def orc_type(field):
if pa.types.is_boolean(field):
return pyorc.Boolean()
elif pa.types.is_int8(field):
return pyorc.TinyInt()
elif pa.types.is_int16(field):
return pyorc.SmallInt()
elif pa.types.is_int32(field):
return pyorc.Int()
elif pa.types.is_int64(field):
return pyorc.BigInt()
elif pa.types.is_float32(field):
return pyorc.Float()
elif pa.types.is_float64(field):
return pyorc.Double()
elif pa.types.is_decimal(field):
return pyorc.Decimal(field.precision, field.scale)
elif pa.types.is_list(field):
return pyorc.Array(orc_type(field.value_type))
elif pa.types.is_timestamp(field):
return pyorc.Timestamp()
elif pa.types.is_date(field):
return pyorc.Date()
elif pa.types.is_binary(field):
return pyorc.Binary()
elif pa.types.is_string(field):
return pyorc.String()
else:
raise ValueError('Cannot Convert %s' % field)
def test_empty_statistics():
buff = BytesIO()
orc_schema = po.Struct(
a=po.BigInt(),
b=po.Double(),
c=po.String(),
d=po.Decimal(11, 2),
e=po.Date(),
f=po.Timestamp(),
g=po.Boolean(),
h=po.Binary(),
i=po.BigInt(),
# One column with non null value, else cudf/pyorc readers crash
)
data = tuple([None] * (len(orc_schema.fields) - 1) + [1])
with po.Writer(buff, orc_schema) as writer:
writer.write(data)
got = cudf.io.orc.read_orc_statistics([buff])
# Check for both file and stripe stats
for stats in got:
# Similar expected stats for the first 6 columns in this case
for col_name in ascii_lowercase[:6]:
assert stats[0][col_name].get("number_of_values") == 0
assert stats[0][col_name].get("has_null") is True
assert stats[0][col_name].get("minimum") is None
assert stats[0][col_name].get("maximum") is None
for col_name in ascii_lowercase[:3]:
assert stats[0][col_name].get("sum") == 0
# Sum for decimal column is a string
assert stats[0]["d"].get("sum") == "0"
assert stats[0]["g"].get("number_of_values") == 0
assert stats[0]["g"].get("has_null") is True
assert stats[0]["g"].get("true_count") == 0
assert stats[0]["g"].get("false_count") == 0
assert stats[0]["h"].get("number_of_values") == 0
assert stats[0]["h"].get("has_null") is True
assert stats[0]["h"].get("sum") == 0
assert stats[0]["i"].get("number_of_values") == 1
assert stats[0]["i"].get("has_null") is False
assert stats[0]["i"].get("minimum") == 1
assert stats[0]["i"].get("maximum") == 1
assert stats[0]["i"].get("sum") == 1
PANDAS_TO_ORC_TYPES = {
cudf.dtype("int8"): pyorc.TinyInt(),
pd.Int8Dtype(): pyorc.TinyInt(),
pd.Int16Dtype(): pyorc.SmallInt(),
pd.Int32Dtype(): pyorc.Int(),
pd.Int64Dtype(): pyorc.BigInt(),
pd.BooleanDtype(): pyorc.Boolean(),
cudf.dtype("bool_"): pyorc.Boolean(),
cudf.dtype("int16"): pyorc.SmallInt(),
cudf.dtype("int32"): pyorc.Int(),
cudf.dtype("int64"): pyorc.BigInt(),
cudf.dtype("O"): pyorc.String(),
pd.StringDtype(): pyorc.String(),
cudf.dtype("float32"): pyorc.Float(),
cudf.dtype("float64"): pyorc.Double(),
cudf.dtype("<M8[ns]"): pyorc.Timestamp(),
cudf.dtype("<M8[ms]"): pyorc.Timestamp(),
cudf.dtype("<M8[us]"): pyorc.Timestamp(),
}
ORC_TO_PANDAS_TYPES = {
pyorc.TinyInt().name: pd.Int8Dtype(),
pyorc.Int().name: pd.Int32Dtype(),
pyorc.Boolean().name: pd.BooleanDtype(),
pyorc.SmallInt().name: pd.Int16Dtype(),
pyorc.BigInt().name: pd.Int64Dtype(),
pyorc.String().name: cudf.dtype("O"),
pyorc.Float().name: cudf.dtype("float32"),
pyorc.Double().name: cudf.dtype("float64"),
pyorc.Timestamp().name: cudf.dtype("<M8[ns]"),
}
PANDAS_TO_ORC_TYPES = {
np.dtype("int8"): pyorc.TinyInt(),
pd.Int8Dtype(): pyorc.TinyInt(),
pd.Int16Dtype(): pyorc.SmallInt(),
pd.Int32Dtype(): pyorc.Int(),
pd.Int64Dtype(): pyorc.BigInt(),
pd.BooleanDtype(): pyorc.Boolean(),
np.dtype("bool_"): pyorc.Boolean(),
np.dtype("int16"): pyorc.SmallInt(),
np.dtype("int32"): pyorc.Int(),
np.dtype("int64"): pyorc.BigInt(),
np.dtype("O"): pyorc.String(),
pd.StringDtype(): pyorc.String(),
np.dtype("float32"): pyorc.Float(),
np.dtype("float64"): pyorc.Double(),
np.dtype("<M8[ns]"): pyorc.Timestamp(),
np.dtype("<M8[ms]"): pyorc.Timestamp(),
np.dtype("<M8[us]"): pyorc.Timestamp(),
}
ORC_TO_PANDAS_TYPES = {
pyorc.TinyInt().name: pd.Int8Dtype(),
pyorc.Int().name: pd.Int32Dtype(),
pyorc.Boolean().name: pd.BooleanDtype(),
pyorc.SmallInt().name: pd.Int16Dtype(),
pyorc.BigInt().name: pd.Int64Dtype(),
pyorc.String().name: np.dtype("O"),
pyorc.Float().name: np.dtype("float32"),
pyorc.Double().name: np.dtype("float64"),
pyorc.Timestamp().name: np.dtype("<M8[ns]"),
}
