def generate_latest(registry=core.REGISTRY):
'''Returns the metrics from the registry in latest text format as a string.'''
output = []
for metric in registry.collect():
output.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name, metric.type))
for sample in metric.samples:
print(sample)
if len(sample) == 3:
name, labels, value = sample
timestamp = None
else:
name, labels, value, timestamp = sample
if labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\',
r'\\').replace('\n',
r'\n').replace('"', r'\"'))
for k, v in sorted(labels.items())
]))
else:
labelstr = ''
output.append('{0}{1} {2}{3}\n'.format(
name, labelstr, core._floatToGoString(value),
' %s' % timestamp if timestamp else ''))
return ''.join(output).encode('utf-8')
def generate_latest_target(self, request):
target = request.args.get('target', [None])[0]
section = request.args.get('section', ['default'])[0]
output = []
for metric in self.collect(target, section):
output.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n',
r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name,
metric.type))
for name, labels, value in metric.samples:
if labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\', r'\\').replace('\n',
r'\n').replace(
'"', r'\"'))
for k, v in sorted(labels.items())
]))
else:
labelstr = ''
output.append('{0}{1} {2}\n'.format(name, labelstr,
_floatToGoString(value)))
if output != []:
request.write(''.join(output).encode('utf-8'))
request.finish()
else:
request.setResponseCode(500, message=('cannot connect to vmware'))
request.finish()
return
def generate_latest():
output = []
for metric in gen_metrics():
# print('metric.name -->', metric.name)
# print('metric.documentation -->', metric.documentation)
# print('metric.samples -->', metric.samples)
output.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name, metric.type))
for name, labels, value in metric.samples:
if labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\',
r'\\').replace('\n',
r'\n').replace('"', r'\"'))
for k, v in sorted(labels.items())
]))
else:
labelstr = ''
output.append('{0}{1} {2}\n'.format(name, labelstr,
_floatToGoString(value)))
# print(metric)
# print(''.join(output))
return ''.join(output)
def generate_latest_metrics(self, request):
""" gets the latest metrics """
section = request.args.get('section', ['default'])[0]
if self.config[section].get('vsphere_host') and self.config[
section].get('vsphere_host') != "None":
vsphere_host = self.config[section].get('vsphere_host')
elif request.args.get('target', [None])[0]:
vsphere_host = request.args.get('target', [None])[0]
elif request.args.get('vsphere_host', [None])[0]:
vsphere_host = request.args.get('vsphere_host')[0]
else:
request.setResponseCode(500)
log("No vsphere_host or target defined")
request.write('No vsphere_host or target defined!\n')
request.finish()
output = []
for metric in self.collect(vsphere_host, section):
output.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n',
r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name,
metric.type))
for name, labels, value in metric.samples:
if labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\', r'\\').replace('\n',
r'\n').replace(
'"', r'\"'))
for k, v in sorted(labels.items())
]))
else:
labelstr = ''
if isinstance(value, int):
value = float(value)
if isinstance(value, long): # noqa: F821
value = float(value)
if isinstance(value, float):
output.append('{0}{1} {2}\n'.format(
name, labelstr, _floatToGoString(value)))
if output != []:
request.write(''.join(output).encode('utf-8'))
request.finish()
else:
request.setResponseCode(500, message=('cannot connect to vmware'))
request.finish()
return
def sample_line(s):
if s.labels:
labelstr = '{{{0}}}'.format(','.join(
['{0}="{1}"'.format(
k, v.replace('\\', r'\\').replace('\n', r'\n').replace('"', r'\"'))
for k, v in sorted(s.labels.items())]))
else:
labelstr = ''
timestamp = ''
if s.timestamp is not None:
# Convert to milliseconds.
timestamp = ' {0:d}'.format(int(float(s.timestamp) * 1000))
return '{0}{1} {2}{3}\n'.format(
s.name, labelstr, core._floatToGoString(s.value), timestamp)
def generate_latest(registry=core.REGISTRY):
'''Returns the metrics from the registry in latest text format as a string.'''
output = []
for metric in registry.collect():
mname = metric.name
mtype = metric.type
# Munging from OpenMetrics into Prometheus format.
if mtype == 'counter':
mname = mname + '_total'
elif mtype == 'info':
mname = mname + '_info'
mtype = 'gauge'
elif mtype == 'stateset':
mtype = 'gauge'
elif mtype == 'gaugehistogram':
# A gauge histogram is really a gauge,
# but this captures the strucutre better.
mtype = 'histogram'
elif mtype == 'unknown':
mtype = 'untyped'
output.append('# HELP {0} {1}'.format(
mname,
metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(mname, mtype))
for s in metric.samples:
if s.name == metric.name + '_created':
continue # Ignore OpenMetrics specific sample. TODO: Make these into a gauge.
