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ewertonvsilva
ewertonvsilva
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"""A word-counting workflow.""" # pytype: skip-file import argparse import logging import re import os import apache_beam as beam from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions class WordExtractingDoFn(beam.DoFn): """Parse each line of input text into words.""" def process(self, element): """Returns an iterator over the words of this element. The element is a line of text. If the line is blank, note that, too. Args: element: the element being processed Returns: The processed element. """ return re.findall(r'[\w\']+', element, re.UNICODE) def run(argv=None, save_main_session=True): """Main entry point; defines and runs the wordcount pipeline.""" parser = argparse.ArgumentParser() parser.add_argument( '--input', dest='input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Input file to process.') parser.add_argument( '--output', dest='output', default='gs://<bucket>/newoutput', help='Output file to write results to.') #argv = [ # '--project=<...>', # '--region=us-central1', # '--runner=DataflowRunner', # '--staging_location=gs://<...>/temp/', # '--temp_location=gs://<...>/temp/', # '--template_location=gs://<...>/templates/word_count_template' # ] known_args, pipeline_args = parser.parse_known_args(argv) pipeline_args.extend([ '--runner=DataflowRunner', '--project=<project-name>', '--region=<region>', '--staging_location=gs://<bucket>/', '--temp_location=gs://<bucket>/temp', '--job_name=your-wordcount-job', ]) # We use the save_main_session option because one or more DoFn's in this # workflow rely on global context (e.g., a module imported at module level). pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = save_main_session # The pipeline will be run on exiting the with block. with beam.Pipeline(options=pipeline_options) as p: lines = p | 'Read' >> ReadFromText(known_args.input) counts = ( lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str)) | 'PairWIthOne' >> beam.Map(lambda x: (x, 1)) | 'GroupAndSum' >> beam.CombinePerKey(sum)) # Format the counts into a PCollection of strings. def format_result(word, count): return '%s: %d' % (word, count) output = counts | 'Format' >> beam.MapTuple(format_result) # Write the output using a "Write" transform that has side effects. # pylint: disable=expression-not-assigned output | 'Write' >> WriteToText(known_args.output) if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) run() 
"""A word-counting workflow.""" # pytype: skip-file import argparse import logging import re import os import apache_beam as beam from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions class WordExtractingDoFn(beam.DoFn): """Parse each line of input text into words.""" def process(self, element): """Returns an iterator over the words of this element. The element is a line of text. If the line is blank, note that, too. Args: element: the element being processed Returns: The processed element. """ return re.findall(r'[\w\']+', element, re.UNICODE) def run(argv=None, save_main_session=True): """Main entry point; defines and runs the wordcount pipeline.""" parser = argparse.ArgumentParser() parser.add_argument( '--input', dest='input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Input file to process.') parser.add_argument( '--output', dest='output', default='gs://<bucket>/newoutput', help='Output file to write results to.') #argv = [ # '--project=<...>', # '--region=us-central1', # '--runner=DataflowRunner', # '--staging_location=gs://<...>/temp/', # '--temp_location=gs://<...>/temp/', # '--template_location=gs://<...>/templates/word_count_template' # ] known_args, pipeline_args = parser.parse_known_args(argv) pipeline_args.extend([ '--runner=DataflowRunner', '--project=<project-name>', '--region=<region>', '--staging_location=gs://<bucket>/', '--temp_location=gs://<bucket>/temp', '--job_name=your-wordcount-job', ]) # We use the save_main_session option because one or more DoFn's in this # workflow rely on global context (e.g., a module imported at module level). pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = save_main_session # The pipeline will be run on exiting the with block. with beam.Pipeline(options=pipeline_options) as p: lines = p | 'Read' >> ReadFromText(known_args.input) counts = ( lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str)) | 'PairWIthOne' >> beam.Map(lambda x: (x, 1)) | 'GroupAndSum' >> beam.CombinePerKey(sum)) # Format the counts into a PCollection of strings. def format_result(word, count): return '%s: %d' % (word, count) output = counts | 'Format' >> beam.MapTuple(format_result) # Write the output using a "Write" transform that has side effects. # pylint: disable=expression-not-assigned output | 'Write' >> WriteToText(known_args.output) if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) run() 
"""A word-counting workflow.""" # pytype: skip-file import argparse import logging import re import os import apache_beam as beam from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions class WordExtractingDoFn(beam.DoFn): """Parse each line of input text into words.""" def process(self, element): """Returns an iterator over the words of this element. The element is a line of text. If the line is blank, note that, too. Args: element: the element being processed Returns: The processed element. """ return re.findall(r'[\w\']+', element, re.UNICODE) def run(argv=None, save_main_session=True): """Main entry point; defines and runs the wordcount pipeline.""" parser = argparse.ArgumentParser() parser.add_argument( '--input', dest='input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Input file to process.') parser.add_argument( '--output', dest='output', default='gs://<bucket>/newoutput', help='Output file to write results to.') #argv = [ # '--project=<...>', # '--region=us-central1', # '--runner=DataflowRunner', # '--staging_location=gs://<...>/temp/', # '--temp_location=gs://<...>/temp/', # '--template_location=gs://<...>/templates/word_count_template' # ] known_args, pipeline_args = parser.parse_known_args(argv) pipeline_args.extend([ '--runner=DataflowRunner', '--project=<project-name>', '--region=<region>', '--staging_location=gs://<bucket>/', '--temp_location=gs://<bucket>/temp', '--job_name=your-wordcount-job', ]) # We use the save_main_session option because one or more DoFn's in this # workflow rely on global context (e.g., a module imported at module level). pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = save_main_session # The pipeline will be run on exiting the with block. with beam.Pipeline(options=pipeline_options) as p: lines = p | 'Read' >> ReadFromText(known_args.input) counts = ( lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str)) | 'PairWIthOne' >> beam.Map(lambda x: (x, 1)) | 'GroupAndSum' >> beam.CombinePerKey(sum)) # Format the counts into a PCollection of strings. def format_result(word, count): return '%s: %d' % (word, count) output = counts | 'Format' >> beam.MapTuple(format_result) # Write the output using a "Write" transform that has side effects. # pylint: disable=expression-not-assigned output | 'Write' >> WriteToText(known_args.output) if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) run() 
"""A word-counting workflow.""" # pytype: skip-file import argparse import logging import re import os import apache_beam as beam from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions class WordExtractingDoFn(beam.DoFn): """Parse each line of input text into words.""" def process(self, element): """Returns an iterator over the words of this element. The element is a line of text. If the line is blank, note that, too. Args: element: the element being processed Returns: The processed element. """ return re.findall(r'[\w\']+', element, re.UNICODE) def run(argv=None, save_main_session=True): """Main entry point; defines and runs the wordcount pipeline.""" parser = argparse.ArgumentParser() parser.add_argument( '--input', dest='input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Input file to process.') parser.add_argument( '--output', dest='output', default='gs://<bucket>/newoutput', help='Output file to write results to.') #argv = [ # '--project=<...>', # '--region=us-central1', # '--runner=DataflowRunner', # '--staging_location=gs://<...>/temp/', # '--temp_location=gs://<...>/temp/', # '--template_location=gs://<...>/templates/word_count_template' # ] known_args, pipeline_args = parser.parse_known_args(argv) pipeline_args.extend([ '--runner=DataflowRunner', '--project=<project-name>', '--region=<region>', '--staging_location=gs://<bucket>/', '--temp_location=gs://<bucket>/temp', '--job_name=your-wordcount-job', ]) # We use the save_main_session option because one or more DoFn's in this # workflow rely on global context (e.g., a module imported at module level). pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = save_main_session # The pipeline will be run on exiting the with block. with beam.Pipeline(options=pipeline_options) as p: lines = p | 'Read' >> ReadFromText(known_args.input) counts = ( lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str)) | 'PairWIthOne' >> beam.Map(lambda x: (x, 1)) | 'GroupAndSum' >> beam.CombinePerKey(sum)) # Format the counts into a PCollection of strings. def format_result(word, count): return '%s: %d' % (word, count) output = counts | 'Format' >> beam.MapTuple(format_result) # Write the output using a "Write" transform that has side effects. # pylint: disable=expression-not-assigned output | 'Write' >> WriteToText(known_args.output) if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) run()
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ewertonvsilva
ewertonvsilva
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You DAG is ok, the problem is on the Beam Python file, there is an error when you send the Dataflow args in the argv. The best approach is extend pipeline_args. And the job is not being submitted because you are sending the argv in the beam.Pipeline.

