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"""

TPC-H Problem Statement Query 10:

The Returned Item Reporting Query finds the top 20 customers, in terms of their effect on lost

revenue for a given quarter, who have returned parts. The query considers only parts that were

ordered in the specified quarter. The query lists the customer's name, address, nation, phone

number, account balance, comment information and revenue lost. The customers are listed in

descending order of lost revenue. Revenue lost is defined as

sum(l_extendedprice*(1-l_discount)) for all qualifying lineitems.

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.

"""

from datetime import datetime

import pyarrow as pa

from datafusion import SessionContext, col, lit

from datafusion import functions as F

from util import get_data_path

DATE_START_OF_QUARTER = "1993-10-01"

date_start_of_quarter = lit(datetime.strptime(DATE_START_OF_QUARTER, "%Y-%m-%d").date())

interval_one_quarter = lit(pa.scalar((0, 92, 0), type=pa.month_day_nano_interval()))

# Load the dataframes we need

ctx = SessionContext()

df_customer = ctx.read_parquet(get_data_path("customer.parquet")).select(

"c_custkey",

"c_nationkey",

"c_name",

"c_acctbal",

"c_address",

"c_phone",

"c_comment",

)

df_lineitem = ctx.read_parquet(get_data_path("lineitem.parquet")).select(

"l_extendedprice", "l_discount", "l_orderkey", "l_returnflag"

)

df_orders = ctx.read_parquet(get_data_path("orders.parquet")).select(

"o_orderkey", "o_custkey", "o_orderdate"

)

df_nation = ctx.read_parquet(get_data_path("nation.parquet")).select(

"n_nationkey", "n_name", "n_regionkey"

)

# limit to returns

df_lineitem = df_lineitem.filter(col("l_returnflag") == lit("R"))

# Rather than aggregate by all of the customer fields as you might do looking at the specification,

# we can aggregate by o_custkey and then join in the customer data at the end.

df = df_orders.filter(col("o_orderdate") >= date_start_of_quarter).filter(

col("o_orderdate") < date_start_of_quarter + interval_one_quarter

)

df = df.join(df_lineitem, left_on=["o_orderkey"], right_on=["l_orderkey"], how="inner")

# Compute the revenue

df = df.aggregate(

[col("o_custkey")],

[F.sum(col("l_extendedprice") * (lit(1) - col("l_discount"))).alias("revenue")],

)

# Now join in the customer data

df = df.join(df_customer, left_on=["o_custkey"], right_on=["c_custkey"], how="inner")

df = df.join(df_nation, left_on=["c_nationkey"], right_on=["n_nationkey"], how="inner")

# These are the columns the problem statement requires

df = df.select(

"c_custkey",

"c_name",

"revenue",

"c_acctbal",

"n_name",

"c_address",

"c_phone",

"c_comment",

)

# Sort the results in descending order

df = df.sort(col("revenue").sort(ascending=False))

# Only return the top 20 results

df = df.limit(20)

df.show()