def step_with(name, start, stop, status):
return has_properties(name=name,
title=name,
attrib=all_of(
has_entry('start', has_float(greater_than_or_equal_to(start))),
has_entry('stop', has_float(less_than_or_equal_to(stop))),
has_entry('status', status)))
def test_hourly_team_score_it(self):
state_verifier = PipelineStateMatcher(PipelineState.DONE)
query = ('SELECT COUNT(*) FROM [%s:%s.%s]' % (self.project,
self.dataset.name,
self.OUTPUT_TABLE))
bigquery_verifier = BigqueryMatcher(self.project,
query,
self.DEFAULT_EXPECTED_CHECKSUM)
extra_opts = {'input': self.DEFAULT_INPUT_FILE,
'dataset': self.dataset.name,
'window_duration': 1,
'on_success_matcher': all_of(state_verifier,
bigquery_verifier)}
# Register clean up before pipeline execution
# Note that actual execution happens in reverse order.
self.addCleanup(self._cleanup_dataset)
self.addCleanup(utils.delete_bq_table, self.project,
self.dataset.name, self.OUTPUT_TABLE)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
hourly_team_score.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_multiple_destinations_transform(self):
output_table_1 = '%s%s' % (self.output_table, 1)
output_table_2 = '%s%s' % (self.output_table, 2)
output_table_3 = '%s%s' % (self.output_table, 3)
output_table_4 = '%s%s' % (self.output_table, 4)
pipeline_verifiers = [
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_1,
data=[(d['name'], d['language'])
for d in _ELEMENTS
if 'language' in d]),
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_2,
data=[(d['name'], d['foundation'])
for d in _ELEMENTS
if 'foundation' in d]),
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_3,
data=[(d['name'], d['language'])
for d in _ELEMENTS
if 'language' in d]),
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_4,
data=[(d['name'], d['foundation'])
for d in _ELEMENTS
if 'foundation' in d])]
args = self.test_pipeline.get_full_options_as_args(
on_success_matcher=all_of(*pipeline_verifiers))
with beam.Pipeline(argv=args) as p:
input = p | beam.Create(_ELEMENTS)
# Get all input in same machine
input = (input
| beam.Map(lambda x: (None, x))
| beam.GroupByKey()
| beam.FlatMap(lambda elm: elm[1]))
_ = (input |
"WriteWithMultipleDestsFreely" >> bigquery.WriteToBigQuery(
table=lambda x: (output_table_1
if 'language' in x
else output_table_2),
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=beam.io.BigQueryDisposition.WRITE_EMPTY))
_ = (input |
"WriteWithMultipleDests" >> bigquery.WriteToBigQuery(
table=lambda x: (output_table_3
if 'language' in x
else output_table_4),
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=beam.io.BigQueryDisposition.WRITE_EMPTY,
max_file_size=20,
max_files_per_bundle=-1))
def test_bigquery_tornadoes_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
project = test_pipeline.get_option('project')
dataset = 'BigQueryTornadoesIT'
table = 'monthly_tornadoes_%s' % int(round(time.time() * 1000))
output_table = '.'.join([dataset, table])
query = 'SELECT month, tornado_count FROM `%s`' % output_table
pipeline_verifiers = [PipelineStateMatcher(),
BigqueryMatcher(
project=project,
query=query,
checksum=self.DEFAULT_CHECKSUM)]
extra_opts = {'output': output_table,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Register cleanup before pipeline execution.
# Note that actual execution happens in reverse order.
self.addCleanup(utils.delete_bq_table, project, dataset, table)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
bigquery_tornadoes.run(
test_pipeline.get_full_options_as_args(**extra_opts))
def run_bigquery_io_read_pipeline(self, input_size):
test_pipeline = TestPipeline(is_integration_test=True)
pipeline_verifiers = [PipelineStateMatcher(),]
extra_opts = {'input_table': self.DEFAULT_DATASET + "." +
self.DEFAULT_TABLE_PREFIX + input_size,
'num_records': self.NUM_RECORDS[input_size],
'on_success_matcher': all_of(*pipeline_verifiers)}
bigquery_io_read_pipeline.run(test_pipeline.get_full_options_as_args(
**extra_opts))
def test_many_elements():
Box = xmlfied('box', foos=Many(Element(name='foo')))
box = Box(foos=['a', 'b', 'c'])
assert_that(etree.tostring(box.toxml()), all_of(
string_contains_in_order('<box>', '<foos>', '<foo>', 'a', '</foo>', '</foos>', '</box>'),
string_contains_in_order('<box>', '<foos>', '<foo>', 'b', '</foo>', '</foos>', '</box>'),
string_contains_in_order('<box>', '<foos>', '<foo>', 'c', '</foo>', '</foos>', '</box>'),
))
def test_step_attach(timed_report_for):
report, start, stop = timed_report_for("""
import pytest
def test_ololo_pewpew():
with pytest.allure.step(title='withattach'):
pytest.allure.attach('myattach', 'abcdef')
""")
assert_that(report.findall('.//test-case/steps/step'), contains(all_of(step_with('withattach', start, stop, Status.PASSED),
has_property('attachments',
has_property('attachment',
has_entry('title', 'myattach'))))))
def test_one_job_fails_all_jobs_fail(self):
