def _do_map(input_reader, processor_func, finalize_func, params, _shards,
_output_writer, _output_writer_kwargs, _job_name, _queue_name,
*processor_args, **processor_kwargs):
handler_spec = qualname(unpacker)
handler_params = {
"func": qualname(processor_func)
if callable(processor_func) else processor_func,
"args": processor_args,
"kwargs": processor_kwargs
}
handler_params.update(params)
pipelines = []
pipelines.append(
MapperPipeline(_job_name,
handler_spec=handler_spec,
input_reader_spec=qualname(input_reader),
output_writer_spec=qualname(_output_writer)
if _output_writer else None,
params=handler_params,
shards=_shards))
if finalize_func:
pipelines.append(
CallbackPipeline(
qualname(finalize_func) if callable(finalize_func) else
finalize_func, *processor_args, **processor_kwargs))
new_pipeline = DynamicPipeline(pipelines)
new_pipeline.start(queue_name=_queue_name or 'default')
return new_pipeline
def run_transform():
JOB_ID_PREFIX = 'ch12_%d' % int(time.time())
TMP_PATH = 'tmp/mapreduce/%s' % JOB_ID_PREFIX
# Extract from BigQuery to GCS.
run_bigquery_job(
JOB_ID_PREFIX, 'extract', {
'sourceTable': table_reference('add_zip_input'),
'destinationUri': 'gs://%s/%s/input-*' % (GCS_BUCKET, TMP_PATH),
'destinationFormat': 'NEWLINE_DELIMITED_JSON',
})
# Run the mapper job to annotate the records.
mapper = MapperPipeline(
'Add Zip',
'add_zip.apply',
'mapreduce.input_readers.FileInputReader',
'mapreduce.output_writers._GoogleCloudStorageOutputWriter',
params={
'files': ['/gs/%s/%s/input-*' % (GCS_BUCKET, TMP_PATH)],
'format': 'lines',
'output_writer': {
'bucket_name': GCS_BUCKET,
'naming_format': TMP_PATH + '/output-$num',
}
})
mapper.start()
wait_for_pipeline(mapper.pipeline_id)
# Load from GCS into BigQuery.
run_bigquery_job(
JOB_ID_PREFIX, 'load', {
'destinationTable': table_reference('add_zip_output'),
'sourceUris': ['gs://%s/%s/output-*' % (GCS_BUCKET, TMP_PATH)],
'sourceFormat': 'NEWLINE_DELIMITED_JSON',
'schema': OUTPUT_SCHEMA,
'writeDisposition': 'WRITE_TRUNCATE',
})
