venv/lib/python3.10/site-packages/pyarrow/tests/parquet/test_dataset.py

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# to you under the Apache License, Version 2.0 (the
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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 datetime
import inspect
import os
import pathlib

import numpy as np
import pytest
import unittest.mock as mock

import pyarrow as pa
import pyarrow.compute as pc
from pyarrow.fs import (FileSelector, FileSystem, LocalFileSystem,
                        PyFileSystem, SubTreeFileSystem, FSSpecHandler)
from pyarrow.tests import util
from pyarrow.util import guid

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


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

except ImportError:
    pd = tm = None


# Marks all of the tests in this module
# Ignore these with pytest ... -m 'not parquet'
pytestmark = [pytest.mark.parquet, pytest.mark.dataset]


def test_filesystem_uri(tempdir):
    table = pa.table({"a": [1, 2, 3]})

    directory = tempdir / "data_dir"
    directory.mkdir()
    path = directory / "data.parquet"
    pq.write_table(table, str(path))

    # filesystem object
    result = pq.read_table(
        path, filesystem=LocalFileSystem())
    assert result.equals(table)

    # filesystem URI
    result = pq.read_table(
        "data_dir/data.parquet", filesystem=util._filesystem_uri(tempdir))
    assert result.equals(table)


@pytest.mark.pandas
def test_read_partitioned_directory(tempdir):
    local = LocalFileSystem()
    _partition_test_for_filesystem(local, tempdir)


@pytest.mark.pandas
def test_read_partitioned_columns_selection(tempdir):
    # ARROW-3861 - do not include partition columns in resulting table when
    # `columns` keyword was passed without those columns
    local = LocalFileSystem()
    base_path = tempdir
    _partition_test_for_filesystem(local, base_path)

    dataset = pq.ParquetDataset(base_path)
    result = dataset.read(columns=["values"])
    assert result.column_names == ["values"]


@pytest.mark.pandas
def test_filters_equivalency(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1]
    string_keys = ['a', 'b', 'c']
    boolean_keys = [True, False]
    partition_spec = [
        ['integer', integer_keys],
        ['string', string_keys],
        ['boolean', boolean_keys]
    ]

    df = pd.DataFrame({
        'integer': np.array(integer_keys, dtype='i4').repeat(15),
        'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2),
        'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3),
        'values': np.arange(30),
    })

    _generate_partition_directories(local, base_path, partition_spec, df)

    # Old filters syntax:
    #  integer == 1 AND string != b AND boolean == True
    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[('integer', '=', 1), ('string', '!=', 'b'),
                 ('boolean', '==', 'True')],
    )
    table = dataset.read()
    result_df = (table.to_pandas().reset_index(drop=True))

    assert 0 not in result_df['integer'].values
    assert 'b' not in result_df['string'].values
    assert False not in result_df['boolean'].values

    # filters in disjunctive normal form:
    #  (integer == 1 AND string != b AND boolean == True) OR
    #  (integer == 2 AND boolean == False)
    # TODO(ARROW-3388): boolean columns are reconstructed as string
    filters = [
        [
            ('integer', '=', 1),
            ('string', '!=', 'b'),
            ('boolean', '==', 'True')
        ],
        [('integer', '=', 0), ('boolean', '==', 'False')]
    ]
    dataset = pq.ParquetDataset(
        base_path, filesystem=local, filters=filters)
    table = dataset.read()
    result_df = table.to_pandas().reset_index(drop=True)

    # Check that all rows in the DF fulfill the filter
    df_filter_1 = (result_df['integer'] == 1) \
        & (result_df['string'] != 'b') \
        & (result_df['boolean'] == 'True')
    df_filter_2 = (np.array(result_df['integer']) == 0) \
        & (result_df['boolean'] == 'False')
    assert df_filter_1.sum() > 0
    assert df_filter_2.sum() > 0
    assert result_df.shape[0] == (df_filter_1.sum() + df_filter_2.sum())

    for filters in [[[('string', '==', b'1\0a')]],
                    [[('string', '==', '1\0a')]]]:
        dataset = pq.ParquetDataset(
            base_path, filesystem=local, filters=filters)
        assert dataset.read().num_rows == 0


@pytest.mark.pandas
def test_filters_cutoff_exclusive_integer(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1, 2, 3, 4]
    partition_spec = [
        ['integers', integer_keys],
    ]
    N = 5

    df = pd.DataFrame({
        'index': np.arange(N),
        'integers': np.array(integer_keys, dtype='i4'),
    }, columns=['index', 'integers'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[
            ('integers', '<', 4),
            ('integers', '>', 1),
        ],
    )
    table = dataset.read()
    result_df = (table.to_pandas()
                      .sort_values(by='index')
                      .reset_index(drop=True))

    result_list = [x for x in map(int, result_df['integers'].values)]
    assert result_list == [2, 3]


