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javadatagenerator.py
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javadatagenerator.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.
#
import logging
import numpy as np
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.transforms.external import JavaExternalTransform
from apache_beam.options.pipeline_options import PipelineOptions
"""A Python multi-language pipeline that produces a set of strings generated from Java.
This example uses the `JavaExternalTransform` API, hence the corresponding Java transform does not
have to be specifically registered with an expansion service.
Example commands for executing the program:
DirectRunner:
$ python javadatagenerator.py --runner DirectRunner --environment_type=DOCKER --output output --expansion_service_port <PORT>
DataflowRunner:
$ python javadatagenerator.py \
--runner DataflowRunner \
--temp_location $TEMP_LOCATION \
--project $GCP_PROJECT \
--region $GCP_REGION \
--job_name $JOB_NAME \
--num_workers $NUM_WORKERS \
--output "gs://$GCS_BUCKET/javadatagenerator/output" \
--expansion_service_port <PORT>
"""
def run(output_path, expansion_service_port, pipeline_args):
pipeline_options = PipelineOptions(pipeline_args)
with beam.Pipeline(options=pipeline_options) as p:
DataConfig = typing.NamedTuple(
'DataConfig', [('prefix', str), ('length', int), ('suffix', str)])
data_config = DataConfig(prefix='start', length=20, suffix='end')
java_transform = JavaExternalTransform(
'org.apache.beam.examples.multilanguage.JavaDataGenerator',
expansion_service=('localhost:%s' % expansion_service_port)).create(np.int32(100)).withDataConfig(data_config)
data = p | 'Generate' >> java_transform
data | 'Write' >> WriteToText(output_path)
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
import argparse
parser = argparse.ArgumentParser()
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.output,
known_args.expansion_service_port,
pipeline_args)