def get_metrics_reporting_registry(
process_registry: CollectorRegistry,
) -> CollectorRegistry:
"""
Get the metrics registry for reporting metrics.
If we're running under gunicorn, then each worker has its own process and
its own process collector. For reporting, we need a fresh registry with a
multiprocess collector that points to the metrics folder (created empty at
startup, with a database file for each process and metric type). It will
use the databases in this folder to generate combined metrics across the
processes, and will not double-count the reporting process's metrics.
If we're not running under gunicorn, then return the passed per-process
registry, which is the only metrics registry. In the single-process case,
We could use the default prometheus_client.REGISTRY, but it makes tests
easier to write if it is possible to replace the registry with a fresh one.
"""
try:
settings = config.Settings()
prometheus_multiproc_dir = settings.prometheus_multiproc_dir
except ValidationError:
prometheus_multiproc_dir = None
if prometheus_multiproc_dir:
registry = CollectorRegistry()
MultiProcessCollector(registry, path=prometheus_multiproc_dir)
return registry
return process_registry
def __init__(self, app=None, export_defaults=True,
defaults_prefix='flask', group_by='path',
buckets=None, static_labels=None, registry=None):
"""
Create a new multiprocess-aware Prometheus metrics export configuration.
:param app: the Flask application (can be `None`)
:param export_defaults: expose all HTTP request latencies
and number of HTTP requests
:param defaults_prefix: string to prefix the default exported
metrics name with (when either `export_defaults=True` or
`export_defaults(..)` is called)
:param group_by: group default HTTP metrics by
this request property, like `path`, `endpoint`, `url_rule`, etc.
(defaults to `path`)
:param buckets: the time buckets for request latencies
(will use the default when `None`)
:param static_labels: static labels to attach to each of the
metrics exposed by this metrics instance
:param registry: the Prometheus Registry to use (can be `None` and it
will be registered with `prometheus_client.multiprocess.MultiProcessCollector`)
"""
_check_multiproc_env_var()
registry = registry or CollectorRegistry()
MultiProcessCollector(registry)
super(MultiprocessPrometheusMetrics, self).__init__(
app=app, path=None, export_defaults=export_defaults,
defaults_prefix=defaults_prefix, group_by=group_by,
buckets=buckets, static_labels=static_labels,
registry=registry
)
def get_metrics() -> str:
"""Get collected metrics."""
registry = REGISTRY
if 'PROMETHEUS_MULTIPROC_DIR' in os.environ:
registry = CollectorRegistry()
MultiProcessCollector(registry)
return generate_latest(registry)
def get_registry(name):
if name not in collector_registries.keys():
collector_registries[name] = CollectorRegistry()
if prometheus_multiproc_dir is not None:
MultiProcessCollector(collector_registries[name])
return collector_registries[name]
def metrics(request: Request) -> Response:
if "prometheus_multiproc_dir" in os.environ:
registry = CollectorRegistry()
MultiProcessCollector(registry)
else:
registry = REGISTRY
return Response(generate_latest(registry), headers={"Content-Type": CONTENT_TYPE_LATEST})
def get_metrics():
registry = CollectorRegistry()
MultiProcessCollector(registry)
data = generate_latest(registry)
response_headers = [
('Content-type', CONTENT_TYPE_LATEST),
('Content-Length', str(len(data))),
]
return data, 200, response_headers
def collect_metrics():
registry = CollectorRegistry()
MultiProcessCollector(registry)
PlatformCollector(registry)
ExternalMetrics(registry)
data = generate_latest(registry)
return Response(data, mimetype=CONTENT_TYPE_LATEST)
def __init__(self, app, endpoint="/metrics"):
self.app = None
self.endpoint = endpoint
self.registry = CollectorRegistry()
MultiProcessCollector(self.registry)
self._metrics = {}
if app is not None:
self.init_app(app)
def test_deprecation_warning(self):
os.environ['prometheus_multiproc_dir'] = self.tempdir
with warnings.catch_warnings(record=True) as w:
values.ValueClass = get_value_class()
registry = CollectorRegistry()
collector = MultiProcessCollector(registry)
Counter('c', 'help', registry=None)
assert os.environ['PROMETHEUS_MULTIPROC_DIR'] == self.tempdir
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "PROMETHEUS_MULTIPROC_DIR" in str(w[-1].message)
def metrics(request: Request) -> Response:
if "prometheus_multiproc_dir" in os.environ:
registry = CollectorRegistry()
MultiProcessCollector(registry)
logger.info(
f"Metrics multiprocess :[{os.environ['prometheus_multiproc_dir']}]"
)
else:
registry = REGISTRY
logger.info(f"Metrics no multiprocess")
return Response(generate_latest(registry), media_type=CONTENT_TYPE_LATEST)
def get_metrics(request):
"""Pyramid view that return the metrics"""
if prom.IS_MULTIPROC:
registry = CollectorRegistry()
MultiProcessCollector(registry)
else:
registry = REGISTRY
request.response.content_type = CONTENT_TYPE_LATEST
resp = Response(content_type=CONTENT_TYPE_LATEST, )
resp.body = generate_latest(registry)
return resp
def prometheus_registry():
"""
Configure prometheus_client.
