def create_task_event(self, type, iteration):
return {
"worker": "test",
"type": type,
"task": self.task_id,
"iter": iteration,
"timestamp": es_factory.get_timestamp_millis()
}
def _create_task_event(self, type_, task, iteration):
return {
"worker": "test",
"type": type_,
"task": task,
"iter": iteration,
"timestamp": es_factory.get_timestamp_millis(),
}
def _create_task_event(type_, task, iteration, **kwargs):
return {
"worker": "test",
"type": type_,
"task": task,
"iter": iteration,
"timestamp": kwargs.get("timestamp") or es_factory.get_timestamp_millis(),
**kwargs,
}
def add_events(self, company_id, events, worker, allow_locked_tasks=False):
actions = []
task_ids = set()
task_iteration = defaultdict(lambda: 0)
task_last_events = nested_dict(
3, dict) # task_id -> metric_hash -> variant_hash -> MetricEvent
for event in events:
# remove spaces from event type
if "type" not in event:
raise errors.BadRequest("Event must have a 'type' field",
event=event)
event_type = event["type"].replace(" ", "_")
if event_type not in EVENT_TYPES:
raise errors.BadRequest(
"Invalid event type {}".format(event_type),
event=event,
types=EVENT_TYPES,
)
event["type"] = event_type
# @timestamp indicates the time the event is written, not when it happened
event["@timestamp"] = es_factory.get_es_timestamp_str()
# for backward bomba-tavili-tea
if "ts" in event:
event["timestamp"] = event.pop("ts")
# set timestamp and worker if not sent
if "timestamp" not in event:
event["timestamp"] = es_factory.get_timestamp_millis()
if "worker" not in event:
event["worker"] = worker
# force iter to be a long int
iter = event.get("iter")
if iter is not None:
iter = int(iter)
event["iter"] = iter
# used to have "values" to indicate array. no need anymore
if "values" in event:
event["value"] = event["values"]
del event["values"]
index_name = EventMetrics.get_index_name(company_id, event_type)
es_action = {
"_op_type": "index", # overwrite if exists with same ID
"_index": index_name,
"_type": "event",
"_source": event,
}
# for "log" events, don't assing custom _id - whatever is sent, is written (not overwritten)
if event_type != "log":
es_action["_id"] = self._get_event_id(event)
else:
es_action["_id"] = dbutils.id()
task_id = event.get("task")
if task_id is not None:
es_action["_routing"] = task_id
task_ids.add(task_id)
if (iter is not None and event.get("metric")
not in self._skip_iteration_for_metric):
task_iteration[task_id] = max(iter,
task_iteration[task_id])
if event_type == EventType.metrics_scalar.value:
self._update_last_metric_event_for_task(
task_last_events=task_last_events,
task_id=task_id,
event=event)
else:
es_action["_routing"] = task_id
actions.append(es_action)
if task_ids:
# verify task_ids
with translate_errors_context(), TimingContext(
"mongo", "task_by_ids"):
extra_msg = None
query = Q(id__in=task_ids, company=company_id)
if not allow_locked_tasks:
query &= Q(status__nin=LOCKED_TASK_STATUSES)
extra_msg = "or task published"
res = Task.objects(query).only("id")
if len(res) < len(task_ids):
invalid_task_ids = tuple(
set(task_ids) - set(r.id for r in res))
raise errors.bad_request.InvalidTaskId(
extra_msg, company=company_id, ids=invalid_task_ids)
errors_in_bulk = []
added = 0
chunk_size = 500
with translate_errors_context(), TimingContext("es",
"events_add_batch"):
# TODO: replace it with helpers.parallel_bulk in the future once the parallel pool leak is fixed
with closing(
helpers.streaming_bulk(
self.es,
actions,
chunk_size=chunk_size,
# thread_count=8,
refresh=True,
)) as it:
for success, info in it:
if success:
added += chunk_size
else:
errors_in_bulk.append(info)
remaining_tasks = set()
now = datetime.utcnow()
for task_id in task_ids:
# Update related tasks. For reasons of performance, we prefer to update all of them and not only those
# who's events were successful
updated = self._update_task(
company_id=company_id,
task_id=task_id,
now=now,
iter_max=task_iteration.get(task_id),
last_events=task_last_events.get(task_id),
)
if not updated:
remaining_tasks.add(task_id)
continue
if remaining_tasks:
TaskBLL.set_last_update(remaining_tasks,
company_id,
last_update=now)
