class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.pids = []
self.event = Event()
self.exchange_space_name = 'test_granules'
self.exchange_point_name = 'science_data'
self.i = 0
self.purge_queues()
self.queue_buffer = []
self.streams = []
self.addCleanup(self.stop_all_ingestion)
def purge_queues(self):
xn = self.container.ex_manager.create_xn_queue('science_granule_ingestion')
xn.purge()
def tearDown(self):
self.purge_queues()
for pid in self.pids:
self.container.proc_manager.terminate_process(pid)
IngestionManagementIntTest.clean_subscriptions()
for queue in self.queue_buffer:
if isinstance(queue, ExchangeNameQueue):
queue.delete()
elif isinstance(queue, str):
xn = self.container.ex_manager.create_xn_queue(queue)
xn.delete()
#--------------------------------------------------------------------------------
# Helper/Utility methods
#--------------------------------------------------------------------------------
def create_dataset(self, parameter_dict_id=''):
'''
Creates a time-series dataset
'''
tdom, sdom = time_series_domain()
sdom = sdom.dump()
tdom = tdom.dump()
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
dataset_id = self.dataset_management.create_dataset('test_dataset_%i'%self.i, parameter_dictionary_id=parameter_dict_id, spatial_domain=sdom, temporal_domain=tdom)
return dataset_id
def get_datastore(self, dataset_id):
'''
Gets an instance of the datastore
This method is primarily used to defeat a bug where integration tests in multiple containers may sometimes
delete a CouchDB datastore and the other containers are unaware of the new state of the datastore.
'''
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def get_ingestion_config(self):
'''
Grab the ingestion configuration from the resource registry
'''
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration,id_only=True)
return ingest_configs[0]
def launch_producer(self, stream_id=''):
'''
Launch the producer
'''
pid = self.container.spawn_process('better_data_producer', 'ion.processes.data.example_data_producer', 'BetterDataProducer', {'process':{'stream_id':stream_id}})
self.pids.append(pid)
def make_simple_dataset(self):
'''
Makes a stream, a stream definition and a dataset, the essentials for most of these tests
'''
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('ctd stream %i' % self.i, 'xp1', stream_definition_id=stream_def_id)
dataset_id = self.create_dataset(pdict_id)
self.get_datastore(dataset_id)
self.i += 1
return stream_id, route, stream_def_id, dataset_id
def publish_hifi(self,stream_id,stream_route,offset=0):
'''
Publish deterministic data
'''
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def publish_fake_data(self,stream_id, route):
'''
Make four granules
'''
for i in xrange(4):
self.publish_hifi(stream_id,route,i)
def start_ingestion(self, stream_id, dataset_id):
'''
Starts ingestion/persistence for a given dataset
'''
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
def stop_ingestion(self, stream_id):
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id)
def stop_all_ingestion(self):
try:
[self.stop_ingestion(sid) for sid in self.streams]
except:
pass
def validate_granule_subscription(self, msg, route, stream_id):
'''
Validation for granule format
'''
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info('%s', rdt.pretty_print())
self.assertIsInstance(msg,Granule,'Message is improperly formatted. (%s)' % type(msg))
self.event.set()
def wait_until_we_have_enough_granules(self, dataset_id='',data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
extents = self.dataset_management.dataset_extents(dataset_id, 'time')[0]
granule = self.data_retriever.retrieve_last_data_points(dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
if rdt['time'] and rdt['time'][0] != rdt._pdict.get_context('time').fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
#--------------------------------------------------------------------------------
# Test Methods
#--------------------------------------------------------------------------------
@attr('SMOKE')
def test_dm_end_2_end(self):
#--------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
#--------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_definition = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('producer', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
#--------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
#--------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
#--------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
#--------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id,40)
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt['time'][:10] == np.arange(10)).all(),'%s' % rdt['time'][:])
self.assertTrue((rdt['binary'][:10] == np.array(['hi']*10, dtype='object')).all())
#--------------------------------------------------------------------------------
# Now to try the streamed approach
#--------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream('replay_out', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream_id)
log.info('Process ID: %s', process_id)
replay_client = ReplayClient(process_id)
#--------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
#--------------------------------------------------------------------------------
xp = self.container.ex_manager.create_xp(self.exchange_point_name)
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5), 'The process never launched')
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
subscriber.stop()
self.data_retriever.cancel_replay_agent(self.replay_id)
#--------------------------------------------------------------------------------
# Test the slicing capabilities
#--------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id, query={'tdoa':slice(0,5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt['time'] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b,bool) else b)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_coverage_transform(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_parsed()
stream_def_id = self.pubsub_management.create_stream_definition('ctd parsed', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = ph.get_rdt(stream_def_id)
ph.fill_parsed_rdt(rdt)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], rdt['time'])
np.testing.assert_array_almost_equal(rdt_out['temp'], rdt['temp'])
np.testing.assert_array_almost_equal(rdt_out['conductivity_L1'], np.array([42.914]))
np.testing.assert_array_almost_equal(rdt_out['temp_L1'], np.array([20.]))
np.testing.assert_array_almost_equal(rdt_out['pressure_L1'], np.array([3.068]))
np.testing.assert_array_almost_equal(rdt_out['density'], np.array([1021.7144739593881]))
np.testing.assert_array_almost_equal(rdt_out['salinity'], np.array([30.935132729668283]))
def test_qc_events(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_qc_pdict()
stream_def_id = self.pubsub_management.create_stream_definition('qc stream def', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('qc stream', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
config = DotDict()
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id, config=config)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.arange(10) * 3
verified = Event()
def verification(event, *args, **kwargs):
self.assertEquals(event.qc_parameter, 'temp_qc')
self.assertEquals(event.temporal_value, 7)
verified.set()
es = EventSubscriber(event_type=OT.ParameterQCEvent, origin=dataset_id, callback=verification, auto_delete=True)
es.start()
self.addCleanup(es.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(verified.wait(10))
def test_lookup_values_ingest_replay(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsub_management.create_stream_definition('lookups', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
config = DotDict()
config.process.lookup_docs = ['test1', 'test2']
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id, config=config)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
stored_value_manager = StoredValueManager(self.container)
stored_value_manager.stored_value_cas('test1',{'offset_a':10.0, 'offset_b':13.1})
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(20))
np.testing.assert_array_almost_equal(rdt_out['temp'], np.array([20.] * 20))
np.testing.assert_array_almost_equal(rdt_out['calibrated'], np.array([30.]*20))
np.testing.assert_array_equal(rdt_out['offset_b'], np.array([rdt_out.fill_value('offset_b')] * 20))
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20,40)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor.event.clear()
stored_value_manager.stored_value_cas('test1',{'offset_a':20.0})
stored_value_manager.stored_value_cas('coefficient_document',{'offset_b':10.0})
gevent.sleep(2)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(40))
np.testing.assert_array_almost_equal(rdt_out['temp'], np.array([20.] * 20 + [20.] * 20))
np.testing.assert_array_equal(rdt_out['offset_b'], np.array([10.] * 40))
np.testing.assert_array_almost_equal(rdt_out['calibrated'], np.array([30.]*20 + [40.]*20))
np.testing.assert_array_almost_equal(rdt_out['calibrated_b'], np.array([40.] * 20 + [50.] * 20))
@unittest.skip('Doesnt work')
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode')
def test_replay_pause(self):
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
replay_stream, replay_route = self.pubsub_management.create_stream('replay', 'xp1', stream_definition_id=stream_def_id)
dataset_id = self.create_dataset(pdict_id)
scov = DatasetManagementService._get_simplex_coverage(dataset_id)
bb = CoverageCraft(scov)
bb.rdt['time'] = np.arange(100)
bb.rdt['temp'] = np.random.random(100) + 30
bb.sync_with_granule()
DatasetManagementService._persist_coverage(dataset_id, bb.coverage) # This invalidates it for multi-host configurations
# Set up the subscriber to verify the data
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
xp = self.container.ex_manager.create_xp('xp1')
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
# Set up the replay agent and the client wrapper
# 1) Define the Replay (dataset and stream to publish on)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream)
# 2) Make a client to the interact with the process (optionall provide it a process to bind with)
replay_client = ReplayClient(process_id)
# 3) Start the agent (launch the process)
self.data_retriever.start_replay_agent(self.replay_id)