This is run the first time the /metrics endpoint is used.
"""
# The multiprocess configuration makes it compatible with gunicorn.
# https://github.com/prometheus/client_python/#multiprocess-mode-eg-gunicorn
from prometheus_client import CollectorRegistry
from prometheus_client.multiprocess import MultiProcessCollector
registry = CollectorRegistry()
MultiProcessCollector(registry) # This has a side effect, apparently.
return registry
def __init__(self, app=None, **kwargs):
"""
Create a new multiprocess-aware Prometheus metrics export configuration.
:param registry: the Prometheus Registry to use (can be `None` and it
will be registered with `prometheus_client.multiprocess.MultiProcessCollector`)
"""
_check_multiproc_env_var()
registry = kwargs.get('registry') or CollectorRegistry()
MultiProcessCollector(registry)
super(MultiprocessPrometheusMetrics, self).__init__(app=app,
path=None,
registry=registry,
**kwargs)
def set_config(self, config: ValueExtractor) -> None:
super().set_config(config)
registry = get_registry(self.config.get('registry', REGISTRY))
if MULTIPROC_DIR:
registry = MultiProcessCollector(registry)
self._registry = registry
port: int = self.config.get_int('port', default=0)
if port:
start_http_server(port=port, registry=registry)
graphite: Optional[ValueExtractor] = self.config.get('graphite')
if graphite:
address = graphite.get('address')
if isinstance(address, str):
addr = address.split(':')
address = (addr[0], int(addr[-1]))
gb = GraphiteBridge(address, registry=registry)
interval: float = graphite.get_duration('interval', 60)
prefix: str = graphite.get('prefix', '')
gb.start(interval, prefix=prefix)
def __init__(self, collectors: Iterable[Any]):
"""Builds a metric encoder using the given list of collectors. A collector is broadly
defined here as any type that implements the `collect` method.
:param collectors: The collectors to fetch metrics from
:type collectors: Iterable[Any]
"""
self._registry = CollectorRegistry(auto_describe=True)
# Always use a new registry for collecting mmapped files under multiprocess mode
if "PROMETHEUS_MULTIPROC_DIR" in os.environ:
MultiProcessCollector(self._registry)
# Do not double register metrics that implement MultiProcessValue
# See: prometheus_client/values.py#L31
collectors = filter(
lambda c: not isinstance(c, LiveMetricRegistry) and
not isinstance(c, MetricWrapperBase) and hasattr(c, "collect"),
collectors,
)
for c in collectors:
self._registry.register(c)
def prometheus_cleanup_worker(pid):
"""Aggregate dead worker's metrics into a single archive file."""
mark_process_dead(pid)
prom_dir = os.environ['prometheus_multiproc_dir']
worker_files = [
'histogram_{}.db'.format(pid),
'counter_{}.db'.format(pid),
]
paths = _filter_exists(os.path.join(prom_dir, f) for f in worker_files)
# check at least one worker file exists
if not paths:
return
histogram_path = os.path.join(prom_dir, histogram_archive)
counter_path = os.path.join(prom_dir, counter_archive)
archive_paths = _filter_exists([histogram_path, counter_path])
collect_paths = paths + archive_paths
collector = MultiProcessCollector(None)
try:
metrics = collector.merge(collect_paths, accumulate=False)
except AttributeError:
metrics = legacy_collect(collect_paths)
tmp_histogram = tempfile.NamedTemporaryFile(delete=False)
tmp_counter = tempfile.NamedTemporaryFile(delete=False)
write_metrics(metrics, tmp_histogram.name, tmp_counter.name)
# ensure reader does get partial state
with prometheus_lock:
os.rename(tmp_histogram.name, histogram_path)
os.rename(tmp_counter.name, counter_path)
for path in paths:
os.unlink(path)
def metrics():
return Response(
generate_latest(MultiProcessCollector(CollectorRegistry())),
mimetype=CONTENT_TYPE_LATEST,
)
def display_metrics():
registry = prometheus_client.CollectorRegistry()
MultiProcessCollector(registry)
data = prometheus_client.generate_latest(registry=registry)
return data.decode()
def test_prometheus_cleanup(registry):
pid = 1
def getpid():
return pid
# override use of os.getpid. _ValueClass is recreated after every test,
# so we don't need to clean up
from prometheus_client import core
core._ValueClass = core._MultiProcessValue(getpid)
histogram = metrics.Histogram(
name='histogram',
documentation='test histogram',
labelnames=['foo', 'bar', 'baz'],
statsd='{name}.{label}',
registry=registry,
)
counter = metrics.Counter(
name='counter',
documentation='test counter',
labelnames=['foo', 'bar', 'baz'],
statsd='{name}.{label}',
registry=registry,
)
from prometheus_client.multiprocess import MultiProcessCollector
collector = MultiProcessCollector(registry)
labels = {'foo': 'foo', 'bar': 'bar', 'baz': 'baz'}
def collect():
return {m.name: m for m in collector.collect()}
def files():
return list(sorted(os.listdir(os.environ['prometheus_multiproc_dir'])))
counter.inc(1, **labels)
histogram.observe(0.5, **labels)
histogram.observe(2.5, **labels)
assert files() == [
'counter_1.db',
'histogram_1.db',
]
before = collect()
metrics.prometheus_cleanup_worker(pid)
after = collect()
assert files() == [
'counter_archive.db',
'histogram_archive.db',
]