@pytest.mark.xfail(
    # different error with use_legacy_datasets because result_df is no longer
    # categorical
    raises=(TypeError, AssertionError),
    reason='Loss of type information in creation of categoricals.'
)
@pytest.mark.pandas
def test_filters_cutoff_exclusive_datetime(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    date_keys = [
        datetime.date(2018, 4, 9),
        datetime.date(2018, 4, 10),
        datetime.date(2018, 4, 11),
        datetime.date(2018, 4, 12),
        datetime.date(2018, 4, 13)
    ]
    partition_spec = [
        ['dates', date_keys]
    ]
    N = 5

    df = pd.DataFrame({
        'index': np.arange(N),
        'dates': np.array(date_keys, dtype='datetime64'),
    }, columns=['index', 'dates'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[
            ('dates', '<', "2018-04-12"),
            ('dates', '>', "2018-04-10")
        ],
    )
    table = dataset.read()
    result_df = (table.to_pandas()
                      .sort_values(by='index')
                      .reset_index(drop=True))

    expected = pd.Categorical(
        np.array([datetime.date(2018, 4, 11)], dtype='datetime64'),
        categories=np.array(date_keys, dtype='datetime64'))

    assert result_df['dates'].values == expected


@pytest.mark.pandas
def test_filters_inclusive_datetime(tempdir):
    # ARROW-11480
    path = tempdir / 'timestamps.parquet'

    pd.DataFrame({
        "dates": pd.date_range("2020-01-01", periods=10, freq="D"),
        "id": range(10)
    }).to_parquet(path, use_deprecated_int96_timestamps=True)

    table = pq.read_table(path, filters=[
        ("dates", "<=", datetime.datetime(2020, 1, 5))
    ])

    assert table.column('id').to_pylist() == [0, 1, 2, 3, 4]


@pytest.mark.pandas
def test_filters_inclusive_integer(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1, 2, 3, 4]
    partition_spec = [
        ['integers', integer_keys],
    ]
    N = 5

    df = pd.DataFrame({
        'index': np.arange(N),
        'integers': np.array(integer_keys, dtype='i4'),
    }, columns=['index', 'integers'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[
            ('integers', '<=', 3),
            ('integers', '>=', 2),
        ],
    )
    table = dataset.read()
    result_df = (table.to_pandas()
                 .sort_values(by='index')
                 .reset_index(drop=True))

    result_list = [int(x) for x in map(int, result_df['integers'].values)]
    assert result_list == [2, 3]


@pytest.mark.pandas
def test_filters_inclusive_set(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1]
    string_keys = ['a', 'b', 'c']
    boolean_keys = [True, False]
    partition_spec = [
        ['integer', integer_keys],
        ['string', string_keys],
        ['boolean', boolean_keys]
    ]

    df = pd.DataFrame({
        'integer': np.array(integer_keys, dtype='i4').repeat(15),
        'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2),
        'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3),
        'values': np.arange(30),
    })

    _generate_partition_directories(local, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[('string', 'in', 'ab')],
    )
    table = dataset.read()
    result_df = (table.to_pandas().reset_index(drop=True))

    assert 'a' in result_df['string'].values
    assert 'b' in result_df['string'].values
    assert 'c' not in result_df['string'].values

    dataset = pq.ParquetDataset(
        base_path, filesystem=local,
        filters=[('integer', 'in', [1]), ('string', 'in', ('a', 'b')),
                 ('boolean', 'not in', {'False'})],
    )
    table = dataset.read()
    result_df = (table.to_pandas().reset_index(drop=True))

    assert 0 not in result_df['integer'].values
    assert 'c' not in result_df['string'].values
    assert False not in result_df['boolean'].values


@pytest.mark.pandas
def test_filters_invalid_pred_op(tempdir):
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1, 2, 3, 4]
    partition_spec = [
        ['integers', integer_keys],
    ]
    N = 5

    df = pd.DataFrame({
        'index': np.arange(N),
        'integers': np.array(integer_keys, dtype='i4'),
    }, columns=['index', 'integers'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    with pytest.raises(TypeError):
        pq.ParquetDataset(base_path,
                          filesystem=local,
                          filters=[('integers', 'in', 3), ])

    with pytest.raises(ValueError):
        pq.ParquetDataset(base_path,
                          filesystem=local,
                          filters=[('integers', '=<', 3), ])

    # Dataset API returns empty table
    dataset = pq.ParquetDataset(base_path,
                                filesystem=local,
                                filters=[('integers', 'in', set()), ])
    assert dataset.read().num_rows == 0

    dataset = pq.ParquetDataset(base_path,
                                filesystem=local,
                                filters=[('integers', '!=', {3})])
    with pytest.raises(NotImplementedError):
        assert dataset.read().num_rows == 0


@pytest.mark.pandas
def test_filters_invalid_column(tempdir):
    # ARROW-5572 - raise error on invalid name in filter specification
    # works with new dataset
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1, 2, 3, 4]
    partition_spec = [['integers', integer_keys]]
    N = 5

    df = pd.DataFrame({
        'index': np.arange(N),
        'integers': np.array(integer_keys, dtype='i4'),
    }, columns=['index', 'integers'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    msg = r"No match for FieldRef.Name\(non_existent_column\)"
    with pytest.raises(ValueError, match=msg):
        pq.ParquetDataset(base_path, filesystem=local,
                          filters=[('non_existent_column', '<', 3), ]).read()


