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+%global _empty_manifest_terminate_build 0
+Name:		python-prometheus-client
+Version:	0.16.0
+Release:	1
+Summary:	Python client for the Prometheus monitoring system.
+License:	Apache Software License 2.0
+URL:		https://github.com/prometheus/client_python
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d0/55/9e34c73e1e490b105b4cd13d08497b1f7cb086a260e4161b7b7c2928b196/prometheus_client-0.16.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-twisted
+
+%description
+# Prometheus Python Client
+
+The official Python client for [Prometheus](https://prometheus.io).
+
+## Three Step Demo
+
+**One**: Install the client:
+```
+pip install prometheus-client
+```
+
+**Two**: Paste the following into a Python interpreter:
+```python
+from prometheus_client import start_http_server, Summary
+import random
+import time
+
+# Create a metric to track time spent and requests made.
+REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
+
+# Decorate function with metric.
+@REQUEST_TIME.time()
+def process_request(t):
+    """A dummy function that takes some time."""
+    time.sleep(t)
+
+if __name__ == '__main__':
+    # Start up the server to expose the metrics.
+    start_http_server(8000)
+    # Generate some requests.
+    while True:
+        process_request(random.random())
+```
+
+**Three**: Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+From one easy to use decorator you get:
+  * `request_processing_seconds_count`: Number of times this function was called.
+  * `request_processing_seconds_sum`: Total amount of time spent in this function.
+
+Prometheus's `rate` function allows calculation of both requests per second,
+and latency over time from this data.
+
+In addition if you're on Linux the `process` metrics expose CPU, memory and
+other information about the process for free!
+
+## Installation
+
+```
+pip install prometheus-client
+```
+
+This package can be found on
+[PyPI](https://pypi.python.org/pypi/prometheus_client).
+
+## Instrumenting
+
+Four types of metric are offered: Counter, Gauge, Summary and Histogram.
+See the documentation on [metric types](http://prometheus.io/docs/concepts/metric_types/)
+and [instrumentation best practices](https://prometheus.io/docs/practices/instrumentation/#counter-vs-gauge-summary-vs-histogram)
+on how to use them.
+
+### Counter
+
+Counters go up, and reset when the process restarts.
+
+
+```python
+from prometheus_client import Counter
+c = Counter('my_failures', 'Description of counter')
+c.inc()     # Increment by 1
+c.inc(1.6)  # Increment by given value
+```
+
+If there is a suffix of `_total` on the metric name, it will be removed. When
+exposing the time series for counter, a `_total` suffix will be added. This is
+for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
+requires the `_total` suffix.
+
+There are utilities to count exceptions raised:
+
+```python
+@c.count_exceptions()
+def f():
+  pass
+
+with c.count_exceptions():
+  pass
+
+# Count only one type of exception
+with c.count_exceptions(ValueError):
+  pass
+```
+
+### Gauge
+
+Gauges can go up and down.
+
+```python
+from prometheus_client import Gauge
+g = Gauge('my_inprogress_requests', 'Description of gauge')
+g.inc()      # Increment by 1
+g.dec(10)    # Decrement by given value
+g.set(4.2)   # Set to a given value
+```
+
+There are utilities for common use cases:
+
+```python
+g.set_to_current_time()   # Set to current unixtime
+
+# Increment when entered, decrement when exited.
+@g.track_inprogress()
+def f():
+  pass
+
+with g.track_inprogress():
+  pass
+```
+
+A Gauge can also take its value from a callback:
+
+```python
+d = Gauge('data_objects', 'Number of objects')
+my_dict = {}
+d.set_function(lambda: len(my_dict))
+```
+
+### Summary
+
+Summaries track the size and number of events.
+
+```python
+from prometheus_client import Summary
+s = Summary('request_latency_seconds', 'Description of summary')
+s.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+There are utilities for timing code:
+
+```python
+@s.time()
+def f():
+  pass
+
+with s.time():
+  pass
+```
+
+The Python client doesn't store or expose quantile information at this time.
+
+### Histogram
+
+Histograms track the size and number of events in buckets.
+This allows for aggregatable calculation of quantiles.
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+The default buckets are intended to cover a typical web/rpc request from milliseconds to seconds.
+They can be overridden by passing `buckets` keyword argument to `Histogram`.
+
+There are utilities for timing code:
+
+```python
+@h.time()
+def f():
+  pass
+
+with h.time():
+  pass
+```
+
+### Info
+
+Info tracks key-value information, usually about a whole target.
+
+```python
+from prometheus_client import Info
+i = Info('my_build_version', 'Description of info')
+i.info({'version': '1.2.3', 'buildhost': 'foo@bar'})
+```
+
+### Enum
+
+Enum tracks which of a set of states something is currently in.
+
+```python
+from prometheus_client import Enum
+e = Enum('my_task_state', 'Description of enum',
+        states=['starting', 'running', 'stopped'])
+e.state('running')
+```
+
+### Labels
+
+All metrics can have labels, allowing grouping of related time series.
+
+See the best practices on [naming](http://prometheus.io/docs/practices/naming/)
+and [labels](http://prometheus.io/docs/practices/instrumentation/#use-labels).
+
+Taking a counter as an example:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+c.labels('post', '/submit').inc()
+```
+
+Labels can also be passed as keyword-arguments:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels(method='get', endpoint='/').inc()
+c.labels(method='post', endpoint='/submit').inc()
+```
+
+Metrics with labels are not initialized when declared, because the client can't
+know what values the label can have. It is recommended to initialize the label
+values by calling the `.labels()` method alone:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/')
+c.labels('post', '/submit')
+```
+
+### Exemplars
+
+Exemplars can be added to counter and histogram metrics. Exemplars can be
+specified by passing a dict of label value pairs to be exposed as the exemplar.
+For example with a counter:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc(exemplar={'trace_id': 'abc123'})
+c.labels('post', '/submit').inc(1.0, {'trace_id': 'def456'})
+```
+
+And with a histogram:
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7, {'trace_id': 'abc123'})
+```
+
+### Disabling `_created` metrics
+
+By default counters, histograms, and summaries export an additional series
+suffixed with `_created` and a value of the unix timestamp for when the metric
+was created. If this information is not helpful, it can be disabled by setting
+the environment variable `PROMETHEUS_DISABLE_CREATED_SERIES=True`.
+
+### Process Collector
+
+The Python client automatically exports metrics about process CPU usage, RAM,
+file descriptors and start time. These all have the prefix `process`, and
+are only currently available on Linux.
+
+The namespace and pid constructor arguments allows for exporting metrics about
+other processes, for example:
+```
+ProcessCollector(namespace='mydaemon', pid=lambda: open('/var/run/daemon.pid').read())
+```
+
+### Platform Collector
+
+The client also automatically exports some metadata about Python. If using Jython,
+metadata about the JVM in use is also included. This information is available as 
+labels on the `python_info` metric. The value of the metric is 1, since it is the 
+labels that carry information.
+
+### Disabling Default Collector metrics
+
+By default the collected `process`, `gc`, and `platform` collector metrics are exported.