# 4) Start replaying...
replay_client.start_replay()
# Wait till we get some granules
self.assertTrue(self.event.wait(5))
# We got granules, pause the replay, clear the queue and allow the process to finish consuming
replay_client.pause_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure there's no remaining messages being consumed
self.assertFalse(self.event.wait(1))
# Resume the replay and wait until we start getting granules again
replay_client.resume_replay()
self.assertTrue(self.event.wait(5))
# Stop the replay, clear the queues
replay_client.stop_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure that it did indeed stop
self.assertFalse(self.event.wait(1))
subscriber.stop()
def test_retrieve_and_transform(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(ctd_stream_id, dataset_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
sal_stream_def_id = self.pubsub_management.create_stream_definition('sal data', parameter_dictionary_id=salinity_pdict_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.random.randn(10) * 10 + 30
rdt['conductivity'] = np.random.randn(10) * 2 + 10
rdt['pressure'] = np.random.randn(10) * 1 + 12
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt['time'] = np.arange(10,20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 20)
granule = self.data_retriever.retrieve(dataset_id,
None,
None,
'ion.processes.data.transforms.ctd.ctd_L2_salinity',
'CTDL2SalinityTransformAlgorithm',
kwargs=dict(params=sal_stream_def_id))
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt['salinity']:
self.assertNotEquals(i,0)
self.streams.append(ctd_stream_id)
self.stop_ingestion(ctd_stream_id)
def test_last_granule(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id,route, 0)
self.publish_hifi(stream_id,route, 1)
self.wait_until_we_have_enough_granules(dataset_id,20) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id, 10)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(10) + 10
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id,5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(15,20)
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_replay_with_parameters(self):
#--------------------------------------------------------------------------------
# Create the configurations and the dataset
#--------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('replay_with_params', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.publish_fake_data(stream_id, route)
self.assertTrue(dataset_monitor.event.wait(30))
query = {
'start_time': 0 - 2208988800,
'end_time': 20 - 2208988800,
'stride_time' : 2,
'parameters': ['time','temp']
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id,query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
comp = np.arange(0,20,2) == rdt['time']
self.assertTrue(comp.all(),'%s' % rdt.pretty_print())
self.assertEquals(set(rdt.iterkeys()), set(['time','temp']))
extents = self.dataset_management.dataset_extents(dataset_id=dataset_id, parameters=['time','temp'])
self.assertTrue(extents['time']>=20)
self.assertTrue(extents['temp']>=20)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_repersist_data(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id,route,0)
self.publish_hifi(stream_id,route,1)
self.wait_until_we_have_enough_granules(dataset_id,20)
config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id,dataset_id=dataset_id)
self.publish_hifi(stream_id,route,2)
self.publish_hifi(stream_id,route,3)
self.wait_until_we_have_enough_granules(dataset_id,40)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(0,40)
if not isinstance(comp,bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_correct_time(self):
# There are 2208988800 seconds between Jan 1 1900 and Jan 1 1970, i.e.
# the conversion factor between unix and NTP time
unix_now = np.floor(time.time())
ntp_now = unix_now + 2208988800
unix_ago = unix_now - 20
ntp_ago = unix_ago + 2208988800
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_simplex_coverage(dataset_id)
coverage.insert_timesteps(20)
coverage.set_parameter_values('time', np.arange(ntp_ago,ntp_now))
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertTrue( np.abs(temporal_bounds[0] - unix_ago) < 2)
self.assertTrue( np.abs(temporal_bounds[1] - unix_now) < 2)
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_empty_coverage_time(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_coverage(dataset_id)
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertEquals([coverage.get_parameter_context('time').fill_value] *2, temporal_bounds)
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_out_of_band_retrieve(self):
# Setup the environemnt
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
# Fill the dataset
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id,40)
# Retrieve the data
granule = DataRetrieverService.retrieve_oob(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
self.assertTrue((rdt['time'] == np.arange(40)).all())
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_retrieve_cache(self):
DataRetrieverService._refresh_interval = 1
datasets = [self.make_simple_dataset() for i in xrange(10)]
for stream_id, route, stream_def_id, dataset_id in datasets:
coverage = DatasetManagementService._get_simplex_coverage(dataset_id)
coverage.insert_timesteps(10)
coverage.set_parameter_values('time', np.arange(10))
coverage.set_parameter_values('temp', np.arange(10))
# Verify cache hit and refresh
dataset_ids = [i[3] for i in datasets]
self.assertTrue(dataset_ids[0] not in DataRetrieverService._retrieve_cache)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age = DataRetrieverService._retrieve_cache[dataset_ids[0]]
# Verify that it was hit and it's now in there
self.assertTrue(dataset_ids[0] in DataRetrieverService._retrieve_cache)
gevent.sleep(DataRetrieverService._refresh_interval + 0.2)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age2 = DataRetrieverService._retrieve_cache[dataset_ids[0]]
self.assertTrue(age2 != age)
for dataset_id in dataset_ids:
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(dataset_ids[0] not in DataRetrieverService._retrieve_cache)
stream_id, route, stream_def, dataset_id = datasets[0]
self.start_ingestion(stream_id, dataset_id)
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(dataset_id in DataRetrieverService._retrieve_cache)
DataRetrieverService._refresh_interval = 100
self.publish_hifi(stream_id,route,1)
self.wait_until_we_have_enough_granules(dataset_id, data_size=20)
event = gevent.event.Event()
with gevent.Timeout(20):
while not event.wait(0.1):
if dataset_id not in DataRetrieverService._retrieve_cache:
event.set()
self.assertTrue(event.is_set())
def publish_and_wait(self, dataset_id, granule):
stream_ids, _ = self.resource_registry.find_objects(dataset_id, PRED.hasStream,id_only=True)
stream_id=stream_ids[0]
route = self.pubsub_management.read_stream_route(stream_id)
publisher = StandaloneStreamPublisher(stream_id,route)
dataset_monitor = DatasetMonitor(dataset_id)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_thorough_gap_analysis(self):
dataset_id = self.test_ingestion_gap_analysis()
vcov = DatasetManagementService._get_coverage(dataset_id)
self.assertIsInstance(vcov,ViewCoverage)
ccov = vcov.reference_coverage
self.assertIsInstance(ccov, ComplexCoverage)
self.assertEquals(len(ccov._reference_covs), 3)
def test_ingestion_gap_analysis(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
connection1 = uuid4().hex
connection2 = uuid4().hex
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [0]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index='0'))
rdt['time'] = [1]
rdt['temp'] = [1]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index=1))
rdt['time'] = [2]
rdt['temp'] = [2]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index='3')) # Gap, missed message
rdt['time'] = [3]
rdt['temp'] = [3]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index='3')) # Gap, new connection
rdt['time'] = [4]
rdt['temp'] = [4]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index='4'))
rdt['time'] = [5]
rdt['temp'] = [5]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index=5))
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['time'], np.arange(6))
np.testing.assert_array_equal(rdt['temp'], np.arange(6))
return dataset_id
@unittest.skip('Outdated due to ingestion retry')
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_ingestion_failover(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
event = Event()
def cb(*args, **kwargs):
event.set()
sub = EventSubscriber(event_type="ExceptionEvent", callback=cb, origin="stream_exception")
sub.start()
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id, 40)
file_path = DatasetManagementService._get_coverage_path(dataset_id)
master_file = os.path.join(file_path, '%s_master.hdf5' % dataset_id)
with open(master_file, 'w') as f:
f.write('this will crash HDF')
self.publish_hifi(stream_id, route, 5)
self.assertTrue(event.wait(10))
sub.stop()
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_coverage_types(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
cov = DatasetManagementService._get_coverage(dataset_id=dataset_id)
self.assertIsInstance(cov, ViewCoverage)
cov = DatasetManagementService._get_simplex_coverage(dataset_id=dataset_id)
self.assertIsInstance(cov, SimplexCoverage)
class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.event = Event()
self.exchange_space_name = 'test_granules'
self.exchange_point_name = 'science_data'
self.i = 0
self.cci = 0
#--------------------------------------------------------------------------------
# Helper/Utility methods
#--------------------------------------------------------------------------------
def create_dataset(self, parameter_dict_id=''):
'''
Creates a time-series dataset
'''
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
dataset = Dataset('test_dataset_%i'%self.i)
dataset_id = self.dataset_management.create_dataset(dataset, parameter_dictionary_id=parameter_dict_id)
self.addCleanup(self.dataset_management.delete_dataset, dataset_id)
return dataset_id
def get_datastore(self, dataset_id):
'''
Gets an instance of the datastore
This method is primarily used to defeat a bug where integration tests in multiple containers may sometimes
delete a CouchDB datastore and the other containers are unaware of the new state of the datastore.