@pytest.mark.pandas
@pytest.mark.parametrize("filters",
                         ([('integers', '<', 3)],
                          [[('integers', '<', 3)]],
                          pc.field('integers') < 3,
                          pc.field('nested', 'a') < 3,
                          pc.field('nested', 'b').cast(pa.int64()) < 3))
@pytest.mark.parametrize("read_method", ("read_table", "read_pandas"))
def test_filters_read_table(tempdir, filters, read_method):
    read = getattr(pq, read_method)
    # test that filters keyword is passed through in read_table
    local = LocalFileSystem()
    base_path = tempdir

    integer_keys = [0, 1, 2, 3, 4]
    partition_spec = [
        ['integers', integer_keys],
    ]
    N = len(integer_keys)

    df = pd.DataFrame({
        'index': np.arange(N),
        'integers': np.array(integer_keys, dtype='i4'),
        'nested': np.array([{'a': i, 'b': str(i)} for i in range(N)])
    })

    _generate_partition_directories(local, base_path, partition_spec, df)

    kwargs = dict(filesystem=local, filters=filters)

    table = read(base_path, **kwargs)
    assert table.num_rows == 3


@pytest.mark.pandas
def test_partition_keys_with_underscores(tempdir):
    # ARROW-5666 - partition field values with underscores preserve underscores
    local = LocalFileSystem()
    base_path = tempdir

    string_keys = ["2019_2", "2019_3"]
    partition_spec = [
        ['year_week', string_keys],
    ]
    N = 2

    df = pd.DataFrame({
        'index': np.arange(N),
        'year_week': np.array(string_keys, dtype='object'),
    }, columns=['index', 'year_week'])

    _generate_partition_directories(local, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(base_path)
    result = dataset.read()
    assert result.column("year_week").to_pylist() == string_keys


@pytest.mark.s3
def test_read_s3fs(s3_example_s3fs, ):
    fs, path = s3_example_s3fs
    path = path + "/test.parquet"
    table = pa.table({"a": [1, 2, 3]})
    _write_table(table, path, filesystem=fs)

    result = _read_table(path, filesystem=fs)
    assert result.equals(table)


@pytest.mark.s3
def test_read_directory_s3fs(s3_example_s3fs):
    fs, directory = s3_example_s3fs
    path = directory + "/test.parquet"
    table = pa.table({"a": [1, 2, 3]})
    _write_table(table, path, filesystem=fs)

    result = _read_table(directory, filesystem=fs)
    assert result.equals(table)


@pytest.mark.pandas
def test_read_single_file_list(tempdir):
    data_path = str(tempdir / 'data.parquet')

    table = pa.table({"a": [1, 2, 3]})
    _write_table(table, data_path)

    result = pq.ParquetDataset([data_path]).read()
    assert result.equals(table)


@pytest.mark.pandas
@pytest.mark.s3
def test_read_partitioned_directory_s3fs(s3_example_s3fs):
    fs, path = s3_example_s3fs
    _partition_test_for_filesystem(fs, path)


def _partition_test_for_filesystem(fs, base_path):
    foo_keys = [0, 1]
    bar_keys = ['a', 'b', 'c']
    partition_spec = [
        ['foo', foo_keys],
        ['bar', bar_keys]
    ]
    N = 30

    df = pd.DataFrame({
        'index': np.arange(N),
        'foo': np.array(foo_keys, dtype='i4').repeat(15),
        'bar': np.tile(np.tile(np.array(bar_keys, dtype=object), 5), 2),
        'values': np.random.randn(N)
    }, columns=['index', 'foo', 'bar', 'values'])

    _generate_partition_directories(fs, base_path, partition_spec, df)

    dataset = pq.ParquetDataset(base_path, filesystem=fs)
    table = dataset.read()
    result_df = (table.to_pandas()
                 .sort_values(by='index')
                 .reset_index(drop=True))

    expected_df = (df.sort_values(by='index')
                   .reset_index(drop=True)
                   .reindex(columns=result_df.columns))

    # With pandas 2.0.0 Index can store all numeric dtypes (not just
    # int64/uint64/float64). Using astype() to create a categorical
    # column preserves original dtype (int32)
    expected_df['foo'] = expected_df['foo'].astype("category")
    expected_df['bar'] = expected_df['bar'].astype("category")

    assert (result_df.columns == ['index', 'values', 'foo', 'bar']).all()

    tm.assert_frame_equal(result_df, expected_df)


def _generate_partition_directories(fs, base_dir, partition_spec, df):
    # partition_spec : list of lists, e.g. [['foo', [0, 1, 2],
    #                                       ['bar', ['a', 'b', 'c']]
    # part_table : a pyarrow.Table to write to each partition
    if not isinstance(fs, FileSystem):
        fs = PyFileSystem(FSSpecHandler(fs))