# If one of the import jobs fails, then other jobs must not be performed.
# This is to avoid reinsertion of some records when a pipeline fails and
# is rerun.
output_table_1 = '%s%s' % (self.output_table, 1)
output_table_2 = '%s%s' % (self.output_table, 2)
self.bigquery_client.get_or_create_table(
self.project, self.dataset_id,
output_table_1.split('.')[1],
bigquery_tools.parse_table_schema_from_json(self.BIG_QUERY_SCHEMA),
None, None)
self.bigquery_client.get_or_create_table(
self.project, self.dataset_id,
output_table_2.split('.')[1],
bigquery_tools.parse_table_schema_from_json(
self.BIG_QUERY_SCHEMA_2), None, None)
pipeline_verifiers = [
BigqueryFullResultMatcher(project=self.project,
query="SELECT name, language FROM %s" %
output_table_1,
data=[]),
BigqueryFullResultMatcher(project=self.project,
query="SELECT name, foundation FROM %s" %
output_table_2,
data=[])
]
args = self.test_pipeline.get_full_options_as_args(
experiments='use_beam_bq_sink')
with self.assertRaises(Exception):
with beam.Pipeline(argv=args) as p:
input = p | beam.Create(_ELEMENTS)
input2 = p | "Broken record" >> beam.Create(
['language_broken_record'])
input = (input, input2) | beam.Flatten()
_ = (input
| "WriteWithMultipleDests" >> bigquery.WriteToBigQuery(
table=lambda x:
(output_table_1
if 'language' in x else output_table_2),
create_disposition=(
beam.io.BigQueryDisposition.CREATE_IF_NEEDED),
write_disposition=beam.io.BigQueryDisposition.
WRITE_APPEND))
hamcrest_assert(p, all_of(*pipeline_verifiers))
def test_leader_board_it(self):
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
success_condition = 'total_score=5000 LIMIT 1'
users_query = ('SELECT total_score FROM [%s:%s.%s] '
'WHERE %s' %
(self.project, self.dataset.name,
self.OUTPUT_TABLE_USERS, success_condition))
bq_users_verifier = BigqueryMatcher(self.project, users_query,
self.DEFAULT_EXPECTED_CHECKSUM)
teams_query = ('SELECT total_score FROM [%s:%s.%s] '
'WHERE %s' %
(self.project, self.dataset.name,
self.OUTPUT_TABLE_TEAMS, success_condition))
bq_teams_verifier = BigqueryMatcher(self.project, teams_query,
self.DEFAULT_EXPECTED_CHECKSUM)
extra_opts = {
'subscription':
self.input_sub.full_name,
'dataset':
self.dataset.name,
'topic':
self.input_topic.full_name,
'team_window_duration':
1,
'wait_until_finish_duration':
self.WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher':
all_of(state_verifier, bq_users_verifier, bq_teams_verifier)
}
# Register cleanup before pipeline execution.
# Note that actual execution happens in reverse order.
self.addCleanup(self._cleanup_pubsub)
self.addCleanup(self._cleanup_dataset)
self.addCleanup(utils.delete_bq_table, self.project, self.dataset.name,
self.OUTPUT_TABLE_USERS)
self.addCleanup(utils.delete_bq_table, self.project, self.dataset.name,
self.OUTPUT_TABLE_TEAMS)
# Generate input data and inject to PubSub.
test_utils.wait_for_subscriptions_created(
[self.input_topic, self.input_sub])
self._inject_pubsub_game_events(self.input_topic,
self.DEFAULT_INPUT_COUNT)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
leader_board.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def testNoMatchIfArgumentFailsToSatisfyAllOfManyOtherMatchers(self):
self.assert_does_not_match(
"matcher in the middle",
all_of(
equal_to("good"),
equal_to("good"),
equal_to("good"),
equal_to("bad"),
equal_to("good"),
equal_to("good"),
),
"good",
)
def test_cyrillic_exc(report_for):
report = report_for(u"""
# -*- coding: utf-8 -*-
def test_foo():
raise Exception(u'русские буквы')
""")
assert_that(report.findall('.//failure'), contains(
all_of(has_property('message', u'Exception: русские буквы'),
has_property('stack-trace', u'''def test_foo():
> raise Exception(u'русские буквы')
E Exception: русские буквы
test_cyrillic_exc.py:3: Exception'''))))
def run_datastore_write(self, limit=None):
test_pipeline = TestPipeline(is_integration_test=True)
current_time = datetime.now().strftime("%m%d%H%M%S")
seed = random.randint(0, 100000)
kind = 'testkind%s%d' % (current_time, seed)
pipeline_verifiers = [PipelineStateMatcher()]
extra_opts = {'kind': kind,
'num_entities': self.NUM_ENTITIES,
'on_success_matcher': all_of(*pipeline_verifiers)}
if limit is not None:
extra_opts['limit'] = limit
datastore_write_it_pipeline.run(test_pipeline.get_full_options_as_args(
**extra_opts))
def test_unicode_labels(report_for):
report = report_for(u"""
# -*- coding: utf-8 -*-
import allure
@allure.feature(u'русские буквы')
@allure.story(u'еще русские буквы')
def test_a():
pass
""")
assert_that(report, all_of(
has_label('test_a', 'feature', u'русские буквы'),
has_label('test_a', 'story', u'еще русские буквы')))
def test_cyrillic_exc(report_for):
report = report_for(u"""
# -*- coding: utf-8 -*-
def test_foo():
raise Exception(u'русские буквы')
""")
assert_that(report.findall('.//failure'), contains(
all_of(has_property('message', u'Exception: русские буквы'),
has_property('stack-trace', u'''def test_foo():
> raise Exception(u'русские буквы')
E Exception: русские буквы
test_cyrillic_exc.py:3: Exception'''))))
def _test_streaming(self, with_attributes):
"""Runs IT pipeline with message verifier.
Args:
with_attributes: False - Reads and writes message data only.
True - Reads and writes message data and attributes. Also verifies
id_label and timestamp_attribute features.