+If this information is not helpful, it can be disabled using the following:
+```python
+import prometheus_client
+
+prometheus_client.REGISTRY.unregister(prometheus_client.GC_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PLATFORM_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PROCESS_COLLECTOR)
+```
+
+## Exporting
+
+There are several options for exporting metrics.
+
+### HTTP
+
+Metrics are usually exposed over HTTP, to be read by the Prometheus server.
+
+The easiest way to do this is via `start_http_server`, which will start a HTTP
+server in a daemon thread on the given port:
+
+```python
+from prometheus_client import start_http_server
+
+start_http_server(8000)
+```
+
+Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+To add Prometheus exposition to an existing HTTP server, see the `MetricsHandler` class
+which provides a `BaseHTTPRequestHandler`. It also serves as a simple example of how
+to write a custom endpoint.
+
+#### Twisted
+
+To use prometheus with [twisted](https://twistedmatrix.com/), there is `MetricsResource` which exposes metrics as a twisted resource.
+
+```python
+from prometheus_client.twisted import MetricsResource
+from twisted.web.server import Site
+from twisted.web.resource import Resource
+from twisted.internet import reactor
+
+root = Resource()
+root.putChild(b'metrics', MetricsResource())
+
+factory = Site(root)
+reactor.listenTCP(8000, factory)
+reactor.run()
+```
+
+#### WSGI
+
+To use Prometheus with [WSGI](http://wsgi.readthedocs.org/en/latest/), there is
+`make_wsgi_app` which creates a WSGI application.
+
+```python
+from prometheus_client import make_wsgi_app
+from wsgiref.simple_server import make_server
+
+app = make_wsgi_app()
+httpd = make_server('', 8000, app)
+httpd.serve_forever()
+```
+
+Such an application can be useful when integrating Prometheus metrics with WSGI
+apps.
+
+The method `start_wsgi_server` can be used to serve the metrics through the
+WSGI reference implementation in a new thread.
+
+```python
+from prometheus_client import start_wsgi_server
+
+start_wsgi_server(8000)
+```
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_wsgi_app(disable_compression=True)
+```
+
+#### ASGI
+
+To use Prometheus with [ASGI](http://asgi.readthedocs.org/en/latest/), there is
+`make_asgi_app` which creates an ASGI application.
+
+```python
+from prometheus_client import make_asgi_app
+
+app = make_asgi_app()
+```
+Such an application can be useful when integrating Prometheus metrics with ASGI
+apps.
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing 
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_asgi_app(disable_compression=True)
+```
+
+#### Flask
+
+To use Prometheus with [Flask](http://flask.pocoo.org/) we need to serve metrics through a Prometheus WSGI application. This can be achieved using [Flask's application dispatching](http://flask.pocoo.org/docs/latest/patterns/appdispatch/). Below is a working example.
+
+Save the snippet below in a `myapp.py` file
+
+```python
+from flask import Flask
+from werkzeug.middleware.dispatcher import DispatcherMiddleware
+from prometheus_client import make_wsgi_app
+
+# Create my app
+app = Flask(__name__)
+
+# Add prometheus wsgi middleware to route /metrics requests
+app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
+    '/metrics': make_wsgi_app()
+})
+```
+
+Run the example web application like this
+
+```bash
+# Install uwsgi if you do not have it
+pip install uwsgi
+uwsgi --http 127.0.0.1:8000 --wsgi-file myapp.py --callable app
+```
+
+Visit http://localhost:8000/metrics to see the metrics
+
+### Node exporter textfile collector
+
+The [textfile collector](https://github.com/prometheus/node_exporter#textfile-collector)
+allows machine-level statistics to be exported out via the Node exporter.
+
+This is useful for monitoring cronjobs, or for writing cronjobs to expose metrics
+about a machine system that the Node exporter does not support or would not make sense
+to perform at every scrape (for example, anything involving subprocesses).
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, write_to_textfile
+
+registry = CollectorRegistry()
+g = Gauge('raid_status', '1 if raid array is okay', registry=registry)
+g.set(1)
+write_to_textfile('/configured/textfile/path/raid.prom', registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+## Exporting to a Pushgateway
+
+The [Pushgateway](https://github.com/prometheus/pushgateway)
+allows ephemeral and batch jobs to expose their metrics to Prometheus.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+Pushgateway functions take a grouping key. `push_to_gateway` replaces metrics
+with the same grouping key, `pushadd_to_gateway` only replaces metrics with the
+same name and grouping key and `delete_from_gateway` deletes metrics with the
+given job and grouping key. See the
+[Pushgateway documentation](https://github.com/prometheus/pushgateway/blob/master/README.md)
+for more information.
+
+`instance_ip_grouping_key` returns a grouping key with the instance label set
+to the host's IP address.
+
+### Handlers for authentication
+
+If the push gateway you are connecting to is protected with HTTP Basic Auth,
+you can use a special handler to set the Authorization header.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import basic_auth_handler
+
+def my_auth_handler(url, method, timeout, headers, data):
+    username = 'foobar'
+    password = 'secret123'
+    return basic_auth_handler(url, method, timeout, headers, data, username, password)
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+TLS Auth is also supported when using the push gateway with a special handler.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import tls_auth_handler
+
+
+def my_auth_handler(url, method, timeout, headers, data):
+    certfile = 'client-crt.pem'
+    keyfile = 'client-key.pem'
+    return tls_auth_handler(url, method, timeout, headers, data, certfile, keyfile)
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+## Bridges
+
+It is also possible to expose metrics to systems other than Prometheus.
+This allows you to take advantage of Prometheus instrumentation even
+if you are not quite ready to fully transition to Prometheus yet.
+
+### Graphite
+
+Metrics are pushed over TCP in the Graphite plaintext format.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003))
+# Push once.
+gb.push()
+# Push every 10 seconds in a daemon thread.
+gb.start(10.0)
+```
+
+Graphite [tags](https://grafana.com/blog/2018/01/11/graphite-1.1-teaching-an-old-dog-new-tricks/) are also supported.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003), tags=True)
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+gb.push()
+```
+
+## Custom Collectors
+
+Sometimes it is not possible to directly instrument code, as it is not
+in your control. This requires you to proxy metrics from other systems.
+
+To do so you need to create a custom collector, for example:
+
+```python
+from prometheus_client.core import GaugeMetricFamily, CounterMetricFamily, REGISTRY
+
+class CustomCollector(object):
+    def collect(self):
+        yield GaugeMetricFamily('my_gauge', 'Help text', value=7)
+        c = CounterMetricFamily('my_counter_total', 'Help text', labels=['foo'])
+        c.add_metric(['bar'], 1.7)
+        c.add_metric(['baz'], 3.8)
+        yield c
+
+REGISTRY.register(CustomCollector())
+```
+
+`SummaryMetricFamily`, `HistogramMetricFamily` and `InfoMetricFamily` work similarly.
+
+A collector may implement a `describe` method which returns metrics in the same
+format as `collect` (though you don't have to include the samples). This is
+used to predetermine the names of time series a `CollectorRegistry` exposes and
+thus to detect collisions and duplicate registrations.
