Metadata-Version: 2.1
Name: prometheus-flask-exporter
Version: 0.8.1
Summary: Prometheus metrics exporter for Flask
Home-page: https://github.com/rycus86/prometheus_flask_exporter
Author: Viktor Adam
Author-email: rycus86@gmail.com
License: MIT
Download-URL: https://github.com/rycus86/prometheus_flask_exporter/archive/0.8.1.tar.gz
Description: # Prometheus Flask exporter
        
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        This library provides HTTP request metrics to export into
        [Prometheus](https://prometheus.io/).
        It can also track method invocations using convenient functions.
        
        ## Usage
        
        ```python
        from flask import Flask, request
        from prometheus_flask_exporter import PrometheusMetrics
        
        app = Flask(__name__)
        metrics = PrometheusMetrics(app)
        
        # static information as metric
        metrics.info('app_info', 'Application info', version='1.0.3')
        
        @app.route('/')
        def main():
            pass  # requests tracked by default
        
        @app.route('/skip')
        @metrics.do_not_track()
        def skip():
            pass  # default metrics are not collected
        
        @app.route('/<item_type>')
        @metrics.do_not_track()
        @metrics.counter('invocation_by_type', 'Number of invocations by type',
                 labels={'item_type': lambda: request.view_args['type']})
        def by_type(item_type):
            pass  # only the counter is collected, not the default metrics
        
        @app.route('/long-running')
        @metrics.gauge('in_progress', 'Long running requests in progress')
        def long_running():
            pass
        
        @app.route('/status/<int:status>')
        @metrics.do_not_track()
        @metrics.summary('requests_by_status', 'Request latencies by status',
                         labels={'status': lambda r: r.status_code})
        @metrics.histogram('requests_by_status_and_path', 'Request latencies by status and path',
                           labels={'status': lambda r: r.status_code, 'path': lambda: request.path})
        def echo_status(status):
            return 'Status: %s' % status, status
        ```
        
        ## Default metrics
        
        The following metrics are exported by default
        (unless the `export_defaults` is set to `False`).
        
        - `flask_http_request_duration_seconds` (Histogram)
          Labels: `method`, `path` and `status`.
          Flask HTTP request duration in seconds for all Flask requests.
        - `flask_http_request_total` (Counter)
          Labels: `method` and `status`.
          Total number of HTTP requests for all Flask requests.
        - `flask_exporter_info` (Gauge)
          Information about the Prometheus Flask exporter itself (e.g. `version`).
        
        The prefix for the default metrics can be controlled by the `defaults_prefix` parameter.
        Is you don't want to use any prefix, pass the `prometheus_flask_exporter.NO_PREFIX` value in.
        
        ## Configuration
        
        By default, the metrics are exposed on the same Flask application on the
        `/metrics` endpoint and using the core Prometheus registry.
        If this doesn't suit your needs, set the `path` argument to `None` and/or
        the `export_defaults` argument to `False` plus change the `registry`
        argument if needed.
        
        The `group_by` constructor argument controls what
        the default request duration metric is tracked by: endpoint (function)
        instead of URI path (the default). This parameter also accepts a function
        to extract the value from the request, or a name of a property of the request object.
        Examples:
        
        ```python
        PrometheusMetrics(app, group_by='path')         # the default
        PrometheusMetrics(app, group_by='endpoint')     # by endpoint
        PrometheusMetrics(app, group_by='url_rule')     # by URL rule
        
        def custom_rule(req):  # the Flask request object
            """ The name of the function becomes the label name. """
            return '%s::%s' % (req.method, req.path)
        
        PrometheusMetrics(app, group_by=custom_rule)    # by a function
        
        # Error: this is not supported:
        PrometheusMetrics(app, group_by=lambda r: r.path)
        ```
        
        > The `group_by_endpoint` argument is deprecated since 0.4.0,
        > please use the new `group_by` argument.
        
        The `register_endpoint` allows exposing the metrics endpoint on a specific path.
        It also allows passing in a Flask application to register it on but defaults
        to the main one if not defined.
        
        Similarly, the `start_http_server` allows exposing the endpoint on an
        independent Flask application on a selected HTTP port.
        It also supports overriding the endpoint's path and the HTTP listen address.
        
        ## Labels
        
        When defining labels for metrics on functions,
        the following values are supported in the dictionary:
        
        - A simple static value
        - A no-argument callable
        - A single argument callable that will receive the Flask response
          as the argument
        
        Label values are evaluated within the request context.
        
        ## Application information
        
        The `PrometheusMetrics.info(..)` method provides a way to expose
        information as a `Gauge` metric, the application version for example.
        
        The metric is returned from the method to allow changing its value
        from the default `1`:
        
        ```python
        metrics = PrometheusMetrics(app)
        info = metrics.info('dynamic_info', 'Something dynamic')
        ...
        info.set(42.1)
        ```
        
        ## Examples
        
        See some simple examples visualized on a Grafana dashboard by running
        the demo in the [examples/sample-signals](https://github.com/rycus86/prometheus_flask_exporter/tree/master/examples/sample-signals) folder.
        
