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https://issues.apache.org/jira/browse/ARROW-17740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17613157#comment-17613157
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Weston Pace commented on ARROW-17740:
-------------------------------------
{quote}
Looks like there's something even more deadly. The following error is reported
even if the column used for the join and ultimately the column to be selected
is not of type List.
{quote}
Can you clarify? Is the column part of the join payload or not? If it is not
part of the payload at all then that is a new issue. If it is part of the
payload then I think that is ARROW-17216
{quote}
I'm giving up on Acero。I have also tested DuckDB and DuckDB also performs
poorly when reading many columns. Any other suggestions? Otherwise, I'm gonna
have to tank.
{quote}
I am not aware of anyone actively working on performance for the many-columns
case.
> [c++][compute]Is there any other way to use Join besides Acero？
> ---------------------------------------------------------------
>
> Key: ARROW-17740
> URL: https://issues.apache.org/jira/browse/ARROW-17740
> Project: Apache Arrow
> Issue Type: Improvement
> Reporter: LinGeLin
> Priority: Major
> Attachments: data.zip, image-2022-09-30-14-32-48-405.png,
> join_test.zip, test.cpp, test_join.cpp, test_join1.cpp, v4test.py
>
>
> Acero performs poorly, and coredump occurs frequently！
>
> In the scenario I'm working on, I'll read one Parquet file and then several
> other Parquet files. These files will have the same column name (UUID). I
> need to join (by UUID), project (remove UUID), and filter (some custom
> filtering) the results of the two reads. I found that Acero could only be
> used to do join, but when I tested it, Acero performance was very poor and
> very unstable, coredump often happened. Is there another way? Or just another
> way to do a join!
>
> my project commit:
> [链接|https://github.com/LinGeLin/io/commit/9b1b06d8d74154f0768bf5258cc3eaa2b9e20701]
> tensorflow ==2.6.2
> you can build tfio as follows:
> ./configure.sh
> bazel build -s- -verbose_failures $BAZEL_OPTIMIZATION //tensorflow_io/...
> //tensorflow_io_gcs_filesystem/... --compilation_mode=opt --copt=-msse4.2
> --copt=-mfma --copt=-mavx2
> python setup.py bdist_wheel --data bazel-bin
> pip install dist/tensorflow_io-0.21.0-cp38-cp38-linux_x86_64.whl
> --force-reinstall --no-deps
>
> run v4test.py to test the dataset
>
> Data.zip contains several parquet files, which are stored on S3 in my
> scenario.
> I have copied some of the code into test.cpp and can only see the general
> flow, not compiled
>
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