examples/tpch/q07_volume_shipping.py

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"""
TPC-H Problem Statement Query 7:

The Volume Shipping Query finds, for two given nations, the gross discounted revenues derived from
lineitems in which parts were shipped from a supplier in either nation to a customer in the other
nation during 1995 and 1996. The query lists the supplier nation, the customer nation, the year,
and the revenue from shipments that took place in that year. The query orders the answer by
Supplier nation, Customer nation, and year (all ascending).

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
        supp_nation,
        cust_nation,
        l_year,
        sum(volume) as revenue
    from
        (
                select
                        n1.n_name as supp_nation,
                        n2.n_name as cust_nation,
                        extract(year from l_shipdate) as l_year,
                        l_extendedprice * (1 - l_discount) as volume
                from
                        supplier,
                        lineitem,
                        orders,
                        customer,
                        nation n1,
                        nation n2
                where
                        s_suppkey = l_suppkey
                        and o_orderkey = l_orderkey
                        and c_custkey = o_custkey
                        and s_nationkey = n1.n_nationkey
                        and c_nationkey = n2.n_nationkey
                        and (
                                (n1.n_name = 'FRANCE' and n2.n_name = 'GERMANY')
                                or (n1.n_name = 'GERMANY' and n2.n_name = 'FRANCE')
                        )
                        and l_shipdate between date '1995-01-01' and date '1996-12-31'
        ) as shipping
    group by
        supp_nation,
        cust_nation,
        l_year
    order by
        supp_nation,
        cust_nation,
        l_year;
"""

from datetime import date

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

# Variables of interest to query over

nation_1 = lit("FRANCE")
nation_2 = lit("GERMANY")

START_DATE = date(1995, 1, 1)
END_DATE = date(1996, 12, 31)


# Load the dataframes we need

ctx = SessionContext()

df_supplier = ctx.read_parquet(get_data_path("supplier.parquet")).select(
    "s_suppkey", "s_nationkey"
)
df_lineitem = ctx.read_parquet(get_data_path("lineitem.parquet")).select(
    "l_shipdate", "l_extendedprice", "l_discount", "l_suppkey", "l_orderkey"
)
df_orders = ctx.read_parquet(get_data_path("orders.parquet")).select(
    "o_orderkey", "o_custkey"
)
df_customer = ctx.read_parquet(get_data_path("customer.parquet")).select(
    "c_custkey", "c_nationkey"
)
df_nation = ctx.read_parquet(get_data_path("nation.parquet")).select(
    "n_nationkey", "n_name"
)


# Filter to time of interest
df_lineitem = df_lineitem.filter(
    col("l_shipdate") >= lit(START_DATE), col("l_shipdate") <= lit(END_DATE)
)


# Limit the nation table to the two nations of interest.
df_nation = df_nation.filter(F.in_list(col("n_name"), [nation_1, nation_2]))


# Limit suppliers to either nation
df_supplier = df_supplier.join(
    df_nation, left_on="s_nationkey", right_on="n_nationkey"
).select("s_suppkey", col("n_name").alias("supp_nation"))

# Limit customers to either nation
df_customer = df_customer.join(
    df_nation, left_on="c_nationkey", right_on="n_nationkey"
).select("c_custkey", col("n_name").alias("cust_nation"))

# Join up all the data frames from line items, and make sure the supplier and customer are in
# different nations.
df = (
    df_lineitem.join(df_orders, left_on="l_orderkey", right_on="o_orderkey")
    .join(df_customer, left_on="o_custkey", right_on="c_custkey")
    .join(df_supplier, left_on="l_suppkey", right_on="s_suppkey")
    .filter(col("cust_nation") != col("supp_nation"))
)

# Extract out two values for every line item
df = df.with_columns(
    l_year=F.datepart(lit("year"), col("l_shipdate")).cast(pa.int32()),
    volume=col("l_extendedprice") * (lit(1.0) - col("l_discount")),
)

# Aggregate and sort per the spec.
df = df.aggregate(
    ["supp_nation", "cust_nation", "l_year"],
    [F.sum(col("volume")).alias("revenue")],
).sort_by("supp_nation", "cust_nation", "l_year")

df.show()