+
+Usually custom collectors do not have to implement `describe`. If `describe` is
+not implemented and the CollectorRegistry was created with `auto_describe=True`
+(which is the case for the default registry) then `collect` will be called at
+registration time instead of `describe`. If this could cause problems, either
+implement a proper `describe`, or if that's not practical have `describe`
+return an empty list.
+
+
+## Multiprocess Mode (E.g. Gunicorn)
+
+Prometheus client libraries presume a threaded model, where metrics are shared
+across workers. This doesn't work so well for languages such as Python where
+it's common to have processes rather than threads to handle large workloads.
+
+To handle this the client library can be put in multiprocess mode.
+This comes with a number of limitations:
+
+- Registries can not be used as normal, all instantiated metrics are exported
+  - Registering metrics to a registry later used by a `MultiProcessCollector`
+    may cause duplicate metrics to be exported
+- Custom collectors do not work (e.g. cpu and memory metrics)
+- Info and Enum metrics do not work
+- The pushgateway cannot be used
+- Gauges cannot use the `pid` label
+- Exemplars are not supported
+
+There's several steps to getting this working:
+
+**1. Deployment**:
+
+The `PROMETHEUS_MULTIPROC_DIR` environment variable must be set to a directory
+that the client library can use for metrics. This directory must be wiped
+between process/Gunicorn runs (before startup is recommended).
+
+This environment variable should be set from a start-up shell script,
+and not directly from Python (otherwise it may not propagate to child processes).
+
+**2. Metrics collector**:
+
+The application must initialize a new `CollectorRegistry`, and store the
+multi-process collector inside. It is a best practice to create this registry
+inside the context of a request to avoid metrics registering themselves to a
+collector used by a `MultiProcessCollector`. If a registry with metrics
+registered is used by a `MultiProcessCollector` duplicate metrics may be
+exported, one for multiprocess, and one for the process serving the request.
+
+```python
+from prometheus_client import multiprocess
+from prometheus_client import generate_latest, CollectorRegistry, CONTENT_TYPE_LATEST, Counter
+
+MY_COUNTER = Counter('my_counter', 'Description of my counter')
+
+# Expose metrics.
+def app(environ, start_response):
+    registry = CollectorRegistry()
+    multiprocess.MultiProcessCollector(registry)
+    data = generate_latest(registry)
+    status = '200 OK'
+    response_headers = [
+        ('Content-type', CONTENT_TYPE_LATEST),
+        ('Content-Length', str(len(data)))
+    ]
+    start_response(status, response_headers)
+    return iter([data])
+```
+
+**3. Gunicorn configuration**:
+
+The `gunicorn` configuration file needs to include the following function:
+
+```python
+from prometheus_client import multiprocess
+
+def child_exit(server, worker):
+    multiprocess.mark_process_dead(worker.pid)
+```
+
+**4. Metrics tuning (Gauge)**:
+
+When `Gauge`s are used in multiprocess applications,
+you must decide how to handle the metrics reported by each process.
+Gauges have several modes they can run in, which can be selected with the `multiprocess_mode` parameter.
+
+- 'all': Default. Return a timeseries per process (alive or dead), labelled by the process's `pid` (the label is added internally).
+- 'min': Return a single timeseries that is the minimum of the values of all processes (alive or dead).
+- 'max': Return a single timeseries that is the maximum of the values of all processes (alive or dead).
+- 'sum': Return a single timeseries that is the sum of the values of all processes (alive or dead).
+
+Prepend 'live' to the beginning of the mode to return the same result but only considering living processes
+(e.g., 'liveall, 'livesum', 'livemax', 'livemin').
+
+```python
+from prometheus_client import Gauge
+
+# Example gauge
+IN_PROGRESS = Gauge("inprogress_requests", "help", multiprocess_mode='livesum')
+```
+
+
+## Parser
+
+The Python client supports parsing the Prometheus text format.
+This is intended for advanced use cases where you have servers
+exposing Prometheus metrics and need to get them into some other
+system.
+
+```python
+from prometheus_client.parser import text_string_to_metric_families
+for family in text_string_to_metric_families(u"my_gauge 1.0\n"):
+  for sample in family.samples:
+    print("Name: {0} Labels: {1} Value: {2}".format(*sample))
+```
+
+## Links
+
+* [Releases](https://github.com/prometheus/client_python/releases): The releases page shows the history of the project and acts as a changelog.
+* [PyPI](https://pypi.python.org/pypi/prometheus_client)
+
+
+%package -n python3-prometheus-client
+Summary:	Python client for the Prometheus monitoring system.
+Provides:	python-prometheus-client
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-prometheus-client
+# Prometheus Python Client
+
+The official Python client for [Prometheus](https://prometheus.io).
+
+## Three Step Demo
+
+**One**: Install the client:
+```
+pip install prometheus-client
+```
+
+**Two**: Paste the following into a Python interpreter:
+```python
+from prometheus_client import start_http_server, Summary
+import random
+import time
+
+# Create a metric to track time spent and requests made.
+REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
+
+# Decorate function with metric.
+@REQUEST_TIME.time()
+def process_request(t):
+    """A dummy function that takes some time."""
+    time.sleep(t)
+
+if __name__ == '__main__':
+    # Start up the server to expose the metrics.
+    start_http_server(8000)
+    # Generate some requests.
+    while True:
+        process_request(random.random())
+```
+
+**Three**: Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+From one easy to use decorator you get:
+  * `request_processing_seconds_count`: Number of times this function was called.
+  * `request_processing_seconds_sum`: Total amount of time spent in this function.
+
+Prometheus's `rate` function allows calculation of both requests per second,
+and latency over time from this data.
+
+In addition if you're on Linux the `process` metrics expose CPU, memory and
+other information about the process for free!
+
+## Installation
+
+```
+pip install prometheus-client
+```
+
+This package can be found on
+[PyPI](https://pypi.python.org/pypi/prometheus_client).
+
+## Instrumenting
+
+Four types of metric are offered: Counter, Gauge, Summary and Histogram.
+See the documentation on [metric types](http://prometheus.io/docs/concepts/metric_types/)
+and [instrumentation best practices](https://prometheus.io/docs/practices/instrumentation/#counter-vs-gauge-summary-vs-histogram)
+on how to use them.
+
+### Counter
+
+Counters go up, and reset when the process restarts.
+
+
+```python
+from prometheus_client import Counter
+c = Counter('my_failures', 'Description of counter')
+c.inc()     # Increment by 1
+c.inc(1.6)  # Increment by given value
+```
+
+If there is a suffix of `_total` on the metric name, it will be removed. When
+exposing the time series for counter, a `_total` suffix will be added. This is
+for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
+requires the `_total` suffix.
+
+There are utilities to count exceptions raised:
+
+```python
+@c.count_exceptions()
+def f():
+  pass
+
+with c.count_exceptions():
+  pass
+
+# Count only one type of exception
+with c.count_exceptions(ValueError):
+  pass
+```
+
+### Gauge
+
+Gauges can go up and down.
