GH-38709: [C++] Protect against PREALLOCATE preprocessor defined on macOS

[cholmcc]

Rationale for this change

The macOS header sys/vnode.h defines PREALLOCATE as a preprocessor macro, which causes a conflict in arrow/compute/kernel.h where the same name is defined as an identifier.

Other BSDs does not seem to define this macro.

What changes are included in this PR?

#undef PREALLOCATE on macOS and if defined.

Are these changes tested?

Somewhat, in a different context.

Are there any user-facing changes?

If some code specific to macOS actually uses the PREALLOCATE macro, then that code may have problems. However, it is unlikely that any user code would use this macro as it seems to be intended for internal use in the BSD kernel of macOS.

• Closes: [C++] Conflict between arrow::compute::MemAllocation::PREALLOCATE and system header vnode.h preprocessor define #38709

[github-actions]

Thanks for opening a pull request!

If this is not a minor PR. Could you open an issue for this pull request on GitHub? https://github.com/apache/arrow/issues/new/choose

Opening GitHub issues ahead of time contributes to the Openness of the Apache Arrow project.

Then could you also rename the pull request title in the following format?

GH-${GITHUB_ISSUE_ID}: [${COMPONENT}] ${SUMMARY}

or

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In the case of PARQUET issues on JIRA the title also supports:

PARQUET-${JIRA_ISSUE_ID}: [${COMPONENT}] ${SUMMARY}

See also:

• Other pull requests
• Contribution Guidelines - How to contribute patches

[github-actions]

⚠️ GitHub issue #38709 has been automatically assigned in GitHub to PR creator.

[kou]

+1

[pitrou]

Sidenote: this is why we should discourage ENUM_VALUE in favor of kEnumValue in new code :-)

[kou]

Oh, I forgot to merge this.
I'll merge this.

[conbench-apache-arrow]

After merging your PR, Conbench analyzed the 5 benchmarking runs that have been run so far on merge-commit 61a4a80.

There were 2 benchmark results indicating a performance regression:

• Commit Run on ursa-i9-9960x at 2023-11-29 15:34:23Z
  • dataset-selectivity (Python) with dataset=nyctaxi_multi_ipc_s3, selectivity=10%
  • dataset-selectivity (Python) with dataset=nyctaxi_multi_ipc_s3, selectivity=100%

The full Conbench report has more details.