"""
# Set on_success_matcher to verify pipeline state and pubsub output. These
# verifications run on a (remote) worker.
# Expect the state to be RUNNING since a streaming pipeline is usually
# never DONE. The test runner will cancel the pipeline after verification.
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
expected_messages = self.EXPECTED_OUTPUT_MESSAGES[self.runner_name]
if not with_attributes:
expected_messages = [
pubsub_msg.data.decode('utf-8')
for pubsub_msg in expected_messages
]
if self.runner_name == 'TestDirectRunner':
strip_attributes = None
else:
strip_attributes = [self.ID_LABEL, self.TIMESTAMP_ATTRIBUTE]
pubsub_msg_verifier = PubSubMessageMatcher(
self.project,
self.output_sub.name,
expected_messages,
timeout=MESSAGE_MATCHER_TIMEOUT_S,
with_attributes=with_attributes,
strip_attributes=strip_attributes)
extra_opts = {
'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
'wait_until_finish_duration': TEST_PIPELINE_DURATION_MS,
'on_success_matcher': all_of(state_verifier, pubsub_msg_verifier)
}
# Generate input data and inject to PubSub.
for msg in self.INPUT_MESSAGES[self.runner_name]:
self.pub_client.publish(self.input_topic.name, msg.data,
**msg.attributes)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
pubsub_it_pipeline.run_pipeline(
argv=self.test_pipeline.get_full_options_as_args(**extra_opts),
with_attributes=with_attributes,
id_label=self.ID_LABEL,
timestamp_attribute=self.TIMESTAMP_ATTRIBUTE)
def test_big_query_standard_sql(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
extra_opts = {'query': STANDARD_QUERY,
'output': self.output_table,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': True,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_nested_steps(timed_report_for):
report, start, stop = timed_report_for("""
import pytest
def test_ololo_pewpew():
with pytest.allure.step(title='outer'):
with pytest.allure.step(title='inner'):
assert False
""")
assert_that(report.findall('.//test-case/steps/step'), contains(all_of(step_with('outer', start, stop, Status.FAILED),
has_property('steps',
has_property('step',
step_with('inner', start, stop, Status.FAILED))))))
def test_leader_board_it(self):
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
success_condition = 'total_score=5000 LIMIT 1'
users_query = ('SELECT total_score FROM [%s:%s.%s] '
'WHERE %s' % (self.project,
self.dataset.name,
self.OUTPUT_TABLE_USERS,
success_condition))
bq_users_verifier = BigqueryMatcher(self.project,
users_query,
self.DEFAULT_EXPECTED_CHECKSUM)
teams_query = ('SELECT total_score FROM [%s:%s.%s] '
'WHERE %s' % (self.project,
self.dataset.name,
self.OUTPUT_TABLE_TEAMS,
success_condition))
bq_teams_verifier = BigqueryMatcher(self.project,
teams_query,
self.DEFAULT_EXPECTED_CHECKSUM)
extra_opts = {'subscription': self.input_sub.full_name,
'dataset': self.dataset.name,
'topic': self.input_topic.full_name,
'team_window_duration': 1,
'wait_until_finish_duration':
self.WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(state_verifier,
bq_users_verifier,
bq_teams_verifier)}
# Register cleanup before pipeline execution.
# Note that actual execution happens in reverse order.
self.addCleanup(self._cleanup_pubsub)
self.addCleanup(self._cleanup_dataset)
self.addCleanup(utils.delete_bq_table, self.project,
self.dataset.name, self.OUTPUT_TABLE_USERS)
self.addCleanup(utils.delete_bq_table, self.project,
self.dataset.name, self.OUTPUT_TABLE_TEAMS)
# Generate input data and inject to PubSub.
test_utils.wait_for_subscriptions_created([self.input_topic,
self.input_sub])
self._inject_pubsub_game_events(self.input_topic, self.DEFAULT_INPUT_COUNT)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
leader_board.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_big_query_standard_sql(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
extra_opts = {'query': STANDARD_QUERY,
'output': self.output_table,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': True,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_add_environment_in_testcase(report_for, result, environment_xml):
report_for("""
import pytest
import allure
def test_dummy():
allure.environment(foo='bar')
assert %s
""" % result)
assert_that(
environment_xml().findall('.//parameter')[0],
all_of(has_property('name', 'foo'), has_property('key', 'foo'),
has_property('value', 'bar')))
def test_nested_steps(timed_report_for):
report, start, stop = timed_report_for("""
import pytest
def test_ololo_pewpew():
with pytest.allure.step(title='outer'):
with pytest.allure.step(title='inner'):
assert False
""")
assert_that(report.findall('.//test-case/steps/step'), contains(all_of(step_with('outer', start, stop, Status.FAILED),
has_property('steps',
has_property('step',
step_with('inner', start, stop, Status.FAILED))))))
def test_many_elements():
Box = xmlfied('box', foos=WrappedMany(Element(name='foo')))
box = Box(foos=['a', 'b', 'c'])
assert_that(
etree.tostring(box.toxml()),
all_of(
string_contains_in_order('<box>', '<foos>', '<foo>', 'a', '</foo>',
'</foos>', '</box>'),
string_contains_in_order('<box>', '<foos>', '<foo>', 'b', '</foo>',
'</foos>', '</box>'),
string_contains_in_order('<box>', '<foos>', '<foo>', 'c', '</foo>',
'</foos>', '</box>')))
def run_datastore_write(self, limit=None):
test_pipeline = TestPipeline(is_integration_test=True)
current_time = datetime.now().strftime("%m%d%H%M%S")
seed = random.randint(0, 100000)
kind = 'testkind%s%d' % (current_time, seed)