    DEPTH = len(partition_spec)

    pathsep = getattr(fs, "pathsep", getattr(fs, "sep", "/"))

    def _visit_level(base_dir, level, part_keys):
        name, values = partition_spec[level]
        for value in values:
            this_part_keys = part_keys + [(name, value)]

            level_dir = pathsep.join([
                str(base_dir),
                '{}={}'.format(name, value)
            ])
            fs.create_dir(level_dir)

            if level == DEPTH - 1:
                # Generate example data
                from pyarrow.fs import FileType

                file_path = pathsep.join([level_dir, guid()])
                filtered_df = _filter_partition(df, this_part_keys)
                part_table = pa.Table.from_pandas(filtered_df)
                with fs.open_output_stream(file_path) as f:
                    _write_table(part_table, f)
                assert fs.get_file_info(file_path).type != FileType.NotFound
                assert fs.get_file_info(file_path).type == FileType.File

                file_success = pathsep.join([level_dir, '_SUCCESS'])
                with fs.open_output_stream(file_success) as f:
                    pass
            else:
                _visit_level(level_dir, level + 1, this_part_keys)
                file_success = pathsep.join([level_dir, '_SUCCESS'])
                with fs.open_output_stream(file_success) as f:
                    pass

    _visit_level(base_dir, 0, [])


def _filter_partition(df, part_keys):
    predicate = np.ones(len(df), dtype=bool)

    to_drop = []
    for name, value in part_keys:
        to_drop.append(name)

        # to avoid pandas warning
        if isinstance(value, (datetime.date, datetime.datetime)):
            value = pd.Timestamp(value)

        predicate &= df[name] == value

    return df[predicate].drop(to_drop, axis=1)


@pytest.mark.pandas
def test_filter_before_validate_schema(tempdir):
    # ARROW-4076 apply filter before schema validation
    # to avoid checking unneeded schemas

    # create partitioned dataset with mismatching schemas which would
    # otherwise raise if first validation all schemas
    dir1 = tempdir / 'A=0'
    dir1.mkdir()
    table1 = pa.Table.from_pandas(pd.DataFrame({'B': [1, 2, 3]}))
    pq.write_table(table1, dir1 / 'data.parquet')

    dir2 = tempdir / 'A=1'
    dir2.mkdir()
    table2 = pa.Table.from_pandas(pd.DataFrame({'B': ['a', 'b', 'c']}))
    pq.write_table(table2, dir2 / 'data.parquet')

    # read single file using filter
    table = pq.read_table(tempdir, filters=[[('A', '==', 0)]])
    assert table.column('B').equals(pa.chunked_array([[1, 2, 3]]))


@pytest.mark.pandas
def test_read_multiple_files(tempdir):
    nfiles = 10
    size = 5

    dirpath = tempdir / guid()
    dirpath.mkdir()

    test_data = []
    paths = []
    for i in range(nfiles):
        df = _test_dataframe(size, seed=i)

        # Hack so that we don't have a dtype cast in v1 files
        df['uint32'] = df['uint32'].astype(np.int64)

        path = dirpath / '{}.parquet'.format(i)

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

        test_data.append(table)
        paths.append(path)

    # Write a _SUCCESS.crc file
    (dirpath / '_SUCCESS.crc').touch()

    def read_multiple_files(paths, columns=None, use_threads=True, **kwargs):
        dataset = pq.ParquetDataset(paths, **kwargs)
        return dataset.read(columns=columns, use_threads=use_threads)

    result = read_multiple_files(paths)
    expected = pa.concat_tables(test_data)

    assert result.equals(expected)

    # Read column subset
    to_read = [0, 2, 6, result.num_columns - 1]

    col_names = [result.field(i).name for i in to_read]
    out = pq.read_table(dirpath, columns=col_names)
    expected = pa.Table.from_arrays([result.column(i) for i in to_read],
                                    names=col_names,
                                    metadata=result.schema.metadata)
    assert out.equals(expected)

    # Read with multiple threads
    pq.read_table(dirpath, use_threads=True)

    # Test failure modes with non-uniform metadata
    bad_apple = _test_dataframe(size, seed=i).iloc[:, :4]
    bad_apple_path = tempdir / '{}.parquet'.format(guid())

    t = pa.Table.from_pandas(bad_apple)
    _write_table(t, bad_apple_path)

    # TODO(dataset) Dataset API skips bad files

    # bad_meta = pq.read_metadata(bad_apple_path)

    # with pytest.raises(ValueError):
    #     read_multiple_files(paths + [bad_apple_path])

    # with pytest.raises(ValueError):
    #     read_multiple_files(paths, metadata=bad_meta)

    # mixed_paths = [bad_apple_path, paths[0]]

    # with pytest.raises(ValueError):
    #     read_multiple_files(mixed_paths)


@pytest.mark.pandas
def test_dataset_read_pandas(tempdir):
    nfiles = 5
    size = 5

    dirpath = tempdir / guid()
    dirpath.mkdir()

    test_data = []
    frames = []
    paths = []
    for i in range(nfiles):
        df = _test_dataframe(size, seed=i)
        df.index = np.arange(i * size, (i + 1) * size)
        df.index.name = 'index'

        path = dirpath / '{}.parquet'.format(i)

        table = pa.Table.from_pandas(df)
        _write_table(table, path)
        test_data.append(table)
        frames.append(df)
        paths.append(path)

    dataset = pq.ParquetDataset(dirpath)
    columns = ['uint8', 'strings']
    result = dataset.read_pandas(columns=columns).to_pandas()
    expected = pd.concat([x[columns] for x in frames])

    tm.assert_frame_equal(result, expected)

