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"""
TPC-H Problem Statement Query 12:

The Shipping Modes and Order Priority Query counts, by ship mode, for lineitems actually received
by customers in a given year, the number of lineitems belonging to orders for which the
l_receiptdate exceeds the l_commitdate for two different specified ship modes. Only lineitems that
were actually shipped before the l_commitdate are considered. The late lineitems are partitioned
into two groups, those with priority URGENT or HIGH, and those with a priority other than URGENT or
HIGH.

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

SHIP_MODE_1 = "MAIL"
SHIP_MODE_2 = "SHIP"
DATE_OF_INTEREST = "1994-01-01"

# Load the dataframes we need

ctx = SessionContext()

df_orders = ctx.read_parquet(get_data_path("orders.parquet")).select(
    "o_orderkey", "o_orderpriority"
)
df_lineitem = ctx.read_parquet(get_data_path("lineitem.parquet")).select(
    "l_orderkey", "l_shipmode", "l_commitdate", "l_shipdate", "l_receiptdate"
)

date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()

interval = pa.scalar((0, 365, 0), type=pa.month_day_nano_interval())


df = df_lineitem.filter(col("l_receiptdate") >= lit(date)).filter(
    col("l_receiptdate") < lit(date) + lit(interval)
)

# Note: It is not recommended to use array_has because it treats the second argument as an argument
# so if you pass it col("l_shipmode") it will pass the entire array to process which is very slow.
# Instead check the position of the entry is not null.
df = df.filter(
    ~F.array_position(
        F.make_array(lit(SHIP_MODE_1), lit(SHIP_MODE_2)), col("l_shipmode")
    ).is_null()
)

# Since we have only two values, it's much easier to do this as a filter where the l_shipmode
# matches either of the two values, but we want to show doing some array operations in this
# example. If you want to see this done with filters, comment out the above line and uncomment
# this one.
# df = df.filter((col("l_shipmode") == lit(SHIP_MODE_1)) | (col("l_shipmode") == lit(SHIP_MODE_2))) # noqa: ERA001


# We need order priority, so join order df to line item
df = df.join(df_orders, left_on=["l_orderkey"], right_on=["o_orderkey"], how="inner")

# Restrict to line items we care about based on the problem statement.
df = df.filter(col("l_commitdate") < col("l_receiptdate"))

df = df.filter(col("l_shipdate") < col("l_commitdate"))

df = df.with_column(
    "high_line_value",
    F.case(col("o_orderpriority"))
    .when(lit("1-URGENT"), lit(1))
    .when(lit("2-HIGH"), lit(1))
    .otherwise(lit(0)),
)

# Aggregate the results
df = df.aggregate(
    [col("l_shipmode")],
    [
        F.sum(col("high_line_value")).alias("high_line_count"),
        F.count(col("high_line_value")).alias("all_lines_count"),
    ],
)

# Compute the final output
df = df.select(
    col("l_shipmode"),
    col("high_line_count"),
    (col("all_lines_count") - col("high_line_count")).alias("low_line_count"),
)

df = df.sort(col("l_shipmode").sort())

df.show()
