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# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements. See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership. The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing,

# software distributed under the License is distributed on an

# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

# KIND, either express or implied. See the License for the

# specific language governing permissions and limitations

# under the License.

import os

import time

import pyarrow as pa

import pyarrow.compute as pc

from datafusion import SessionContext, col, lit, udf

from datafusion import functions as F

path = os.path.dirname(os.path.abspath(__file__))

filepath = os.path.join(path, "./tpch/data/lineitem.parquet")

# This example serves to demonstrate alternate approaches to answering the

# question "return all of the rows that have a specific combination of these

# values". We have the combinations we care about provided as a python

# list of tuples. There is no built in function that supports this operation,

# but it can be explicitly specified via a single expression or we can

# use a user defined function.

ctx = SessionContext()

# These part keys and suppliers are chosen because there are

# cases where two suppliers each have two of the part keys

# but we are interested in these specific combinations.

values_of_interest = [

(1530, 4031, "N"),

(6530, 1531, "N"),

(5618, 619, "N"),

(8118, 8119, "N"),

]

partkeys = [lit(r[0]) for r in values_of_interest]

suppkeys = [lit(r[1]) for r in values_of_interest]

returnflags = [lit(r[2]) for r in values_of_interest]

df_lineitem = ctx.read_parquet(filepath).select(

"l_partkey", "l_suppkey", "l_returnflag"

)

start_time = time.time()

df_simple_filter = df_lineitem.filter(

F.in_list(col("l_partkey"), partkeys),

F.in_list(col("l_suppkey"), suppkeys),

F.in_list(col("l_returnflag"), returnflags),

)

num_rows = df_simple_filter.count()

print(

f"Simple filtering has number {num_rows} rows and took {time.time() - start_time} s"

)

print("This is the incorrect number of rows!")

start_time = time.time()

# Explicitly check for the combinations of interest.

# This works but is not scalable.

filter_expr = (

(

(col("l_partkey") == values_of_interest[0][0])

& (col("l_suppkey") == values_of_interest[0][1])

& (col("l_returnflag") == values_of_interest[0][2])

)

| (

(col("l_partkey") == values_of_interest[1][0])

& (col("l_suppkey") == values_of_interest[1][1])

& (col("l_returnflag") == values_of_interest[1][2])

)

| (

(col("l_partkey") == values_of_interest[2][0])

& (col("l_suppkey") == values_of_interest[2][1])

& (col("l_returnflag") == values_of_interest[2][2])

)

| (

(col("l_partkey") == values_of_interest[3][0])

& (col("l_suppkey") == values_of_interest[3][1])

& (col("l_returnflag") == values_of_interest[3][2])

)

)

df_explicit_filter = df_lineitem.filter(filter_expr)

num_rows = df_explicit_filter.count()

print(

f"Explicit filtering has number {num_rows} rows and took {time.time() - start_time} s"

)

start_time = time.time()

# Instead try a python UDF

def is_of_interest_impl(

partkey_arr: pa.Array,

suppkey_arr: pa.Array,

returnflag_arr: pa.Array,

) -> pa.Array:

result = []

for idx, partkey in enumerate(partkey_arr):

partkey = partkey.as_py()

suppkey = suppkey_arr[idx].as_py()

returnflag = returnflag_arr[idx].as_py()

value = (partkey, suppkey, returnflag)

result.append(value in values_of_interest)

return pa.array(result)

is_of_interest = udf(

is_of_interest_impl,

[pa.int64(), pa.int64(), pa.utf8()],

pa.bool_(),

"stable",

)

df_udf_filter = df_lineitem.filter(

is_of_interest(col("l_partkey"), col("l_suppkey"), col("l_returnflag"))

)

num_rows = df_udf_filter.count()

print(f"UDF filtering has number {num_rows} rows and took {time.time() - start_time} s")

start_time = time.time()

# Now use a user defined function but lean on the built in pyarrow array

# functions so we never convert rows to python objects.

# To see other pyarrow compute functions see

# https://arrow.apache.org/docs/python/api/compute.html

#

# It is important that the number of rows in the returned array

# matches the original array, so we cannot use functions like

# filtered_partkey_arr.filter(filtered_suppkey_arr).

def udf_using_pyarrow_compute_impl(

partkey_arr: pa.Array,

suppkey_arr: pa.Array,

returnflag_arr: pa.Array,

) -> pa.Array:

results = None

for partkey, suppkey, returnflag in values_of_interest:

filtered_partkey_arr = pc.equal(partkey_arr, partkey)

filtered_suppkey_arr = pc.equal(suppkey_arr, suppkey)

filtered_returnflag_arr = pc.equal(returnflag_arr, returnflag)

resultant_arr = pc.and_(filtered_partkey_arr, filtered_suppkey_arr)

resultant_arr = pc.and_(resultant_arr, filtered_returnflag_arr)

if results is None:

results = resultant_arr

else:

results = pc.or_(results, resultant_arr)

return results

udf_using_pyarrow_compute = udf(

udf_using_pyarrow_compute_impl,

[pa.int64(), pa.int64(), pa.utf8()],

pa.bool_(),

"stable",

)

df_udf_pyarrow_compute = df_lineitem.filter(

udf_using_pyarrow_compute(col("l_partkey"), col("l_suppkey"), col("l_returnflag"))

)

num_rows = df_udf_pyarrow_compute.count()

print(

f"UDF filtering using pyarrow compute has number {num_rows} rows and took {time.time() - start_time} s"

)

start_time = time.time()