    # also be able to pass the columns as a set (ARROW-12314)
    result = dataset.read_pandas(columns=set(columns)).to_pandas()
    assert result.shape == expected.shape
    # column order can be different because of using a set
    tm.assert_frame_equal(result.reindex(columns=expected.columns), expected)


@pytest.mark.pandas
def test_dataset_memory_map(tempdir):
    # ARROW-2627: Check that we can use ParquetDataset with memory-mapping
    dirpath = tempdir / guid()
    dirpath.mkdir()

    df = _test_dataframe(10, seed=0)
    path = dirpath / '{}.parquet'.format(0)
    table = pa.Table.from_pandas(df)
    _write_table(table, path, version='2.6')

    dataset = pq.ParquetDataset(
        dirpath, memory_map=True)
    assert dataset.read().equals(table)


@pytest.mark.pandas
def test_dataset_enable_buffered_stream(tempdir):
    dirpath = tempdir / guid()
    dirpath.mkdir()

    df = _test_dataframe(10, seed=0)
    path = dirpath / '{}.parquet'.format(0)
    table = pa.Table.from_pandas(df)
    _write_table(table, path, version='2.6')

    with pytest.raises(ValueError):
        pq.ParquetDataset(
            dirpath, buffer_size=-64)

    for buffer_size in [128, 1024]:
        dataset = pq.ParquetDataset(
            dirpath, buffer_size=buffer_size)
        assert dataset.read().equals(table)


@pytest.mark.pandas
def test_dataset_enable_pre_buffer(tempdir):
    dirpath = tempdir / guid()
    dirpath.mkdir()

    df = _test_dataframe(10, seed=0)
    path = dirpath / '{}.parquet'.format(0)
    table = pa.Table.from_pandas(df)
    _write_table(table, path, version='2.6')

    for pre_buffer in (True, False):
        dataset = pq.ParquetDataset(
            dirpath, pre_buffer=pre_buffer)
        assert dataset.read().equals(table)
        actual = pq.read_table(dirpath, pre_buffer=pre_buffer)
        assert actual.equals(table)


def _make_example_multifile_dataset(base_path, nfiles=10, file_nrows=5):
    test_data = []
    paths = []
    for i in range(nfiles):
        df = _test_dataframe(file_nrows, seed=i)
        path = base_path / '{}.parquet'.format(i)

        test_data.append(_write_table(df, path))
        paths.append(path)
    return paths


def _assert_dataset_paths(dataset, paths):
    paths = [str(path.as_posix()) for path in paths]
    assert set(paths) == set(dataset.files)


@pytest.mark.pandas
@pytest.mark.parametrize('dir_prefix', ['_', '.'])
def test_ignore_private_directories(tempdir, dir_prefix):
    dirpath = tempdir / guid()
    dirpath.mkdir()

    paths = _make_example_multifile_dataset(dirpath, nfiles=10,
                                            file_nrows=5)

    # private directory
    (dirpath / '{}staging'.format(dir_prefix)).mkdir()

    dataset = pq.ParquetDataset(dirpath)

    _assert_dataset_paths(dataset, paths)


@pytest.mark.pandas
def test_ignore_hidden_files_dot(tempdir):
    dirpath = tempdir / guid()
    dirpath.mkdir()

    paths = _make_example_multifile_dataset(dirpath, nfiles=10,
                                            file_nrows=5)

    with (dirpath / '.DS_Store').open('wb') as f:
        f.write(b'gibberish')

    with (dirpath / '.private').open('wb') as f:
        f.write(b'gibberish')

    dataset = pq.ParquetDataset(dirpath)

    _assert_dataset_paths(dataset, paths)


@pytest.mark.pandas
def test_ignore_hidden_files_underscore(tempdir):
    dirpath = tempdir / guid()
    dirpath.mkdir()

    paths = _make_example_multifile_dataset(dirpath, nfiles=10,
                                            file_nrows=5)

    with (dirpath / '_committed_123').open('wb') as f:
        f.write(b'abcd')

    with (dirpath / '_started_321').open('wb') as f:
        f.write(b'abcd')

    dataset = pq.ParquetDataset(dirpath)

    _assert_dataset_paths(dataset, paths)


@pytest.mark.pandas
@pytest.mark.parametrize('dir_prefix', ['_', '.'])
def test_ignore_no_private_directories_in_base_path(tempdir, dir_prefix):
    # ARROW-8427 - don't ignore explicitly listed files if parent directory
    # is a private directory
    dirpath = tempdir / "{0}data".format(dir_prefix) / guid()
    dirpath.mkdir(parents=True)

    paths = _make_example_multifile_dataset(dirpath, nfiles=10,
                                            file_nrows=5)

    dataset = pq.ParquetDataset(paths)
    _assert_dataset_paths(dataset, paths)