+
+```python
+from prometheus_client import Gauge
+g = Gauge('my_inprogress_requests', 'Description of gauge')
+g.inc()      # Increment by 1
+g.dec(10)    # Decrement by given value
+g.set(4.2)   # Set to a given value
+```
+
+There are utilities for common use cases:
+
+```python
+g.set_to_current_time()   # Set to current unixtime
+
+# Increment when entered, decrement when exited.
+@g.track_inprogress()
+def f():
+  pass
+
+with g.track_inprogress():
+  pass
+```
+
+A Gauge can also take its value from a callback:
+
+```python
+d = Gauge('data_objects', 'Number of objects')
+my_dict = {}
+d.set_function(lambda: len(my_dict))
+```
+
+### Summary
+
+Summaries track the size and number of events.
+
+```python
+from prometheus_client import Summary
+s = Summary('request_latency_seconds', 'Description of summary')
+s.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+There are utilities for timing code:
+
+```python
+@s.time()
+def f():
+  pass
+
+with s.time():
+  pass
+```
+
+The Python client doesn't store or expose quantile information at this time.
+
+### Histogram
+
+Histograms track the size and number of events in buckets.
+This allows for aggregatable calculation of quantiles.
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+The default buckets are intended to cover a typical web/rpc request from milliseconds to seconds.
+They can be overridden by passing `buckets` keyword argument to `Histogram`.
+
+There are utilities for timing code:
+
+```python
+@h.time()
+def f():
+  pass
+
+with h.time():
+  pass
+```
+
+### Info
+
+Info tracks key-value information, usually about a whole target.
+
+```python
+from prometheus_client import Info
+i = Info('my_build_version', 'Description of info')
+i.info({'version': '1.2.3', 'buildhost': 'foo@bar'})
+```
+
+### Enum
+
+Enum tracks which of a set of states something is currently in.
+
+```python
+from prometheus_client import Enum
+e = Enum('my_task_state', 'Description of enum',
+        states=['starting', 'running', 'stopped'])
+e.state('running')
+```
+
+### Labels
+
+All metrics can have labels, allowing grouping of related time series.
+
+See the best practices on [naming](http://prometheus.io/docs/practices/naming/)
+and [labels](http://prometheus.io/docs/practices/instrumentation/#use-labels).
+
+Taking a counter as an example:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+c.labels('post', '/submit').inc()
+```
+
+Labels can also be passed as keyword-arguments:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels(method='get', endpoint='/').inc()
+c.labels(method='post', endpoint='/submit').inc()
+```
+
+Metrics with labels are not initialized when declared, because the client can't
+know what values the label can have. It is recommended to initialize the label
+values by calling the `.labels()` method alone:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/')
+c.labels('post', '/submit')
+```
+
+### Exemplars
+
+Exemplars can be added to counter and histogram metrics. Exemplars can be
+specified by passing a dict of label value pairs to be exposed as the exemplar.
+For example with a counter:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc(exemplar={'trace_id': 'abc123'})
+c.labels('post', '/submit').inc(1.0, {'trace_id': 'def456'})
+```
+
+And with a histogram:
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7, {'trace_id': 'abc123'})
+```
+
+### Disabling `_created` metrics
+
+By default counters, histograms, and summaries export an additional series
+suffixed with `_created` and a value of the unix timestamp for when the metric
+was created. If this information is not helpful, it can be disabled by setting
+the environment variable `PROMETHEUS_DISABLE_CREATED_SERIES=True`.
+
+### Process Collector
+
+The Python client automatically exports metrics about process CPU usage, RAM,
+file descriptors and start time. These all have the prefix `process`, and
+are only currently available on Linux.
+
+The namespace and pid constructor arguments allows for exporting metrics about
+other processes, for example:
+```
+ProcessCollector(namespace='mydaemon', pid=lambda: open('/var/run/daemon.pid').read())
+```
+
+### Platform Collector
+
+The client also automatically exports some metadata about Python. If using Jython,
+metadata about the JVM in use is also included. This information is available as 
+labels on the `python_info` metric. The value of the metric is 1, since it is the 
+labels that carry information.
+
+### Disabling Default Collector metrics
+
+By default the collected `process`, `gc`, and `platform` collector metrics are exported.
+If this information is not helpful, it can be disabled using the following:
+```python
+import prometheus_client
+
+prometheus_client.REGISTRY.unregister(prometheus_client.GC_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PLATFORM_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PROCESS_COLLECTOR)
+```
+
+## Exporting
+
+There are several options for exporting metrics.
+
+### HTTP
+
+Metrics are usually exposed over HTTP, to be read by the Prometheus server.
+
+The easiest way to do this is via `start_http_server`, which will start a HTTP
+server in a daemon thread on the given port:
+
+```python
+from prometheus_client import start_http_server
+
+start_http_server(8000)
+```
+
+Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+To add Prometheus exposition to an existing HTTP server, see the `MetricsHandler` class
+which provides a `BaseHTTPRequestHandler`. It also serves as a simple example of how
+to write a custom endpoint.
+
+#### Twisted
+
+To use prometheus with [twisted](https://twistedmatrix.com/), there is `MetricsResource` which exposes metrics as a twisted resource.
+
+```python
+from prometheus_client.twisted import MetricsResource
+from twisted.web.server import Site
+from twisted.web.resource import Resource
+from twisted.internet import reactor
+
+root = Resource()
+root.putChild(b'metrics', MetricsResource())
+
+factory = Site(root)
+reactor.listenTCP(8000, factory)
+reactor.run()
+```
+
+#### WSGI
+
+To use Prometheus with [WSGI](http://wsgi.readthedocs.org/en/latest/), there is
+`make_wsgi_app` which creates a WSGI application.
+
+```python
+from prometheus_client import make_wsgi_app
+from wsgiref.simple_server import make_server
+
+app = make_wsgi_app()
+httpd = make_server('', 8000, app)
+httpd.serve_forever()
+```
+
+Such an application can be useful when integrating Prometheus metrics with WSGI
+apps.
+
+The method `start_wsgi_server` can be used to serve the metrics through the
+WSGI reference implementation in a new thread.
+
+```python
+from prometheus_client import start_wsgi_server
+
+start_wsgi_server(8000)
+```
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_wsgi_app(disable_compression=True)
+```
+
+#### ASGI
+
+To use Prometheus with [ASGI](http://asgi.readthedocs.org/en/latest/), there is
+`make_asgi_app` which creates an ASGI application.
+
+```python
+from prometheus_client import make_asgi_app
+
+app = make_asgi_app()
+```
+Such an application can be useful when integrating Prometheus metrics with ASGI
+apps.
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing 
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_asgi_app(disable_compression=True)
+```
+
+#### Flask
+
+To use Prometheus with [Flask](http://flask.pocoo.org/) we need to serve metrics through a Prometheus WSGI application. This can be achieved using [Flask's application dispatching](http://flask.pocoo.org/docs/latest/patterns/appdispatch/). Below is a working example.
