def annotate_machine_pu_util(internal_graph, node):
source = InfoGraphNode.get_machine_name_of_pu(node)
machine = InfoGraphNode.get_node(internal_graph, source)
machine_util = InfoGraphNode.get_compute_utilization(machine)
if 'intel/use/compute/utilization' not in machine_util.columns:
sum_util = None
cpu_metric = 'intel/procfs/cpu/utilization_percentage'
pu_util_df = InfoGraphNode.get_compute_utilization(node)
if cpu_metric in pu_util_df.columns:
pu_util = pu_util_df[cpu_metric]
pu_util = pu_util.fillna(0)
machine_util[InfoGraphNode.get_attributes(node)['name']] = pu_util
InfoGraphNode.set_compute_utilization(machine, machine_util)
else:
LOG.info('CPU util not Found use for node {}'.format(InfoGraphNode.get_name(node)))
else:
LOG.debug('Found use for node {}'.format(InfoGraphNode.get_name(node)))
def get_annotated_graph(self,
graph,
ts_from,
ts_to,
utilization=False,
saturation=False):
"""
Collect data from cimmaron tsdb in relation to the specified graph and
time windows and store an annotated subgraph in specified directory
:param graph: (NetworkX Graph) Graph to be annotated with data
:param ts_from: (str) Epoch time representation of start time
:param ts_to: (str) Epoch time representation of stop time
:param utilization: (bool) if True the method calculates also
utilization for each node, if available
:return: NetworkX Graph annotated with telemetry data
"""
TelemetryAnnotation._get_annotated_graph_input_validation(
graph, ts_from, ts_to)
internal_graph = graph.copy()
self.internal_graph = internal_graph
for node in internal_graph.nodes(data=True):
if isinstance(self.telemetry, SnapAnnotation):
queries = list()
try:
queries = self.telemetry.get_queries(
internal_graph, node, ts_from, ts_to)
# queries = self.telemetry.get_queries(graph, node, ts_from, ts_to)
except Exception as e:
LOG.error("Exception: {}".format(e))
LOG.error(e)
import traceback
traceback.print_exc()
if len(queries) != 0:
InfoGraphNode.set_queries(node, queries)
telemetry_data = self.telemetry.get_data(node)
InfoGraphNode.set_telemetry_data(node, telemetry_data)
if utilization and not telemetry_data.empty:
SnapUtils.utilization(internal_graph, node,
self.telemetry)
# if only procfs is available, results needs to be
# propagated at machine level
if InfoGraphNode.get_type(
node) == InfoGraphNodeType.PHYSICAL_PU:
SnapUtils.annotate_machine_pu_util(
internal_graph, node)
if InfoGraphNode.node_is_disk(node):
SnapUtils.annotate_machine_disk_util(
internal_graph, node)
if InfoGraphNode.node_is_nic(node):
SnapUtils.annotate_machine_network_util(
internal_graph, node)
if saturation:
SnapUtils.saturation(internal_graph, node,
self.telemetry)
elif isinstance(self.telemetry, PrometheusAnnotation):
queries = list()
try:
queries = self.telemetry.get_queries(
internal_graph, node, ts_from, ts_to)
# queries = self.telemetry.get_queries(graph, node, ts_from, ts_to)
except Exception as e:
LOG.error("Exception: {}".format(e))
LOG.error(e)
import traceback
traceback.print_exc()
if len(queries) != 0:
InfoGraphNode.set_queries(node, queries)
telemetry_data = self.telemetry.get_data(node)
InfoGraphNode.set_telemetry_data(node, telemetry_data)
# if utilization and not telemetry_data.empty:
#PrometheusUtils.utilization(internal_graph, node, self.telemetry)
# if only procfs is available, results needs to be
# propagated at machine level
#if InfoGraphNode.get_type(node) == InfoGraphNodeType.PHYSICAL_PU:
# PrometheusUtils.annotate_machine_pu_util(internal_graph, node)
#if InfoGraphNode.node_is_disk(node):
# PrometheusUtils.annotate_machine_disk_util(internal_graph, node)
#if InfoGraphNode.node_is_nic(node):
# PrometheusUtils.annotate_machine_network_util(internal_graph, node)
#if saturation:
#PrometheusUtils.saturation(internal_graph, node, self.telemetry)
else:
telemetry_data = self.telemetry.get_data(node)
InfoGraphNode.set_telemetry_data(node, telemetry_data)
if utilization and not telemetry_data.empty:
SnapUtils.utilization(internal_graph, node, self.telemetry)
# if only procfs is available, results needs to be
# propagated at machine level
if InfoGraphNode.get_type(
node) == InfoGraphNodeType.PHYSICAL_PU:
source = InfoGraphNode.get_machine_name_of_pu(node)
machine = InfoGraphNode.get_node(
internal_graph, source)
machine_util = InfoGraphNode.get_compute_utilization(
machine)
if '/intel/use/compute/utilization' not in machine_util.columns:
sum_util = None
pu_util = InfoGraphNode.get_compute_utilization(
node
)['intel/procfs/cpu/utilization_percentage']
pu_util = pu_util.fillna(0)
if 'intel/procfs/cpu/utilization_percentage' in machine_util.columns:
machine_util = machine_util[
'intel/procfs/cpu/utilization_percentage']
machine_util = machine_util.fillna(0)
sum_util = machine_util.add(pu_util,
fill_value=0)
else:
sum_util = pu_util
if isinstance(sum_util, pandas.Series):
# sum_util.index.name = None
sum_util = pandas.DataFrame(
sum_util,
columns=[
'intel/procfs/cpu/utilization_percentage'
])
InfoGraphNode.set_compute_utilization(
machine, sum_util)
else:
LOG.debug('Found use for node {}'.format(
InfoGraphNode.get_name(node)))
if saturation:
self._saturation(internal_graph, node, self.telemetry)
return internal_graph
def annotate_machine_pu_util(internal_graph, node):
source = InfoGraphNode.get_machine_name_of_pu(node)
machine = InfoGraphNode.get_node(internal_graph, source)
machine_util = InfoGraphNode.get_compute_utilization(machine)
InfoGraphNode.set_compute_utilization(machine, pandas.DataFrame())
def utilization_scores(graph):
"""
Returns a dictionary with the scores of
all the nodes of the graph.
