def get_queue_metrics(
call: APICall, company_id, req_model: GetMetricsRequest
) -> GetMetricsResponse:
ret = queue_bll.metrics.get_queue_metrics(
company_id=company_id,
from_date=req_model.from_date,
to_date=req_model.to_date,
interval=req_model.interval,
queue_ids=req_model.queue_ids,
)
queue_dicts = {
queue: extract_properties_to_lists(
["date", "avg_waiting_time", "queue_length"], data
)
for queue, data in ret.items()
}
return GetMetricsResponse(
queues=[
QueueMetrics(
queue=queue,
dates=data["date"],
avg_waiting_times=data["avg_waiting_time"],
queue_lengths=data["queue_length"],
)
if data
else QueueMetrics(queue=queue)
for queue, data in queue_dicts.items()
]
)
def get_iterations_data(self, iter_buckets: dict) -> dict:
"""
Convert a list of bucket entries to `x`s array and `y`s array
"""
return extract_properties_to_lists(
("x", "y"),
iter_buckets[self.name]["buckets"],
self._get_iterations_data_single,
)
def get_activity_series(active_only: bool = False) -> ActivityReportSeries:
ret = worker_bll.stats.get_activity_report(
company_id=company_id,
from_date=req_model.from_date,
to_date=req_model.to_date,
interval=req_model.interval,
active_only=active_only,
)
if not ret:
return ActivityReportSeries(dates=[], counts=[])
count_by_date = extract_properties_to_lists(["date", "count"], ret)
return ActivityReportSeries(dates=count_by_date["date"],
counts=count_by_date["count"])
def _get_variant_metric_stats(
metric: str,
agg_names: Sequence[str],
stats: Sequence[dict],
variant: Optional[str] = None,
) -> MetricStats:
stat_by_name = extract_properties_to_lists(agg_names, stats)
return MetricStats(
metric=metric,
variant=variant,
dates=stat_by_name["date"],
stats=[
AggregationStats(aggregation=name, values=aggs)
for name, aggs in stat_by_name.items() if name != "date"
],
)
task_id=task_id,
batch_size=min(batch_size, 10_000),
navigate_earlier=False,
from_key_value=from_key_value,
metric_variants=metric_variants,
key=key,
)
if not res.events:
break
events.extend(res.events)
from_key_value = str(events[-1][scalar_key.field])
key = str(key)
variants = {
variant: extract_properties_to_lists(["value", scalar_key.field],
events,
target_keys=["y", key])
for variant, events in bucketize(events, key=itemgetter(
"variant")).items()
}
call.kpis["events"] = len(events)
scroll = ScalarMetricsIterRawScroll(
from_key_value=str(events[-1][scalar_key.field]) if events else None,
total=total,
request=request,
)
return make_response(
returned=len(events),