    # ARROW-9644 - don't ignore full directory with underscore in base path
    dataset = pq.ParquetDataset(dirpath)
    _assert_dataset_paths(dataset, paths)


def test_ignore_custom_prefixes(tempdir):
    # ARROW-9573 - allow override of default ignore_prefixes
    part = ["xxx"] * 3 + ["yyy"] * 3
    table = pa.table([
        pa.array(range(len(part))),
        pa.array(part).dictionary_encode(),
    ], names=['index', '_part'])

    pq.write_to_dataset(table, str(tempdir), partition_cols=['_part'])

    private_duplicate = tempdir / '_private_duplicate'
    private_duplicate.mkdir()
    pq.write_to_dataset(table, str(private_duplicate),
                        partition_cols=['_part'])

    read = pq.read_table(
        tempdir, ignore_prefixes=['_private'])

    assert read.equals(table)


def test_empty_directory(tempdir):
    # ARROW-5310
    empty_dir = tempdir / 'dataset'
    empty_dir.mkdir()

    dataset = pq.ParquetDataset(empty_dir)
    result = dataset.read()
    assert result.num_rows == 0
    assert result.num_columns == 0


def _test_write_to_dataset_with_partitions(base_path,
                                           filesystem=None,
                                           schema=None,
                                           index_name=None):
    import pandas as pd
    import pandas.testing as tm

    import pyarrow.parquet as pq

    # ARROW-1400
    output_df = pd.DataFrame({
        'group1': list('aaabbbbccc'),
        'group2': list('eefeffgeee'),
        'num': list(range(10)),
        'nan': [np.nan] * 10,
        'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]').astype(
            'datetime64[ns]')
    })
    cols = output_df.columns.tolist()
    partition_by = ['group1', 'group2']
    output_table = pa.Table.from_pandas(output_df, schema=schema, safe=False,
                                        preserve_index=False)
    pq.write_to_dataset(output_table, base_path, partition_by,
                        filesystem=filesystem)

    metadata_path = os.path.join(str(base_path), '_common_metadata')

    if filesystem is not None:
        with filesystem.open(metadata_path, 'wb') as f:
            pq.write_metadata(output_table.schema, f)
    else:
        pq.write_metadata(output_table.schema, metadata_path)

    dataset = pq.ParquetDataset(base_path,
                                filesystem=filesystem)
    # ARROW-2209: Ensure the dataset schema also includes the partition columns
    # NB schema property is an arrow and not parquet schema
    dataset_cols = set(dataset.schema.names)

    assert dataset_cols == set(output_table.schema.names)

    input_table = dataset.read()
    input_df = input_table.to_pandas()

    # Read data back in and compare with original DataFrame
    # Partitioned columns added to the end of the DataFrame when read
    input_df_cols = input_df.columns.tolist()
    assert partition_by == input_df_cols[-1 * len(partition_by):]

    input_df = input_df[cols]
    # Partitioned columns become 'categorical' dtypes
    for col in partition_by:
        output_df[col] = output_df[col].astype('category')

    if schema:
        expected_date_type = schema.field('date').type.to_pandas_dtype()
        output_df["date"] = output_df["date"].astype(expected_date_type)

    tm.assert_frame_equal(output_df, input_df)


def _test_write_to_dataset_no_partitions(base_path,
                                         filesystem=None):
    import pandas as pd

    import pyarrow.parquet as pq

    # ARROW-1400
    output_df = pd.DataFrame({
        'group1': list('aaabbbbccc'),
        'group2': list('eefeffgeee'),
        'num': list(range(10)),
        'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]').astype(
            'datetime64[ns]')
    })
    cols = output_df.columns.tolist()
    output_table = pa.Table.from_pandas(output_df)

    if filesystem is None:
        filesystem = LocalFileSystem()
    elif not isinstance(filesystem, FileSystem):
        filesystem = PyFileSystem(FSSpecHandler(filesystem))

    # Without partitions, append files to root_path
    n = 5
    for i in range(n):
        pq.write_to_dataset(output_table, base_path,
                            filesystem=filesystem)

    selector = FileSelector(str(base_path), allow_not_found=False,
                            recursive=True)

    infos = filesystem.get_file_info(selector)
    output_files = [info for info in infos if info.path.endswith(".parquet")]
    assert len(output_files) == n

    # Deduplicated incoming DataFrame should match
    # original outgoing Dataframe
    input_table = pq.ParquetDataset(
        base_path, filesystem=filesystem
    ).read()
    input_df = input_table.to_pandas()
    input_df = input_df.drop_duplicates()
    input_df = input_df[cols]
    tm.assert_frame_equal(output_df, input_df)


@pytest.mark.pandas
def test_write_to_dataset_with_partitions(tempdir):
    _test_write_to_dataset_with_partitions(str(tempdir))


@pytest.mark.pandas
def test_write_to_dataset_with_partitions_and_schema(tempdir):
    schema = pa.schema([pa.field('group1', type=pa.string()),
                        pa.field('group2', type=pa.string()),
                        pa.field('num', type=pa.int64()),
                        pa.field('nan', type=pa.int32()),
                        pa.field('date', type=pa.timestamp(unit='us'))])
    _test_write_to_dataset_with_partitions(
        str(tempdir), schema=schema)


@pytest.mark.pandas
def test_write_to_dataset_with_partitions_and_index_name(tempdir):
    _test_write_to_dataset_with_partitions(
        str(tempdir), index_name='index_name')


@pytest.mark.pandas
def test_write_to_dataset_no_partitions(tempdir):
    _test_write_to_dataset_no_partitions(str(tempdir))