+
+Save the snippet below in a `myapp.py` file
+
+```python
+from flask import Flask
+from werkzeug.middleware.dispatcher import DispatcherMiddleware
+from prometheus_client import make_wsgi_app
+
+# Create my app
+app = Flask(__name__)
+
+# Add prometheus wsgi middleware to route /metrics requests
+app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
+    '/metrics': make_wsgi_app()
+})
+```
+
+Run the example web application like this
+
+```bash
+# Install uwsgi if you do not have it
+pip install uwsgi
+uwsgi --http 127.0.0.1:8000 --wsgi-file myapp.py --callable app
+```
+
+Visit http://localhost:8000/metrics to see the metrics
+
+### Node exporter textfile collector
+
+The [textfile collector](https://github.com/prometheus/node_exporter#textfile-collector)
+allows machine-level statistics to be exported out via the Node exporter.
+
+This is useful for monitoring cronjobs, or for writing cronjobs to expose metrics
+about a machine system that the Node exporter does not support or would not make sense
+to perform at every scrape (for example, anything involving subprocesses).
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, write_to_textfile
+
+registry = CollectorRegistry()
+g = Gauge('raid_status', '1 if raid array is okay', registry=registry)
+g.set(1)
+write_to_textfile('/configured/textfile/path/raid.prom', registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+## Exporting to a Pushgateway
+
+The [Pushgateway](https://github.com/prometheus/pushgateway)
+allows ephemeral and batch jobs to expose their metrics to Prometheus.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+Pushgateway functions take a grouping key. `push_to_gateway` replaces metrics
+with the same grouping key, `pushadd_to_gateway` only replaces metrics with the
+same name and grouping key and `delete_from_gateway` deletes metrics with the
+given job and grouping key. See the
+[Pushgateway documentation](https://github.com/prometheus/pushgateway/blob/master/README.md)
+for more information.
+
+`instance_ip_grouping_key` returns a grouping key with the instance label set
+to the host's IP address.
+
+### Handlers for authentication
+
+If the push gateway you are connecting to is protected with HTTP Basic Auth,
+you can use a special handler to set the Authorization header.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import basic_auth_handler
+
+def my_auth_handler(url, method, timeout, headers, data):
+    username = 'foobar'
+    password = 'secret123'
+    return basic_auth_handler(url, method, timeout, headers, data, username, password)
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+TLS Auth is also supported when using the push gateway with a special handler.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import tls_auth_handler
+
+
+def my_auth_handler(url, method, timeout, headers, data):
+    certfile = 'client-crt.pem'
+    keyfile = 'client-key.pem'
+    return tls_auth_handler(url, method, timeout, headers, data, certfile, keyfile)
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+## Bridges
+
+It is also possible to expose metrics to systems other than Prometheus.
+This allows you to take advantage of Prometheus instrumentation even
+if you are not quite ready to fully transition to Prometheus yet.
+
+### Graphite
+
+Metrics are pushed over TCP in the Graphite plaintext format.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003))
+# Push once.
+gb.push()
+# Push every 10 seconds in a daemon thread.
+gb.start(10.0)
+```
+
+Graphite [tags](https://grafana.com/blog/2018/01/11/graphite-1.1-teaching-an-old-dog-new-tricks/) are also supported.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003), tags=True)
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+gb.push()
+```
+
+## Custom Collectors
+
+Sometimes it is not possible to directly instrument code, as it is not
+in your control. This requires you to proxy metrics from other systems.
+
+To do so you need to create a custom collector, for example:
+
+```python
+from prometheus_client.core import GaugeMetricFamily, CounterMetricFamily, REGISTRY
+
+class CustomCollector(object):
+    def collect(self):
+        yield GaugeMetricFamily('my_gauge', 'Help text', value=7)
+        c = CounterMetricFamily('my_counter_total', 'Help text', labels=['foo'])
+        c.add_metric(['bar'], 1.7)
+        c.add_metric(['baz'], 3.8)
+        yield c
+
+REGISTRY.register(CustomCollector())
+```
+
+`SummaryMetricFamily`, `HistogramMetricFamily` and `InfoMetricFamily` work similarly.
+
+A collector may implement a `describe` method which returns metrics in the same
+format as `collect` (though you don't have to include the samples). This is
+used to predetermine the names of time series a `CollectorRegistry` exposes and
+thus to detect collisions and duplicate registrations.
+
+Usually custom collectors do not have to implement `describe`. If `describe` is
+not implemented and the CollectorRegistry was created with `auto_describe=True`
+(which is the case for the default registry) then `collect` will be called at
+registration time instead of `describe`. If this could cause problems, either
+implement a proper `describe`, or if that's not practical have `describe`
+return an empty list.
+
+
+## Multiprocess Mode (E.g. Gunicorn)
+
+Prometheus client libraries presume a threaded model, where metrics are shared
+across workers. This doesn't work so well for languages such as Python where
+it's common to have processes rather than threads to handle large workloads.
+
+To handle this the client library can be put in multiprocess mode.
+This comes with a number of limitations:
+
+- Registries can not be used as normal, all instantiated metrics are exported
+  - Registering metrics to a registry later used by a `MultiProcessCollector`
+    may cause duplicate metrics to be exported
+- Custom collectors do not work (e.g. cpu and memory metrics)
+- Info and Enum metrics do not work
+- The pushgateway cannot be used
+- Gauges cannot use the `pid` label
+- Exemplars are not supported
+
+There's several steps to getting this working:
+
+**1. Deployment**:
+
+The `PROMETHEUS_MULTIPROC_DIR` environment variable must be set to a directory
+that the client library can use for metrics. This directory must be wiped
+between process/Gunicorn runs (before startup is recommended).
+
+This environment variable should be set from a start-up shell script,
+and not directly from Python (otherwise it may not propagate to child processes).
+
+**2. Metrics collector**:
+
+The application must initialize a new `CollectorRegistry`, and store the
+multi-process collector inside. It is a best practice to create this registry
+inside the context of a request to avoid metrics registering themselves to a
+collector used by a `MultiProcessCollector`. If a registry with metrics
+registered is used by a `MultiProcessCollector` duplicate metrics may be
+exported, one for multiprocess, and one for the process serving the request.
+
+```python
+from prometheus_client import multiprocess
+from prometheus_client import generate_latest, CollectorRegistry, CONTENT_TYPE_LATEST, Counter
+
+MY_COUNTER = Counter('my_counter', 'Description of my counter')
+
+# Expose metrics.
+def app(environ, start_response):
+    registry = CollectorRegistry()
+    multiprocess.MultiProcessCollector(registry)
+    data = generate_latest(registry)
+    status = '200 OK'
+    response_headers = [
+        ('Content-type', CONTENT_TYPE_LATEST),
+        ('Content-Length', str(len(data)))
+    ]
+    start_response(status, response_headers)
+    return iter([data])
+```
+
+**3. Gunicorn configuration**:
+
+The `gunicorn` configuration file needs to include the following function:
+
+```python
+from prometheus_client import multiprocess
+
+def child_exit(server, worker):
+    multiprocess.mark_process_dead(worker.pid)
+```
+
+**4. Metrics tuning (Gauge)**:
+
+When `Gauge`s are used in multiprocess applications,
+you must decide how to handle the metrics reported by each process.