'''
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def get_ingestion_config(self):
'''
Grab the ingestion configuration from the resource registry
'''
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration,id_only=True)
return ingest_configs[0]
def launch_producer(self, stream_id=''):
'''
Launch the producer
'''
pid = self.container.spawn_process('better_data_producer', 'ion.processes.data.example_data_producer', 'BetterDataProducer', {'process':{'stream_id':stream_id}})
self.addCleanup(self.container.terminate_process, pid)
def make_simple_dataset(self):
'''
Makes a stream, a stream definition and a dataset, the essentials for most of these tests
'''
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition('ctd data %i' % self.i, parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('ctd stream %i' % self.i, 'xp1', stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
dataset_id = self.create_dataset(pdict_id)
# self.get_datastore(dataset_id)
self.i += 1
return stream_id, route, stream_def_id, dataset_id
def publish_hifi(self,stream_id,stream_route,offset=0):
'''
Publish deterministic data
'''
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def publish_fake_data(self,stream_id, route):
'''
Make four granules
'''
for i in xrange(4):
self.publish_hifi(stream_id,route,i)
def start_ingestion(self, stream_id, dataset_id):
'''
Starts ingestion/persistence for a given dataset
'''
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
def stop_ingestion(self, stream_id):
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id)
def validate_granule_subscription(self, msg, route, stream_id):
'''
Validation for granule format
'''
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info('%s', rdt.pretty_print())
self.assertIsInstance(msg,Granule,'Message is improperly formatted. (%s)' % type(msg))
self.event.set()
def wait_until_we_have_enough_granules(self, dataset_id='',data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
extents = self.dataset_management.dataset_extents(dataset_id, 'time')
granule = self.data_retriever.retrieve_last_data_points(dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
if rdt['time'] and rdt['time'][0] != rdt._pdict.get_context('time').fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
#--------------------------------------------------------------------------------
# Test Methods
#--------------------------------------------------------------------------------
def test_dm_end_2_end(self):
#--------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
#--------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_definition = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('producer', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
#--------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
#--------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
#--------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
#--------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id,40)
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt['time'][:10] == np.arange(10)).all(),'%s' % rdt['time'][:])
self.assertTrue((rdt['binary'][:10] == np.array(['hi']*10, dtype='object')).all())
#--------------------------------------------------------------------------------
# Now to try the streamed approach
#--------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream('replay_out', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream_id)
log.info('Process ID: %s', process_id)
replay_client = ReplayClient(process_id)
#--------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
#--------------------------------------------------------------------------------
sub_id = self.pubsub_management.create_subscription(self.exchange_space_name,stream_ids=[replay_stream_id])
self.addCleanup(self.pubsub_management.delete_subscription, sub_id)
self.pubsub_management.activate_subscription(sub_id)
self.addCleanup(self.pubsub_management.deactivate_subscription, sub_id)
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
subscriber.start()
self.addCleanup(subscriber.stop)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5), 'The process never launched')
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
self.data_retriever.cancel_replay_agent(self.replay_id)
#--------------------------------------------------------------------------------
# Test the slicing capabilities
#--------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id, query={'tdoa':slice(0,5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt['time'] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b,bool) else b)
def test_coverage_transform(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_parsed()
stream_def_id = self.pubsub_management.create_stream_definition('ctd parsed', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = ph.get_rdt(stream_def_id)
ph.fill_parsed_rdt(rdt)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], rdt['time'])
np.testing.assert_array_almost_equal(rdt_out['temp'], rdt['temp'])
np.testing.assert_allclose(rdt_out['conductivity_L1'], np.array([42.914]))
np.testing.assert_allclose(rdt_out['temp_L1'], np.array([20.]))
np.testing.assert_allclose(rdt_out['pressure_L1'], np.array([3.068]))
np.testing.assert_allclose(rdt_out['density'], np.array([1021.7144739593881], dtype='float32'))
np.testing.assert_allclose(rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
def test_ingestion_pause(self):
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
ingestion_config_id = self.get_ingestion_config()
self.start_ingestion(ctd_stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, ctd_stream_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
self.ingestion_management.pause_data_stream(ctd_stream_id, ingestion_config_id)
monitor.event.clear()
rdt['time'] = np.arange(10,20)
publisher.publish(rdt.to_granule())
self.assertFalse(monitor.event.wait(1))
self.ingestion_management.resume_data_stream(ctd_stream_id, ingestion_config_id)
self.assertTrue(monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_almost_equal(rdt2['time'], np.arange(20))
def test_last_granule(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
self.publish_hifi(stream_id,route, 0)
self.publish_hifi(stream_id,route, 1)
self.wait_until_we_have_enough_granules(dataset_id,20) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id, 10)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(10) + 10
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id,5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(15,20)
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
def test_replay_with_parameters(self):
#--------------------------------------------------------------------------------
# Create the configurations and the dataset
#--------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('replay_with_params', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.publish_fake_data(stream_id, route)
self.assertTrue(dataset_monitor.wait())
query = {
'start_time': 0 - 2208988800,
'end_time': 19 - 2208988800,
'stride_time' : 2,
'parameters': ['time','temp']
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id,query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
np.testing.assert_array_equal(rdt['time'], np.arange(0,20,2))
self.assertEquals(set(rdt.iterkeys()), set(['time','temp']))
extents = self.dataset_management.dataset_extents(dataset_id=dataset_id, parameters=['time','temp'])
self.assertTrue(extents['time']>=20)
self.assertTrue(extents['temp']>=20)
def test_repersist_data(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id,route,0)
self.publish_hifi(stream_id,route,1)
self.wait_until_we_have_enough_granules(dataset_id,20)
config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id,dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
self.publish_hifi(stream_id,route,2)
self.publish_hifi(stream_id,route,3)
self.wait_until_we_have_enough_granules(dataset_id,40)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(0,40)
if not isinstance(comp,bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)
@unittest.skip('deprecated')
def test_correct_time(self):
# There are 2208988800 seconds between Jan 1 1900 and Jan 1 1970, i.e.
# the conversion factor between unix and NTP time
unix_now = np.floor(time.time())
ntp_now = unix_now + 2208988800
unix_ago = unix_now - 20
ntp_ago = unix_ago + 2208988800
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_simplex_coverage(dataset_id, mode='a')
coverage.insert_timesteps(20)
coverage.set_parameter_values('time', np.arange(ntp_ago,ntp_now))
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertTrue( np.abs(temporal_bounds[0] - unix_ago) < 2)
self.assertTrue( np.abs(temporal_bounds[1] - unix_now) < 2)
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_out_of_band_retrieve(self):
# Setup the environemnt
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
# Fill the dataset
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id,40)
# Retrieve the data
granule = DataRetrieverService.retrieve_oob(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
self.assertTrue((rdt['time'] == np.arange(40)).all())
def publish_and_wait(self, dataset_id, granule):
stream_ids, _ = self.resource_registry.find_objects(dataset_id, PRED.hasStream,id_only=True)
stream_id=stream_ids[0]
route = self.pubsub_management.read_stream_route(stream_id)
publisher = StandaloneStreamPublisher(stream_id,route)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(granule)
self.assertTrue(dataset_monitor.wait())
def test_sparse_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_sparse()
stream_def_id = self.pubsub_management.create_stream_definition('sparse', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
dataset_id = self.create_dataset(pdict_id)
self.start_ingestion(stream_id,dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
# Publish initial granule
# the first one has the sparse value set inside it, sets lat to 45 and lon to -71
ntp_now = time.time() + 2208988800
rdt = ph.get_rdt(stream_def_id)
rdt['time'] = [ntp_now]
rdt['internal_timestamp'] = [ntp_now]
rdt['temp'] = [300000]
rdt['preferred_timestamp'] = ['driver_timestamp']
rdt['port_timestamp'] = [ntp_now]
rdt['quality_flag'] = ['']
rdt['lat'] = [45]
rdt['conductivity'] = [4341400]
rdt['driver_timestamp'] = [ntp_now]
rdt['lon'] = [-71]
rdt['pressure'] = [256.8]
publisher = StandaloneStreamPublisher(stream_id, route)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
# Check the values and make sure they're correct
np.testing.assert_allclose(rdt_out['time'], rdt['time'])
np.testing.assert_allclose(rdt_out['temp'], rdt['temp'])
np.testing.assert_allclose(rdt_out['lat'], np.array([45]))
np.testing.assert_allclose(rdt_out['lon'], np.array([-71]))
np.testing.assert_allclose(rdt_out['conductivity_L1'], np.array([42.914]))
np.testing.assert_allclose(rdt_out['temp_L1'], np.array([20.]))
np.testing.assert_allclose(rdt_out['pressure_L1'], np.array([3.068]))
np.testing.assert_allclose(rdt_out['density'], np.array([1021.7144739593881], dtype='float32'))
np.testing.assert_allclose(rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
# We're going to change the lat/lon
rdt = ph.get_rdt(stream_def_id)
rdt['time'] = time.time() + 2208988800
rdt['lat'] = [46]
rdt['lon'] = [-73]
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_allclose(rdt_out['time'], rdt['time'])
for i in xrange(9):
ntp_now = time.time() + 2208988800
rdt['time'] = [ntp_now]
rdt['internal_timestamp'] = [ntp_now]
rdt['temp'] = [300000]
rdt['preferred_timestamp'] = ['driver_timestamp']
rdt['port_timestamp'] = [ntp_now]
rdt['quality_flag'] = [None]
rdt['conductivity'] = [4341400]
rdt['driver_timestamp'] = [ntp_now]
rdt['pressure'] = [256.8]
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_allclose(rdt_out['pressure'], np.array([256.8] * 10))
np.testing.assert_allclose(rdt_out['lat'], np.array([45] + [46] * 9))
np.testing.assert_allclose(rdt_out['lon'], np.array([-71] + [-73] * 9))
class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url("res/deploy/r2deploy.yml")
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.pids = []
self.event = Event()
self.exchange_space_name = "test_granules"
self.exchange_point_name = "science_data"
self.i = 0
self.purge_queues()
self.queue_buffer = []
self.streams = []
self.addCleanup(self.stop_all_ingestion)
def purge_queues(self):
xn = self.container.ex_manager.create_xn_queue("science_granule_ingestion")
xn.purge()
def tearDown(self):
self.purge_queues()
for pid in self.pids:
self.container.proc_manager.terminate_process(pid)
IngestionManagementIntTest.clean_subscriptions()
for queue in self.queue_buffer:
if isinstance(queue, ExchangeNameQueue):
queue.delete()
elif isinstance(queue, str):
xn = self.container.ex_manager.create_xn_queue(queue)
xn.delete()
# --------------------------------------------------------------------------------
# Helper/Utility methods
# --------------------------------------------------------------------------------
def create_dataset(self, parameter_dict_id=""):
"""
Creates a time-series dataset
"""
tdom, sdom = time_series_domain()
sdom = sdom.dump()
tdom = tdom.dump()
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name(
"ctd_parsed_param_dict", id_only=True
)
dataset_id = self.dataset_management.create_dataset(
"test_dataset_%i" % self.i,
parameter_dictionary_id=parameter_dict_id,
spatial_domain=sdom,
temporal_domain=tdom,
)
return dataset_id
def get_datastore(self, dataset_id):
"""
Gets an instance of the datastore
This method is primarily used to defeat a bug where integration tests in multiple containers may sometimes
delete a CouchDB datastore and the other containers are unaware of the new state of the datastore.