@pytest.mark.pandas
def test_write_to_dataset_pathlib(tempdir):
    _test_write_to_dataset_with_partitions(tempdir / "test1")
    _test_write_to_dataset_no_partitions(tempdir / "test2")


@pytest.mark.pandas
@pytest.mark.s3
def test_write_to_dataset_pathlib_nonlocal(tempdir, s3_example_s3fs):
    # pathlib paths are only accepted for local files
    fs, _ = s3_example_s3fs

    with pytest.raises(TypeError, match="path-like objects are only allowed"):
        _test_write_to_dataset_with_partitions(
            tempdir / "test1", filesystem=fs)

    with pytest.raises(TypeError, match="path-like objects are only allowed"):
        _test_write_to_dataset_no_partitions(
            tempdir / "test2", filesystem=fs)


@pytest.mark.pandas
@pytest.mark.s3
def test_write_to_dataset_with_partitions_s3fs(s3_example_s3fs):
    fs, path = s3_example_s3fs

    _test_write_to_dataset_with_partitions(
        path, filesystem=fs)


@pytest.mark.pandas
@pytest.mark.s3
def test_write_to_dataset_no_partitions_s3fs(s3_example_s3fs):
    fs, path = s3_example_s3fs

    _test_write_to_dataset_no_partitions(
        path, filesystem=fs)


@pytest.mark.pandas
def test_write_to_dataset_filesystem(tempdir):
    df = pd.DataFrame({'A': [1, 2, 3]})
    table = pa.Table.from_pandas(df)
    path = str(tempdir)

    pq.write_to_dataset(table, path, filesystem=LocalFileSystem())
    result = pq.read_table(path)
    assert result.equals(table)


def _make_dataset_for_pickling(tempdir, N=100):
    path = tempdir / 'data.parquet'
    local = LocalFileSystem()

    df = pd.DataFrame({
        'index': np.arange(N),
        'values': np.random.randn(N)
    }, columns=['index', 'values'])
    table = pa.Table.from_pandas(df)

    num_groups = 3
    with pq.ParquetWriter(path, table.schema) as writer:
        for i in range(num_groups):
            writer.write_table(table)

    reader = pq.ParquetFile(path)
    assert reader.metadata.num_row_groups == num_groups

    metadata_path = tempdir / '_metadata'
    with local.open_output_stream(str(metadata_path)) as f:
        pq.write_metadata(table.schema, f)

    dataset = pq.ParquetDataset(
        tempdir, filesystem=local)

    return dataset


@pytest.mark.pandas
def test_pickle_dataset(tempdir, pickle_module):
    def is_pickleable(obj):
        return obj == pickle_module.loads(pickle_module.dumps(obj))

    dataset = _make_dataset_for_pickling(tempdir)
    assert is_pickleable(dataset)


@pytest.mark.pandas
def test_partitioned_dataset(tempdir):
    # ARROW-3208: Segmentation fault when reading a Parquet partitioned dataset
    # to a Parquet file
    path = tempdir / "ARROW-3208"
    df = pd.DataFrame({
        'one': [-1, 10, 2.5, 100, 1000, 1, 29.2],
        'two': [-1, 10, 2, 100, 1000, 1, 11],
        'three': [0, 0, 0, 0, 0, 0, 0]
    })
    table = pa.Table.from_pandas(df)
    pq.write_to_dataset(table, root_path=str(path),
                        partition_cols=['one', 'two'])
    table = pq.ParquetDataset(path).read()
    pq.write_table(table, path / "output.parquet")


def test_dataset_read_dictionary(tempdir):
    path = tempdir / "ARROW-3325-dataset"
    t1 = pa.table([[util.rands(10) for i in range(5)] * 10], names=['f0'])
    t2 = pa.table([[util.rands(10) for i in range(5)] * 10], names=['f0'])
    pq.write_to_dataset(t1, root_path=str(path))
    pq.write_to_dataset(t2, root_path=str(path))

    result = pq.ParquetDataset(
        path, read_dictionary=['f0']).read()

    # The order of the chunks is non-deterministic
    ex_chunks = [t1[0].chunk(0).dictionary_encode(),
                 t2[0].chunk(0).dictionary_encode()]

    assert result[0].num_chunks == 2
    c0, c1 = result[0].chunk(0), result[0].chunk(1)
    if c0.equals(ex_chunks[0]):
        assert c1.equals(ex_chunks[1])
    else:
        assert c0.equals(ex_chunks[1])
        assert c1.equals(ex_chunks[0])


def test_read_table_schema(tempdir):
    # test that schema keyword is passed through in read_table
    table = pa.table({'a': pa.array([1, 2, 3], pa.int32())})
    pq.write_table(table, tempdir / "data1.parquet")
    pq.write_table(table, tempdir / "data2.parquet")

    schema = pa.schema([('a', 'int64')])

    # reading single file (which is special cased in the code)
    result = pq.read_table(tempdir / "data1.parquet", schema=schema)
    expected = pa.table({'a': [1, 2, 3]}, schema=schema)
    assert result.equals(expected)