+Gauges have several modes they can run in, which can be selected with the `multiprocess_mode` parameter.
+
+- 'all': Default. Return a timeseries per process (alive or dead), labelled by the process's `pid` (the label is added internally).
+- 'min': Return a single timeseries that is the minimum of the values of all processes (alive or dead).
+- 'max': Return a single timeseries that is the maximum of the values of all processes (alive or dead).
+- 'sum': Return a single timeseries that is the sum of the values of all processes (alive or dead).
+
+Prepend 'live' to the beginning of the mode to return the same result but only considering living processes
+(e.g., 'liveall, 'livesum', 'livemax', 'livemin').
+
+```python
+from prometheus_client import Gauge
+
+# Example gauge
+IN_PROGRESS = Gauge("inprogress_requests", "help", multiprocess_mode='livesum')
+```
+
+
+## Parser
+
+The Python client supports parsing the Prometheus text format.
+This is intended for advanced use cases where you have servers
+exposing Prometheus metrics and need to get them into some other
+system.
+
+```python
+from prometheus_client.parser import text_string_to_metric_families
+for family in text_string_to_metric_families(u"my_gauge 1.0\n"):
+  for sample in family.samples:
+    print("Name: {0} Labels: {1} Value: {2}".format(*sample))
+```
+
+## Links
+
+* [Releases](https://github.com/prometheus/client_python/releases): The releases page shows the history of the project and acts as a changelog.
+* [PyPI](https://pypi.python.org/pypi/prometheus_client)
+
+
+%package help
+Summary:	Development documents and examples for prometheus-client
+Provides:	python3-prometheus-client-doc
+%description help
+# Prometheus Python Client
+
+The official Python client for [Prometheus](https://prometheus.io).
+
+## Three Step Demo
+
+**One**: Install the client:
+```
+pip install prometheus-client
+```
+
+**Two**: Paste the following into a Python interpreter:
+```python
+from prometheus_client import start_http_server, Summary
+import random
+import time
+
+# Create a metric to track time spent and requests made.
+REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
+
+# Decorate function with metric.
+@REQUEST_TIME.time()
+def process_request(t):
+    """A dummy function that takes some time."""
+    time.sleep(t)
+
+if __name__ == '__main__':
+    # Start up the server to expose the metrics.
+    start_http_server(8000)
+    # Generate some requests.
+    while True:
+        process_request(random.random())
+```
+
+**Three**: Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+From one easy to use decorator you get:
+  * `request_processing_seconds_count`: Number of times this function was called.
+  * `request_processing_seconds_sum`: Total amount of time spent in this function.
+
+Prometheus's `rate` function allows calculation of both requests per second,
+and latency over time from this data.
+
+In addition if you're on Linux the `process` metrics expose CPU, memory and
+other information about the process for free!
+
+## Installation
+
+```
+pip install prometheus-client
+```
+
+This package can be found on
+[PyPI](https://pypi.python.org/pypi/prometheus_client).
+
+## Instrumenting
+
+Four types of metric are offered: Counter, Gauge, Summary and Histogram.
+See the documentation on [metric types](http://prometheus.io/docs/concepts/metric_types/)
+and [instrumentation best practices](https://prometheus.io/docs/practices/instrumentation/#counter-vs-gauge-summary-vs-histogram)
+on how to use them.
+
+### Counter
+
+Counters go up, and reset when the process restarts.
+
+
+```python
+from prometheus_client import Counter
+c = Counter('my_failures', 'Description of counter')
+c.inc()     # Increment by 1
+c.inc(1.6)  # Increment by given value
+```
+
+If there is a suffix of `_total` on the metric name, it will be removed. When
+exposing the time series for counter, a `_total` suffix will be added. This is
+for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
+requires the `_total` suffix.
+
+There are utilities to count exceptions raised:
+
+```python
+@c.count_exceptions()
+def f():
+  pass
+
+with c.count_exceptions():
+  pass
+
+# Count only one type of exception
+with c.count_exceptions(ValueError):
+  pass
+```
+
+### Gauge
+
+Gauges can go up and down.
+
+```python
+from prometheus_client import Gauge
+g = Gauge('my_inprogress_requests', 'Description of gauge')
+g.inc()      # Increment by 1
+g.dec(10)    # Decrement by given value
+g.set(4.2)   # Set to a given value
+```
+
+There are utilities for common use cases:
+
+```python
+g.set_to_current_time()   # Set to current unixtime
+
+# Increment when entered, decrement when exited.
+@g.track_inprogress()
+def f():
+  pass
+
+with g.track_inprogress():
+  pass
+```
+
+A Gauge can also take its value from a callback:
+
+```python
+d = Gauge('data_objects', 'Number of objects')
+my_dict = {}
+d.set_function(lambda: len(my_dict))
+```
+
+### Summary
+
+Summaries track the size and number of events.
+
+```python
+from prometheus_client import Summary
+s = Summary('request_latency_seconds', 'Description of summary')
+s.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+There are utilities for timing code:
+
+```python
+@s.time()
+def f():
+  pass
+
+with s.time():
+  pass
+```
+
+The Python client doesn't store or expose quantile information at this time.
+
+### Histogram
+
+Histograms track the size and number of events in buckets.
+This allows for aggregatable calculation of quantiles.
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7)    # Observe 4.7 (seconds in this case)
+```
+
+The default buckets are intended to cover a typical web/rpc request from milliseconds to seconds.
+They can be overridden by passing `buckets` keyword argument to `Histogram`.
+
+There are utilities for timing code:
+
+```python
+@h.time()
+def f():
+  pass
+
+with h.time():
+  pass
+```
+
+### Info
+
+Info tracks key-value information, usually about a whole target.
+
+```python
+from prometheus_client import Info
+i = Info('my_build_version', 'Description of info')
+i.info({'version': '1.2.3', 'buildhost': 'foo@bar'})
+```
+
+### Enum
+
+Enum tracks which of a set of states something is currently in.
+
+```python
+from prometheus_client import Enum
+e = Enum('my_task_state', 'Description of enum',
+        states=['starting', 'running', 'stopped'])
+e.state('running')
+```
+
+### Labels
+
+All metrics can have labels, allowing grouping of related time series.
+
+See the best practices on [naming](http://prometheus.io/docs/practices/naming/)
+and [labels](http://prometheus.io/docs/practices/instrumentation/#use-labels).
+
+Taking a counter as an example:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+c.labels('post', '/submit').inc()
+```
+
+Labels can also be passed as keyword-arguments:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels(method='get', endpoint='/').inc()
+c.labels(method='post', endpoint='/submit').inc()
+```
+
+Metrics with labels are not initialized when declared, because the client can't
+know what values the label can have. It is recommended to initialize the label
+values by calling the `.labels()` method alone:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/')
+c.labels('post', '/submit')
+```
+
+### Exemplars
+
+Exemplars can be added to counter and histogram metrics. Exemplars can be
+specified by passing a dict of label value pairs to be exposed as the exemplar.
