Not able to run python pipeline through airflow BeamRunPythonPipelineOperator - google-cloud-platform

Question

I'm not able to run python pipeline through airflow BeamRunPythonPipelineOperator. Below is my complete code:
DAG FILE
import os
from datetime import datetime, timedelta
from airflow.utils.dates import days_ago
from airflow import DAG
from airflow.providers.google.cloud.operators.dataflow import DataflowConfiguration
from airflow.providers.apache.beam.operators.beam import BeamRunPythonPipelineOperator
from airflow.providers.google.cloud.operators.dataflow import DataflowTemplatedJobStartOperator
default_args = {
"owner": "<...>",
"start_date": days_ago(1),
'dataflow_default_options': {
"project": "<...>",
}
}
dag = DAG(
dag_id="word_count",
default_args=default_args,
schedule_interval="#once"
)
start_python_pipeline_dataflow_runner = BeamRunPythonPipelineOperator(
task_id="start_python_pipeline_dataflow_runner",
runner="DataflowRunner",
py_file="gs://<...>/word_count.py",
pipeline_options={
'input':"gs://<...>/kinglear.txt",
'output':"gs://<...>/output.txt",
'temp_location':"gs://<...>/temp/",
'staging_location':"gs://<...>/temp/",
},
py_options=[],
py_requirements=['apache-beam[gcp]==2.26.0'],
py_interpreter='python3',
py_system_site_packages=False,
dataflow_config=DataflowConfiguration(
job_name='{{task.task_id}}', project_id="<...>", location="us-central1"
),
dag=dag,
)
Python File (word_count.py)
"""A word-counting workflow."""
# pytype: skip-file
import argparse
import logging
import re
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://<...>/kinglear.txt',
help='Input file to process.')
parser.add_argument(
'--output',
dest='output',
default='gs://<...>/output.txt',
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)
# 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(argv=argv,options=pipeline_options) as p:
# Read the text file[pattern] into a PCollection.
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()
Below is the screenshot of the composer:
I am not able to see the dataflow job in the console, as well as count, result in the bucket. Could anyone suggest to me the right approach or any suggestions on this?

Answer 1

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()