import quincy 17.2.0

[ceph.git] / ceph / src / arrow / python / pyarrow / tests / parquet / test_data_types.py

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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import decimal
import io

import numpy as np
import pytest

import pyarrow as pa
from pyarrow.tests import util
from pyarrow.tests.parquet.common import (_check_roundtrip,
                                          parametrize_legacy_dataset)

try:
    import pyarrow.parquet as pq
    from pyarrow.tests.parquet.common import _read_table, _write_table
except ImportError:
    pq = None


try:
    import pandas as pd
    import pandas.testing as tm

    from pyarrow.tests.pandas_examples import (dataframe_with_arrays,
                                               dataframe_with_lists)
    from pyarrow.tests.parquet.common import alltypes_sample
except ImportError:
    pd = tm = None


pytestmark = pytest.mark.parquet


# General roundtrip of data types
# -----------------------------------------------------------------------------


@pytest.mark.pandas
@parametrize_legacy_dataset
@pytest.mark.parametrize('chunk_size', [None, 1000])
def test_parquet_2_0_roundtrip(tempdir, chunk_size, use_legacy_dataset):
    df = alltypes_sample(size=10000, categorical=True)

    filename = tempdir / 'pandas_roundtrip.parquet'
    arrow_table = pa.Table.from_pandas(df)
    assert arrow_table.schema.pandas_metadata is not None

    _write_table(arrow_table, filename, version='2.6',
                 coerce_timestamps='ms', chunk_size=chunk_size)
    table_read = pq.read_pandas(
        filename, use_legacy_dataset=use_legacy_dataset)
    assert table_read.schema.pandas_metadata is not None

    read_metadata = table_read.schema.metadata
    assert arrow_table.schema.metadata == read_metadata

    df_read = table_read.to_pandas()
    tm.assert_frame_equal(df, df_read)


@pytest.mark.pandas
@parametrize_legacy_dataset
def test_parquet_1_0_roundtrip(tempdir, use_legacy_dataset):
    size = 10000
    np.random.seed(0)
    df = pd.DataFrame({
        'uint8': np.arange(size, dtype=np.uint8),
        'uint16': np.arange(size, dtype=np.uint16),
        'uint32': np.arange(size, dtype=np.uint32),
        'uint64': np.arange(size, dtype=np.uint64),
        'int8': np.arange(size, dtype=np.int16),
        'int16': np.arange(size, dtype=np.int16),
        'int32': np.arange(size, dtype=np.int32),
        'int64': np.arange(size, dtype=np.int64),
        'float32': np.arange(size, dtype=np.float32),
        'float64': np.arange(size, dtype=np.float64),
        'bool': np.random.randn(size) > 0,
        'str': [str(x) for x in range(size)],
        'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
        'empty_str': [''] * size
    })
    filename = tempdir / 'pandas_roundtrip.parquet'
    arrow_table = pa.Table.from_pandas(df)
    _write_table(arrow_table, filename, version='1.0')
    table_read = _read_table(filename, use_legacy_dataset=use_legacy_dataset)
    df_read = table_read.to_pandas()

    # We pass uint32_t as int64_t if we write Parquet version 1.0
    df['uint32'] = df['uint32'].values.astype(np.int64)

    tm.assert_frame_equal(df, df_read)


# Dictionary
# -----------------------------------------------------------------------------


def _simple_table_write_read(table, use_legacy_dataset):
    bio = pa.BufferOutputStream()
    pq.write_table(table, bio)
    contents = bio.getvalue()
    return pq.read_table(
        pa.BufferReader(contents), use_legacy_dataset=use_legacy_dataset
    )


@pytest.mark.pandas
@parametrize_legacy_dataset
def test_direct_read_dictionary(use_legacy_dataset):
    # ARROW-3325
    repeats = 10
    nunique = 5

    data = [
        [util.rands(10) for i in range(nunique)] * repeats,

    ]
    table = pa.table(data, names=['f0'])

    bio = pa.BufferOutputStream()
    pq.write_table(table, bio)
    contents = bio.getvalue()

    result = pq.read_table(pa.BufferReader(contents),
                           read_dictionary=['f0'],
                           use_legacy_dataset=use_legacy_dataset)

    # Compute dictionary-encoded subfield
    expected = pa.table([table[0].dictionary_encode()], names=['f0'])
    assert result.equals(expected)