if s.labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\',
r'\\').replace('\n',
r'\n').replace('"', r'\"'))
for k, v in sorted(s.labels.items())
]))
else:
labelstr = ''
timestamp = ''
if s.timestamp is not None:
# Convert to milliseconds.
timestamp = ' {0:d}'.format(int(float(s.timestamp) * 1000))
output.append('{0}{1} {2}{3}\n'.format(
s.name, labelstr, core._floatToGoString(s.value), timestamp))
return ''.join(output).encode('utf-8')
def _generate_output_text(self, metric):
lines = []
lines.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
lines.append('\n# TYPE {0} {1}\n'.format(metric.name, metric.type))
for name, labels, value in metric.samples:
labelstr = ''
if labels:
labelstr = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\',
r'\\').replace('\n',
r'\n').replace('"', r'\"'))
for k, v in sorted(labels.items())
]))
lines.append('{0}{1} {2}\n'.format(name, labelstr,
_floatToGoString(value)))
return lines
def generate_latest(registry=REGISTRY):
'''Returns the metrics from the registry in latest text format as a string.'''
output = []
for metric in registry.collect():
output.append('# HELP {0} {1}'.format(
metric.name,
metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name, metric.type))
for name, labels, value in metric.samples:
if labels:
label_text = '{{{0}}}'.format(','.join([
'{0}="{1}"'.format(
k,
v.replace('\\', r'\\').replace('\n', r'\n').replace(
'"', r'\"') if v else '')
for k, v in sorted(labels.items())
]))
else:
label_text = ''
output.append('{0}{1} {2}\n'.format(name, label_text,
core._floatToGoString(value)))
return ''.join(output).encode('utf-8')
def generate_latest(registry=core.REGISTRY):
'''Returns the metrics from the registry in latest text format as a string.'''
output = []
for metric in registry.collect():
output.append('# HELP {0} {1}'.format(
metric.name, metric.documentation.replace('\\', r'\\').replace('\n', r'\n')))
output.append('\n# TYPE {0} {1}\n'.format(metric.name, metric.type))
for sample in metric.samples:
if len(sample) == 3:
name, labels, value = sample
timestamp = None
else:
name, labels, value, timestamp = sample
if labels:
labelstr = '{{{0}}}'.format(','.join(
['{0}="{1}"'.format(
k, v.replace('\\', r'\\').replace('\n', r'\n').replace('"', r'\"'))
for k, v in sorted(labels.items())]))
else:
labelstr = ''
output.append('{0}{1} {2}{3}\n'.format(name, labelstr, core._floatToGoString(value),
' %s' % timestamp if timestamp else ''))
return ''.join(output).encode('utf-8')
def collect(files):
"""This almost verbatim from MultiProcessCollector.collect().
The original collects all results in a format designed to be scraped. We
instead need to collect limited results, in a format that can be written
back to disk. To facilitate this, this version of collect() preserves label
ordering, and does not aggregate the histograms.
Specifically, it differs from the original:
1. it takes its files as an argument, rather than hardcoding '*.db'
2. it does not accumulate histograms
3. it preserves label order, to facilitate being inserted back into an mmap
file.
It needs to be kept up to date with changes to prometheus_client as much as
possible, or until changes are landed upstream to allow reuse of collect().
"""
from prometheus_client import core
metrics = {}
for f in files:
if not os.path.exists(f):
continue
# verbatim from here...
parts = os.path.basename(f).split('_')
typ = parts[0]
d = core._MmapedDict(f, read_mode=True)
for key, value in d.read_all_values():
metric_name, name, labelnames, labelvalues = json.loads(key)
metric = metrics.get(metric_name)
if metric is None:
metric = core.Metric(metric_name, 'Multiprocess metric', typ)
metrics[metric_name] = metric
if typ == 'gauge':
pid = parts[2][:-3]
metric._multiprocess_mode = parts[1]
metric.add_sample(
name,
tuple(zip(labelnames, labelvalues)) + (('pid', pid), ),
value,
)
else:
# The duplicates and labels are fixed in the next for.
metric.add_sample(
name,
tuple(zip(labelnames, labelvalues)),
value,
)
d.close()
for metric in metrics.values():
samples = defaultdict(float)
buckets = {}
for name, labels, value in metric.samples:
if metric.type == 'gauge':
without_pid = tuple(l for l in labels if l[0] != 'pid')
if metric._multiprocess_mode == 'min':
current = samples.setdefault((name, without_pid), value)
if value < current:
samples[(name, without_pid)] = value
elif metric._multiprocess_mode == 'max':
current = samples.setdefault((name, without_pid), value)
if value > current:
samples[(name, without_pid)] = value
elif metric._multiprocess_mode == 'livesum':
samples[(name, without_pid)] += value
else: # all/liveall
samples[(name, labels)] = value
elif metric.type == 'histogram':
bucket = tuple(float(l[1]) for l in labels if l[0] == 'le')
if bucket:
# _bucket
without_le = tuple(l for l in labels if l[0] != 'le')
buckets.setdefault(without_le, {})
buckets[without_le].setdefault(bucket[0], 0.0)
buckets[without_le][bucket[0]] += value
else:
# _sum/_count
samples[(name, labels)] += value
else:
# Counter and Summary.
samples[(name, labels)] += value
# end of verbatim copy
# modified to remove accumulation
if metric.type == 'histogram':
for labels, values in buckets.items():
for bucket, value in sorted(values.items()):
key = (
metric.name + '_bucket',
labels + (('le', core._floatToGoString(bucket)), ),
)
samples[key] = value
# Convert to correct sample format.
metric.samples = [
# OrderedDict used instead of dict
(name, OrderedDict(labels), value)
for (name, labels), value in samples.items()
]
return metrics.values()
def legacy_collect(files):
"""This almost verbatim from MultiProcessCollector.collect(), pre 0.4.0
The original collects all results in a format designed to be scraped. We
instead need to collect limited results, in a format that can be written
back to disk. To facilitate this, this version of collect() preserves label
ordering, and does not aggregate the histograms.
Specifically, it differs from the original:
1. it takes its files as an argument, rather than hardcoding '*.db'
2. it does not accumulate histograms
3. it preserves label order, to facilitate being inserted back into an mmap
file.
It needs to be kept up to date with changes to prometheus_client as much as
possible, or until changes are landed upstream to allow reuse of collect().
"""
from prometheus_client import core
metrics = {}
for f in files:
if not os.path.exists(f):
continue
# verbatim from here...
parts = os.path.basename(f).split('_')
typ = parts[0]
d = core._MmapedDict(f, read_mode=True)
for key, value in d.read_all_values():
# Note: key format changed in 0.4+
metric_name, name, labelnames, labelvalues = json.loads(key)
metric = metrics.get(metric_name)
if metric is None:
metric = core.Metric(metric_name, 'Multiprocess metric', typ)
metrics[metric_name] = metric
if typ == 'gauge':
pid = parts[2][:-3]
metric._multiprocess_mode = parts[1]
metric.add_sample(
name,
tuple(zip(labelnames, labelvalues)) + (('pid', pid), ),
value,
)
else:
# The duplicates and labels are fixed in the next for.
metric.add_sample(
name,
tuple(zip(labelnames, labelvalues)),
value,
)
d.close()
for metric in metrics.values():
samples = defaultdict(float)
buckets = {}
for name, labels, value in metric.samples:
if metric.type == 'gauge':
without_pid = tuple(l for l in labels if l[0] != 'pid')
if metric._multiprocess_mode == 'min':
current = samples.setdefault((name, without_pid), value)
if value < current:
samples[(name, without_pid)] = value
elif metric._multiprocess_mode == 'max':
current = samples.setdefault((name, without_pid), value)
if value > current:
samples[(name, without_pid)] = value
elif metric._multiprocess_mode == 'livesum':
samples[(name, without_pid)] += value
else: # all/liveall
samples[(name, labels)] = value
elif metric.type == 'histogram':
bucket = tuple(float(l[1]) for l in labels if l[0] == 'le')
if bucket:
# _bucket
without_le = tuple(l for l in labels if l[0] != 'le')
buckets.setdefault(without_le, {})
buckets[without_le].setdefault(bucket[0], 0.0)
buckets[without_le][bucket[0]] += value
else:
# _sum/_count
samples[(name, labels)] += value
else:
# Counter and Summary.
samples[(name, labels)] += value
# end of verbatim copy
# modified to remove accumulation
if metric.type == 'histogram':
for labels, values in buckets.items():
for bucket, value in sorted(values.items()):
key = (
metric.name + '_bucket',
labels + (('le', core._floatToGoString(bucket)),),
)
samples[key] = value
# Convert to correct sample format.
metric.samples = [
# OrderedDict used instead of dict
(name, OrderedDict(labels), value)
for (name, labels), value in samples.items()
]
return metrics.values()