assert before == after
# magic!
pid += 1
# new worker, create some new metrics, check they are all combined
counter.inc(2, **labels)
histogram.observe(0.5, **labels)
histogram.observe(2.5, **labels)
later = collect()
assert files() == [
'counter_2.db',
'counter_archive.db',
'histogram_2.db',
'histogram_archive.db',
]
# check counter is correct
assert later['counter'].samples == [
Sample(counter_name('counter_total'), labels, 3.0),
]
expected_histogram = [
Sample('histogram_bucket', dict(le='0.005', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.01', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.025', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.05', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.075', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.1', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.25', **labels), 0.0),
Sample('histogram_bucket', dict(le='0.5', **labels), 2.0),
Sample('histogram_bucket', dict(le='0.75', **labels), 2.0),
Sample('histogram_bucket', dict(le='1.0', **labels), 2.0),
Sample('histogram_bucket', dict(le='2.5', **labels), 4.0),
Sample('histogram_bucket', dict(le='5.0', **labels), 4.0),
Sample('histogram_bucket', dict(le='7.5', **labels), 4.0),
Sample('histogram_bucket', dict(le='10.0', **labels), 4.0),
Sample('histogram_bucket', dict(le='+Inf', **labels), 4.0),
Sample('histogram_count', labels, 4.0),
Sample('histogram_sum', labels, 6.0),
]
# check histogram is correct
later['histogram'].samples.sort(key=metrics.histogram_sorter)
assert later['histogram'].samples == expected_histogram
# check the final files produce the correct numbers
metrics.prometheus_cleanup_worker(pid)
final = collect()
assert files() == [
'counter_archive.db',
'histogram_archive.db',
]
final['histogram'].samples.sort(key=metrics.histogram_sorter)
assert later == final
@contact: [email protected] @time: 2018/5/8 上午10:18 """ import os import prometheus_client from flask import Flask, Response, request from flask import jsonify from prometheus_client import CollectorRegistry, multiprocess from prometheus_client import Counter from prometheus_client.multiprocess import MultiProcessCollector # REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request') REGISTRY = CollectorRegistry(auto_describe=False) MultiProcessCollector(REGISTRY) requests_total = Counter("app:requests_total", "Total count of requests", ["method", "url_rule"], registry=REGISTRY) app = Flask(__name__) def record(func): """ func 即为调用该函数的方法 """ def req(*args, **kwargs): requests_total.labels(method=request.method, url_rule=request.path.lower()).inc()
def setUp(self):
self.tempdir = tempfile.mkdtemp()
os.environ['PROMETHEUS_MULTIPROC_DIR'] = self.tempdir
values.ValueClass = MultiProcessValue(lambda: 123)
self.registry = CollectorRegistry()
self.collector = MultiProcessCollector(self.registry)
def metrics(request):
log.info('Serving metrics')
registry = CollectorRegistry()
MultiProcessCollector(registry)
data = generate_latest(registry)
return Response(data, content_type='text/plain')
def setUp(self):
self.tempdir = tempfile.mkdtemp()
os.environ['prometheus_multiproc_dir'] = self.tempdir
core._ValueClass = core._MultiProcessValue(lambda: 123)
self.registry = CollectorRegistry()
MultiProcessCollector(self.registry, self.tempdir)
def create_registry():
registry = CollectorRegistry()
MultiProcessCollector(registry)
return registry