(col("l_shipdate") <= lit(greatest_ship_date) - lit(interval))

# Aggregate the results

disc_price = col("l_extendedprice") * (lit(1) - col("l_discount"))

df = df.aggregate(
    ["l_returnflag", "l_linestatus"],
    [
        F.sum(col("l_quantity")).alias("sum_qty"),
        F.sum(col("l_extendedprice")).alias("sum_base_price"),
        F.sum(disc_price).alias("sum_disc_price"),
        F.sum(disc_price * (lit(1) + col("l_tax"))).alias("sum_charge"),
        F.avg(col("l_quantity")).alias("avg_qty"),
        F.avg(col("l_extendedprice")).alias("avg_price"),
        F.avg(col("l_discount")).alias("avg_disc"),
        F.count_star().alias("count_order"),
    ],
)

# Sort per the expected result

df = df.sort_by("l_returnflag", "l_linestatus")

# Note: There appears to be a discrepancy between what is returned here and what is in the generated
# answers file for the case of return flag N and line status O, but I did not investigate further.

df.show()