examples/multi-language/python/wordcount_external.py

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import logging

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external_transform_provider import ExternalTransformProvider
from apache_beam.typehints.row_type import RowTypeConstraint
"""A Python multi-language pipeline that counts words using multiple Java SchemaTransforms.

This pipeline reads an input text file then extracts the words, counts them, and writes the results Java 
SchemaTransforms. The transforms are listed below and can be found in 
src/main/java/org/apache/beam/examples/schematransforms/:
- `ExtractWordsProvider`
- `JavaCountProvider`
- `WriteWordsProvider`

These Java transforms are accessible to the Python pipeline via an expansion service. Check out the
[`README.md`](https://github.com/apache/beam/blob/master/examples/multi-language/README.md#1-start-the-expansion-service)
for instructions on how to download the jar and run this expansion service.

This example aims to demonstrate how to use the `ExternalTransformProvider` utility, which dynamically generates and
provides user-friendly wrappers for external transforms. 

Example commands for executing this program:

DirectRunner:
$ python wordcount_external.py \
      --runner DirectRunner \
      --input <INPUT FILE> \
      --output <OUTPUT FILE> \
      --expansion_service_port <PORT>

DataflowRunner:
$ python wordcount_external.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/wordcount_external/output" \
      --expansion_service_port <PORT>
"""

EXTRACT_IDENTIFIER = "beam:schematransform:org.apache.beam:extract_words:v1"
COUNT_IDENTIFIER = "beam:schematransform:org.apache.beam:count:v1"
WRITE_IDENTIFIER = "beam:schematransform:org.apache.beam:write_words:v1"


def run(input_path, output_path, expansion_service_port, pipeline_args):
    pipeline_options = PipelineOptions(pipeline_args)

    with beam.Pipeline(options=pipeline_options) as p:
        expansion_service = BeamJarExpansionService(
            "examples:multi-language:shadowJar")
        if expansion_service_port:
            expansion_service = "localhost:" + expansion_service_port

        provider = ExternalTransformProvider(expansion_service)
        # Retrieve portable transforms
        Extract = provider.get_urn(EXTRACT_IDENTIFIER)
        Count = provider.get_urn(COUNT_IDENTIFIER)
        Write = provider.get_urn(WRITE_IDENTIFIER)

        _ = (p
             | 'Read' >> beam.io.ReadFromText(input_path)
             | 'Prepare Rows' >> beam.Map(lambda line: beam.Row(line=line))
             | 'Extract Words' >> Extract(drop=["king", "palace"])
             | 'Count Words' >> Count()
             | 'Format Text' >> beam.Map(lambda row: beam.Row(line="%s: %s" % (
                 row.word, row.count))).with_output_types(
                     RowTypeConstraint.from_fields([('line', str)]))
             | 'Write' >> Write(file_path_prefix=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=False,
                        help='Expansion service port. If left empty, the '
                        'existing multi-language examples service will '
                        'be used by default.')
    known_args, pipeline_args = parser.parse_known_args()

    run(known_args.input, known_args.output, known_args.expansion_service_port,
        pipeline_args)