"""
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def get_ingestion_config(self):
"""
Grab the ingestion configuration from the resource registry
"""
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration, id_only=True)
return ingest_configs[0]
def launch_producer(self, stream_id=""):
"""
Launch the producer
"""
pid = self.container.spawn_process(
"better_data_producer",
"ion.processes.data.example_data_producer",
"BetterDataProducer",
{"process": {"stream_id": stream_id}},
)
self.pids.append(pid)
def make_simple_dataset(self):
"""
Makes a stream, a stream definition and a dataset, the essentials for most of these tests
"""
pdict_id = self.dataset_management.read_parameter_dictionary_by_name("ctd_parsed_param_dict", id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition("ctd data", parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream(
"ctd stream %i" % self.i, "xp1", stream_definition_id=stream_def_id
)
dataset_id = self.create_dataset(pdict_id)
self.get_datastore(dataset_id)
self.i += 1
return stream_id, route, stream_def_id, dataset_id
def publish_hifi(self, stream_id, stream_route, offset=0):
"""
Publish deterministic data
"""
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt["time"] = np.arange(10) + (offset * 10)
rdt["temp"] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def publish_fake_data(self, stream_id, route):
"""
Make four granules
"""
for i in xrange(4):
self.publish_hifi(stream_id, route, i)
def start_ingestion(self, stream_id, dataset_id):
"""
Starts ingestion/persistence for a given dataset
"""
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.persist_data_stream(
stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id
)
def stop_ingestion(self, stream_id):
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(
stream_id=stream_id, ingestion_configuration_id=ingest_config_id
)
def stop_all_ingestion(self):
try:
[self.stop_ingestion(sid) for sid in self.streams]
except:
pass
def validate_granule_subscription(self, msg, route, stream_id):
"""
Validation for granule format
"""
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info("%s", rdt.pretty_print())
self.assertIsInstance(msg, Granule, "Message is improperly formatted. (%s)" % type(msg))
self.event.set()
def wait_until_we_have_enough_granules(self, dataset_id="", data_size=40):
"""
Loops until there is a sufficient amount of data in the dataset
"""
done = False
with gevent.Timeout(40):
while not done:
extents = self.dataset_management.dataset_extents(dataset_id, "time")[0]
granule = self.data_retriever.retrieve_last_data_points(dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
if rdt["time"] and rdt["time"][0] != rdt._pdict.get_context("time").fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
# --------------------------------------------------------------------------------
# Test Methods
# --------------------------------------------------------------------------------
@attr("SMOKE")
def test_dm_end_2_end(self):
# --------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
# --------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name("ctd_parsed_param_dict", id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext("binary", param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context("binary", bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext("records", param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context("records", rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
"replay_pdict", parameter_context_ids=context_ids, temporal_context="time"
)
stream_definition = self.pubsub_management.create_stream_definition(
"ctd data", parameter_dictionary_id=pdict_id
)
stream_id, route = self.pubsub_management.create_stream(
"producer", exchange_point=self.exchange_point_name, stream_definition_id=stream_definition
)
# --------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
# --------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id
)
# --------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
# --------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id, 40)
# --------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
# --------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt["time"][:10] == np.arange(10)).all(), "%s" % rdt["time"][:])
self.assertTrue((rdt["binary"][:10] == np.array(["hi"] * 10, dtype="object")).all())
# --------------------------------------------------------------------------------
# Now to try the streamed approach
# --------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream(
"replay_out", exchange_point=self.exchange_point_name, stream_definition_id=stream_definition
)
self.replay_id, process_id = self.data_retriever.define_replay(
dataset_id=dataset_id, stream_id=replay_stream_id
)
log.info("Process ID: %s", process_id)
replay_client = ReplayClient(process_id)
# --------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
# --------------------------------------------------------------------------------
xp = self.container.ex_manager.create_xp(self.exchange_point_name)
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5), "The process never launched")
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
subscriber.stop()
self.data_retriever.cancel_replay_agent(self.replay_id)
# --------------------------------------------------------------------------------
# Test the slicing capabilities
# --------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id, query={"tdoa": slice(0, 5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt["time"] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b, bool) else b)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
@unittest.skip("Doesnt work")
@attr("LOCOINT")
@unittest.skipIf(os.getenv("CEI_LAUNCH_TEST", False), "Skip test while in CEI LAUNCH mode")
def test_replay_pause(self):
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name("ctd_parsed_param_dict", id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext("binary", param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context("binary", bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext("records", param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context("records", rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
"replay_pdict", parameter_context_ids=context_ids, temporal_context="time"
)
stream_def_id = self.pubsub_management.create_stream_definition(
"replay_stream", parameter_dictionary_id=pdict_id
)
replay_stream, replay_route = self.pubsub_management.create_stream(
"replay", "xp1", stream_definition_id=stream_def_id
)
dataset_id = self.create_dataset(pdict_id)
scov = DatasetManagementService._get_coverage(dataset_id)
bb = CoverageCraft(scov)
bb.rdt["time"] = np.arange(100)
bb.rdt["temp"] = np.random.random(100) + 30
bb.sync_with_granule()
DatasetManagementService._persist_coverage(
dataset_id, bb.coverage
) # This invalidates it for multi-host configurations
# Set up the subscriber to verify the data
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
xp = self.container.ex_manager.create_xp("xp1")
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
# Set up the replay agent and the client wrapper
# 1) Define the Replay (dataset and stream to publish on)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream)
# 2) Make a client to the interact with the process (optionall provide it a process to bind with)
replay_client = ReplayClient(process_id)
# 3) Start the agent (launch the process)
self.data_retriever.start_replay_agent(self.replay_id)
# 4) Start replaying...
replay_client.start_replay()
# Wait till we get some granules
self.assertTrue(self.event.wait(5))
# We got granules, pause the replay, clear the queue and allow the process to finish consuming
replay_client.pause_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure there's no remaining messages being consumed
self.assertFalse(self.event.wait(1))
# Resume the replay and wait until we start getting granules again
replay_client.resume_replay()
self.assertTrue(self.event.wait(5))
# Stop the replay, clear the queues
replay_client.stop_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure that it did indeed stop
self.assertFalse(self.event.wait(1))
subscriber.stop()
def test_retrieve_and_transform(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(ctd_stream_id, dataset_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
"ctd_parsed_param_dict", id_only=True
)
sal_stream_def_id = self.pubsub_management.create_stream_definition(
"sal data", parameter_dictionary_id=salinity_pdict_id
)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt["time"] = np.arange(10)
rdt["temp"] = np.random.randn(10) * 10 + 30
rdt["conductivity"] = np.random.randn(10) * 2 + 10
rdt["pressure"] = np.random.randn(10) * 1 + 12
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt["time"] = np.arange(10, 20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 20)
granule = self.data_retriever.retrieve(
dataset_id,
None,
None,
"ion.processes.data.transforms.ctd.ctd_L2_salinity",
"CTDL2SalinityTransformAlgorithm",
kwargs=dict(params=sal_stream_def_id),
)
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt["salinity"]:
self.assertNotEquals(i, 0)
self.streams.append(ctd_stream_id)
self.stop_ingestion(ctd_stream_id)
def test_last_granule(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id, route, 0)
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id, 20) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id, 10)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt["time"] == np.arange(10) + 10
if not isinstance(comp, bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id, 5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt["time"] == np.arange(15, 20)
if not isinstance(comp, bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_replay_with_parameters(self):
# --------------------------------------------------------------------------------
# Create the configurations and the dataset
# --------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name("ctd_parsed_param_dict", id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext("binary", param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context("binary", bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext("records", param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context("records", rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
"replay_pdict", parameter_context_ids=context_ids, temporal_context="time"
)
stream_def_id = self.pubsub_management.create_stream_definition(
"replay_stream", parameter_dictionary_id=pdict_id
)
stream_id, route = self.pubsub_management.create_stream(
"replay_with_params", exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id
)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id
)
# --------------------------------------------------------------------------------
# Coerce the datastore into existence (beats race condition)
# --------------------------------------------------------------------------------
self.get_datastore(dataset_id)
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id, 40)
query = {
"start_time": 0 - 2208988800,
"end_time": 20 - 2208988800,
"stride_time": 2,
"parameters": ["time", "temp"],
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id, query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
comp = np.arange(0, 20, 2) == rdt["time"]
self.assertTrue(comp.all(), "%s" % rdt.pretty_print())
self.assertEquals(set(rdt.iterkeys()), set(["time", "temp"]))
extents = self.dataset_management.dataset_extents(dataset_id=dataset_id, parameters=["time", "temp"])
self.assertTrue(extents["time"] >= 20)
self.assertTrue(extents["temp"] >= 20)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_repersist_data(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id, route, 0)
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id, 20)
config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id
)
self.publish_hifi(stream_id, route, 2)
self.publish_hifi(stream_id, route, 3)
self.wait_until_we_have_enough_granules(dataset_id, 40)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt["time"] == np.arange(0, 40)
if not isinstance(comp, bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
@attr("LOCOINT")
@unittest.skipIf(
os.getenv("CEI_LAUNCH_TEST", False),
"Host requires file-system access to coverage files, CEI mode does not support.",
)
def test_correct_time(self):
