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Metadata-Version: 2.1 Name: prometheus-client Version: 0.8.0 Summary: Python client for the Prometheus monitoring system. Home-page: https://github.com/prometheus/client_python Author: Brian Brazil Author-email: brian.brazil@robustperception.io License: Apache Software License 2.0 Keywords: prometheus monitoring instrumentation client Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Information Technology Classifier: Intended Audience :: System Administrators Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.6 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: Implementation :: CPython Classifier: Programming Language :: Python :: Implementation :: PyPy Classifier: Topic :: System :: Monitoring Classifier: License :: OSI Approved :: Apache Software License Description-Content-Type: text/markdown Provides-Extra: twisted Requires-Dist: twisted ; extra == 'twisted' # Prometheus Python Client The official Python 2 and 3 client for [Prometheus](http://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() ``` ### 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. ## 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) ``` #### 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. #### 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_dispatch = DispatcherMiddleware(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_dispatch ``` 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) ``` ## 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) ``` ## 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` and `HistogramMetricFamily` 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 (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 - 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 There's several steps to getting this working: 1. Gunicorn 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 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. ```python from prometheus_client import multiprocess from prometheus_client import generate_latest, CollectorRegistry, CONTENT_TYPE_LATEST # 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` metrics are used, additional tuning needs to be performed. 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. - 'liveall': Return a timeseries per process that is still alive. - 'livesum': Return a single timeseries that is the sum of the values of alive processes. - 'max': Return a single timeseries that is the maximum of the values of all processes, alive or dead. - 'min': Return a single timeseries that is the minimum of the values of all processes, alive or dead. ```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)) ```