Hello,
I have a set of tfrecord files, obtained by converting parquet files with
Spark. Each file is roughly 1GB and I have 11 of those.
I would expect simple statistics gathering (ie counting number of items of
all files) to scale linearly with respect to the number of cores on my
system.
I am able to reproduce the issue with the minimal snippet below
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.runners.portability import fn_api_runner
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.portability import python_urns
import sys
pipeline_options = PipelineOptions(['--direct_num_workers', '4'])
file_pattern = 'part-r-00*
runner=fn_api_runner.FnApiRunner(
default_environment=beam_runner_api_pb2.Environment(
urn=python_urns.SUBPROCESS_SDK,
payload=b'%s -m apache_beam.runners.worker.sdk_worker_main'
% sys.executable.encode('ascii')))
p = beam.Pipeline(runner=runner, options=pipeline_options)
lines = (p | 'read' >> beam.io.tfrecordio.ReadFromTFRecord(file_pattern)
| beam.combiners.Count.Globally()
| beam.io.WriteToText('/tmp/output'))
p.run()
Only one combination of apache_beam revision / worker type seems to work (I
refer to https://beam.apache.org/documentation/runners/direct/ for the
worker types)
* beam 2.16; neither multithread nor multiprocess achieve high cpu usage on
multiple cores
* beam 2.17: able to achieve high cpu usage on all 4 cores
* beam 2.18: not tested the mulithreaded mode but the multiprocess mode
fails when trying to serialize the Environment instance most likely because
of a change from 2.17 to 2.18.
I also tried briefly SparkRunner with version 2.16 but was no able to
achieve any throughput.
What is the recommnended way to achieve what I am trying to ? How can I
troubleshoot ?