# Compensate for always adding chunk_size on success (last chunk is probably smaller)
added = min(added, len(actions))
return added, errors_in_bulk
def add_events(self,
company_id,
events,
worker,
allow_locked_tasks=False) -> Tuple[int, int, dict]:
actions = []
task_ids = set()
task_iteration = defaultdict(lambda: 0)
task_last_scalar_events = nested_dict(
3, dict) # task_id -> metric_hash -> variant_hash -> MetricEvent
task_last_events = nested_dict(
3, dict) # task_id -> metric_hash -> event_type -> MetricEvent
errors_per_type = defaultdict(int)
valid_tasks = self._get_valid_tasks(
company_id,
task_ids={
event["task"]
for event in events if event.get("task") is not None
},
allow_locked_tasks=allow_locked_tasks,
)
for event in events:
# remove spaces from event type
event_type = event.get("type")
if event_type is None:
errors_per_type["Event must have a 'type' field"] += 1
continue
event_type = event_type.replace(" ", "_")
if event_type not in EVENT_TYPES:
errors_per_type[f"Invalid event type {event_type}"] += 1
continue
task_id = event.get("task")
if task_id is None:
errors_per_type["Event must have a 'task' field"] += 1
continue
if task_id not in valid_tasks:
errors_per_type["Invalid task id"] += 1
continue
event["type"] = event_type
# @timestamp indicates the time the event is written, not when it happened
event["@timestamp"] = es_factory.get_es_timestamp_str()
# for backward bomba-tavili-tea
if "ts" in event:
event["timestamp"] = event.pop("ts")
# set timestamp and worker if not sent
if "timestamp" not in event:
event["timestamp"] = es_factory.get_timestamp_millis()
if "worker" not in event:
event["worker"] = worker
# force iter to be a long int
iter = event.get("iter")
if iter is not None:
iter = int(iter)
event["iter"] = iter
# used to have "values" to indicate array. no need anymore
if "values" in event:
event["value"] = event["values"]
del event["values"]
event["metric"] = event.get("metric") or ""
event["variant"] = event.get("variant") or ""
index_name = EventMetrics.get_index_name(company_id, event_type)
es_action = {
"_op_type": "index", # overwrite if exists with same ID
"_index": index_name,
"_type": "event",
"_source": event,
}
# for "log" events, don't assing custom _id - whatever is sent, is written (not overwritten)
if event_type != "log":
es_action["_id"] = self._get_event_id(event)
else:
es_action["_id"] = dbutils.id()
es_action["_routing"] = task_id
task_ids.add(task_id)
if (iter is not None and event.get("metric")
not in self._skip_iteration_for_metric):
task_iteration[task_id] = max(iter, task_iteration[task_id])
self._update_last_metric_events_for_task(
last_events=task_last_events[task_id],
event=event,
)
if event_type == EventType.metrics_scalar.value:
self._update_last_scalar_events_for_task(
last_events=task_last_scalar_events[task_id], event=event)
actions.append(es_action)
added = 0
if actions:
chunk_size = 500
with translate_errors_context(), TimingContext(
"es", "events_add_batch"):
# TODO: replace it with helpers.parallel_bulk in the future once the parallel pool leak is fixed
with closing(
helpers.streaming_bulk(
self.es,
actions,
chunk_size=chunk_size,
# thread_count=8,
refresh=True,
)) as it:
for success, info in it:
if success:
added += chunk_size
else:
errors_per_type[
"Error when indexing events batch"] += 1
remaining_tasks = set()
now = datetime.utcnow()
for task_id in task_ids:
# Update related tasks. For reasons of performance, we prefer to update
# all of them and not only those who's events were successful
updated = self._update_task(
company_id=company_id,
task_id=task_id,
now=now,
iter_max=task_iteration.get(task_id),
last_scalar_events=task_last_scalar_events.get(
task_id),
last_events=task_last_events.get(task_id),
)
if not updated:
remaining_tasks.add(task_id)
continue
if remaining_tasks:
TaskBLL.set_last_update(remaining_tasks,
company_id,
last_update=now)
# Compensate for always adding chunk_size on success (last chunk is probably smaller)
added = min(added, len(actions))
if not added:
raise errors.bad_request.EventsNotAdded(**errors_per_type)
errors_count = sum(errors_per_type.values())
return added, errors_count, errors_per_type