def test_model_bigqueryio(self):
# We cannot test BigQueryIO functionality in unit tests, therefore we limit
# ourselves to making sure the pipeline containing BigQuery sources and
# sinks can be built.
#
# To run locally, set `run_locally` to `True`. You will also have to set
# `project`, `dataset` and `table` to the BigQuery table the test will write
# to.
run_locally = False
if run_locally:
project = 'my-project'
dataset = 'samples' # this must already exist
table = 'model_bigqueryio' # this will be created if needed
options = PipelineOptions().view_as(GoogleCloudOptions)
options.project = project
with beam.Pipeline(options=options) as p:
snippets.model_bigqueryio(p, project, dataset, table)
else:
p = TestPipeline()
snippets.model_bigqueryio(p)
def test_model_bigqueryio(self):
# We cannot test BigQueryIO functionality in unit tests, therefore we limit
# ourselves to making sure the pipeline containing BigQuery sources and
# sinks can be built.
#
# To run locally, set `run_locally` to `True`. You will also have to set
# `project`, `dataset` and `table` to the BigQuery table the test will write
# to.
run_locally = False
if run_locally:
project = 'my-project'
dataset = 'samples' # this must already exist
table = 'model_bigqueryio' # this will be created if needed
options = PipelineOptions().view_as(GoogleCloudOptions)
options.project = project
with beam.Pipeline(options=options) as p:
snippets.model_bigqueryio(p, project, dataset, table)
else:
p = TestPipeline()
snippets.model_bigqueryio(p)
def test_model_bigqueryio(self):
# We cannot test BigQueryIO functionality in unit tests therefore we limit
# ourselves to making sure the pipeline containing BigQuery sources and
# sinks can be built.
snippets.model_bigqueryio()
def test_model_bigqueryio(self): # We cannot test BigQueryIO functionality in unit tests therefore we limit # ourselves to making sure the pipeline containing BigQuery sources and # sinks can be built. snippets.model_bigqueryio()