@pytest.mark.pandas
@parametrize_legacy_dataset
def test_direct_read_dictionary_subfield(use_legacy_dataset):
    repeats = 10
    nunique = 5

    data = [
        [[util.rands(10)] for i in range(nunique)] * repeats,
    ]
    table = pa.table(data, names=['f0'])

    bio = pa.BufferOutputStream()
    pq.write_table(table, bio)
    contents = bio.getvalue()
    result = pq.read_table(pa.BufferReader(contents),
                           read_dictionary=['f0.list.item'],
                           use_legacy_dataset=use_legacy_dataset)

    arr = pa.array(data[0])
    values_as_dict = arr.values.dictionary_encode()

    inner_indices = values_as_dict.indices.cast('int32')
    new_values = pa.DictionaryArray.from_arrays(inner_indices,
                                                values_as_dict.dictionary)

    offsets = pa.array(range(51), type='int32')
    expected_arr = pa.ListArray.from_arrays(offsets, new_values)
    expected = pa.table([expected_arr], names=['f0'])

    assert result.equals(expected)
    assert result[0].num_chunks == 1


@parametrize_legacy_dataset
def test_dictionary_array_automatically_read(use_legacy_dataset):
    # ARROW-3246

    # Make a large dictionary, a little over 4MB of data
    dict_length = 4000
    dict_values = pa.array([('x' * 1000 + '_{}'.format(i))
                            for i in range(dict_length)])

    num_chunks = 10
    chunk_size = 100
    chunks = []
    for i in range(num_chunks):
        indices = np.random.randint(0, dict_length,
                                    size=chunk_size).astype(np.int32)
        chunks.append(pa.DictionaryArray.from_arrays(pa.array(indices),
                                                     dict_values))

    table = pa.table([pa.chunked_array(chunks)], names=['f0'])
    result = _simple_table_write_read(table, use_legacy_dataset)

    assert result.equals(table)

    # The only key in the metadata was the Arrow schema key
    assert result.schema.metadata is None


# Decimal
# -----------------------------------------------------------------------------


@pytest.mark.pandas
@parametrize_legacy_dataset
def test_decimal_roundtrip(tempdir, use_legacy_dataset):
    num_values = 10

    columns = {}
    for precision in range(1, 39):
        for scale in range(0, precision + 1):
            with util.random_seed(0):
                random_decimal_values = [
                    util.randdecimal(precision, scale)
                    for _ in range(num_values)
                ]
            column_name = ('dec_precision_{:d}_scale_{:d}'
                           .format(precision, scale))
            columns[column_name] = random_decimal_values

    expected = pd.DataFrame(columns)
    filename = tempdir / 'decimals.parquet'
    string_filename = str(filename)
    table = pa.Table.from_pandas(expected)
    _write_table(table, string_filename)
    result_table = _read_table(
        string_filename, use_legacy_dataset=use_legacy_dataset)
    result = result_table.to_pandas()
    tm.assert_frame_equal(result, expected)


@pytest.mark.pandas
@pytest.mark.xfail(
    raises=OSError, reason='Parquet does not support negative scale'
)
def test_decimal_roundtrip_negative_scale(tempdir):
    expected = pd.DataFrame({'decimal_num': [decimal.Decimal('1.23E4')]})
    filename = tempdir / 'decimals.parquet'
    string_filename = str(filename)
    t = pa.Table.from_pandas(expected)
    _write_table(t, string_filename)
    result_table = _read_table(string_filename)
    result = result_table.to_pandas()
    tm.assert_frame_equal(result, expected)


# List types
# -----------------------------------------------------------------------------


@parametrize_legacy_dataset
@pytest.mark.parametrize('dtype', [int, float])
def test_single_pylist_column_roundtrip(tempdir, dtype, use_legacy_dataset):
    filename = tempdir / 'single_{}_column.parquet'.format(dtype.__name__)
    data = [pa.array(list(map(dtype, range(5))))]
    table = pa.Table.from_arrays(data, names=['a'])
    _write_table(table, filename)
    table_read = _read_table(filename, use_legacy_dataset=use_legacy_dataset)
    for i in range(table.num_columns):
        col_written = table[i]
        col_read = table_read[i]
        assert table.field(i).name == table_read.field(i).name
        assert col_read.num_chunks == 1
        data_written = col_written.chunk(0)
        data_read = col_read.chunk(0)
        assert data_written.equals(data_read)