+For example with a counter:
+
+```python
+from prometheus_client import Counter
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc(exemplar={'trace_id': 'abc123'})
+c.labels('post', '/submit').inc(1.0, {'trace_id': 'def456'})
+```
+
+And with a histogram:
+
+```python
+from prometheus_client import Histogram
+h = Histogram('request_latency_seconds', 'Description of histogram')
+h.observe(4.7, {'trace_id': 'abc123'})
+```
+
+### Disabling `_created` metrics
+
+By default counters, histograms, and summaries export an additional series
+suffixed with `_created` and a value of the unix timestamp for when the metric
+was created. If this information is not helpful, it can be disabled by setting
+the environment variable `PROMETHEUS_DISABLE_CREATED_SERIES=True`.
+
+### Process Collector
+
+The Python client automatically exports metrics about process CPU usage, RAM,
+file descriptors and start time. These all have the prefix `process`, and
+are only currently available on Linux.
+
+The namespace and pid constructor arguments allows for exporting metrics about
+other processes, for example:
+```
+ProcessCollector(namespace='mydaemon', pid=lambda: open('/var/run/daemon.pid').read())
+```
+
+### Platform Collector
+
+The client also automatically exports some metadata about Python. If using Jython,
+metadata about the JVM in use is also included. This information is available as 
+labels on the `python_info` metric. The value of the metric is 1, since it is the 
+labels that carry information.
+
+### Disabling Default Collector metrics
+
+By default the collected `process`, `gc`, and `platform` collector metrics are exported.
+If this information is not helpful, it can be disabled using the following:
+```python
+import prometheus_client
+
+prometheus_client.REGISTRY.unregister(prometheus_client.GC_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PLATFORM_COLLECTOR)
+prometheus_client.REGISTRY.unregister(prometheus_client.PROCESS_COLLECTOR)
+```
+
+## Exporting
+
+There are several options for exporting metrics.
+
+### HTTP
+
+Metrics are usually exposed over HTTP, to be read by the Prometheus server.
+
+The easiest way to do this is via `start_http_server`, which will start a HTTP
+server in a daemon thread on the given port:
+
+```python
+from prometheus_client import start_http_server
+
+start_http_server(8000)
+```
+
+Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
+
+To add Prometheus exposition to an existing HTTP server, see the `MetricsHandler` class
+which provides a `BaseHTTPRequestHandler`. It also serves as a simple example of how
+to write a custom endpoint.
+
+#### Twisted
+
+To use prometheus with [twisted](https://twistedmatrix.com/), there is `MetricsResource` which exposes metrics as a twisted resource.
+
+```python
+from prometheus_client.twisted import MetricsResource
+from twisted.web.server import Site
+from twisted.web.resource import Resource
+from twisted.internet import reactor
+
+root = Resource()
+root.putChild(b'metrics', MetricsResource())
+
+factory = Site(root)
+reactor.listenTCP(8000, factory)
+reactor.run()
+```
+
+#### WSGI
+
+To use Prometheus with [WSGI](http://wsgi.readthedocs.org/en/latest/), there is
+`make_wsgi_app` which creates a WSGI application.
+
+```python
+from prometheus_client import make_wsgi_app
+from wsgiref.simple_server import make_server
+
+app = make_wsgi_app()
+httpd = make_server('', 8000, app)
+httpd.serve_forever()
+```
+
+Such an application can be useful when integrating Prometheus metrics with WSGI
+apps.
+
+The method `start_wsgi_server` can be used to serve the metrics through the
+WSGI reference implementation in a new thread.
+
+```python
+from prometheus_client import start_wsgi_server
+
+start_wsgi_server(8000)
+```
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_wsgi_app(disable_compression=True)
+```
+
+#### ASGI
+
+To use Prometheus with [ASGI](http://asgi.readthedocs.org/en/latest/), there is
+`make_asgi_app` which creates an ASGI application.
+
+```python
+from prometheus_client import make_asgi_app
+
+app = make_asgi_app()
+```
+Such an application can be useful when integrating Prometheus metrics with ASGI
+apps.
+
+By default, the WSGI application will respect `Accept-Encoding:gzip` headers used by Prometheus
+and compress the response if such a header is present. This behaviour can be disabled by passing 
+`disable_compression=True` when creating the app, like this:
+
+```python
+app = make_asgi_app(disable_compression=True)
+```
+
+#### Flask
+
+To use Prometheus with [Flask](http://flask.pocoo.org/) we need to serve metrics through a Prometheus WSGI application. This can be achieved using [Flask's application dispatching](http://flask.pocoo.org/docs/latest/patterns/appdispatch/). Below is a working example.
+
+Save the snippet below in a `myapp.py` file
+
+```python
+from flask import Flask
+from werkzeug.middleware.dispatcher import DispatcherMiddleware
+from prometheus_client import make_wsgi_app
+
+# Create my app
+app = Flask(__name__)
+
+# Add prometheus wsgi middleware to route /metrics requests
+app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
+    '/metrics': make_wsgi_app()
+})
+```
+
+Run the example web application like this
+
+```bash
+# Install uwsgi if you do not have it
+pip install uwsgi
+uwsgi --http 127.0.0.1:8000 --wsgi-file myapp.py --callable app
+```
+
+Visit http://localhost:8000/metrics to see the metrics
+
+### Node exporter textfile collector
+
+The [textfile collector](https://github.com/prometheus/node_exporter#textfile-collector)
+allows machine-level statistics to be exported out via the Node exporter.
+
+This is useful for monitoring cronjobs, or for writing cronjobs to expose metrics
+about a machine system that the Node exporter does not support or would not make sense
+to perform at every scrape (for example, anything involving subprocesses).
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, write_to_textfile
+
+registry = CollectorRegistry()
+g = Gauge('raid_status', '1 if raid array is okay', registry=registry)
+g.set(1)
+write_to_textfile('/configured/textfile/path/raid.prom', registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+## Exporting to a Pushgateway
+
+The [Pushgateway](https://github.com/prometheus/pushgateway)
+allows ephemeral and batch jobs to expose their metrics to Prometheus.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry)
+```
+
+A separate registry is used, as the default registry may contain other metrics
+such as those from the Process Collector.
+
+Pushgateway functions take a grouping key. `push_to_gateway` replaces metrics
+with the same grouping key, `pushadd_to_gateway` only replaces metrics with the
+same name and grouping key and `delete_from_gateway` deletes metrics with the
+given job and grouping key. See the
+[Pushgateway documentation](https://github.com/prometheus/pushgateway/blob/master/README.md)
+for more information.
+
+`instance_ip_grouping_key` returns a grouping key with the instance label set
+to the host's IP address.
+
+### Handlers for authentication
+
+If the push gateway you are connecting to is protected with HTTP Basic Auth,
+you can use a special handler to set the Authorization header.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import basic_auth_handler
+
+def my_auth_handler(url, method, timeout, headers, data):
+    username = 'foobar'
+    password = 'secret123'
+    return basic_auth_handler(url, method, timeout, headers, data, username, password)
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+TLS Auth is also supported when using the push gateway with a special handler.
