/usr/local/lib64/python3.6/site-packages/pyarrow/tests
Edit: /usr/local/lib64/python3.6/site-packages/pyarrow/tests/test_orc.py (9221B)
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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 pytest
import decimal
import datetime
import pyarrow as pa
# Marks all of the tests in this module
# Ignore these with pytest ... -m 'not orc'
pytestmark = pytest.mark.orc
try:
from pandas.testing import assert_frame_equal
import pandas as pd
except ImportError:
pass
@pytest.fixture(scope="module")
def datadir(base_datadir):
return base_datadir / "orc"
def fix_example_values(actual_cols, expected_cols):
"""
Fix type of expected values (as read from JSON) according to
actual ORC datatype.
"""
for name in expected_cols:
expected = expected_cols[name]
actual = actual_cols[name]
if (name == "map" and
[d.keys() == {'key', 'value'} for m in expected for d in m]):
# convert [{'key': k, 'value': v}, ...] to [(k, v), ...]
for i, m in enumerate(expected):
expected_cols[name][i] = [(d['key'], d['value']) for d in m]
continue
typ = actual[0].__class__
if issubclass(typ, datetime.datetime):
# timestamp fields are represented as strings in JSON files
expected = pd.to_datetime(expected)
elif issubclass(typ, datetime.date):
# date fields are represented as strings in JSON files
expected = expected.dt.date
elif typ is decimal.Decimal:
converted_decimals = [None] * len(expected)
# decimal fields are represented as reals in JSON files
for i, (d, v) in enumerate(zip(actual, expected)):
if not pd.isnull(v):
exp = d.as_tuple().exponent
factor = 10 ** -exp
converted_decimals[i] = (
decimal.Decimal(round(v * factor)).scaleb(exp))
expected = pd.Series(converted_decimals)
expected_cols[name] = expected
def check_example_values(orc_df, expected_df, start=None, stop=None):
if start is not None or stop is not None:
expected_df = expected_df[start:stop].reset_index(drop=True)
assert_frame_equal(orc_df, expected_df, check_dtype=False)
def check_example_file(orc_path, expected_df, need_fix=False):
"""
Check a ORC file against the expected columns dictionary.
"""
from pyarrow import orc
orc_file = orc.ORCFile(orc_path)
# Exercise ORCFile.read()
table = orc_file.read()
assert isinstance(table, pa.Table)
table.validate()
# This workaround needed because of ARROW-3080
orc_df = pd.DataFrame(table.to_pydict())
assert set(expected_df.columns) == set(orc_df.columns)
# reorder columns if necessary
if not orc_df.columns.equals(expected_df.columns):
expected_df = expected_df.reindex(columns=orc_df.columns)
if need_fix:
fix_example_values(orc_df, expected_df)
check_example_values(orc_df, expected_df)
# Exercise ORCFile.read_stripe()
json_pos = 0
for i in range(orc_file.nstripes):
batch = orc_file.read_stripe(i)
check_example_values(pd.DataFrame(batch.to_pydict()),
expected_df,
start=json_pos,
stop=json_pos + len(batch))
json_pos += len(batch)
assert json_pos == orc_file.nrows
@pytest.mark.pandas
@pytest.mark.parametrize('filename', [
'TestOrcFile.test1.orc',
'TestOrcFile.testDate1900.orc',
'decimal.orc'
])
def test_example_using_json(filename, datadir):
"""
Check a ORC file example against the equivalent JSON file, as given
in the Apache ORC repository (the JSON file has one JSON object per
line, corresponding to one row in the ORC file).
