examples/tpch/q03_shipping_priority.py

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"""
TPC-H Problem Statement Query 3:

The Shipping Priority Query retrieves the shipping priority and potential revenue, defined as the
sum of l_extendedprice * (1-l_discount), of the orders having the largest revenue among those that
had not been shipped as of a given date. Orders are listed in decreasing order of revenue. If more
than 10 unshipped orders exist, only the 10 orders with the largest revenue are listed.

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_orderkey,
        sum(l_extendedprice * (1 - l_discount)) as revenue,
        o_orderdate,
        o_shippriority
    from
        customer,
        orders,
        lineitem
    where
        c_mktsegment = 'BUILDING'
        and c_custkey = o_custkey
        and l_orderkey = o_orderkey
        and o_orderdate < date '1995-03-15'
        and l_shipdate > date '1995-03-15'
    group by
        l_orderkey,
        o_orderdate,
        o_shippriority
    order by
        revenue desc,
        o_orderdate limit 10;
"""

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

SEGMENT_OF_INTEREST = "BUILDING"
DATE_OF_INTEREST = "1995-03-15"

# Load the dataframes we need

ctx = SessionContext()

df_customer = ctx.read_parquet(get_data_path("customer.parquet")).select(
    "c_mktsegment", "c_custkey"
)
df_orders = ctx.read_parquet(get_data_path("orders.parquet")).select(
    "o_orderdate", "o_shippriority", "o_custkey", "o_orderkey"
)
df_lineitem = ctx.read_parquet(get_data_path("lineitem.parquet")).select(
    "l_orderkey", "l_extendedprice", "l_discount", "l_shipdate"
)

# Limit dataframes to the rows of interest

df_customer = df_customer.filter(col("c_mktsegment") == SEGMENT_OF_INTEREST)
df_orders = df_orders.filter(col("o_orderdate") < lit(DATE_OF_INTEREST))
df_lineitem = df_lineitem.filter(col("l_shipdate") > lit(DATE_OF_INTEREST))

# Join all 3 dataframes

df = df_customer.join(df_orders, left_on="c_custkey", right_on="o_custkey").join(
    df_lineitem, left_on="o_orderkey", right_on="l_orderkey"
)

# Compute the revenue

df = df.aggregate(
    ["l_orderkey"],
    [
        F.first_value(col("o_orderdate")).alias("o_orderdate"),
        F.first_value(col("o_shippriority")).alias("o_shippriority"),
        F.sum(col("l_extendedprice") * (lit(1.0) - col("l_discount"))).alias("revenue"),
    ],
)

# Sort by priority, take 10, and project in the order expected by the spec.

df = (
    df.sort(col("revenue").sort(ascending=False), "o_orderdate")
    .limit(10)
    .select("l_orderkey", "revenue", "o_orderdate", "o_shippriority")
)

# Show result

df.show()