# There are 2208988800 seconds between Jan 1 1900 and Jan 1 1970, i.e.
# the conversion factor between unix and NTP time
unix_now = np.floor(time.time())
ntp_now = unix_now + 2208988800
unix_ago = unix_now - 20
ntp_ago = unix_ago + 2208988800
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_coverage(dataset_id)
coverage.insert_timesteps(20)
coverage.set_parameter_values("time", np.arange(ntp_ago, ntp_now))
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertTrue(np.abs(temporal_bounds[0] - unix_ago) < 2)
self.assertTrue(np.abs(temporal_bounds[1] - unix_now) < 2)
@attr("LOCOINT")
@unittest.skipIf(
os.getenv("CEI_LAUNCH_TEST", False),
"Host requires file-system access to coverage files, CEI mode does not support.",
)
def test_empty_coverage_time(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_coverage(dataset_id)
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertEquals([coverage.get_parameter_context("time").fill_value] * 2, temporal_bounds)
@attr("LOCOINT")
@unittest.skipIf(
os.getenv("CEI_LAUNCH_TEST", False),
"Host requires file-system access to coverage files, CEI mode does not support.",
)
def test_out_of_band_retrieve(self):
# Setup the environemnt
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
# Fill the dataset
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id, 40)
# Retrieve the data
granule = DataRetrieverService.retrieve_oob(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
self.assertTrue((rdt["time"] == np.arange(40)).all())
@attr("LOCOINT")
@unittest.skipIf(
os.getenv("CEI_LAUNCH_TEST", False),
"Host requires file-system access to coverage files, CEI mode does not support.",
)
def test_retrieve_cache(self):
DataRetrieverService._refresh_interval = 1
datasets = [self.make_simple_dataset() for i in xrange(10)]
for stream_id, route, stream_def_id, dataset_id in datasets:
coverage = DatasetManagementService._get_coverage(dataset_id)
coverage.insert_timesteps(10)
coverage.set_parameter_values("time", np.arange(10))
coverage.set_parameter_values("temp", np.arange(10))
# Verify cache hit and refresh
dataset_ids = [i[3] for i in datasets]
self.assertTrue(dataset_ids[0] not in DataRetrieverService._retrieve_cache)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age = DataRetrieverService._retrieve_cache[dataset_ids[0]]
# Verify that it was hit and it's now in there
self.assertTrue(dataset_ids[0] in DataRetrieverService._retrieve_cache)
gevent.sleep(DataRetrieverService._refresh_interval + 0.2)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age2 = DataRetrieverService._retrieve_cache[dataset_ids[0]]
self.assertTrue(age2 != age)
for dataset_id in dataset_ids:
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(dataset_ids[0] not in DataRetrieverService._retrieve_cache)
stream_id, route, stream_def, dataset_id = datasets[0]
self.start_ingestion(stream_id, dataset_id)
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(dataset_id in DataRetrieverService._retrieve_cache)
DataRetrieverService._refresh_interval = 100
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id, data_size=20)
event = gevent.event.Event()
with gevent.Timeout(20):
while not event.wait(0.1):
if dataset_id not in DataRetrieverService._retrieve_cache:
event.set()
self.assertTrue(event.is_set())
@unittest.skip("Outdated due to ingestion retry")
@attr("LOCOINT")
@unittest.skipIf(
os.getenv("CEI_LAUNCH_TEST", False),
"Host requires file-system access to coverage files, CEI mode does not support.",
)
def test_ingestion_failover(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
event = Event()
def cb(*args, **kwargs):
event.set()
sub = EventSubscriber(event_type="ExceptionEvent", callback=cb, origin="stream_exception")
sub.start()
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id, 40)
file_path = DatasetManagementService._get_coverage_path(dataset_id)
master_file = os.path.join(file_path, "%s_master.hdf5" % dataset_id)
with open(master_file, "w") as f:
f.write("this will crash HDF")
self.publish_hifi(stream_id, route, 5)
self.assertTrue(event.wait(10))
sub.stop()
class ExhaustiveParameterTest(IonIntegrationTestCase):
def setUp(self):
self.i=0
self._start_container()
self.container.start_rel_from_url('res/deploy/r2params.yml')
self.dataset_management = DatasetManagementServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.data_product_management = DataProductManagementServiceClient()
self.resource_registry = self.container.resource_registry
self.data_retriever = DataRetrieverServiceClient()
pdicts, _ = self.resource_registry.find_resources(restype='ParameterDictionary', id_only=False)
self.dp_ids = []
for pdict in pdicts:
stream_def_id = self.pubsub_management.create_stream_definition(pdict.name, parameter_dictionary_id=pdict._id)
dp_id = self.make_dp(stream_def_id)
if dp_id: self.dp_ids.append(dp_id)
def make_dp(self, stream_def_id):
stream_def = self.resource_registry.read(stream_def_id)
dp_obj = DataProduct(
name=stream_def.name,
description=stream_def.name,
processing_level_code='Parsed_Canonical')
data_product_id = self.data_product_management.create_data_product(dp_obj, stream_definition_id=stream_def_id)
self.data_product_management.activate_data_product_persistence(data_product_id)
return data_product_id
def fill_values(self, ptype, size):
if isinstance(ptype, ArrayType):
return ['blah'] * size
elif isinstance(ptype, QuantityType):
return np.sin(np.arange(size, dtype=ptype.value_encoding) * 2 * np.pi / 3)
elif isinstance(ptype, RecordType):
return [{'record': 'ok'}] * size
elif isinstance(ptype, ConstantRangeType):
return (1,1000)
elif isinstance(ptype, ConstantType):
return np.dtype(ptype.value_encoding).type(1)
elif isinstance(ptype, CategoryType):
return ptype.categories.keys()[0]
else:
return
def wait_until_we_have_enough_granules(self, dataset_id='',data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
granule = self.data_retriever.retrieve_last_data_points(dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
extents = self.dataset_management.dataset_extents(dataset_id, rdt._pdict.temporal_parameter_name)[0]
if rdt[rdt._pdict.temporal_parameter_name] and rdt[rdt._pdict.temporal_parameter_name][0] != rdt._pdict.get_context(rdt._pdict.temporal_parameter_name).fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
def write_to_data_product(self,data_product_id):
dataset_ids, _ = self.resource_registry.find_objects(data_product_id, 'hasDataset', id_only=True)
dataset_id = dataset_ids.pop()
stream_ids , _ = self.resource_registry.find_objects(data_product_id, 'hasStream', id_only=True)
stream_id = stream_ids.pop()
stream_def_ids, _ = self.resource_registry.find_objects(stream_id, 'hasStreamDefinition', id_only=True)
stream_def_id = stream_def_ids.pop()
route = self.pubsub_management.read_stream_route(stream_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
time_param = rdt._pdict.temporal_parameter_name
if time_param is None:
print '%s has no temporal parameter' % self.resource_registry.read(data_product_id).name
return
rdt[time_param] = np.arange(40)
for field in rdt.fields:
if field == rdt._pdict.temporal_parameter_name:
continue
rdt[field] = self.fill_values(rdt._pdict.get_context(field).param_type,40)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id,40)
granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(granule)
bad = []
for field in rdt.fields:
if not np.array_equal(rdt[field], rdt_out[field]):
print '%s' % field
print '%s != %s' % (rdt[field], rdt_out[field])
bad.append(field)
return bad
def test_data_products(self):
bad_data_products = {}
for dp_id in self.dp_ids:
try:
bad_fields = self.write_to_data_product(dp_id)
if bad_fields:
bad_data_products[dp_id] = "Couldn't write and retrieve %s." % bad_fields
except:
import traceback
bad_data_products[dp_id] = traceback.format_exc()
for dp_id, tb in bad_data_products.iteritems():
print '----------'
print 'Problem with %s' % self.resource_registry.read(dp_id).name
print tb
print '----------'
if bad_data_products:
raise AssertionError('There are bad parameter dictionaries.')
