myenv/lib/python3.11/site-packages/benchmarks/parquet.py

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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np

import pyarrow as pa
try:
    import pyarrow.parquet as pq
except ImportError:
    pq = None
from pyarrow.tests.util import rands


class ParquetWriteBinary(object):

    def setup(self):
        nuniques = 100000
        value_size = 50
        length = 1000000
        num_cols = 10

        unique_values = np.array([rands(value_size) for
                                  i in range(nuniques)], dtype='O')
        values = unique_values[np.random.randint(0, nuniques, size=length)]
        self.table = pa.table([pa.array(values) for i in range(num_cols)],
                              names=['f{}'.format(i) for i in range(num_cols)])
        self.table_df = self.table.to_pandas()

    def time_write_binary_table(self):
        out = pa.BufferOutputStream()
        pq.write_table(self.table, out)

    def time_write_binary_table_uncompressed(self):
        out = pa.BufferOutputStream()
        pq.write_table(self.table, out, compression='none')

    def time_write_binary_table_no_dictionary(self):
        out = pa.BufferOutputStream()
        pq.write_table(self.table, out, use_dictionary=False)

    def time_convert_pandas_and_write_binary_table(self):
        out = pa.BufferOutputStream()
        pq.write_table(pa.table(self.table_df), out)


def generate_dict_strings(string_size, nunique, length, random_order=True):
    uniques = np.array([rands(string_size) for i in range(nunique)], dtype='O')
    if random_order:
        indices = np.random.randint(0, nunique, size=length).astype('i4')
    else:
        indices = np.arange(nunique).astype('i4').repeat(length // nunique)
    return pa.DictionaryArray.from_arrays(indices, uniques)


def generate_dict_table(num_cols, string_size, nunique, length,
                        random_order=True):
    data = generate_dict_strings(string_size, nunique, length,
                                 random_order=random_order)
    return pa.table([
        data for i in range(num_cols)
    ], names=['f{}'.format(i) for i in range(num_cols)])


class ParquetWriteDictionaries(object):

    param_names = ('nunique',)
    params = [(1000), (100000)]

    def setup(self, nunique):
        self.num_cols = 10
        self.value_size = 32
        self.nunique = nunique
        self.length = 10000000

        self.table = generate_dict_table(self.num_cols, self.value_size,
                                         self.nunique, self.length)
        self.table_sequential = generate_dict_table(self.num_cols,
                                                    self.value_size,
                                                    self.nunique, self.length,
                                                    random_order=False)

    def time_write_random_order(self, nunique):
        pq.write_table(self.table, pa.BufferOutputStream())

    def time_write_sequential(self, nunique):
        pq.write_table(self.table_sequential, pa.BufferOutputStream())


class ParquetManyColumns(object):

    total_cells = 10000000
    param_names = ('num_cols',)
    params = [100, 1000, 10000]

    def setup(self, num_cols):
        num_rows = self.total_cells // num_cols
        self.table = pa.table({'c' + str(i): np.random.randn(num_rows)
                               for i in range(num_cols)})

        out = pa.BufferOutputStream()
        pq.write_table(self.table, out)
        self.buf = out.getvalue()

    def time_write(self, num_cols):
        out = pa.BufferOutputStream()
        pq.write_table(self.table, out)

    def time_read(self, num_cols):
        pq.read_table(self.buf)