Nick Gates created ARROW-14965:
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Summary: Contention when reading Parquet files with multi-threading
Key: ARROW-14965
URL: https://issues.apache.org/jira/browse/ARROW-14965
Project: Apache Arrow
Issue Type: Improvement
Components: Python
Affects Versions: 6.0.0
Reporter: Nick Gates
I'm attempting to read a table from multiple Parquet files where I already know
which row_groups I want to read from each file. I also want to apply a filter
expression while reading. To do this my code looks roughly like this:
{code:java}
def read_file():
format = ds.ParquetFileFormat(...)
fragment = format.make_fragment(filepath, row_groups=[0, 1, 2, ...])
scanner = ds.Scanner.from_fragment(
fragment,
use_threads=True,
use_async=False,
filter=...
)
return scanner.to_reader().read_all()
with ThreadPoolExecutor() as pool:
pa.concat_tables(pool.map(read_file, file_paths)) {code}
Running with a ProcessPoolExecutor, each of my 13 read_file calls takes at most
2 seconds. However, with a ThreadPoolExecutor some of the read_file calls take
20+ seconds.
I've tried running this with various combinations of use_threads and use_async
to try and see what's happening. The code blocks are sourced from py-spy, and
identifying contention was done with viztracer.
*use_threads: False, use_async: False*
* It looks like pyarrow._dataset.Scanner.to_reader doesn't release the GIL:
[https://github.com/apache/arrow/blob/be9a22b9b76d9cd83d85d52ffc2844056d90f367/python/pyarrow/_dataset.pyx#L3278-L3283]
* pyarrow._dataset.from_fragment seems to be contended. Py-spy suggests this
is around getting the physical_schema from the fragment?
{code:java}
from_fragment (pyarrow/_dataset.cpython-37m-x86_64-linux-gnu.so)
__pyx_getprop_7pyarrow_8_dataset_8Fragment_physical_schema
(pyarrow/_dataset.cpython-37m-x86_64-linux-gnu.so)
__pthread_cond_timedwait (libpthread-2.17.so) {code}
*use_threads: False, use_async: True*
* There's no longer any contention for pyarrow._dataset.from_fragment
* But there's lots of contention for pyarrow.lib.RecordBatchReader.read_all
{code:java}
arrow::RecordBatchReader::ReadAll (pyarrow/libarrow.so.600)
arrow::dataset::(anonymous namespace)::ScannerRecordBatchReader::ReadNext
(pyarrow/libarrow_dataset.so.600)
arrow::Iterator<arrow::dataset::TaggedRecordBatch>::Next<arrow::GeneratorIterator<arrow::dataset::TaggedRecordBatch>
> (pyarrow/libarrow_dataset.so.600)
arrow::FutureImpl::Wait (pyarrow/libarrow.so.600)
std::condition_variable::wait (libstdc++.so.6.0.19){code}
*use_threads: True, use_async: False*
* Appears to be some contention on Scanner.to_reader
* But most contention remains for RecordBatchReader.read_all
{code:java}
arrow::RecordBatchReader::ReadAll (pyarrow/libarrow.so.600)
arrow::dataset::(anonymous namespace)::ScannerRecordBatchReader::ReadNext
(pyarrow/libarrow_dataset.so.600)
arrow::Iterator<arrow::dataset::TaggedRecordBatch>::Next<arrow::FunctionIterator<arrow::dataset::(anonymous
namespace)::SyncScanner::ScanBatches(arrow::Iterator<std::shared_ptr<arrow::dataset::ScanTask>
>)::{lambda()#1}, arrow::dataset::TaggedRecordBatch> >
(pyarrow/libarrow_dataset.so.600)
std::condition_variable::wait (libstdc++.so.6.0.19)
__pthread_cond_wait (libpthread-2.17.so) {code}
*use_threads: True, use_async: True*
* Contention again mostly for RecordBatchReader.read_all, but seems to
complete in ~12 seconds rather than 20
{code:java}
arrow::RecordBatchReader::ReadAll (pyarrow/libarrow.so.600)
arrow::dataset::(anonymous namespace)::ScannerRecordBatchReader::ReadNext
(pyarrow/libarrow_dataset.so.600)
arrow::Iterator<arrow::dataset::TaggedRecordBatch>::Next<arrow::GeneratorIterator<arrow::dataset::TaggedRecordBatch>
> (pyarrow/libarrow_dataset.so.600)
arrow::FutureImpl::Wait (pyarrow/libarrow.so.600)
std::condition_variable::wait (libstdc++.so.6.0.19)
__pthread_cond_wait (libpthread-2.17.so) {code}
Is this expected behaviour? Or should it be possible to achieve the same
performance from multi-threading as from multi-processing?
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