    # reading multiple fields
    result = pq.read_table(tempdir, schema=schema)
    expected = pa.table({'a': [1, 2, 3, 1, 2, 3]}, schema=schema)
    assert result.equals(expected)

    result = pq.ParquetDataset(tempdir, schema=schema)
    expected = pa.table({'a': [1, 2, 3, 1, 2, 3]}, schema=schema)
    assert result.read().equals(expected)


def test_read_table_duplicate_column_selection(tempdir):
    # test that duplicate column selection gives duplicate columns
    table = pa.table({'a': pa.array([1, 2, 3], pa.int32()),
                      'b': pa.array([1, 2, 3], pa.uint8())})
    pq.write_table(table, tempdir / "data.parquet")

    result = pq.read_table(tempdir / "data.parquet", columns=['a', 'a'])
    expected_schema = pa.schema([('a', 'int32'), ('a', 'int32')])

    assert result.column_names == ['a', 'a']
    assert result.schema == expected_schema


def test_dataset_partitioning(tempdir):
    import pyarrow.dataset as ds

    # create small dataset with directory partitioning
    root_path = tempdir / "test_partitioning"
    (root_path / "2012" / "10" / "01").mkdir(parents=True)

    table = pa.table({'a': [1, 2, 3]})
    pq.write_table(
        table, str(root_path / "2012" / "10" / "01" / "data.parquet"))

    # This works with new dataset API

    # read_table
    part = ds.partitioning(field_names=["year", "month", "day"])
    result = pq.read_table(
        str(root_path), partitioning=part)
    assert result.column_names == ["a", "year", "month", "day"]

    result = pq.ParquetDataset(
        str(root_path), partitioning=part).read()
    assert result.column_names == ["a", "year", "month", "day"]


def test_parquet_dataset_new_filesystem(tempdir):
    # Ensure we can pass new FileSystem object to ParquetDataset
    table = pa.table({'a': [1, 2, 3]})
    pq.write_table(table, tempdir / 'data.parquet')
    filesystem = SubTreeFileSystem(str(tempdir), LocalFileSystem())
    dataset = pq.ParquetDataset('.', filesystem=filesystem)
    result = dataset.read()
    assert result.equals(table)


def test_parquet_dataset_partitions_piece_path_with_fsspec(tempdir):
    # ARROW-10462 ensure that on Windows we properly use posix-style paths
    # as used by fsspec
    fsspec = pytest.importorskip("fsspec")
    filesystem = fsspec.filesystem('file')
    table = pa.table({'a': [1, 2, 3]})
    pq.write_table(table, tempdir / 'data.parquet')

    # pass a posix-style path (using "/" also on Windows)
    path = str(tempdir).replace("\\", "/")
    dataset = pq.ParquetDataset(
        path, filesystem=filesystem)
    # ensure the piece path is also posix-style
    expected = path + "/data.parquet"
    assert dataset.fragments[0].path == expected


def test_parquet_write_to_dataset_exposed_keywords(tempdir):
    table = pa.table({'a': [1, 2, 3]})
    path = tempdir / 'partitioning'

    paths_written = []

    def file_visitor(written_file):
        paths_written.append(written_file.path)

    basename_template = 'part-{i}.parquet'

    pq.write_to_dataset(table, path, partitioning=["a"],
                        file_visitor=file_visitor,
                        basename_template=basename_template)

    expected_paths = {
        path / '1' / 'part-0.parquet',
        path / '2' / 'part-0.parquet',
        path / '3' / 'part-0.parquet'
    }
    paths_written_set = set(map(pathlib.Path, paths_written))
    assert paths_written_set == expected_paths


@pytest.mark.parametrize("write_dataset_kwarg", (
    ("create_dir", True),
    ("create_dir", False),
))
def test_write_to_dataset_kwargs_passed(tempdir, write_dataset_kwarg):
    """Verify kwargs in pq.write_to_dataset are passed onto ds.write_dataset"""
    import pyarrow.dataset as ds

    table = pa.table({"a": [1, 2, 3]})
    path = tempdir / 'out.parquet'

    signature = inspect.signature(ds.write_dataset)
    key, arg = write_dataset_kwarg

    # kwarg not in pq.write_to_dataset, but will be passed to ds.write_dataset
    assert key not in inspect.signature(pq.write_to_dataset).parameters
    assert key in signature.parameters

    with mock.patch.object(ds, "write_dataset", autospec=True)\
            as mock_write_dataset:
        pq.write_to_dataset(table, path, **{key: arg})
        _name, _args, kwargs = mock_write_dataset.mock_calls[0]
        assert kwargs[key] == arg


@pytest.mark.pandas
def test_write_to_dataset_category_observed(tempdir):
    # if we partition on a categorical variable with "unobserved" categories
    # (values present in the dictionary, but not in the actual data)
    # ensure those are not creating empty files/directories
    df = pd.DataFrame({
        "cat": pd.Categorical(["a", "b", "a"], categories=["a", "b", "c"]),
        "col": [1, 2, 3]
    })
    table = pa.table(df)
    path = tempdir / "dataset"
    pq.write_to_dataset(
        table, tempdir / "dataset", partition_cols=["cat"]
    )
    subdirs = [f.name for f in path.iterdir() if f.is_dir()]
    assert len(subdirs) == 2
    assert "cat=c" not in subdirs