[jira] [Created] (ARROW-1695) [Serialization] Fix reference counting of numpy arrays created in custom serialializer

["Philipp Moritz (JIRA)" <ji...@apache.org>]

Philipp Moritz created ARROW-1695:
-------------------------------------

             Summary: [Serialization] Fix reference counting of numpy arrays created in custom serialializer
                 Key: ARROW-1695
                 URL: https://issues.apache.org/jira/browse/ARROW-1695
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 0.7.1
            Reporter: Philipp Moritz
             Fix For: 0.8.0


The problem happens with the following code:

{code}
import numpy as np
import pyarrow
import sys

class Bar(object):
    pass

def bar_custom_serializer(obj):
    x = np.zeros(4)
    return x

def bar_custom_deserializer(serialized_obj):
    return serialized_obj

pyarrow._default_serialization_context.register_type(Bar, "Bar", pickle=False, custom_serializer=bar_custom_serializer, custom_deserializer=bar_custom_deserializer)

pyarrow.serialize(Bar())
{code}

After execution of pyarrow.serialize, the interpreter crashes in the garbage collection routine.

This happens if a numpy array is returned in the custom serializer but there is no other reference to the numpy array. The reason this is not a problem in the current code is that so far we haven't created new numpy arrays in the custom serializer.

I think the problem here is that the numpy array hits reference count zero between the end of SerializeSequences in python_to_arrow.cc and the call to NdarrayToTensor. I'll push a fix later today, which just increases and decreases the reference counts at the appropriate places.



--
This message was sent by Atlassian JIRA
(v6.4.14#64029)