:param graph: InfoGraph
:return: dict[node_name] = score
"""
res = dict()
for node in graph.nodes(data=True):
node_name = InfoGraphNode.get_name(node)
res[node_name] = dict()
util = InfoGraphNode.get_utilization(node)
import analytics_engine.common as common
LOG = common.LOG
res[node_name]['compute'] = 0
res[node_name]['disk'] = 0
res[node_name]['network'] = 0
res[node_name]['memory'] = 0
if (isinstance(util, pandas.DataFrame) and
util.empty) or \
(not isinstance(util, pandas.DataFrame) and
util==None):
continue
# intel/use/
if 'intel/use/compute/utilization' in util:
res[node_name]['compute'] = (
util.get('intel/use/compute/utilization').mean()) / 100.0
elif 'intel/procfs/cpu/utilization_percentage' in util:
res[node_name]['compute'] = (util.get(
'intel/procfs/cpu/utilization_percentage').mean()) / 100.0
if 'intel/use/memory/utilization' in util:
res[node_name]['memory'] = (
util.get('intel/use/memory/utilization').mean()) / 100.0
elif 'intel/procfs/memory/utilization_percentage' in util:
res[node_name]['memory'] = (
util.get('intel/procfs/memory/utilization_percentage'
).mean()) / 100.0
if 'intel/use/disk/utilization' in util:
res[node_name]['disk'] = (
util.get('intel/use/disk/utilization').mean()) / 100.0
elif 'intel/procfs/disk/utilization_percentage' in util:
res[node_name]['disk'] = (util.get(
'intel/procfs/disk/utilization_percentage').mean()) / 100.0
if 'intel/use/network/utilization' in util:
res[node_name]['network'] = (
util.get('intel/use/network/utilization').mean()) / 100.0
elif 'intel/psutil/net/utilization_percentage' in util:
res[node_name]['network'] = (util.get(
'intel/psutil/net/utilization_percentage').mean()) / 100.0
# special handling of cpu, disk & network utilization if node is a machine
if InfoGraphNode.node_is_machine(node):
# mean from all cpu columns
cpu_util = InfoGraphNode.get_compute_utilization(node)
cpu_util['total'] = [
sum(row) / len(row) for index, row in cpu_util.iterrows()
]
res[node_name]['compute'] = cpu_util['total'].mean() / 100
# mean from all disk columns
disk_util = InfoGraphNode.get_disk_utilization(node)
if disk_util.empty:
res[node_name]['disk'] = 0.0
else:
disk_util['total'] = [
sum(row) / len(row)
for index, row in disk_util.iterrows()
]
res[node_name]['disk'] = disk_util['total'].mean() / 100
# mean from all nic columns
net_util = InfoGraphNode.get_network_utilization(node)
if net_util.empty:
res[node_name]['network'] = 0.0
else:
net_util['total'] = [
sum(row) / len(row)
for index, row in net_util.iterrows()
]
res[node_name]['network'] = net_util['total'].mean() / 100
# custom metric
if InfoGraphNode.get_type(
node) == InfoGraphNodeType.DOCKER_CONTAINER:
node_name = InfoGraphNode.get_docker_id(node)
res[node_name] = {}
if 'intel/docker/stats/cgroups/cpu_stats/cpu_usage/percentage' in util.columns:
res[node_name]['compute'] = util[
'intel/docker/stats/cgroups/cpu_stats/cpu_usage/percentage'].mean(
) / 100
else:
res[node_name]['compute'] = 0
if 'intel/docker/stats/cgroups/memory_stats/usage/percentage' in util.columns:
res[node_name]['memory'] = util[
'intel/docker/stats/cgroups/memory_stats/usage/percentage'].mean(
) / 100
else:
res[node_name]['memory'] = 0
if 'intel/docker/stats/network/utilization_percentage' in util.columns:
res[node_name]['network'] = util[
'intel/docker/stats/network/utilization_percentage'].mean(
) / 100
else:
res[node_name]['network'] = 0
if 'intel/docker/stats/cgroups/blkio_stats/io_time_recursive/percentage' in util.columns:
res[node_name]['disk'] = util[
'intel/docker/stats/cgroups/blkio_stats/io_time_recursive/percentage'].mean(
) / 100
else:
res[node_name]['disk'] = 0
return res