/usr/lib/python3/dist-packages/gnocchi/rest/aggregates/api.py is in python3-gnocchi 4.2.0-0ubuntu5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 | # -*- encoding: utf-8 -*-
#
# Copyright © 2016-2017 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import fnmatch
import itertools
import pecan
from pecan import rest
import pyparsing
import six
import voluptuous
from gnocchi import indexer
from gnocchi.rest.aggregates import exceptions
from gnocchi.rest.aggregates import operations as agg_operations
from gnocchi.rest.aggregates import processor
from gnocchi.rest import api
from gnocchi import storage
from gnocchi import utils
def _OperationsSubNodeSchema(v):
return OperationsSubNodeSchema(v)
def MetricSchema(v):
"""metric keyword schema
It could be:
["metric", "metric-ref", "aggregation"]
or
["metric, ["metric-ref", "aggregation"], ["metric-ref", "aggregation"]]
"""
if not isinstance(v, (list, tuple)):
raise voluptuous.Invalid("Expected a tuple/list, got a %s" % type(v))
elif not v:
raise voluptuous.Invalid("Operation must not be empty")
elif len(v) < 2:
raise voluptuous.Invalid("Operation need at least one argument")
elif v[0] != u"metric":
# NOTE(sileht): this error message doesn't looks related to "metric",
# but because that the last schema validated by voluptuous, we have
# good chance (voluptuous.Any is not predictable) to print this
# message even if it's an other operation that invalid.
raise voluptuous.Invalid("'%s' operation invalid" % v[0])
return [u"metric"] + voluptuous.Schema(voluptuous.Any(
voluptuous.ExactSequence([six.text_type, six.text_type]),
voluptuous.All(
voluptuous.Length(min=1),
[voluptuous.ExactSequence([six.text_type, six.text_type])],
)), required=True)(v[1:])
OperationsSchemaBase = [
MetricSchema,
voluptuous.ExactSequence(
[voluptuous.Any(*list(
agg_operations.binary_operators.keys())),
_OperationsSubNodeSchema, _OperationsSubNodeSchema]
),
voluptuous.ExactSequence(
[voluptuous.Any(*list(
agg_operations.unary_operators.keys())),
_OperationsSubNodeSchema]
),
voluptuous.ExactSequence(
[voluptuous.Any(*list(
agg_operations.unary_operators_with_timestamps.keys())),
_OperationsSubNodeSchema]
),
voluptuous.ExactSequence(
[u"aggregate",
voluptuous.Any(*list(agg_operations.AGG_MAP.keys())),
_OperationsSubNodeSchema]
),
voluptuous.ExactSequence(
[u"resample",
voluptuous.Any(*list(agg_operations.AGG_MAP.keys())),
utils.to_timespan, _OperationsSubNodeSchema]
),
voluptuous.ExactSequence(
[u"rolling",
voluptuous.Any(*list(agg_operations.AGG_MAP.keys())),
voluptuous.All(
voluptuous.Coerce(int),
voluptuous.Range(min=1),
),
_OperationsSubNodeSchema]
)
]
OperationsSubNodeSchema = voluptuous.Schema(voluptuous.Any(*tuple(
OperationsSchemaBase + [voluptuous.Coerce(float)]
)), required=True)
def OperationsSchema(v):
if isinstance(v, six.text_type):
try:
v = pyparsing.OneOrMore(
pyparsing.nestedExpr()).parseString(v).asList()[0]
except pyparsing.ParseException as e:
api.abort(400, {"cause": "Invalid operations",
"reason": "Fail to parse the operations string",
"detail": six.text_type(e)})
return voluptuous.Schema(voluptuous.Any(*OperationsSchemaBase),
required=True)(v)
class ReferencesList(list):
"A very simplified OrderedSet with list interface"
def append(self, ref):
if ref not in self:
super(ReferencesList, self).append(ref)
def extend(self, refs):
for ref in refs:
self.append(ref)
def extract_references(nodes):
references = ReferencesList()
if nodes[0] == "metric":
if isinstance(nodes[1], list):
for subnodes in nodes[1:]:
references.append(tuple(subnodes))
else:
references.append(tuple(nodes[1:]))
else:
for subnodes in nodes[1:]:
if isinstance(subnodes, list):
references.extend(extract_references(subnodes))
return references
def get_measures_or_abort(references, operations, start,
stop, granularity, needed_overlap, fill):
try:
return processor.get_measures(
pecan.request.storage,
references,
operations,
start, stop,
granularity, needed_overlap, fill)
except exceptions.UnAggregableTimeseries as e:
api.abort(400, e)
# TODO(sileht): We currently got only one metric for these exceptions but
# we can improve processor to returns all missing metrics at once, so we
# returns a list for the future
except storage.MetricDoesNotExist as e:
api.abort(404, {"cause": "Unknown metrics",
"detail": [str(e.metric.id)]})
except storage.AggregationDoesNotExist as e:
api.abort(404, {"cause": "Metrics with unknown aggregation",
"detail": [(str(e.metric.id), e.method)]})
def ResourceTypeSchema(resource_type):
try:
pecan.request.indexer.get_resource_type(resource_type)
except indexer.NoSuchResourceType as e:
api.abort(400, e)
return resource_type
class AggregatesController(rest.RestController):
FetchSchema = voluptuous.Any({
"operations": OperationsSchema
}, {
"operations": OperationsSchema,
"resource_type": ResourceTypeSchema,
"search": voluptuous.Any(api.ResourceSearchSchema,
api.QueryStringSearchAttrFilter.parse),
})
@pecan.expose("json")
def post(self, start=None, stop=None, granularity=None,
needed_overlap=None, fill=None, groupby=None, **kwargs):
details = api.get_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
@staticmethod
def _get_measures_by_name(resources, metric_wildcards, operations,
start, stop, granularity, needed_overlap, fill,
details):
references = []
for r in resources:
references.extend([
processor.MetricReference(m, agg, r, wildcard)
for wildcard, agg in metric_wildcards
for m in r.metrics if fnmatch.fnmatch(m.name, wildcard)
])
if not references:
api.abort(400, {"cause": "Metrics not found",
"detail": set((m for (m, a) in metric_wildcards))})
response = {
"measures": get_measures_or_abort(
references, operations, start, stop, granularity,
needed_overlap, fill)
}
if details:
response["references"] = set((r.resource for r in references))
return response
|