blob: 82d321f8c9c8325211f91fb47069113a139ef7d0 [file] [log] [blame]
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
#
import logging
import re
import typing
import apache_beam as beam
from apache_beam.io import ReadFromText
from apache_beam.io import WriteToText
from apache_beam.transforms.external import ImplicitSchemaPayloadBuilder
from apache_beam.options.pipeline_options import PipelineOptions
"""A Python multi-language pipeline that counts words.
This pipeline reads an input text file and counts the words using the Java SDK
transform `Count.perElement()`.
Example commands for executing the program:
DirectRunner:
$ python javacount.py --runner DirectRunner --environment_type=DOCKER --input <INPUT FILE> --output output --expansion_service_port <PORT>
DataflowRunner:
$ python javacount.py \
--runner DataflowRunner \
--temp_location $TEMP_LOCATION \
--project $GCP_PROJECT \
--region $GCP_REGION \
--job_name $JOB_NAME \
--num_workers $NUM_WORKERS \
--input "gs://dataflow-samples/shakespeare/kinglear.txt" \
--output "gs://$GCS_BUCKET/javacount/output" \
--expansion_service_port <PORT>
"""
class WordExtractingDoFn(beam.DoFn):
def process(self, element):
return re.findall(r'[\w\']+', element, re.UNICODE)
def run(input_path, output_path, expansion_service_port, pipeline_args):
pipeline_options = PipelineOptions(pipeline_args)
with beam.Pipeline(options=pipeline_options) as p:
lines = p | 'Read' >> ReadFromText(input_path).with_output_types(str)
words = lines | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))
java_output = (
words
| 'JavaCount' >> beam.ExternalTransform(
'beam:transform:org.apache.beam:javacount:v1',
None,
('localhost:%s' % expansion_service_port)))
def format(kv):
key, value = kv
return '%s:%s' % (key, value)
output = java_output | 'Format' >> beam.Map(format)
output | 'Write' >> WriteToText(output_path)
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'--input',
dest='input',
required=True,
help='Input file')
parser.add_argument(
'--output',
dest='output',
required=True,
help='Output file')
parser.add_argument(
'--expansion_service_port',
dest='expansion_service_port',
required=True,
help='Expansion service port')
known_args, pipeline_args = parser.parse_known_args()
run(
known_args.input,
known_args.output,
known_args.expansion_service_port,
pipeline_args)