pipeline_verifiers = [PipelineStateMatcher()]
extra_opts = {'kind': kind,
'num_entities': self.NUM_ENTITIES,
'on_success_matcher': all_of(*pipeline_verifiers)}
if limit is not None:
extra_opts['limit'] = limit
datastore_write_it_pipeline.run(test_pipeline.get_full_options_as_args(
**extra_opts))
def _run_wordcount_it(self, run_wordcount, **opts):
test_pipeline = TestPipeline(is_integration_test=True)
extra_opts = {}
# Set extra options to the pipeline for test purpose
test_output = '/'.join([
test_pipeline.get_option('output'),
str(int(time.time() * 1000)), 'results'
])
extra_opts['output'] = test_output
test_input = test_pipeline.get_option('input')
if test_input:
extra_opts['input'] = test_input
arg_sleep_secs = test_pipeline.get_option('sleep_secs')
sleep_secs = int(
arg_sleep_secs) if arg_sleep_secs is not None else None
expect_checksum = (test_pipeline.get_option('expect_checksum')
or self.DEFAULT_CHECKSUM)
pipeline_verifiers = [
PipelineStateMatcher(),
FileChecksumMatcher(test_output + '*-of-*', expect_checksum,
sleep_secs)
]
extra_opts['on_success_matcher'] = all_of(*pipeline_verifiers)
extra_opts.update(opts)
# Register clean up before pipeline execution
self.addCleanup(delete_files, [test_output + '*'])
publish_to_bq = bool(
test_pipeline.get_option('publish_to_big_query') or False)
# Start measure time for performance test
start_time = time.time()
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
run_wordcount(
test_pipeline.get_full_options_as_args(**extra_opts),
save_main_session=False,
)
end_time = time.time()
run_time = end_time - start_time
if publish_to_bq:
self._publish_metrics(test_pipeline, run_time)
def test_streaming_wordcount_it(self):
# Set extra options to the pipeline for test purpose
pipeline_verifiers = [PipelineStateMatcher(PipelineState.RUNNING)]
extra_opts = {'input_sub': self.input_sub.full_name,
'output_topic': self.output_topic.full_name,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Generate input data and inject to PubSub.
test_utils.wait_for_subscriptions_created([self.input_sub])
self._inject_numbers(self.input_topic, DEFAULT_INPUT_NUMBERS)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
streaming_wordcount.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_big_query_new_types(self):
expected_checksum = test_utils.compute_hash(NEW_TYPES_OUTPUT_EXPECTED)
verify_query = NEW_TYPES_OUTPUT_VERIFY_QUERY % self.output_table
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
self._setup_new_types_env()
extra_opts = {
'query': NEW_TYPES_QUERY % (self.dataset_id, NEW_TYPES_INPUT_TABLE),
'output': self.output_table,
'output_schema': NEW_TYPES_OUTPUT_SCHEMA,
'use_standard_sql': False,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_big_query_new_types(self):
expected_checksum = test_utils.compute_hash(NEW_TYPES_OUTPUT_EXPECTED)
verify_query = NEW_TYPES_OUTPUT_VERIFY_QUERY % self.output_table
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
self._setup_new_types_env()
extra_opts = {
'query': NEW_TYPES_QUERY % (self.dataset_id, NEW_TYPES_INPUT_TABLE),
'output': self.output_table,
'output_schema': NEW_TYPES_OUTPUT_SCHEMA,
'use_standard_sql': False,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_run_example_with_setup_file(self):
pipeline = TestPipeline(is_integration_test=True)
coordinate_output = FileSystems.join(
pipeline.get_option('output'),
'juliaset-{}'.format(str(uuid.uuid4())),
'coordinates.txt')
extra_args = {
'coordinate_output': coordinate_output,
'grid_size': self.GRID_SIZE,
'setup_file': os.path.normpath(
os.path.join(os.path.dirname(__file__), '..', 'setup.py')),
'on_success_matcher': all_of(PipelineStateMatcher(PipelineState.DONE)),
}
args = pipeline.get_full_options_as_args(**extra_args)
juliaset.run(args)
def test_big_query_legacy_sql(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
extra_opts = {'query': LEGACY_QUERY,
'output': self.output_table,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': False,
'wait_until_finish_duration': WAIT_UNTIL_FINISH_DURATION_MS,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def run_bigquery_io_read_pipeline(self, input_size):
test_pipeline = TestPipeline(is_integration_test=True)
pipeline_verifiers = [
PipelineStateMatcher(),
]
extra_opts = {
'input_table':
self.DEFAULT_DATASET + "." + self.DEFAULT_TABLE_PREFIX +
input_size,
'num_records':
self.NUM_RECORDS[input_size],
'on_success_matcher':
all_of(*pipeline_verifiers)
}
bigquery_io_read_pipeline.run(
test_pipeline.get_full_options_as_args(**extra_opts))
def test_wordcount_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
output = '/'.join([test_pipeline.get_option('output'),
test_pipeline.get_option('job_name'),
'results'])
pipeline_verifiers = [PipelineStateMatcher(),
FileChecksumMatcher(output + '*-of-*',
self.DEFAULT_CHECKSUM)]
extra_opts = {'output': output,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
wordcount.run(test_pipeline.get_full_options_as_args(**extra_opts))
def test_many_nested():
Item = xmlfied('item', value=Element())
Box = xmlfied('box', items=WrappedMany(Nested()))
box = Box(items=[])
box.items.append(Item('a'))
box.items.append(Item('a'))
box.items.append(Item('a'))
assert_that(
etree.tostring(box.toxml()),
all_of(
string_contains_in_order('<box>', '<items>', '<item>', 'a',
'</item>', '<item>', 'a', '</item>',
'<item>', 'a', '</item>', '</items>',
'</box>'), ))
def test_one_job_fails_all_jobs_fail(self):
