def _add_correlation_info_to_message(tenant, message):
"""
Pack the message with correlation data. The message will be update by
adding a dictionary named "meniscus" that contains tenant specific
information used in processing the message.
"""
#match the producer by the message pname
producer = tenant_util.find_event_producer(tenant,
producer_name=message['pname'])
#if the producer is not found, create a default producer
if not producer:
producer = EventProducer(_id=None, name="default", pattern="default")
#create correlation dictionary
correlation_dict = {
'tenant_name': tenant.tenant_name,
'ep_id': producer.get_id(),
'pattern': producer.pattern,
'durable': producer.durable,
'encrypted': producer.encrypted,
'@timestamp': timeutils.utcnow(),
'sinks': producer.sinks,
"destinations": dict()
}
#configure sink dispatch
for sink in producer.sinks:
correlation_dict["destinations"][sink] = {
'transaction_id': None,
'transaction_time': None
}
# After successful correlation remove meniscus information from structured
# data so that the client's token is scrubbed form the message.
message['native'].pop('meniscus', None)
message.update({
'meniscus': {
'tenant': tenant.tenant_id,
'correlation': correlation_dict
}
})
# If the message data indicates that the message has normalization rules
# that apply, Queue the message for normalization processing
if normalizer.should_normalize(message):
# send the message to normalization then route to sink
normalizer.normalize_message.delay(message)
else:
# Queue the message for indexing/storage
sinks.route_message(message)
def normalize_message(message):
"""
This code takes a message and normalizes it into a dictionary. This
normalized dictionary is assigned to a field matching the pattern name
of the normalization. This dictionary is then assigned to the message
under the normalized field.
"""
pattern = message['meniscus']['correlation']['pattern']
normalized_doc = json.loads(
_normalizer.normalize(message['msg']).as_json())
message['normalized'] = {
pattern: normalized_doc
}
sinks.route_message(message)
def _add_correlation_info_to_message(tenant, message):
"""
Pack the message with correlation data. The message will be update by
adding a dictionary named "meniscus" that contains tenant specific
information used in processing the message.
"""
#match the producer by the message pname
producer = tenant_util.find_event_producer(tenant,
producer_name=message['pname'])
#if the producer is not found, create a default producer
if not producer:
producer = EventProducer(_id=None, name="default", pattern="default")
#create correlation dictionary
correlation_dict = {
'tenant_name': tenant.tenant_name,
'ep_id': producer.get_id(),
'pattern': producer.pattern,
'durable': producer.durable,
'encrypted': producer.encrypted,
'@timestamp': timeutils.utcnow(),
'sinks': producer.sinks,
"destinations": dict()
}
#configure sink dispatch
for sink in producer.sinks:
correlation_dict["destinations"][sink] = {'transaction_id': None,
'transaction_time': None}
# After successful correlation remove meniscus information from structured
# data so that the client's token is scrubbed form the message.
message['native'].pop('meniscus', None)
message.update({'meniscus': {'tenant': tenant.tenant_id,
'correlation': correlation_dict}})
# If the message data indicates that the message has normalization rules
# that apply, Queue the message for normalization processing
if normalizer.should_normalize(message):
# send the message to normalization then route to sink
normalizer.normalize_message.delay(message)
else:
# Queue the message for indexing/storage
sinks.route_message(message)
