[jira] [Updated] (ARROW-6157) [Python][C++] UnionArray with invalid data passes validation / leads to segfaults
[
https://issues.apache.org/jira/browse/ARROW-6157?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated ARROW-6157:
--
Labels: pull-request-available (was: )
> [Python][C++] UnionArray with invalid data passes validation / leads to
> segfaults
> -
>
> Key: ARROW-6157
> URL: https://issues.apache.org/jira/browse/ARROW-6157
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Reporter: Joris Van den Bossche
>Assignee: Antoine Pitrou
>Priority: Major
> Labels: pull-request-available
> Fix For: 1.0.0
>
>
> From the Python side, you can create an "invalid" UnionArray:
> {code}
> binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
> int64 = pa.array([1, 2, 3], type='int64')
> types = pa.array([0, 1, 0, 0, 2, 1, 0], type='int8') # <- value of 2 is out
> of bound for number of childs
> value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
> a = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
> {code}
> Eg on conversion to python this leads to a segfault:
> {code}
> In [7]: a.to_pylist()
> Segmentation fault (core dumped)
> {code}
> On the other hand, doing an explicit validation does not give an error:
> {code}
> In [8]: a.validate()
> {code}
> Should the validation raise errors for this case? (the C++
> {{ValidateVisitor}} for UnionArray does nothing)
> (so that this can be called from the Python API to avoid creating invalid
> arrays / segfaults there)
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[jira] [Updated] (ARROW-6157) [Python][C++] UnionArray with invalid data passes validation / leads to segfaults
[
https://issues.apache.org/jira/browse/ARROW-6157?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antoine Pitrou updated ARROW-6157:
--
Fix Version/s: 1.0.0
> [Python][C++] UnionArray with invalid data passes validation / leads to
> segfaults
> -
>
> Key: ARROW-6157
> URL: https://issues.apache.org/jira/browse/ARROW-6157
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Reporter: Joris Van den Bossche
>Priority: Major
> Fix For: 1.0.0
>
>
> From the Python side, you can create an "invalid" UnionArray:
> {code}
> binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
> int64 = pa.array([1, 2, 3], type='int64')
> types = pa.array([0, 1, 0, 0, 2, 1, 0], type='int8') # <- value of 2 is out
> of bound for number of childs
> value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
> a = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
> {code}
> Eg on conversion to python this leads to a segfault:
> {code}
> In [7]: a.to_pylist()
> Segmentation fault (core dumped)
> {code}
> On the other hand, doing an explicit validation does not give an error:
> {code}
> In [8]: a.validate()
> {code}
> Should the validation raise errors for this case? (the C++
> {{ValidateVisitor}} for UnionArray does nothing)
> (so that this can be called from the Python API to avoid creating invalid
> arrays / segfaults there)
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[jira] [Updated] (ARROW-6157) [Python][C++] UnionArray with invalid data passes validation / leads to segfaults
[
https://issues.apache.org/jira/browse/ARROW-6157?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joris Van den Bossche updated ARROW-6157:
-
Description:
>From the Python side, you can create an "invalid" UnionArray:
{code}
binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
int64 = pa.array([1, 2, 3], type='int64')
types = pa.array([0, 1, 0, 0, 2, 1, 0], type='int8') # <- value of 2 is out
of bound for number of childs
value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
a = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
{code}
Eg on conversion to python this leads to a segfault:
{code}
In [7]: a.to_pylist()
Segmentation fault (core dumped)
{code}
On the other hand, doing an explicit validation does not give an error:
{code}
In [8]: a.validate()
{code}
Should the validation raise errors for this case? (the C++ {{ValidateVisitor}}
for UnionArray does nothing)
(so that this can be called from the Python API to avoid creating invalid
arrays / segfaults there)
was:
>From the Python side, you can create an "invalid" UnionArray:
{code}
binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
int64 = pa.array([1, 2, 3], type='int64')
types = pa.array([0, 1, 0, 0, 2, 1, 0], type='int8') # <- value of 2 is out
of bound for number of childs
value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
a = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
{code}
Eg on conversion to python this leads to a segfault:
{code}
In [7]: a.to_pylist()
Segmentation fault (core dumped)
{code}
On the other hand, doing an explicit validation does not give an error:
{code}
In [8]: a.validate()
{code}
Should the validation raise errors for this case? (the C++ {{ValidateVisitor}}
for UnionArray does nothing)
> [Python][C++] UnionArray with invalid data passes validation / leads to
> segfaults
> -
>
> Key: ARROW-6157
> URL: https://issues.apache.org/jira/browse/ARROW-6157
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Reporter: Joris Van den Bossche
>Priority: Major
>
> From the Python side, you can create an "invalid" UnionArray:
> {code}
> binary = pa.array([b'a', b'b', b'c', b'd'], type='binary')
> int64 = pa.array([1, 2, 3], type='int64')
> types = pa.array([0, 1, 0, 0, 2, 1, 0], type='int8') # <- value of 2 is out
> of bound for number of childs
> value_offsets = pa.array([0, 0, 2, 1, 1, 2, 3], type='int32')
> a = pa.UnionArray.from_dense(types, value_offsets, [binary, int64])
> {code}
> Eg on conversion to python this leads to a segfault:
> {code}
> In [7]: a.to_pylist()
> Segmentation fault (core dumped)
> {code}
> On the other hand, doing an explicit validation does not give an error:
> {code}
> In [8]: a.validate()
> {code}
> Should the validation raise errors for this case? (the C++
> {{ValidateVisitor}} for UnionArray does nothing)
> (so that this can be called from the Python API to avoid creating invalid
> arrays / segfaults there)
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