# If one of the import jobs fails, then other jobs must not be performed.
# This is to avoid reinsertion of some records when a pipeline fails and
# is rerun.
output_table_1 = '%s%s' % (self.output_table, 1)
output_table_2 = '%s%s' % (self.output_table, 2)
self.bigquery_client.get_or_create_table(
self.project, self.dataset_id, output_table_1.split('.')[1],
bigquery_tools.parse_table_schema_from_json(self.BIG_QUERY_SCHEMA),
None, None)
self.bigquery_client.get_or_create_table(
self.project, self.dataset_id, output_table_2.split('.')[1],
bigquery_tools.parse_table_schema_from_json(self.BIG_QUERY_SCHEMA_2),
None, None)
pipeline_verifiers = [
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_1,
data=[]),
BigqueryFullResultMatcher(
project=self.project,
query="SELECT * FROM %s" % output_table_2,
data=[])]
args = self.test_pipeline.get_full_options_as_args(
experiments='use_beam_bq_sink')
with self.assertRaises(Exception):
with beam.Pipeline(argv=args) as p:
input = p | beam.Create(_ELEMENTS)
input2 = p | "Broken record" >> beam.Create(['language_broken_record'])
input = (input, input2) | beam.Flatten()
_ = (input |
"WriteWithMultipleDests" >> bigquery.WriteToBigQuery(
table=lambda x: (output_table_1
if 'language' in x
else output_table_2),
create_disposition=(
beam.io.BigQueryDisposition.CREATE_IF_NEEDED),
write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND))
hamcrest_assert(p, all_of(*pipeline_verifiers))
def test_big_query_legacy_sql(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
gs_location = 'gs://temp-storage-for-upload-tests/%s' % self.output_table
extra_opts = {'query': LEGACY_QUERY,
'output': self.output_table,
'bq_temp_location': gs_location,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': False,
'on_success_matcher': all_of(*pipeline_verifiers)}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_bigquery_side_input_it(self):
state_verifier = PipelineStateMatcher(PipelineState.DONE)
NUM_GROUPS = 3
extra_opts = {
'output': self.output,
'num_groups': str(NUM_GROUPS),
'on_success_matcher': all_of(state_verifier)
}
# Register clean up before pipeline execution
self.addCleanup(delete_files, [self.output + '*'])
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
bigquery_side_input.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def _test_streaming(self, with_attributes):
"""Runs IT pipeline with message verifier.
Args:
with_attributes: False - Reads and writes message data only.
True - Reads and writes message data and attributes. Also verifies
id_label and timestamp_attribute features.
"""
# Set on_success_matcher to verify pipeline state and pubsub output. These
# verifications run on a (remote) worker.
# Expect the state to be RUNNING since a streaming pipeline is usually
# never DONE. The test runner will cancel the pipeline after verification.
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
expected_messages = self.EXPECTED_OUTPUT_MESSAGES[self.runner_name]
if not with_attributes:
expected_messages = [pubsub_msg.data.decode('utf-8')
for pubsub_msg in expected_messages]
if self.runner_name == 'TestDirectRunner':
strip_attributes = None
else:
strip_attributes = [self.ID_LABEL, self.TIMESTAMP_ATTRIBUTE]
pubsub_msg_verifier = PubSubMessageMatcher(
self.project,
self.output_sub.name,
expected_messages,
timeout=MESSAGE_MATCHER_TIMEOUT_S,
with_attributes=with_attributes,
strip_attributes=strip_attributes)
extra_opts = {'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
'wait_until_finish_duration': TEST_PIPELINE_DURATION_MS,
'on_success_matcher': all_of(state_verifier,
pubsub_msg_verifier)}
# Generate input data and inject to PubSub.
for msg in self.INPUT_MESSAGES[self.runner_name]:
self.pub_client.publish(self.input_topic.name, msg.data, **msg.attributes)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
pubsub_it_pipeline.run_pipeline(
argv=self.test_pipeline.get_full_options_as_args(**extra_opts),
with_attributes=with_attributes,
id_label=self.ID_LABEL,
timestamp_attribute=self.TIMESTAMP_ATTRIBUTE)
def test_step_attach(timed_report_for):
report, start, stop = timed_report_for("""
import pytest
def test_ololo_pewpew():
with pytest.allure.step(title='withattach'):
pytest.allure.attach('myattach', 'abcdef')
""")
assert_that(
report.findall('.//test-case/steps/step'),
contains(
all_of(
step_with('withattach', start, stop, Status.PASSED),
has_property(
'attachments',
has_property('attachment', has_entry('title',
'myattach'))))))
def test_user_score_it(self):
state_verifier = PipelineStateMatcher(PipelineState.DONE)
file_verifier = FileChecksumMatcher(self.output + '*-of-*',
self.DEFAULT_EXPECTED_CHECKSUM)
extra_opts = {'input': self.DEFAULT_INPUT_FILE,
'output': self.output + '/user-score',
'on_success_matcher': all_of(state_verifier,
file_verifier)}
# Register clean up before pipeline execution
self.addCleanup(delete_files, [self.output + '*'])
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
user_score.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_many_nested():
Item = xmlfied('item', value=Element())
Box = xmlfied('box', items=Many(Nested()))
box = Box(items=[])
box.items.append(Item('a'))
box.items.append(Item('a'))
box.items.append(Item('a'))
assert_that(etree.tostring(box.toxml()), all_of(
string_contains_in_order('<box>',
'<items>',
'<item>', 'a', '</item>',
'<item>', 'a', '</item>',
'<item>', 'a', '</item>',
'</items>',
'</box>'),
))
def test_bigquery_tornadoes_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
output_table = ('BigQueryTornadoesIT'
'.monthly_tornadoes_%s' % int(round(time.time() * 1000)))
query = 'SELECT month, tornado_count FROM [%s]' % output_table
pipeline_verifiers = [PipelineStateMatcher(),
BigqueryMatcher(
project=test_pipeline.get_option('project'),
query=query,
checksum=self.DEFAULT_CHECKSUM)]
extra_opts = {'output': output_table,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
bigquery_tornadoes.run(
test_pipeline.get_full_options_as_args(**extra_opts))
def test_bigquery_tornadoes_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
output_table = ('BigQueryTornadoesIT'
'.monthly_tornadoes_%s' % int(round(time.time() * 1000)))
query = 'SELECT month, tornado_count FROM [%s]' % output_table
pipeline_verifiers = [PipelineStateMatcher(),
BigqueryMatcher(
project=test_pipeline.get_option('project'),
query=query,
checksum=self.DEFAULT_CHECKSUM)]
extra_opts = {'output': output_table,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
bigquery_tornadoes.run(
test_pipeline.get_full_options_as_args(**extra_opts))
def test_user_score_it(self):
state_verifier = PipelineStateMatcher(PipelineState.DONE)
file_verifier = FileChecksumMatcher(self.output + '*-of-*',
self.DEFAULT_EXPECTED_CHECKSUM)
extra_opts = {
'input': self.DEFAULT_INPUT_FILE,
'output': self.output + '/user-score',
'on_success_matcher': all_of(state_verifier, file_verifier)
}
# Register clean up before pipeline execution
self.addCleanup(delete_files, [self.output + '*'])
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
user_score.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def run_pipeline(self):