class ExhaustiveParameterTest(IonIntegrationTestCase):
def setUp(self):
self.i = 0
self._start_container()
self.container.start_rel_from_url('res/deploy/r2params.yml')
self.dataset_management = DatasetManagementServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.data_product_management = DataProductManagementServiceClient()
self.resource_registry = self.container.resource_registry
self.data_retriever = DataRetrieverServiceClient()
pdicts, _ = self.resource_registry.find_resources(
restype='ParameterDictionary', id_only=False)
self.dp_ids = []
for pdict in pdicts:
stream_def_id = self.pubsub_management.create_stream_definition(
pdict.name, parameter_dictionary_id=pdict._id)
dp_id = self.make_dp(stream_def_id)
if dp_id: self.dp_ids.append(dp_id)
def make_dp(self, stream_def_id):
tdom, sdom = time_series_domain()
tdom = tdom.dump()
sdom = sdom.dump()
stream_def = self.resource_registry.read(stream_def_id)
dp_obj = DataProduct(name=stream_def.name,
description=stream_def.name,
processing_level_code='Parsed_Canonical',
temporal_domain=tdom,
spatial_domain=sdom)
data_product_id = self.data_product_management.create_data_product(
dp_obj, stream_definition_id=stream_def_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
return data_product_id
def fill_values(self, ptype, size):
if isinstance(ptype, ArrayType):
return ['blah'] * size
elif isinstance(ptype, QuantityType):
return np.sin(
np.arange(size, dtype=ptype.value_encoding) * 2 * np.pi / 3)
elif isinstance(ptype, RecordType):
return [{'record': 'ok'}] * size
elif isinstance(ptype, ConstantRangeType):
return (1, 1000)
elif isinstance(ptype, ConstantType):
return np.dtype(ptype.value_encoding).type(1)
elif isinstance(ptype, CategoryType):
return ptype.categories.keys()[0]
else:
return
def wait_until_we_have_enough_granules(self, dataset_id='', data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
granule = self.data_retriever.retrieve_last_data_points(
dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
extents = self.dataset_management.dataset_extents(
dataset_id, rdt._pdict.temporal_parameter_name)[0]
if rdt[rdt._pdict.temporal_parameter_name] and rdt[
rdt._pdict.
temporal_parameter_name][0] != rdt._pdict.get_context(
rdt._pdict.temporal_parameter_name
).fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
def write_to_data_product(self, data_product_id):
dataset_ids, _ = self.resource_registry.find_objects(data_product_id,
'hasDataset',
id_only=True)
dataset_id = dataset_ids.pop()
stream_ids, _ = self.resource_registry.find_objects(data_product_id,
'hasStream',
id_only=True)
stream_id = stream_ids.pop()
stream_def_ids, _ = self.resource_registry.find_objects(
stream_id, 'hasStreamDefinition', id_only=True)
stream_def_id = stream_def_ids.pop()
route = self.pubsub_management.read_stream_route(stream_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
time_param = rdt._pdict.temporal_parameter_name
if time_param is None:
print '%s has no temporal parameter' % self.resource_registry.read(
data_product_id).name
return
rdt[time_param] = np.arange(40)
for field in rdt.fields:
if field == rdt._pdict.temporal_parameter_name:
continue
rdt[field] = self.fill_values(
rdt._pdict.get_context(field).param_type, 40)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 40)
granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(granule)
bad = []
for field in rdt.fields:
if not np.array_equal(rdt[field], rdt_out[field]):
print '%s' % field
print '%s != %s' % (rdt[field], rdt_out[field])
bad.append(field)
return bad
def test_data_products(self):
bad_data_products = {}
for dp_id in self.dp_ids:
try:
bad_fields = self.write_to_data_product(dp_id)
if bad_fields:
bad_data_products[
dp_id] = "Couldn't write and retrieve %s." % bad_fields
except:
import traceback
bad_data_products[dp_id] = traceback.format_exc()
for dp_id, tb in bad_data_products.iteritems():
print '----------'
print 'Problem with %s' % self.resource_registry.read(dp_id).name
print tb
print '----------'
if bad_data_products:
raise AssertionError('There are bad parameter dictionaries.')
class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.pids = []
self.event = Event()
self.exchange_space_name = 'test_granules'
self.exchange_point_name = 'science_data'
self.i = 0
self.purge_queues()
self.queue_buffer = []
self.streams = []
self.addCleanup(self.stop_all_ingestion)
def purge_queues(self):
xn = self.container.ex_manager.create_xn_queue(
'science_granule_ingestion')
xn.purge()
def tearDown(self):
self.purge_queues()
for pid in self.pids:
self.container.proc_manager.terminate_process(pid)
IngestionManagementIntTest.clean_subscriptions()
for queue in self.queue_buffer:
if isinstance(queue, ExchangeNameQueue):
queue.delete()
elif isinstance(queue, str):
xn = self.container.ex_manager.create_xn_queue(queue)
xn.delete()
#--------------------------------------------------------------------------------
# Helper/Utility methods
#--------------------------------------------------------------------------------
def create_dataset(self, parameter_dict_id=''):
'''
Creates a time-series dataset
'''
tdom, sdom = time_series_domain()
sdom = sdom.dump()
tdom = tdom.dump()
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
dataset_id = self.dataset_management.create_dataset(
'test_dataset_%i' % self.i,
parameter_dictionary_id=parameter_dict_id,
spatial_domain=sdom,
temporal_domain=tdom)
return dataset_id
def get_datastore(self, dataset_id):
'''
Gets an instance of the datastore
This method is primarily used to defeat a bug where integration tests in multiple containers may sometimes
delete a CouchDB datastore and the other containers are unaware of the new state of the datastore.
'''
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(
datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def get_ingestion_config(self):
'''
Grab the ingestion configuration from the resource registry
'''
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(
restype=RT.IngestionConfiguration, id_only=True)
return ingest_configs[0]
def launch_producer(self, stream_id=''):
'''
Launch the producer
'''
pid = self.container.spawn_process(
'better_data_producer', 'ion.processes.data.example_data_producer',
'BetterDataProducer', {'process': {
'stream_id': stream_id
}})
self.pids.append(pid)
def make_simple_dataset(self):
'''
Makes a stream, a stream definition and a dataset, the essentials for most of these tests
'''
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition(
'ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream(
'ctd stream %i' % self.i,
'xp1',
stream_definition_id=stream_def_id)
dataset_id = self.create_dataset(pdict_id)
self.get_datastore(dataset_id)
self.i += 1
return stream_id, route, stream_def_id, dataset_id
def publish_hifi(self, stream_id, stream_route, offset=0):
'''
Publish deterministic data
'''
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(
stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def publish_fake_data(self, stream_id, route):
'''
Make four granules
'''
for i in xrange(4):
self.publish_hifi(stream_id, route, i)
def start_ingestion(self, stream_id, dataset_id):
'''
Starts ingestion/persistence for a given dataset
'''
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.persist_data_stream(
stream_id=stream_id,
ingestion_configuration_id=ingest_config_id,
dataset_id=dataset_id)
def stop_ingestion(self, stream_id):
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(
stream_id=stream_id, ingestion_configuration_id=ingest_config_id)
def stop_all_ingestion(self):
try:
[self.stop_ingestion(sid) for sid in self.streams]
except:
pass
def validate_granule_subscription(self, msg, route, stream_id):
'''
Validation for granule format
'''
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info('%s', rdt.pretty_print())
self.assertIsInstance(
msg, Granule, 'Message is improperly formatted. (%s)' % type(msg))
self.event.set()
def wait_until_we_have_enough_granules(self, dataset_id='', data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
extents = self.dataset_management.dataset_extents(
dataset_id, 'time')[0]
granule = self.data_retriever.retrieve_last_data_points(
dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
if rdt['time'] and rdt['time'][0] != rdt._pdict.get_context(
'time').fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
#--------------------------------------------------------------------------------
# Test Methods
#--------------------------------------------------------------------------------
@attr('SMOKE')
def test_dm_end_2_end(self):
#--------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
#--------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(
pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(
self.dataset_management.create_parameter_context(
'binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(
self.dataset_management.create_parameter_context(
'records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
'replay_pdict',
parameter_context_ids=context_ids,
temporal_context='time')
stream_definition = self.pubsub_management.create_stream_definition(
'ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream(
'producer',
exchange_point=self.exchange_point_name,
stream_definition_id=stream_definition)
#--------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
#--------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id,
ingestion_configuration_id=ingest_config_id,
dataset_id=dataset_id)
#--------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
#--------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id, 40)
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt['time'][:10] == np.arange(10)).all(),
'%s' % rdt['time'][:])
self.assertTrue((rdt['binary'][:10] == np.array(['hi'] * 10,
dtype='object')).all())
#--------------------------------------------------------------------------------
# Now to try the streamed approach
#--------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream(
'replay_out',
exchange_point=self.exchange_point_name,
stream_definition_id=stream_definition)
self.replay_id, process_id = self.data_retriever.define_replay(
dataset_id=dataset_id, stream_id=replay_stream_id)
log.info('Process ID: %s', process_id)
replay_client = ReplayClient(process_id)
#--------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
#--------------------------------------------------------------------------------
xp = self.container.ex_manager.create_xp(self.exchange_point_name)
subscriber = StandaloneStreamSubscriber(
self.exchange_space_name, self.validate_granule_subscription)
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5),
'The process never launched')
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
subscriber.stop()
self.data_retriever.cancel_replay_agent(self.replay_id)
#--------------------------------------------------------------------------------
# Test the slicing capabilities
#--------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id,
query={'tdoa': slice(0, 5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt['time'] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b, bool) else b)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
@unittest.skip('Doesnt work')
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Skip test while in CEI LAUNCH mode')
def test_replay_pause(self):
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(
pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(
self.dataset_management.create_parameter_context(
'binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(
self.dataset_management.create_parameter_context(
'records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
'replay_pdict',
parameter_context_ids=context_ids,
temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition(
'replay_stream', parameter_dictionary_id=pdict_id)
replay_stream, replay_route = self.pubsub_management.create_stream(
'replay', 'xp1', stream_definition_id=stream_def_id)
dataset_id = self.create_dataset(pdict_id)
scov = DatasetManagementService._get_coverage(dataset_id)
bb = CoverageCraft(scov)
bb.rdt['time'] = np.arange(100)
bb.rdt['temp'] = np.random.random(100) + 30
bb.sync_with_granule()
DatasetManagementService._persist_coverage(
dataset_id,
bb.coverage) # This invalidates it for multi-host configurations
# Set up the subscriber to verify the data
subscriber = StandaloneStreamSubscriber(
self.exchange_space_name, self.validate_granule_subscription)
xp = self.container.ex_manager.create_xp('xp1')
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
# Set up the replay agent and the client wrapper
# 1) Define the Replay (dataset and stream to publish on)
self.replay_id, process_id = self.data_retriever.define_replay(
dataset_id=dataset_id, stream_id=replay_stream)
# 2) Make a client to the interact with the process (optionall provide it a process to bind with)
replay_client = ReplayClient(process_id)
# 3) Start the agent (launch the process)
self.data_retriever.start_replay_agent(self.replay_id)