        ![Example dashboard](https://github.com/rycus86/prometheus_flask_exporter/raw/master/examples/sample-signals/dashboard.png)
        
        ## App Factory Pattern
        
        This library also supports the flask [app factory pattern](http://flask.pocoo.org/docs/1.0/patterns/appfactories/). Use the `init_app` method to attach the library to one or more application objects. Note, that to use this mode, you'll need to pass in `None` for the `app` in the constructor.
        
        ```python
        metrics = PrometheusMetrics(app=None, ...)
        # then later:
        metrics.init_app(app)
        ```
        
        ## Debug mode
        
        Please note, that changes being live-reloaded, when running the Flask
        app with `debug=True`, are not going to be reflected in the metrics.
        See [https://github.com/rycus86/prometheus_flask_exporter/issues/4](https://github.com/rycus86/prometheus_flask_exporter/issues/4)
        for more details.
        
        Alternatively - since version `0.5.1` - if you set the `DEBUG_METRICS`
        environment variable, you will get metrics for the latest reloaded code.
        These will be exported on the main Flask app.
        Serving the metrics on a different port is not going to work
        most probably - e.g. `PrometheusMetrics.start_http_server(..)` is not
        expected to work.
        
        ## WSGI
        
        Getting accurate metrics for WSGI apps might require a bit more setup.
        See a working sample app in the `examples` folder, and also the
        [prometheus_flask_exporter#5](https://github.com/rycus86/prometheus_flask_exporter/issues/5) issue.
        
        ### Multiprocess applications
        
        For multiprocess applications (WSGI or otherwise), you can find some
        helper classes in the `prometheus_flask_exporter.multiprocess` module.
        These provide convenience wrappers for exposing metrics in an
        environment where multiple copies of the application will run on a single host.
        
        ```python
        # an extension targeted at Gunicorn deployments
        from prometheus_flask_exporter.multiprocess import GunicornPrometheusMetrics
        
        app = Flask(__name__)
        metrics = GunicornPrometheusMetrics(app)
        
        # then in the Gunicorn config file:
        from prometheus_flask_exporter.multiprocess import GunicornPrometheusMetrics
        
        def when_ready(server):
            GunicornPrometheusMetrics.start_http_server_when_ready(8080)
        
        def child_exit(server, worker):
            GunicornPrometheusMetrics.mark_process_dead_on_child_exit(worker.pid)
        ```
        
        Also see the `GunicornInternalPrometheusMetrics` class if you want to have
        the metrics HTTP endpoint exposed internally, on the same Flask application.
        
        ```python
        # an extension targeted at Gunicorn deployments with an internal metrics endpoint
        from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
        
        app = Flask(__name__)
        metrics = GunicornInternalPrometheusMetrics(app)
        
        # then in the Gunicorn config file:
        from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
        
        def child_exit(server, worker):
            GunicornInternalPrometheusMetrics.mark_process_dead_on_child_exit(worker.pid)
        ```
        
        There's a small wrapper available for [Gunicorn](https://gunicorn.org/) and
        [uWSGI](https://uwsgi-docs.readthedocs.io/en/latest/index.html), for everything
        else you can extend the `prometheus_flask_exporter.multiprocess.MultiprocessPrometheusMetrics` class
        and implement the `should_start_http_server` method at least.
        
        ```python
        from prometheus_flask_exporter.multiprocess import MultiprocessPrometheusMetrics
        
        class MyMultiprocessMetrics(MultiprocessPrometheusMetrics):
            def should_start_http_server(self):
                return this_worker() == primary_worker()
        ```
        
        This should return `True` on one process only, and the underlying
        [Prometheus client library](https://github.com/prometheus/client_python)
        will collect the metrics for all the forked children or siblings.
        
        __Note:__ this needs the `prometheus_multiproc_dir` environment variable
        to point to a valid, writable directory.
        
        You'll also have to call the `metrics.start_http_server()` function
        explicitly somewhere, and the `should_start_http_server` takes care of
        only starting it once.
        The [examples](https://github.com/rycus86/prometheus_flask_exporter/tree/master/examples) folder
        has some working examples on this.
        
        Please also note, that the Prometheus client library does not collect process level
        metrics, like memory, CPU and Python GC stats when multiprocessing is enabled.
        See the [prometheus_flask_exporter#18](https://github.com/rycus86/prometheus_flask_exporter/issues/18)
        issue for some more context and details.
        
        A final caveat is that the metrics HTTP server will listen on __any__ paths
        on the given HTTP port, not only on `/metrics`, and it is not implemented
        at the moment to be able to change this.
        
        ## License
        
        MIT
        
Keywords: prometheus,flask,monitoring,exporter
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: Topic :: System :: Monitoring
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
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
Description-Content-Type: text/markdown
