[C++][Python] Segfault during `pyarrow.dataset.write_dataset` with dataset source read with pre_buffer=True

[austin3dickey]

Describe the bug, including details regarding any error messages, version, and platform.

Please see voltrondata-labs/arrow-benchmarks-ci#166 for further detail.

Since #37854, the arrow-benchmarks-ci runners have been attempting to run the dataset-serialize benchmark on an x86_64 Ubuntu runner using Python 3.8. Each time, somewhere between 0 and 3 cases succeed before we see Fatal Python error: Segmentation fault.

Component(s)

Benchmarking, Python

[austin3dickey]

I was able to reproduce this on my M1 Mac. Steps:

• build Arrow and pyarrow using this as reference (python 3.8)
• Instead of running python -m buildkite.benchmark.run_benchmark_groups, I manually ran the dataset-serialize benchmark by:
  • cloning https://github.com/voltrondata-labs/benchmarks
  • checking out commit 93a57ba (I plan to submit a band-aid patch that avoids this issue in the future)
  • running pip install -e . in that repo
  • creating a data dir and saving it to BENCHMARKS_DATA_DIR
  • creating a temporary output dir and saving it to BENCHMARK_OUTPUT_DIR (only required since I'm using Darwin)
  • setting export DRY_RUN=true so it doesn't try to post to Conbench
  • running conbench dataset-serialize ALL --iterations=6 --all=true --drop-caches=true with sudo permissions

This was able to run one case, and generate a segfault on the second case. Logs look the same as here.

[austin3dickey]

Here is a more stripped-down example:

import pathlib
import shutil
import tempfile
import uuid

import pyarrow.dataset

tempdir = tempfile.TemporaryDirectory()

# First, download https://ursa-labs-taxi-data.s3.us-east-2.amazonaws.com/2009/01/data.parquet
source_ds = pyarrow.dataset.dataset("data.parquet")

for n_rows in [561000, 5610000]:
    for serialization_format in ["parquet", "arrow", "feather", "csv"]:
        data = source_ds.head(
            n_rows,
            # # Uncomment this and the segfault does not happen!
            # fragment_scan_options=pyarrow.dataset.ParquetFragmentScanOptions(
            #     pre_buffer=False
            # ),
        )
        out_dir = pathlib.Path(tempdir.name) / str(uuid.uuid4())

        # This is where the segfault happens, during one of the loops
        print(f"Writing to {serialization_format}")
        pyarrow.dataset.write_dataset(
            data=data,
            format=serialization_format,
            base_dir=out_dir,
            existing_data_behavior="overwrite_or_ignore",
        )
        print("Done")

        shutil.rmtree(out_dir)

I ran this a few times, and there are a mix of symptoms. See:

> python ~/Desktop/test.py
Writing to parquet
[1]    47390 segmentation fault  python ~/Desktop/test.py

> python ~/Desktop/test.py
Writing to parquet
Done
Writing to arrow
[1]    47400 bus error  python ~/Desktop/test.py

> python ~/Desktop/test.py
Writing to parquet
[1]    47413 bus error  python ~/Desktop/test.py

> python ~/Desktop/test.py
Writing to parquet
Done
Writing to arrow
[1]    47431 segmentation fault  python ~/Desktop/test.py

> python ~/Desktop/test.py
Writing to parquet
Done
Writing to arrow
Done
Writing to feather
Done
Writing to csv
Done
Writing to parquet
Done
[1]    58361 segmentation fault  python ~/Desktop/test.py

[austin3dickey]

Edited the above example to be slightly more minimal.

[mapleFU]

Do we have some stack for the coredump?

[austin3dickey]

I need some help generating the stack as I'm fairly new to Arrow development. Could you point me to the correct flags? I think I built in release mode so I'll try switching to debug mode first.

In the meantime I'd appreciate if anyone else can confirm that the above example produces a segfault on their machine.

[austin3dickey]

Without rebuilding (I'm still on release mode) I used lldb:

> lldb /opt/miniconda3/envs/test/bin/python /Users/austin/Desktop/test.py
(lldb) target create "/opt/miniconda3/envs/test/bin/python"
Current executable set to '/opt/miniconda3/envs/test/bin/python' (arm64).
(lldb) settings set -- target.run-args  "/Users/austin/Desktop/test.py"
(lldb) run
Process 77545 launched: '/opt/miniconda3/envs/test/bin/python' (arm64)
Writing to parquet
Process 77545 stopped
* thread #28, stop reason = EXC_BAD_ACCESS (code=1, address=0x5)
    frame #0: 0x0000000105e6bdac libparquet.1400.0.0.dylib`parquet::arrow::(anonymous namespace)::FileReaderImpl::DecodeRowGroups(std::__1::shared_ptr<parquet::arrow::(anonymous namespace)::FileReaderImpl>, std::__1::vector<int, std::__1::allocator<int>> const&, std::__1::vector<int, std::__1::allocator<int>> const&, arrow::internal::Executor*) + 1720
libparquet.1400.0.0.dylib`parquet::arrow::(anonymous namespace)::FileReaderImpl::DecodeRowGroups:
->  0x105e6bdac <+1720>: ldr    x8, [x0]
    0x105e6bdb0 <+1724>: ldr    x9, [x8, #0x30]
    0x105e6bdb4 <+1728>: add    x8, sp, #0x278
    0x105e6bdb8 <+1732>: add    x1, sp, #0x258
Target 0: (python) stopped.
(lldb) bt
* thread #28, stop reason = EXC_BAD_ACCESS (code=1, address=0x5)
  * frame #0: 0x0000000105e6bdac libparquet.1400.0.0.dylib`parquet::arrow::(anonymous namespace)::FileReaderImpl::DecodeRowGroups(std::__1::shared_ptr<parquet::arrow::(anonymous namespace)::FileReaderImpl>, std::__1::vector<int, std::__1::allocator<int>> const&, std::__1::vector<int, std::__1::allocator<int>> const&, arrow::internal::Executor*) + 1720
    frame #1: 0x0000000105e6b52c libparquet.1400.0.0.dylib`parquet::arrow::RowGroupGenerator::ReadOneRowGroup(arrow::internal::Executor*, std::__1::shared_ptr<parquet::arrow::(anonymous namespace)::FileReaderImpl>, int, std::__1::vector<int, std::__1::allocator<int>> const&) + 164
    frame #2: 0x0000000105e753b8 libparquet.1400.0.0.dylib`std::__1::enable_if<is_future<arrow::Future<std::__1::function<arrow::Future<std::__1::shared_ptr<arrow::RecordBatch>> ()>>>::value, void>::type arrow::detail::ContinueFuture::operator()<parquet::arrow::RowGroupGenerator::FetchNext()::'lambda'(), arrow::Future<std::__1::function<arrow::Future<std::__1::shared_ptr<arrow::RecordBatch>> ()>>, arrow::Future<std::__1::function<arrow::Future<std::__1::shared_ptr<arrow::RecordBatch>> ()>>>(arrow::Future<std::__1::function<arrow::Future<std::__1::shared_ptr<arrow::RecordBatch>> ()>>, parquet::arrow::RowGroupGenerator::FetchNext()::'lambda'()&&) const + 96
    frame #3: 0x0000000105e75160 libparquet.1400.0.0.dylib`arrow::Future<arrow::internal::Empty>::ThenOnComplete<parquet::arrow::RowGroupGenerator::FetchNext()::'lambda'(), arrow::Future<arrow::internal::Empty>::PassthruOnFailure<parquet::arrow::RowGroupGenerator::FetchNext()::'lambda'()>>::operator()(arrow::Result<arrow::internal::Empty> const&) && + 72
    frame #4: 0x000000014030fa6c libarrow.1400.0.0.dylib`arrow::ConcreteFutureImpl::RunOrScheduleCallback(std::__1::shared_ptr<arrow::FutureImpl> const&, arrow::FutureImpl::CallbackRecord&&, bool) + 292
    frame #5: 0x000000014030f804 libarrow.1400.0.0.dylib`arrow::ConcreteFutureImpl::DoMarkFinishedOrFailed(arrow::FutureState) + 200
    frame #6: 0x0000000105e671f0 libparquet.1400.0.0.dylib`arrow::Future<arrow::internal::Empty>::DoMarkFinished(arrow::Result<arrow::internal::Empty>) + 148
    frame #7: 0x0000000105e67050 libparquet.1400.0.0.dylib`void arrow::Future<arrow::internal::Empty>::MarkFinished<arrow::internal::Empty, void>(arrow::Status) + 68
    frame #8: 0x0000000105e73ef0 libparquet.1400.0.0.dylib`arrow::Future<arrow::internal::Empty> arrow::internal::Executor::DoTransfer<arrow::internal::Empty, arrow::Future<arrow::internal::Empty>, arrow::Status>(arrow::Future<arrow::internal::Empty>, bool)::'lambda'(arrow::Status const&)::operator()(arrow::Status const&) + 124
    frame #9: 0x000000014030fed4 libarrow.1400.0.0.dylib`arrow::internal::FnOnce<void ()>::FnImpl<arrow::ConcreteFutureImpl::RunOrScheduleCallback(std::__1::shared_ptr<arrow::FutureImpl> const&, arrow::FutureImpl::CallbackRecord&&, bool)::'lambda'()>::invoke() + 40
    frame #10: 0x000000014033c6a8 libarrow.1400.0.0.dylib`void* std::__1::__thread_proxy[abi:v160006]<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, arrow::internal::ThreadPool::LaunchWorkersUnlocked(int)::$_6>>(void*) + 504
    frame #11: 0x0000000196b5bfa8 libsystem_pthread.dylib`_pthread_start + 148

[mapleFU]

Nice, let me use the same way to debug here..

I'm hard to re-produce this using arrow-13.0 in my MacOs...I'll try to build the latest one

[austin3dickey]

Yeah, as discussed in voltrondata-labs/arrow-benchmarks-ci#166 I think the cause is #37854, which wasn't in the 13.0.0 release yet.