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https://issues.apache.org/jira/browse/ARROW-1873?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16274642#comment-16274642
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Wes McKinney commented on ARROW-1873:
-------------------------------------
If I had to guess, the features inner array is overflowing 2 billion elements.
I marked this for 0.8.0 to take a look
> Segmentation fault when loading total 2GB of parquet files
> ----------------------------------------------------------
>
> Key: ARROW-1873
> URL: https://issues.apache.org/jira/browse/ARROW-1873
> Project: Apache Arrow
> Issue Type: Bug
> Reporter: DB Tsai
> Fix For: 0.8.0
>
>
> We are trying to load 100 parquet files, and each of them is around 20MB.
> Before we port [ARROW-1830] into our pyarrow distribution, we use {{glob}} to
> list all the files, and then load them as pandas dataframe through pyarrow.
> The schema of the parquet files is like
> {code:java}
> root
> |-- dateint: integer (nullable = true)
> |-- profileid: long (nullable = true)
> |-- time: long (nullable = true)
> |-- label: double (nullable = true)
> |-- weight: double (nullable = true)
> |-- features: array (nullable = true)
> | |-- element: double (containsNull = true)
> {code}
> If we only load couple of them, it works without any issue. However, when
> loading 100 of them, we got segmentation fault as the following. FYI, if we
> flatten {{features: array[double]}} into top level, the file sizes are around
> the same, and work fine too.
> Is there anything we can try to eliminate this issue? Thanks.
> {code}
> >>> import glob
> >>> files = glob.glob("/home/dbt/data/*")
> >>> data = pq.ParquetDataset(files).read().to_pandas()
> [New Thread 0x7fffe8f84700 (LWP 23769)]
> [New Thread 0x7fffe3b93700 (LWP 23770)]
> [New Thread 0x7fffe3392700 (LWP 23771)]
> [New Thread 0x7fffe2b91700 (LWP 23772)]
> [Thread 0x7fffe2b91700 (LWP 23772) exited]
> [Thread 0x7fffe3b93700 (LWP 23770) exited]
> Thread 4 "python" received signal SIGSEGV, Segmentation fault.
> [Switching to Thread 0x7fffe3392700 (LWP 23771)]
> 0x00007ffff270fc94 in arrow::Status
> arrow::VisitTypeInline<arrow::py::ArrowDeserializer>(arrow::DataType const&,
> arrow::py::ArrowDeserializer*) ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> (gdb) backtrace
> #0 0x00007ffff270fc94 in arrow::Status
> arrow::VisitTypeInline<arrow::py::ArrowDeserializer>(arrow::DataType const&,
> arrow::py::ArrowDeserializer*) ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #1 0x00007ffff2700b5a in
> arrow::py::ConvertColumnToPandas(arrow::py::PandasOptions,
> std::shared_ptr<arrow::Column> const&, _object*, _object**) ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #2 0x00007ffff2714985 in arrow::Status
> arrow::py::ConvertListsLike<arrow::DoubleType>(arrow::py::PandasOptions,
> std::shared_ptr<arrow::Column> const&, _object**) () from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #3 0x00007ffff2716b92 in
> arrow::py::ObjectBlock::Write(std::shared_ptr<arrow::Column> const&, long,
> long) ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #4 0x00007ffff270a489 in
> arrow::py::DataFrameBlockCreator::WriteTableToBlocks(int)::{lambda(int)#1}::operator()(int)
> const ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #5 0x00007ffff270a67c in std::thread::_Impl<std::_Bind_simple<arrow::Status
> arrow::ParallelFor<arrow::py::DataFrameBlockCreator::WriteTableToBlocks(int)::{lambda(int)#1}&>(int,
> int,
> arrow::py::DataFrameBlockCreator::WriteTableToBlocks(int)::{lambda(int)#1}&)::{lambda()#1}
> ()> >::_M_run() ()
> from
> /home/dbt/miniconda3/lib/python3.6/site-packages/pyarrow/../../../libarrow_python.so.0
> #6 0x00007ffff1e30c5c in std::execute_native_thread_routine_compat
> (__p=<optimized out>)
> at
> /opt/conda/conda-bld/compilers_linux-64_1505664199673/work/.build/src/gcc-7.2.0/libstdc++-v3/src/c++11/thread.cc:110
> #7 0x00007ffff7bc16ba in start_thread (arg=0x7fffe3392700) at
> pthread_create.c:333
> #8 0x00007ffff78f73dd in clone () at
> ../sysdeps/unix/sysv/linux/x86_64/clone.S:109
> {code}
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