+
+```python
+from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
+from prometheus_client.exposition import tls_auth_handler
+
+
+def my_auth_handler(url, method, timeout, headers, data):
+    certfile = 'client-crt.pem'
+    keyfile = 'client-key.pem'
+    return tls_auth_handler(url, method, timeout, headers, data, certfile, keyfile)
+
+registry = CollectorRegistry()
+g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
+g.set_to_current_time()
+push_to_gateway('localhost:9091', job='batchA', registry=registry, handler=my_auth_handler)
+```
+
+## Bridges
+
+It is also possible to expose metrics to systems other than Prometheus.
+This allows you to take advantage of Prometheus instrumentation even
+if you are not quite ready to fully transition to Prometheus yet.
+
+### Graphite
+
+Metrics are pushed over TCP in the Graphite plaintext format.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003))
+# Push once.
+gb.push()
+# Push every 10 seconds in a daemon thread.
+gb.start(10.0)
+```
+
+Graphite [tags](https://grafana.com/blog/2018/01/11/graphite-1.1-teaching-an-old-dog-new-tricks/) are also supported.
+
+```python
+from prometheus_client.bridge.graphite import GraphiteBridge
+
+gb = GraphiteBridge(('graphite.your.org', 2003), tags=True)
+c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
+c.labels('get', '/').inc()
+gb.push()
+```
+
+## Custom Collectors
+
+Sometimes it is not possible to directly instrument code, as it is not
+in your control. This requires you to proxy metrics from other systems.
+
+To do so you need to create a custom collector, for example:
+
+```python
+from prometheus_client.core import GaugeMetricFamily, CounterMetricFamily, REGISTRY
+
+class CustomCollector(object):
+    def collect(self):
+        yield GaugeMetricFamily('my_gauge', 'Help text', value=7)
+        c = CounterMetricFamily('my_counter_total', 'Help text', labels=['foo'])
+        c.add_metric(['bar'], 1.7)
+        c.add_metric(['baz'], 3.8)
+        yield c
+
+REGISTRY.register(CustomCollector())
+```
+
+`SummaryMetricFamily`, `HistogramMetricFamily` and `InfoMetricFamily` work similarly.
+
+A collector may implement a `describe` method which returns metrics in the same
+format as `collect` (though you don't have to include the samples). This is
+used to predetermine the names of time series a `CollectorRegistry` exposes and
+thus to detect collisions and duplicate registrations.
+
+Usually custom collectors do not have to implement `describe`. If `describe` is
+not implemented and the CollectorRegistry was created with `auto_describe=True`
+(which is the case for the default registry) then `collect` will be called at
+registration time instead of `describe`. If this could cause problems, either
+implement a proper `describe`, or if that's not practical have `describe`
+return an empty list.
+
+
+## Multiprocess Mode (E.g. Gunicorn)
+
+Prometheus client libraries presume a threaded model, where metrics are shared
+across workers. This doesn't work so well for languages such as Python where
+it's common to have processes rather than threads to handle large workloads.
+
+To handle this the client library can be put in multiprocess mode.
+This comes with a number of limitations:
+
+- Registries can not be used as normal, all instantiated metrics are exported
+  - Registering metrics to a registry later used by a `MultiProcessCollector`
+    may cause duplicate metrics to be exported
+- Custom collectors do not work (e.g. cpu and memory metrics)
+- Info and Enum metrics do not work
+- The pushgateway cannot be used
+- Gauges cannot use the `pid` label
+- Exemplars are not supported
+
+There's several steps to getting this working:
+
+**1. Deployment**:
+
+The `PROMETHEUS_MULTIPROC_DIR` environment variable must be set to a directory
+that the client library can use for metrics. This directory must be wiped
+between process/Gunicorn runs (before startup is recommended).
+
+This environment variable should be set from a start-up shell script,
+and not directly from Python (otherwise it may not propagate to child processes).
+
+**2. Metrics collector**:
+
+The application must initialize a new `CollectorRegistry`, and store the
+multi-process collector inside. It is a best practice to create this registry
+inside the context of a request to avoid metrics registering themselves to a
+collector used by a `MultiProcessCollector`. If a registry with metrics
+registered is used by a `MultiProcessCollector` duplicate metrics may be
+exported, one for multiprocess, and one for the process serving the request.
+
+```python
+from prometheus_client import multiprocess
+from prometheus_client import generate_latest, CollectorRegistry, CONTENT_TYPE_LATEST, Counter
+
+MY_COUNTER = Counter('my_counter', 'Description of my counter')
+
+# Expose metrics.
+def app(environ, start_response):
+    registry = CollectorRegistry()
+    multiprocess.MultiProcessCollector(registry)
+    data = generate_latest(registry)
+    status = '200 OK'
+    response_headers = [
+        ('Content-type', CONTENT_TYPE_LATEST),
+        ('Content-Length', str(len(data)))
+    ]
+    start_response(status, response_headers)
+    return iter([data])
+```
+
+**3. Gunicorn configuration**:
+
+The `gunicorn` configuration file needs to include the following function:
+
+```python
+from prometheus_client import multiprocess
+
+def child_exit(server, worker):
+    multiprocess.mark_process_dead(worker.pid)
+```
+
+**4. Metrics tuning (Gauge)**:
+
+When `Gauge`s are used in multiprocess applications,
+you must decide how to handle the metrics reported by each process.
+Gauges have several modes they can run in, which can be selected with the `multiprocess_mode` parameter.
+
+- 'all': Default. Return a timeseries per process (alive or dead), labelled by the process's `pid` (the label is added internally).
+- 'min': Return a single timeseries that is the minimum of the values of all processes (alive or dead).
+- 'max': Return a single timeseries that is the maximum of the values of all processes (alive or dead).
+- 'sum': Return a single timeseries that is the sum of the values of all processes (alive or dead).
+
+Prepend 'live' to the beginning of the mode to return the same result but only considering living processes
+(e.g., 'liveall, 'livesum', 'livemax', 'livemin').
+
+```python
+from prometheus_client import Gauge
+
+# Example gauge
+IN_PROGRESS = Gauge("inprogress_requests", "help", multiprocess_mode='livesum')
+```
+
+
+## Parser
+
+The Python client supports parsing the Prometheus text format.
+This is intended for advanced use cases where you have servers
+exposing Prometheus metrics and need to get them into some other
+system.
+
+```python
+from prometheus_client.parser import text_string_to_metric_families
+for family in text_string_to_metric_families(u"my_gauge 1.0\n"):
+  for sample in family.samples:
+    print("Name: {0} Labels: {1} Value: {2}".format(*sample))
+```
+
+## Links
+
+* [Releases](https://github.com/prometheus/client_python/releases): The releases page shows the history of the project and acts as a changelog.
+* [PyPI](https://pypi.python.org/pypi/prometheus_client)
+
+
+%prep
+%autosetup -n prometheus-client-0.16.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-prometheus-client -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.16.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..7b94861
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4a0fee6e8109a149621af1d1e1d2e920  prometheus_client-0.16.0.tar.gz