Following is the fixed code:

word_count.py :

"""A word-counting workflow.""" # pytype: skip-file import argparse import logging import re import os import apache_beam as beam from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions class WordExtractingDoFn(beam.DoFn): """Parse each line of input text into words.""" def process(self, element): """Returns an iterator over the words of this element. The element is a line of text. If the line is blank, note that, too. Args: element: the element being processed Returns: The processed element. """ return re.findall(r'[\w\']+', element, re.UNICODE) def run(argv=None, save_main_session=True): """Main entry point; defines and runs the wordcount pipeline.""" parser = argparse.ArgumentParser() parser.add_argument( '--input', dest='input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Input file to process.') parser.add_argument( '--output', dest='output', default='gs://<bucket>/newoutput', help='Output file to write results to.') #argv = [ # '--project=<...>', # '--region=us-central1', # '--runner=DataflowRunner', # '--staging_location=gs://<...>/temp/', # '--temp_location=gs://<...>/temp/', # '--template_location=gs://<...>/templates/word_count_template' # ] known_args, pipeline_args = parser.parse_known_args(argv) pipeline_args.extend([ '--runner=DataflowRunner', '--project=<project-name>', '--region=<region>', '--staging_location=gs://<bucket>/', '--temp_location=gs://<bucket>/temp', '--job_name=your-wordcount-job', ]) # We use the save_main_session option because one or more DoFn's in this # workflow rely on global context (e.g., a module imported at module level). pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = save_main_session # The pipeline will be run on exiting the with block. with beam.Pipeline(options=pipeline_options) as p: lines = p | 'Read' >> ReadFromText(known_args.input) counts = ( lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str)) | 'PairWIthOne' >> beam.Map(lambda x: (x, 1)) | 'GroupAndSum' >> beam.CombinePerKey(sum)) # Format the counts into a PCollection of strings. def format_result(word, count): return '%s: %d' % (word, count) output = counts | 'Format' >> beam.MapTuple(format_result) # Write the output using a "Write" transform that has side effects. # pylint: disable=expression-not-assigned output | 'Write' >> WriteToText(known_args.output) if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) run()