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addprefix.py
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addprefix.py
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#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""A Python multi-language pipeline that adds prefixes to a given set of strings.
This pipeline reads an input text file and adds two prefixes to every line read from the file.
* Prefix 'java'. This is added by a multi-language Java transform named 'JavaPrefix'.
* Prefix 'python'. This is added by a Python transform.
Example commands for executing the program:
DirectRunner:
$ python addprefix.py --runner DirectRunner --environment_type=DOCKER --input <INPUT FILE> --output output --expansion_service_port <PORT>
DataflowRunner:
$ python addprefix.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/javaprefix/output" \
--expansion_service_port <PORT>
"""
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
def run(input_path, output_path, expansion_service_port, pipeline_args):
pipeline_options = PipelineOptions(pipeline_args)
with beam.Pipeline(options=pipeline_options) as p:
input = p | 'Read' >> ReadFromText(input_path).with_output_types(str)
java_output = (
input
| 'JavaPrefix' >> beam.ExternalTransform(
'beam:transform:org.apache.beam:javaprefix:v1',
ImplicitSchemaPayloadBuilder({'prefix': 'java:'}),
('localhost:%s' % expansion_service_port)))
def python_prefix(record):
return 'python:%s' % record
output = java_output | 'PythonPrefix' >> beam.Map(python_prefix)
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)