# Waits for messages to appear in output topic.
expected_msg = [msg.encode('utf-8') for msg in MESSAGES_TO_PUBLISH]
pubsub_msg_verifier = PubSubMessageMatcher(
self.project, self.output_sub.name, expected_msg, timeout=600)
# Checks that pipeline initializes to RUNNING state.
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
extra_opts = {
'wait_until_finish_duration': WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(state_verifier, pubsub_msg_verifier),
'experiment': 'beam_fn_api',
'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
}
argv = self.test_pipeline.get_full_options_as_args(**extra_opts)
return dataflow_exercise_streaming_metrics_pipeline.run(argv)
def _test_streaming(self, with_attributes):
"""Runs IT pipeline with message verifier.
Args:
with_attributes: False - Reads and writes message data only.
True - Reads and writes message data and attributes. Also verifies
id_label and timestamp_attribute features.
"""
# Build expected dataset.
# Set extra options to the pipeline for test purpose
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
expected_messages = self.EXPECTED_OUTPUT_MESSAGES
if not with_attributes:
expected_messages = [
pubsub_msg.data for pubsub_msg in expected_messages
]
pubsub_msg_verifier = PubSubMessageMatcher(
self.project,
OUTPUT_SUB + self.uuid,
expected_messages,
timeout=MESSAGE_MATCHER_TIMEOUT_S,
with_attributes=with_attributes,
strip_attributes=[self.ID_LABEL, self.TIMESTAMP_ATTRIBUTE])
extra_opts = {
'input_subscription': self.input_sub.full_name,
'output_topic': self.output_topic.full_name,
'wait_until_finish_duration': TEST_PIPELINE_DURATION_MS,
'on_success_matcher': all_of(state_verifier, pubsub_msg_verifier)
}
# Generate input data and inject to PubSub.
test_utils.wait_for_subscriptions_created([self.input_sub])
for msg in self.INPUT_MESSAGES:
self.input_topic.publish(msg.data, **msg.attributes)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
pubsub_it_pipeline.run_pipeline(
argv=self.test_pipeline.get_full_options_as_args(**extra_opts),
with_attributes=with_attributes,
id_label=self.ID_LABEL,
timestamp_attribute=self.TIMESTAMP_ATTRIBUTE)
def test_bqfl_streaming(self):
if isinstance(self.test_pipeline.runner, TestDataflowRunner):
self.skipTest("TestStream is not supported on TestDataflowRunner")
output_table = '%s_%s' % (self.output_table, 'ints')
_SIZE = 100
schema = self.BIG_QUERY_STREAMING_SCHEMA
l = [{'Integr': i} for i in range(_SIZE)]
state_matcher = PipelineStateMatcher(PipelineState.RUNNING)
bq_matcher = BigqueryFullResultStreamingMatcher(
project=self.project,
query="SELECT Integr FROM %s"
% output_table,
data=[(i,) for i in range(100)])
args = self.test_pipeline.get_full_options_as_args(
on_success_matcher=all_of(state_matcher, bq_matcher),
experiments='use_beam_bq_sink',
streaming=True)
with beam.Pipeline(argv=args) as p:
stream_source = (TestStream()
.advance_watermark_to(0)
.advance_processing_time(100)
.add_elements(l[:_SIZE//4])
.advance_processing_time(100)
.advance_watermark_to(100)
.add_elements(l[_SIZE//4:2*_SIZE//4])
.advance_processing_time(100)
.advance_watermark_to(200)
.add_elements(l[2*_SIZE//4:3*_SIZE//4])
.advance_processing_time(100)
.advance_watermark_to(300)
.add_elements(l[3*_SIZE//4:])
.advance_processing_time(100)
.advance_watermark_to_infinity())
_ = (p
| stream_source
| bigquery.WriteToBigQuery(output_table,
schema=schema,
method=bigquery.WriteToBigQuery \
.Method.FILE_LOADS,
triggering_frequency=100))
def test_big_query_standard_sql_kms_key(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
extra_opts = {'query': STANDARD_QUERY,
'output': self.output_table,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': True,
'on_success_matcher': all_of(*pipeline_verifiers),
'kms_key': KMS_KEY
}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
table = self.bigquery_client.get_table(
self.project, self.dataset_id, 'output_table')
self.assertEqual(KMS_KEY, table.encryptionConfiguration.kmsKeyName)
def setUp(self):
super(GroupByKeyStreamingTest, self).setUp()
self.topic_short_name = self.pipeline.get_option('pubsub_topic_name')
self.setup_pubsub()
timeout = self.pipeline.get_option('timeout') or DEFAULT_TIMEOUT
expected_num_of_records = self.pipeline.get_option('num_of_records')
pubsub_msg_verifier = PubSubMessageMatcher(
self.project_id,
self.output_sub.name,
expected_msg_len=int(expected_num_of_records),
timeout=int(timeout))
self.extra_opts = {
'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
'metrics_namespace': self.metrics_namespace,
'wait_until_finish_duration': WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(pubsub_msg_verifier)
}
def test_big_query_standard_sql_kms_key(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [PipelineStateMatcher(), BigqueryMatcher(
project=self.project,
query=verify_query,
checksum=expected_checksum)]
extra_opts = {'query': STANDARD_QUERY,
'output': self.output_table,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': True,
'on_success_matcher': all_of(*pipeline_verifiers),
'kms_key': KMS_KEY
}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