# 4) Start replaying...
replay_client.start_replay()
# Wait till we get some granules
self.assertTrue(self.event.wait(5))
# We got granules, pause the replay, clear the queue and allow the process to finish consuming
replay_client.pause_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure there's no remaining messages being consumed
self.assertFalse(self.event.wait(1))
# Resume the replay and wait until we start getting granules again
replay_client.resume_replay()
self.assertTrue(self.event.wait(5))
# Stop the replay, clear the queues
replay_client.stop_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure that it did indeed stop
self.assertFalse(self.event.wait(1))
subscriber.stop()
def test_retrieve_and_transform(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(ctd_stream_id, dataset_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
sal_stream_def_id = self.pubsub_management.create_stream_definition(
'sal data', parameter_dictionary_id=salinity_pdict_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.random.randn(10) * 10 + 30
rdt['conductivity'] = np.random.randn(10) * 2 + 10
rdt['pressure'] = np.random.randn(10) * 1 + 12
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt['time'] = np.arange(10, 20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 20)
granule = self.data_retriever.retrieve(
dataset_id,
None,
None,
'ion.processes.data.transforms.ctd.ctd_L2_salinity',
'CTDL2SalinityTransformAlgorithm',
kwargs=dict(params=sal_stream_def_id))
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt['salinity']:
self.assertNotEquals(i, 0)
self.streams.append(ctd_stream_id)
self.stop_ingestion(ctd_stream_id)
def test_last_granule(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id, route, 0)
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id,
20) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_data_points(
dataset_id, 10)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(10) + 10
if not isinstance(comp, bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(
dataset_id, 5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(15, 20)
if not isinstance(comp, bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_replay_with_parameters(self):
#--------------------------------------------------------------------------------
# Create the configurations and the dataset
#--------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(
pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(
self.dataset_management.create_parameter_context(
'binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(
self.dataset_management.create_parameter_context(
'records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary(
'replay_pdict',
parameter_context_ids=context_ids,
temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition(
'replay_stream', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream(
'replay_with_params',
exchange_point=self.exchange_point_name,
stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id,
ingestion_configuration_id=config_id,
dataset_id=dataset_id)
dataset_modified = Event()
def cb(*args, **kwargs):
dataset_modified.set()
es = EventSubscriber(event_type=OT.DatasetModified,
callback=cb,
origin=dataset_id)
es.start()
self.addCleanup(es.stop)
self.publish_fake_data(stream_id, route)
self.assertTrue(dataset_modified.wait(30))
query = {
'start_time': 0 - 2208988800,
'end_time': 20 - 2208988800,
'stride_time': 2,
'parameters': ['time', 'temp']
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id,
query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
comp = np.arange(0, 20, 2) == rdt['time']
self.assertTrue(comp.all(), '%s' % rdt.pretty_print())
self.assertEquals(set(rdt.iterkeys()), set(['time', 'temp']))
extents = self.dataset_management.dataset_extents(
dataset_id=dataset_id, parameters=['time', 'temp'])
self.assertTrue(extents['time'] >= 20)
self.assertTrue(extents['temp'] >= 20)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
def test_repersist_data(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id, route, 0)
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id, 20)
config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(
stream_id=stream_id, ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(
stream_id=stream_id,
ingestion_configuration_id=config_id,
dataset_id=dataset_id)
self.publish_hifi(stream_id, route, 2)
self.publish_hifi(stream_id, route, 3)
self.wait_until_we_have_enough_granules(dataset_id, 40)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(0, 40)
if not isinstance(comp, bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)
self.streams.append(stream_id)
self.stop_ingestion(stream_id)
@attr('LOCOINT')
@unittest.skipIf(os.getenv(
'CEI_LAUNCH_TEST', False
), 'Host requires file-system access to coverage files, CEI mode does not support.'
)
def test_correct_time(self):
# There are 2208988800 seconds between Jan 1 1900 and Jan 1 1970, i.e.
# the conversion factor between unix and NTP time
unix_now = np.floor(time.time())
ntp_now = unix_now + 2208988800
unix_ago = unix_now - 20
ntp_ago = unix_ago + 2208988800
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
coverage = DatasetManagementService._get_coverage(dataset_id)
coverage.insert_timesteps(20)
coverage.set_parameter_values('time', np.arange(ntp_ago, ntp_now))
temporal_bounds = self.dataset_management.dataset_temporal_bounds(
dataset_id)
self.assertTrue(np.abs(temporal_bounds[0] - unix_ago) < 2)
self.assertTrue(np.abs(temporal_bounds[1] - unix_now) < 2)
@attr('LOCOINT')
@unittest.skipIf(os.getenv(
'CEI_LAUNCH_TEST', False
), 'Host requires file-system access to coverage files, CEI mode does not support.'
)
def test_empty_coverage_time(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
coverage = DatasetManagementService._get_coverage(dataset_id)
temporal_bounds = self.dataset_management.dataset_temporal_bounds(
dataset_id)
self.assertEquals([coverage.get_parameter_context('time').fill_value] *
2, temporal_bounds)
@attr('LOCOINT')
@unittest.skipIf(os.getenv(
'CEI_LAUNCH_TEST', False
), 'Host requires file-system access to coverage files, CEI mode does not support.'
)
def test_out_of_band_retrieve(self):
# Setup the environemnt
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(stream_id, dataset_id)
# Fill the dataset
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id, 40)
# Retrieve the data
granule = DataRetrieverService.retrieve_oob(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
self.assertTrue((rdt['time'] == np.arange(40)).all())
@attr('LOCOINT')
@unittest.skipIf(os.getenv(
'CEI_LAUNCH_TEST', False
), 'Host requires file-system access to coverage files, CEI mode does not support.'
)
def test_retrieve_cache(self):
DataRetrieverService._refresh_interval = 1
datasets = [self.make_simple_dataset() for i in xrange(10)]
for stream_id, route, stream_def_id, dataset_id in datasets:
coverage = DatasetManagementService._get_coverage(dataset_id)
coverage.insert_timesteps(10)
coverage.set_parameter_values('time', np.arange(10))
coverage.set_parameter_values('temp', np.arange(10))
# Verify cache hit and refresh
dataset_ids = [i[3] for i in datasets]
self.assertTrue(
dataset_ids[0] not in DataRetrieverService._retrieve_cache)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age = DataRetrieverService._retrieve_cache[dataset_ids[0]]
# Verify that it was hit and it's now in there
self.assertTrue(dataset_ids[0] in DataRetrieverService._retrieve_cache)
gevent.sleep(DataRetrieverService._refresh_interval + 0.2)
DataRetrieverService._get_coverage(dataset_ids[0]) # Hit the chache
cov, age2 = DataRetrieverService._retrieve_cache[dataset_ids[0]]
self.assertTrue(age2 != age)
for dataset_id in dataset_ids:
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(
dataset_ids[0] not in DataRetrieverService._retrieve_cache)
stream_id, route, stream_def, dataset_id = datasets[0]
self.start_ingestion(stream_id, dataset_id)
DataRetrieverService._get_coverage(dataset_id)
self.assertTrue(dataset_id in DataRetrieverService._retrieve_cache)
DataRetrieverService._refresh_interval = 100
self.publish_hifi(stream_id, route, 1)
self.wait_until_we_have_enough_granules(dataset_id, data_size=20)
event = gevent.event.Event()
with gevent.Timeout(20):
while not event.wait(0.1):
if dataset_id not in DataRetrieverService._retrieve_cache:
event.set()
self.assertTrue(event.is_set())
@unittest.skip('Outdated due to ingestion retry')
@attr('LOCOINT')
@unittest.skipIf(os.getenv(
'CEI_LAUNCH_TEST', False
), 'Host requires file-system access to coverage files, CEI mode does not support.'
