def schema_ext(value):
try:
return utils.UUID(value)
except ValueError as e:
raise voluptuous.Invalid(e)
def post(self,
start=None,
stop=None,
granularity=None,
needed_overlap=None,
fill=None,
groupby=None,
**kwargs):
details = api.get_bool_param('details', kwargs)
if fill is None and needed_overlap is None:
fill = "dropna"
start, stop, granularity, needed_overlap, fill = api.validate_qs(
start, stop, granularity, needed_overlap, fill)
body = api.deserialize_and_validate(self.FetchSchema)
references = extract_references(body["operations"])
if not references:
api.abort(
400, {
"cause": "Operations is invalid",
"reason": "At least one 'metric' is required",
"detail": body["operations"]
})
if "resource_type" in body:
attr_filter = body["search"]
policy_filter = (
pecan.request.auth_helper.get_resource_policy_filter(
pecan.request, "search resource", body["resource_type"]))
if policy_filter:
if attr_filter:
attr_filter = {"and": [policy_filter, attr_filter]}
else:
attr_filter = policy_filter
groupby = sorted(set(api.arg_to_list(groupby)))
sorts = groupby if groupby else api.RESOURCE_DEFAULT_PAGINATION
try:
resources = pecan.request.indexer.list_resources(
body["resource_type"],
attribute_filter=attr_filter,
sorts=sorts)
except indexer.IndexerException as e:
api.abort(400, six.text_type(e))
if not groupby:
return self._get_measures_by_name(resources,
references,
body["operations"],
start,
stop,
granularity,
needed_overlap,
fill,
details=details)
def groupper(r):
return tuple((attr, r[attr]) for attr in groupby)
results = []
for key, resources in itertools.groupby(resources, groupper):
results.append({
"group":
dict(key),
"measures":
self._get_measures_by_name(resources,
references,
body["operations"],
start,
stop,
granularity,
needed_overlap,
fill,
details=details)
})
return results
else:
try:
metric_ids = set(
six.text_type(utils.UUID(m)) for (m, a) in references)
except ValueError as e:
api.abort(
400, {
"cause": "Invalid metric references",
"reason": six.text_type(e),
"detail": references
})
metrics = pecan.request.indexer.list_metrics(
attribute_filter={"in": {
"id": metric_ids
}})
missing_metric_ids = (set(metric_ids) -
set(six.text_type(m.id) for m in metrics))
if missing_metric_ids:
api.abort(
404, {
"cause": "Unknown metrics",
"reason": "Provided metrics don't exists",
"detail": missing_metric_ids
})
number_of_metrics = len(metrics)
if number_of_metrics == 0:
return []
for metric in metrics:
api.enforce("get metric", metric)
metrics_by_ids = dict((six.text_type(m.id), m) for m in metrics)
references = [
processor.MetricReference(metrics_by_ids[m], a)
for (m, a) in references
]
response = {
"measures":
get_measures_or_abort(references, body["operations"], start,
stop, granularity, needed_overlap, fill)
}
if details:
response["references"] = metrics
return response