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https://issues.apache.org/jira/browse/ARROW-14965?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17454174#comment-17454174
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Weston Pace commented on ARROW-14965:
-------------------------------------
That makes a lot of sense. I didn't realize you were using S3. There is
another throttle before you even get to the maxConnections throttle which is
the I/O thread pool size. Although, since this is parquet, it might be the CPU
thread pool size. Can you try modifying the CPU and I/O thread pool sizes to
see if they have an effect on performance? We should also bump that
maxConnections parameter up too.
The python calls are:
[pyarrow.set_cpu_count|https://arrow.apache.org/docs/python/generated/pyarrow.set_cpu_count.html]
pyarrow.set_io_thread_count (which appears to be missing from the docs, I'll
open a ticket on that)
> [Python][C++] 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: C++, Python
> Affects Versions: 6.0.0
> Reporter: Nick Gates
> Priority: Minor
>
> 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(filepath):
> 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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