@parametrize_legacy_dataset
def test_empty_lists_table_roundtrip(use_legacy_dataset):
    # ARROW-2744: Shouldn't crash when writing an array of empty lists
    arr = pa.array([[], []], type=pa.list_(pa.int32()))
    table = pa.Table.from_arrays([arr], ["A"])
    _check_roundtrip(table, use_legacy_dataset=use_legacy_dataset)


@parametrize_legacy_dataset
def test_nested_list_nonnullable_roundtrip_bug(use_legacy_dataset):
    # Reproduce failure in ARROW-5630
    typ = pa.list_(pa.field("item", pa.float32(), False))
    num_rows = 10000
    t = pa.table([
        pa.array(([[0] * ((i + 5) % 10) for i in range(0, 10)] *
                  (num_rows // 10)), type=typ)
    ], ['a'])
    _check_roundtrip(
        t, data_page_size=4096, use_legacy_dataset=use_legacy_dataset)


@parametrize_legacy_dataset
def test_nested_list_struct_multiple_batches_roundtrip(
    tempdir, use_legacy_dataset
):
    # Reproduce failure in ARROW-11024
    data = [[{'x': 'abc', 'y': 'abc'}]]*100 + [[{'x': 'abc', 'y': 'gcb'}]]*100
    table = pa.table([pa.array(data)], names=['column'])
    _check_roundtrip(
        table, row_group_size=20, use_legacy_dataset=use_legacy_dataset)

    # Reproduce failure in ARROW-11069 (plain non-nested structs with strings)
    data = pa.array(
        [{'a': '1', 'b': '2'}, {'a': '3', 'b': '4'}, {'a': '5', 'b': '6'}]*10
    )
    table = pa.table({'column': data})
    _check_roundtrip(
        table, row_group_size=10, use_legacy_dataset=use_legacy_dataset)


def test_writing_empty_lists():
    # ARROW-2591: [Python] Segmentation fault issue in pq.write_table
    arr1 = pa.array([[], []], pa.list_(pa.int32()))
    table = pa.Table.from_arrays([arr1], ['list(int32)'])
    _check_roundtrip(table)


@pytest.mark.pandas
def test_column_of_arrays(tempdir):
    df, schema = dataframe_with_arrays()

    filename = tempdir / 'pandas_roundtrip.parquet'
    arrow_table = pa.Table.from_pandas(df, schema=schema)
    _write_table(arrow_table, filename, version='2.6', coerce_timestamps='ms')
    table_read = _read_table(filename)
    df_read = table_read.to_pandas()
    tm.assert_frame_equal(df, df_read)


@pytest.mark.pandas
def test_column_of_lists(tempdir):
    df, schema = dataframe_with_lists(parquet_compatible=True)

    filename = tempdir / 'pandas_roundtrip.parquet'
    arrow_table = pa.Table.from_pandas(df, schema=schema)
    _write_table(arrow_table, filename, version='2.6')
    table_read = _read_table(filename)
    df_read = table_read.to_pandas()

    tm.assert_frame_equal(df, df_read)


def test_large_list_records():
    # This was fixed in PARQUET-1100

    list_lengths = np.random.randint(0, 500, size=50)
    list_lengths[::10] = 0

    list_values = [list(map(int, np.random.randint(0, 100, size=x)))
                   if i % 8 else None
                   for i, x in enumerate(list_lengths)]

    a1 = pa.array(list_values)

    table = pa.Table.from_arrays([a1], ['int_lists'])
    _check_roundtrip(table)


@pytest.mark.pandas
@parametrize_legacy_dataset
def test_parquet_nested_convenience(tempdir, use_legacy_dataset):
    # ARROW-1684
    df = pd.DataFrame({
        'a': [[1, 2, 3], None, [4, 5], []],
        'b': [[1.], None, None, [6., 7.]],
    })

    path = str(tempdir / 'nested_convenience.parquet')

    table = pa.Table.from_pandas(df, preserve_index=False)
    _write_table(table, path)

    read = pq.read_table(
        path, columns=['a'], use_legacy_dataset=use_legacy_dataset)
    tm.assert_frame_equal(read.to_pandas(), df[['a']])

    read = pq.read_table(
        path, columns=['a', 'b'], use_legacy_dataset=use_legacy_dataset)
    tm.assert_frame_equal(read.to_pandas(), df)


# Binary
# -----------------------------------------------------------------------------


def test_fixed_size_binary():
    t0 = pa.binary(10)
    data = [b'fooooooooo', None, b'barooooooo', b'quxooooooo']
    a0 = pa.array(data, type=t0)

    table = pa.Table.from_arrays([a0],
                                 ['binary[10]'])
    _check_roundtrip(table)