)
def test_ingestion_failover(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(stream_id, dataset_id)
event = Event()
def cb(*args, **kwargs):
event.set()
sub = EventSubscriber(event_type="ExceptionEvent",
callback=cb,
origin="stream_exception")
sub.start()
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id, 40)
file_path = DatasetManagementService._get_coverage_path(dataset_id)
master_file = os.path.join(file_path, '%s_master.hdf5' % dataset_id)
with open(master_file, 'w') as f:
f.write('this will crash HDF')
self.publish_hifi(stream_id, route, 5)
self.assertTrue(event.wait(10))
sub.stop()
class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.pids = []
self.event = Event()
self.exchange_space_name = 'test_granules'
self.exchange_point_name = 'science_data'
self.purge_queues()
self.queue_buffer = []
def purge_queues(self):
xn = self.container.ex_manager.create_xn_queue('science_granule_ingestion')
xn.purge()
def tearDown(self):
self.purge_queues()
for pid in self.pids:
self.container.proc_manager.terminate_process(pid)
IngestionManagementIntTest.clean_subscriptions()
for queue in self.queue_buffer:
if isinstance(queue, ExchangeNameQueue):
queue.delete()
elif isinstance(queue, str):
xn = self.container.ex_manager.create_xn_queue(queue)
xn.delete()
def launch_producer(self, stream_id=''):
#--------------------------------------------------------------------------------
# Launch the producer
#--------------------------------------------------------------------------------
pid = self.container.spawn_process('better_data_producer', 'ion.processes.data.example_data_producer', 'BetterDataProducer', {'process':{'stream_id':stream_id}})
self.pids.append(pid)
def get_ingestion_config(self):
#--------------------------------------------------------------------------------
# Grab the ingestion configuration from the resource registry
#--------------------------------------------------------------------------------
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration,id_only=True)
return ingest_configs[0]
def publish_hifi(self,stream_id,stream_route,offset=0):
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def publish_fake_data(self,stream_id, route):
for i in xrange(4):
self.publish_hifi(stream_id,route,i)
def get_datastore(self, dataset_id):
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def validate_granule_subscription(self, msg, route, stream_id):
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info('%s', rdt.pretty_print())
self.assertIsInstance(msg,Granule,'Message is improperly formatted. (%s)' % type(msg))
self.event.set()
def make_file_data(self):
from interface.objects import File
import uuid
data = 'hello world\n'
rand = str(uuid.uuid4())[:8]
meta = File(name='/examples/' + rand + '.txt', group_id='example1')
return {'body': data, 'meta':meta}
def publish_file(self, stream_id, stream_route):
publisher = StandaloneStreamPublisher(stream_id,stream_route)
publisher.publish(self.make_file_data())
def wait_until_we_have_enough_granules(self, dataset_id='',granules=4):
datastore = self.get_datastore(dataset_id)
dataset = self.dataset_management.read_dataset(dataset_id)
with gevent.timeout.Timeout(40):
success = False
while not success:
success = len(datastore.query_view(dataset.view_name)) >= granules
gevent.sleep(0.1)
log.info(datastore.query_view(dataset.view_name))
def wait_until_we_have_enough_files(self):
datastore = self.container.datastore_manager.get_datastore('filesystem', DataStore.DS_PROFILE.FILESYSTEM)
now = time.time()
timeout = now + 10
done = False
while not done:
if now >= timeout:
raise Timeout('Files are not populating in time.')
if len(datastore.query_view('catalog/file_by_owner')) >= 1:
done = True
now = time.time()
def create_dataset(self, parameter_dict_id=''):
tdom, sdom = time_series_domain()
sdom = sdom.dump()
tdom = tdom.dump()
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
dataset_id = self.dataset_management.create_dataset('test_dataset', parameter_dictionary_id=parameter_dict_id, spatial_domain=sdom, temporal_domain=tdom)
return dataset_id
@unittest.skip('Doesnt work')
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode')
def test_replay_pause(self):
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
replay_stream, replay_route = self.pubsub_management.create_stream('replay', 'xp1', stream_definition_id=stream_def_id)
dataset_id = self.create_dataset(pdict_id)
scov = DatasetManagementService._get_coverage(dataset_id)
bb = CoverageCraft(scov)
bb.rdt['time'] = np.arange(100)
bb.rdt['temp'] = np.random.random(100) + 30
bb.sync_with_granule()
DatasetManagementService._persist_coverage(dataset_id, bb.coverage) # This invalidates it for multi-host configurations
# Set up the subscriber to verify the data
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
xp = self.container.ex_manager.create_xp('xp1')
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
# Set up the replay agent and the client wrapper
# 1) Define the Replay (dataset and stream to publish on)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream)
# 2) Make a client to the interact with the process (optionall provide it a process to bind with)
replay_client = ReplayClient(process_id)
# 3) Start the agent (launch the process)
self.data_retriever.start_replay_agent(self.replay_id)
# 4) Start replaying...
replay_client.start_replay()
# Wait till we get some granules
self.assertTrue(self.event.wait(5))
# We got granules, pause the replay, clear the queue and allow the process to finish consuming
replay_client.pause_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure there's no remaining messages being consumed
self.assertFalse(self.event.wait(1))
# Resume the replay and wait until we start getting granules again
replay_client.resume_replay()
self.assertTrue(self.event.wait(5))
# Stop the replay, clear the queues
replay_client.stop_replay()
gevent.sleep(1)
subscriber.xn.purge()
self.event.clear()
# Make sure that it did indeed stop
self.assertFalse(self.event.wait(1))
subscriber.stop()
@attr('SMOKE')
def test_dm_end_2_end(self):
#--------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
#--------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_definition = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('producer', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
#--------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
#--------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
#--------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
#--------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id,4)
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt['time'][:10] == np.arange(10)).all(),'%s' % rdt['time'][:])
self.assertTrue((rdt['binary'][:10] == np.array(['hi']*10, dtype='object')).all())
#--------------------------------------------------------------------------------
# Now to try the streamed approach
#--------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream('replay_out', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream_id)
log.info('Process ID: %s', process_id)
replay_client = ReplayClient(process_id)
#--------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
#--------------------------------------------------------------------------------
xp = self.container.ex_manager.create_xp(self.exchange_point_name)
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
self.queue_buffer.append(self.exchange_space_name)
subscriber.start()
subscriber.xn.bind(replay_route.routing_key, xp)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5), 'The process never launched')
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
subscriber.stop()
self.data_retriever.cancel_replay_agent(self.replay_id)
#--------------------------------------------------------------------------------
# Test the slicing capabilities
#--------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id, query={'tdoa':slice(0,5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt['time'] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b,bool) else b)
def test_retrieve_and_transform(self):
# Stream definition for the CTD data
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
ctd_stream_id, route = self.pubsub_management.create_stream('ctd stream', 'xp1', stream_definition_id=stream_def_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
sal_stream_def_id = self.pubsub_management.create_stream_definition('sal data', parameter_dictionary_id=salinity_pdict_id)
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
#--------------------------------------------------------------------------------
# Again with this ridiculous problem
#--------------------------------------------------------------------------------
self.get_datastore(dataset_id)
self.ingestion_management.persist_data_stream(stream_id=ctd_stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.random.randn(10) * 10 + 30
rdt['conductivity'] = np.random.randn(10) * 2 + 10
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt['time'] = np.arange(10,20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 2)
granule = self.data_retriever.retrieve(dataset_id,
None,
None,
'ion.processes.data.transforms.ctd.ctd_L2_salinity',
'CTDL2SalinityTransformAlgorithm',
kwargs=dict(params=sal_stream_def_id))
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt['salinity']:
self.assertNotEquals(i,0)
def test_last_granule(self):
#--------------------------------------------------------------------------------
# Create the necessary configurations for the test
#--------------------------------------------------------------------------------
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition('ctd parsed', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('last_granule', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
#--------------------------------------------------------------------------------
# Create the datastore first,
#--------------------------------------------------------------------------------
self.get_datastore(dataset_id)
self.publish_hifi(stream_id,route, 0)
self.publish_hifi(stream_id,route, 1)
self.wait_until_we_have_enough_granules(dataset_id,2) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_granule(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(10) + 10
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id,5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(15,20)
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
def test_replay_with_parameters(self):
#--------------------------------------------------------------------------------
# Create the configurations and the dataset
#--------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('replay_with_params', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
#--------------------------------------------------------------------------------
# Coerce the datastore into existence (beats race condition)
#--------------------------------------------------------------------------------
self.get_datastore(dataset_id)
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id,4)
query = {
'start_time': 0,
'end_time': 20,
'stride_time' : 2,
'parameters': ['time','temp']
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id,query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
comp = np.arange(0,20,2) == rdt['time']
self.assertTrue(comp.all(),'%s' % rdt.pretty_print())
self.assertEquals(set(rdt.iterkeys()), set(['time','temp']))
extents = self.dataset_management.dataset_extents(dataset_id=dataset_id, parameters=['time','temp'])
self.assertTrue(extents['time']>=20)
self.assertTrue(extents['temp']>=20)
def test_repersist_data(self):
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition(name='ctd', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream(name='repersist', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
self.get_datastore(dataset_id)
self.publish_hifi(stream_id,route,0)
self.publish_hifi(stream_id,route,1)
self.wait_until_we_have_enough_granules(dataset_id,2)
self.ingestion_management.unpersist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id,dataset_id=dataset_id)
self.publish_hifi(stream_id,route,2)
self.publish_hifi(stream_id,route,3)
self.wait_until_we_have_enough_granules(dataset_id,4)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(0,40)
if not isinstance(comp,bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)