def get_datastore_actions():
limit = int(request.args.get('limit', 20))
offset = int(request.args.get('offset', 0))
filters = [
filter_service().from_string(f)
for f in json.loads(request.args.get('filters', '[]'))
]
order_by = [
order_by_service().from_string(f)
for f in json.loads(request.args.get('order_by', '[]'))
]
datastore_id = request.args.get('datastore_id')
workflow_id = request.args.get('workflow_id')
if datastore_id:
filters.append(Filter('datastore_id', Operator.EQ, datastore_id))
if workflow_id:
filters.append(Filter('workflow_id', Operator.EQ, workflow_id))
actions = action_service().query_actions(filters, limit, offset, order_by)
return {
'results': [a.to_dict() for a in actions],
'limit': limit,
'offset': offset,
'total': action_service().query_actions_count(filters)
}
def start_datastore(emr_engine, datastore, action):
"""
:type emr_engine: dart.engine.emr.emr.EmrEngine
:type datastore: dart.model.datastore.Datastore
:type action: dart.model.action.Action
"""
cluster_name = 'dart-datastore-%s-%s' % (datastore.id, datastore.data.name)
actions = emr_engine.dart.find_actions([
Filter('datastore_id', Operator.EQ, datastore.id),
Filter('state', Operator.EQ, ActionState.HAS_NEVER_RUN),
Filter('action_type_name', Operator.EQ,
EmrActionTypes.load_dataset.name),
])
instance_groups_args = prepare_instance_groups(
emr_engine, datastore, actions, emr_engine.core_node_limit,
datastore.data.args['data_to_freespace_ratio'])
bootstrap_actions_args = prepare_bootstrap_actions(
datastore, emr_engine.impala_docker_repo_base_url,
emr_engine.impala_version, False, action)
extra_data = {
'instance_groups_args': instance_groups_args,
'bootstrap_action_args': bootstrap_actions_args,
}
if datastore.data.args['dry_run']:
emr_engine.dart.patch_action(action, progress=1, extra_data=extra_data)
return
action = emr_engine.dart.patch_action(action,
progress=0,
extra_data=extra_data)
cluster_id = create_cluster(bootstrap_actions_args, cluster_name,
datastore, emr_engine, instance_groups_args)
emr_engine.dart.patch_datastore(datastore,
extra_data={'cluster_id': cluster_id})
emr_engine.dart.patch_action(action, progress=0.1)
cluster = None
while True:
state = emr_engine.conn.describe_cluster(cluster_id).status.state
# http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/ProcessingCycle.html
# using cluster state since describe_jobflow is deprecated
# https://aws.amazon.com/elasticmapreduce/faqs/
if state in ['STARTING', 'BOOTSTRAPPING', 'RUNNING']:
time.sleep(30)
else:
break
if state not in ['WAITING']:
raise Exception(
'cluster_id=%s not in WAITING state, but in state: %s' %
(cluster_id, cluster.state))
emr_engine.dart.patch_datastore(
datastore,
host=cluster.masterpublicdnsname,
port=21050,
connection_url='jdbc:impala://%s:%s/default' %
(cluster.masterpublicdnsname, 21050))
def _handle_complete_action(self, message_id, message, previous_handler_failed):
if previous_handler_failed:
_logger.error('previous handler for message id=%s failed... see if retrying is possible' % message_id)
return
_logger.info("Complete Action Trigger: message_id={message_id}, message={message}".format(message_id=message_id, message=message))
state = message['action_state']
action = self._action_service.get_action(message['action_id'])
assert isinstance(action, Action)
datastore = self._datastore_service.get_datastore(action.data.datastore_id)
error_message = message.get('error_message')
self._action_service.update_action_state(action, state, error_message or action.data.error_message)
wfid = action.data.workflow_id
wfiid = action.data.workflow_instance_id
wf = self._workflow_service.get_workflow(wfid) if wfid else None
wfi = self._workflow_service.get_workflow_instance(wfiid) if wfiid else None
callbacks = []
try_next_action = True
try:
if state == ActionState.FAILED:
callbacks.append(lambda: self._emailer.send_action_failed_email(action, datastore))
if action.data.on_failure == ActionOnFailure.DEACTIVATE:
try_next_action = False
if wf and wfi:
self._workflow_service.update_workflow_instance_state(wfi, WorkflowInstanceState.FAILED)
if wfi.data.retry_num < wf.data.retries_on_failure:
retry_num = wfi.data.retry_num + 1
callbacks.append(lambda: self._trigger_proxy.trigger_workflow_retry(wfid, retry_num))
else:
self._workflow_service.update_workflow_state(wf, WorkflowState.INACTIVE)
if wf.data.on_failure == WorkflowOnFailure.DEACTIVATE:
self._datastore_service.update_datastore_state(datastore, DatastoreState.INACTIVE)
f1 = Filter('workflow_instance_id', Operator.EQ, wfiid)
f2 = Filter('state', Operator.EQ, ActionState.HAS_NEVER_RUN)
for a in self._action_service.query_actions_all(filters=[f1, f2]):
error_msg = 'A prior action (id=%s) in this workflow instance failed' % action.id
self._action_service.update_action_state(a, ActionState.SKIPPED, error_msg)
callbacks.append(lambda: self._emailer.send_workflow_failed_email(wf, wfi))
else:
self._datastore_service.update_datastore_state(datastore, DatastoreState.INACTIVE)
else:
if wfi and action.data.last_in_workflow:
self._handle_complete_workflow(callbacks, wf, wfi, wfid)
elif state == ActionState.COMPLETED:
if action.data.on_success_email:
callbacks.append(lambda: self._emailer.send_action_completed_email(action, datastore))
if wfi and action.data.last_in_workflow:
self._handle_complete_workflow(callbacks, wf, wfi, wfid)
finally:
for f in callbacks:
f()
if try_next_action:
self._trigger_proxy.try_next_action({'datastore_id': datastore.id, 'log_info': message.get('log_info')})
def get_actions(self, datastore_id=None, workflow_id=None):
""" :type datastore_id: str
:type workflow_id: str
:rtype: list[dart.model.action.Action] """
assert datastore_id or workflow_id, 'datastore_id and/or workflow_id must be provided'
filters = []
if datastore_id:
filters.append(Filter('datastore_id', Operator.EQ, datastore_id))
if workflow_id:
filters.append(Filter('workflow_id', Operator.EQ, workflow_id))
return self.find_actions(filters)
def from_string(self, f_string):
pattern = re.compile(r'\s*(\S+?)\s*(' +
'|'.join(self._operator_handlers.keys()) +
')\s*(\S+)\s*')
m = pattern.match(f_string)
try:
return Filter(m.group(1), m.group(2), m.group(3))
except:
raise DartValidationException('could not parse filter: %s' %
f_string)
def _find_workflow_instances(workflow=None):
limit = int(request.args.get('limit', 20))
offset = int(request.args.get('offset', 0))
filters = [filter_service().from_string(f) for f in json.loads(request.args.get('filters', '[]'))]
if workflow:
filters.append(Filter('workflow_id', Operator.EQ, workflow.id))
workflow_instances = workflow_service().query_workflow_instances(filters, limit, offset)
return {
'results': [d.to_dict() for d in workflow_instances],
'limit': limit,
'offset': offset,
'total': workflow_service().query_workflow_instances_count(filters)
}