table = self.bigquery_client.get_table(
self.project, self.dataset_id, 'output_table')
self.assertEqual(KMS_KEY, table.encryptionConfiguration.kmsKeyName)
def test_wordcount_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
output = '/'.join([
test_pipeline.get_option('output'),
test_pipeline.get_option('job_name'), 'results'
])
pipeline_verifiers = [
PipelineStateMatcher(),
FileChecksumMatcher(output + '*-of-*', self.DEFAULT_CHECKSUM)
]
extra_opts = {
'output': output,
'on_success_matcher': all_of(*pipeline_verifiers)
}
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
wordcount.run(test_pipeline.get_full_options_as_args(**extra_opts))
def test_game_stats_it(self):
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
success_condition = 'mean_duration=300 LIMIT 1'
sessions_query = ('SELECT mean_duration FROM [%s:%s.%s] '
'WHERE %s' % (self.project,
self.dataset.name,
self.OUTPUT_TABLE_SESSIONS,
success_condition))
bq_sessions_verifier = BigqueryMatcher(self.project,
sessions_query,
self.DEFAULT_EXPECTED_CHECKSUM)
# TODO(mariagh): Add teams table verifier once game_stats.py is fixed.
extra_opts = {'subscription': self.input_sub.name,
'dataset': self.dataset.name,
'topic': self.input_topic.name,
'fixed_window_duration': 1,
'user_activity_window_duration': 1,
'wait_until_finish_duration':
self.WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(state_verifier,
bq_sessions_verifier)}
# Register cleanup before pipeline execution.
# Note that actual execution happens in reverse order.
self.addCleanup(self._cleanup_pubsub)
self.addCleanup(self._cleanup_dataset)
self.addCleanup(utils.delete_bq_table, self.project,
self.dataset.name, self.OUTPUT_TABLE_SESSIONS)
self.addCleanup(utils.delete_bq_table, self.project,
self.dataset.name, self.OUTPUT_TABLE_TEAMS)
# Generate input data and inject to PubSub.
self._inject_pubsub_game_events(self.input_topic, self.DEFAULT_INPUT_COUNT)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
game_stats.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_big_query_legacy_sql(self):
verify_query = DIALECT_OUTPUT_VERIFY_QUERY % self.output_table
expected_checksum = test_utils.compute_hash(DIALECT_OUTPUT_EXPECTED)
pipeline_verifiers = [
PipelineStateMatcher(),
BigqueryMatcher(project=self.project,
query=verify_query,
checksum=expected_checksum)
]
gs_location = 'gs://temp-storage-for-upload-tests/%s' % self.output_table
extra_opts = {
'query': LEGACY_QUERY,
'output': self.output_table,
'bq_temp_location': gs_location,
'output_schema': DIALECT_OUTPUT_SCHEMA,
'use_standard_sql': False,
'on_success_matcher': all_of(*pipeline_verifiers)
}
options = self.test_pipeline.get_full_options_as_args(**extra_opts)
big_query_query_to_table_pipeline.run_bq_pipeline(options)
def test_streaming_wordcount_debugging_it(self):
# Set extra options to the pipeline for test purpose
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
pubsub_msg_verifier = PubSubMessageMatcher(
self.project, self.output_sub.name, EXPECTED_MESSAGE, timeout=400)
extra_opts = {
'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
'wait_until_finish_duration': WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(state_verifier, pubsub_msg_verifier)
}
# Generate input data and inject to PubSub.
self._inject_data(self.input_topic, SAMPLE_MESSAGES)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
streaming_wordcount_debugging.run(
self.test_pipeline.get_full_options_as_args(**extra_opts),
save_main_session=False)
def test_datastore_wordcount_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
dataset = test_pipeline.get_option("project")
kind = self.DATASTORE_WORDCOUNT_KIND
output = '/'.join([test_pipeline.get_option('output'),
str(int(time.time() * 1000)),
'datastore_wordcount_results'])
arg_sleep_secs = test_pipeline.get_option('sleep_secs')
sleep_secs = int(arg_sleep_secs) if arg_sleep_secs is not None else None
pipeline_verifiers = [PipelineStateMatcher(),
FileChecksumMatcher(output + '*-of-*',
self.EXPECTED_CHECKSUM,
sleep_secs)]
extra_opts = {'dataset': dataset,
'kind': kind,
'output': output,
'read_only': True,
'on_success_matcher': all_of(*pipeline_verifiers)}
datastore_wordcount.run(test_pipeline.get_full_options_as_args(
**extra_opts))
def test_wordcount_it(self):
test_pipeline = TestPipeline(is_integration_test=True)
# Set extra options to the pipeline for test purpose
output = '/'.join([test_pipeline.get_option('output'),
str(int(time.time())),
'results'])
arg_sleep_secs = test_pipeline.get_option('sleep_secs')
sleep_secs = int(arg_sleep_secs) if arg_sleep_secs is not None else None
pipeline_verifiers = [PipelineStateMatcher(),
FileChecksumMatcher(output + '*-of-*',
self.DEFAULT_CHECKSUM,
sleep_secs)]
extra_opts = {'output': output,
'on_success_matcher': all_of(*pipeline_verifiers)}
# Register clean up before pipeline execution
self.addCleanup(delete_files, [output + '*'])
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
wordcount.run(test_pipeline.get_full_options_as_args(**extra_opts))
def _test_streaming(self, with_attributes):
"""Runs IT pipeline with message verifier.