# Large types
# -----------------------------------------------------------------------------


@pytest.mark.slow
@pytest.mark.large_memory
def test_large_table_int32_overflow():
    size = np.iinfo('int32').max + 1

    arr = np.ones(size, dtype='uint8')

    parr = pa.array(arr, type=pa.uint8())

    table = pa.Table.from_arrays([parr], names=['one'])
    f = io.BytesIO()
    _write_table(table, f)


def _simple_table_roundtrip(table, use_legacy_dataset=False, **write_kwargs):
    stream = pa.BufferOutputStream()
    _write_table(table, stream, **write_kwargs)
    buf = stream.getvalue()
    return _read_table(buf, use_legacy_dataset=use_legacy_dataset)


@pytest.mark.slow
@pytest.mark.large_memory
@parametrize_legacy_dataset
def test_byte_array_exactly_2gb(use_legacy_dataset):
    # Test edge case reported in ARROW-3762
    val = b'x' * (1 << 10)

    base = pa.array([val] * ((1 << 21) - 1))
    cases = [
        [b'x' * 1023],  # 2^31 - 1
        [b'x' * 1024],  # 2^31
        [b'x' * 1025]   # 2^31 + 1
    ]
    for case in cases:
        values = pa.chunked_array([base, pa.array(case)])
        t = pa.table([values], names=['f0'])
        result = _simple_table_roundtrip(
            t, use_legacy_dataset=use_legacy_dataset, use_dictionary=False)
        assert t.equals(result)


@pytest.mark.slow
@pytest.mark.pandas
@pytest.mark.large_memory
@parametrize_legacy_dataset
def test_binary_array_overflow_to_chunked(use_legacy_dataset):
    # ARROW-3762

    # 2^31 + 1 bytes
    values = [b'x'] + [
        b'x' * (1 << 20)
    ] * 2 * (1 << 10)
    df = pd.DataFrame({'byte_col': values})

    tbl = pa.Table.from_pandas(df, preserve_index=False)
    read_tbl = _simple_table_roundtrip(
        tbl, use_legacy_dataset=use_legacy_dataset)

    col0_data = read_tbl[0]
    assert isinstance(col0_data, pa.ChunkedArray)

    # Split up into 2GB chunks
    assert col0_data.num_chunks == 2

    assert tbl.equals(read_tbl)


@pytest.mark.slow
@pytest.mark.pandas
@pytest.mark.large_memory
@parametrize_legacy_dataset
def test_list_of_binary_large_cell(use_legacy_dataset):
    # ARROW-4688
    data = []

    # TODO(wesm): handle chunked children
    # 2^31 - 1 bytes in a single cell
    # data.append([b'x' * (1 << 20)] * 2047 + [b'x' * ((1 << 20) - 1)])

    # A little under 2GB in cell each containing approximately 10MB each
    data.extend([[b'x' * 1000000] * 10] * 214)

    arr = pa.array(data)
    table = pa.Table.from_arrays([arr], ['chunky_cells'])
    read_table = _simple_table_roundtrip(
        table, use_legacy_dataset=use_legacy_dataset)
    assert table.equals(read_table)


def test_large_binary():
    data = [b'foo', b'bar'] * 50
    for type in [pa.large_binary(), pa.large_string()]:
        arr = pa.array(data, type=type)
        table = pa.Table.from_arrays([arr], names=['strs'])
        for use_dictionary in [False, True]:
            _check_roundtrip(table, use_dictionary=use_dictionary)


@pytest.mark.slow
@pytest.mark.large_memory
def test_large_binary_huge():
    s = b'xy' * 997
    data = [s] * ((1 << 33) // len(s))
    for type in [pa.large_binary(), pa.large_string()]:
        arr = pa.array(data, type=type)
        table = pa.Table.from_arrays([arr], names=['strs'])
        for use_dictionary in [False, True]:
            _check_roundtrip(table, use_dictionary=use_dictionary)
        del arr, table


@pytest.mark.large_memory
def test_large_binary_overflow():
    s = b'x' * (1 << 31)
    arr = pa.array([s], type=pa.large_binary())
    table = pa.Table.from_arrays([arr], names=['strs'])
    for use_dictionary in [False, True]:
        writer = pa.BufferOutputStream()
        with pytest.raises(
                pa.ArrowInvalid,
                match="Parquet cannot store strings with size 2GB or more"):
            _write_table(table, writer, use_dictionary=use_dictionary)