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https://issues.apache.org/jira/browse/ARROW-1783?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16266127#comment-16266127
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ASF GitHub Bot commented on ARROW-1783:
---------------------------------------
wesm commented on a change in pull request #1362: ARROW-1783: [Python] Provide
a "component" dict representation of a serialized Python object with minimal
allocation
URL: https://github.com/apache/arrow/pull/1362#discussion_r153078038
##########
File path: cpp/src/arrow/ipc/message.h
##########
@@ -144,33 +144,20 @@ class ARROW_EXPORT MessageReader {
public:
virtual ~MessageReader() = default;
+ /// \brief Create MessageReader that reads from InputStream
+ static std::unique_ptr<MessageReader> Open(io::InputStream* stream);
+
+ /// \brief Create MessageReader that reads from owned InputStream
+ static std::unique_ptr<MessageReader> Open(
+ const std::shared_ptr<io::InputStream>& owned_stream);
+
/// \brief Read next Message from the interface
///
/// \param[out] message an arrow::ipc::Message instance
/// \return Status
virtual Status ReadNextMessage(std::unique_ptr<Message>* message) = 0;
};
-/// \brief Implementation of MessageReader that reads from InputStream
-/// \since 0.5.0
-class ARROW_EXPORT InputStreamMessageReader : public MessageReader {
Review comment:
It was never necessary to export this class
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> [Python] Convert SerializedPyObject to/from sequence of component buffers
> with minimal memory allocation / copying
> ------------------------------------------------------------------------------------------------------------------
>
> Key: ARROW-1783
> URL: https://issues.apache.org/jira/browse/ARROW-1783
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Reporter: Wes McKinney
> Assignee: Wes McKinney
> Labels: pull-request-available
> Fix For: 0.8.0
>
>
> See discussion on Dask org:
> https://github.com/dask/distributed/pull/931
> It would be valuable for downstream users to compute the serialized payload
> as a sequence of memoryview-compatible objects without having to allocate new
> memory on write. This means that the component tensor messages must have
> their metadata and bodies in separate buffers. This will require a bit of
> work internally reassemble the object from a collection of {{pyarrow.Buffer}}
> objects
> see also ARROW-1509
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