"""
# Read JSON file
path = datadir / filename
table = pd.read_json(str(path.with_suffix('.jsn.gz')), lines=True)
check_example_file(path, table, need_fix=True)
def test_orcfile_empty(datadir):
from pyarrow import orc
table = orc.ORCFile(datadir / "TestOrcFile.emptyFile.orc").read()
assert table.num_rows == 0
expected_schema = pa.schema([
("boolean1", pa.bool_()),
("byte1", pa.int8()),
("short1", pa.int16()),
("int1", pa.int32()),
("long1", pa.int64()),
("float1", pa.float32()),
("double1", pa.float64()),
("bytes1", pa.binary()),
("string1", pa.string()),
("middle", pa.struct(
[("list", pa.list_(
pa.struct([("int1", pa.int32()),
("string1", pa.string())])))
])),
("list", pa.list_(
pa.struct([("int1", pa.int32()),
("string1", pa.string())])
)),
("map", pa.map_(pa.string(),
pa.struct([("int1", pa.int32()),
("string1", pa.string())])
)),
])
assert table.schema == expected_schema
def test_orcfile_readwrite():
from pyarrow import orc
buffer_output_stream = pa.BufferOutputStream()
a = pa.array([1, None, 3, None])
b = pa.array([None, "Arrow", None, "ORC"])
table = pa.table({"int64": a, "utf8": b})
orc.write_table(table, buffer_output_stream)
buffer_reader = pa.BufferReader(buffer_output_stream.getvalue())
orc_file = orc.ORCFile(buffer_reader)
output_table = orc_file.read()
assert table.equals(output_table)
# deprecated keyword order
buffer_output_stream = pa.BufferOutputStream()
with pytest.warns(FutureWarning):
orc.write_table(buffer_output_stream, table)
buffer_reader = pa.BufferReader(buffer_output_stream.getvalue())
orc_file = orc.ORCFile(buffer_reader)
output_table = orc_file.read()
assert table.equals(output_table)
def test_column_selection(tempdir):
from pyarrow import orc
# create a table with nested types
inner = pa.field('inner', pa.int64())
middle = pa.field('middle', pa.struct([inner]))
fields = [
pa.field('basic', pa.int32()),
pa.field(
'list', pa.list_(pa.field('item', pa.int32()))
),
pa.field(
'struct', pa.struct([middle, pa.field('inner2', pa.int64())])
),
pa.field(
'list-struct', pa.list_(pa.field(
'item', pa.struct([
pa.field('inner1', pa.int64()),
pa.field('inner2', pa.int64())
])
))
),
pa.field('basic2', pa.int64()),
]
arrs = [
[0], [[1, 2]], [{"middle": {"inner": 3}, "inner2": 4}],
[[{"inner1": 5, "inner2": 6}, {"inner1": 7, "inner2": 8}]], [9]]
table = pa.table(arrs, schema=pa.schema(fields))
path = str(tempdir / 'test.orc')
orc.write_table(table, path)
orc_file = orc.ORCFile(path)
# default selecting all columns
result1 = orc_file.read()
assert result1.equals(table)
# selecting with columns names
result2 = orc_file.read(columns=["basic", "basic2"])
assert result2.equals(table.select(["basic", "basic2"]))
result3 = orc_file.read(columns=["list", "struct", "basic2"])
assert result3.equals(table.select(["list", "struct", "basic2"]))
# using dotted paths
result4 = orc_file.read(columns=["struct.middle.inner"])
expected4 = pa.table({"struct": [{"middle": {"inner": 3}}]})
assert result4.equals(expected4)
result5 = orc_file.read(columns=["struct.inner2"])
expected5 = pa.table({"struct": [{"inner2": 4}]})
assert result5.equals(expected5)
result6 = orc_file.read(
columns=["list", "struct.middle.inner", "struct.inner2"]
)
assert result6.equals(table.select(["list", "struct"]))
result7 = orc_file.read(columns=["list-struct.inner1"])
expected7 = pa.table({"list-struct": [[{"inner1": 5}, {"inner1": 7}]]})
assert result7.equals(expected7)
# selecting with (Arrow-based) field indices
result2 = orc_file.read(columns=[0, 4])
assert result2.equals(table.select(["basic", "basic2"]))
result3 = orc_file.read(columns=[1, 2, 3])
assert result3.equals(table.select(["list", "struct", "list-struct"]))
# error on non-existing name or index
with pytest.raises(IOError):
# liborc returns ParseError, which gets translated into IOError
# instead of ValueError
orc_file.read(columns=["wrong"])
with pytest.raises(ValueError):
orc_file.read(columns=[5])