Args:
with_attributes: False - Reads and writes message data only.
True - Reads and writes message data and attributes. Also verifies
id_label and timestamp_attribute features.
"""
# Build expected dataset.
# Set extra options to the pipeline for test purpose
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
expected_messages = self.EXPECTED_OUTPUT_MESSAGES
if not with_attributes:
expected_messages = [pubsub_msg.data for pubsub_msg in expected_messages]
pubsub_msg_verifier = PubSubMessageMatcher(
self.project,
OUTPUT_SUB + self.uuid,
expected_messages,
timeout=MESSAGE_MATCHER_TIMEOUT_S,
with_attributes=with_attributes,
strip_attributes=[self.ID_LABEL, self.TIMESTAMP_ATTRIBUTE])
extra_opts = {'input_subscription': self.input_sub.full_name,
'output_topic': self.output_topic.full_name,
'wait_until_finish_duration': TEST_PIPELINE_DURATION_MS,
'on_success_matcher': all_of(state_verifier,
pubsub_msg_verifier)}
# Generate input data and inject to PubSub.
test_utils.wait_for_subscriptions_created([self.input_sub])
for msg in self.INPUT_MESSAGES:
self.input_topic.publish(msg.data, **msg.attributes)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
pubsub_it_pipeline.run_pipeline(
argv=self.test_pipeline.get_full_options_as_args(**extra_opts),
with_attributes=with_attributes,
id_label=self.ID_LABEL,
timestamp_attribute=self.TIMESTAMP_ATTRIBUTE)
def test_streaming_wordcount_it(self):
# Build expected dataset.
expected_msg = [('%d: 1' % num) for num in range(DEFAULT_INPUT_NUMBERS)]
# Set extra options to the pipeline for test purpose
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
pubsub_msg_verifier = PubSubMessageMatcher(self.project,
self.output_sub.name,
expected_msg,
timeout=400)
extra_opts = {'input_subscription': self.input_sub.name,
'output_topic': self.output_topic.name,
'wait_until_finish_duration': WAIT_UNTIL_FINISH_DURATION,
'on_success_matcher': all_of(state_verifier,
pubsub_msg_verifier)}
# Generate input data and inject to PubSub.
self._inject_numbers(self.input_topic, DEFAULT_INPUT_NUMBERS)
# Get pipeline options from command argument: --test-pipeline-options,
# and start pipeline job by calling pipeline main function.
streaming_wordcount.run(
self.test_pipeline.get_full_options_as_args(**extra_opts))
def test_return_next_cell_in_valid_range(self):
for x in range(999):
assert_that(
self.next_cell_calculator.next(), all_of(greater_than_or_equal_to(0), less_than_or_equal_to(100))
)
def __init__(self, *element_matchers):
delegates = [has_item(e) for e in element_matchers]
self.matcher = all_of(*delegates)
def has_properties(*keys_valuematchers, **kv_args):
"""Matches if an object has properties satisfying all of a dictionary
of string property names and corresponding value matchers.
:param matcher_dict: A dictionary mapping keys to associated value matchers,
or to expected values for
:py:func:`~hamcrest.core.core.isequal.equal_to` matching.
Note that the keys must be actual keys, not matchers. Any value argument
that is not a matcher is implicitly wrapped in an
:py:func:`~hamcrest.core.core.isequal.equal_to` matcher to check for
equality.
Examples::
has_properties({'foo':equal_to(1), 'bar':equal_to(2)})
has_properties({'foo':1, 'bar':2})
``has_properties`` also accepts a list of keyword arguments:
.. function:: has_properties(keyword1=value_matcher1[, keyword2=value_matcher2[, ...]])
:param keyword1: A keyword to look up.
:param valueMatcher1: The matcher to satisfy for the value, or an expected
value for :py:func:`~hamcrest.core.core.isequal.equal_to` matching.
Examples::
has_properties(foo=equal_to(1), bar=equal_to(2))
has_properties(foo=1, bar=2)
Finally, ``has_properties`` also accepts a list of alternating keys and their
value matchers:
.. function:: has_properties(key1, value_matcher1[, ...])
:param key1: A key (not a matcher) to look up.
:param valueMatcher1: The matcher to satisfy for the value, or an expected
value for :py:func:`~hamcrest.core.core.isequal.equal_to` matching.
Examples::
has_properties('foo', equal_to(1), 'bar', equal_to(2))
has_properties('foo', 1, 'bar', 2)
"""
if len(keys_valuematchers) == 1:
try:
base_dict = keys_valuematchers[0].copy()
for key in base_dict:
base_dict[key] = wrap_shortcut(base_dict[key])
except AttributeError:
raise ValueError('single-argument calls to has_properties must pass a dict as the argument')
else:
if len(keys_valuematchers) % 2:
raise ValueError('has_properties requires key-value pairs')
base_dict = {}
for index in range(int(len(keys_valuematchers) / 2)):
base_dict[keys_valuematchers[2 * index]] = wrap_shortcut(keys_valuematchers[2 * index + 1])
for key, value in kv_args.items():
base_dict[key] = wrap_shortcut(value)
return all_of(*[has_property(property_name, property_value_matcher) for \
property_name, property_value_matcher in base_dict.items()])
