Skip to main content

Generic set of metrics for Python applications.

Project description

metrics-python

Generic set of metrics for Python applications.

We collect metrics utils in this package to hopefully make a generic package we can use in other projects in the future.

Labels

Common labels like app, env, cluster, component, role, etc. is added to the metrics using the scrape config. Adding these metrics is not a responsibility we have in the metrics-python package.

Application info

Some properties from the application is not added as metric labels by default by the scrape config. One example is the application version. metrics-python has a util to expose labels like this to Prometheus.

from metrics_python.generics.info import expose_application_info

expose_application_info(version="your-application-version")

Django

Cache

Cache metrics can be observed by adding patch_caching() to your settings file.

from metrics_python.django.cache import patch_caching

patch_caching()

Middleware

The execution of middlewares can be observed by adding patch_middlewares() to your settings file.

from metrics_python.django.middleware import patch_middlewares

patch_middlewares()

Signals

The execution of signals can be observed by adding patch_signals() to your settings file.

from metrics_python.django.signals import patch_signals

patch_signals()

Query count and duration in views

Database query count, duration, and duplicate queries can be observed by adding the QueryCountMiddleware. Add the middleware as early as possible in the list of middlewares to observe queries executed by other middlewares.

MIDDLEWARE = [
    ...
    "metrics_python.django.middleware.QueryCountMiddleware",
]

Query count and duration in Celery tasks

Database metrics can also be observed in Celery. Execute setup_celery_database_metrics bellow setup_celery_metrics, look into the Celery section of this document for more information.

from metrics_python.django.celery import setup_celery_database_metrics

setup_celery_database_metrics()

Postgres database connection metrics

The get_new_connection method in the PostgreSQL database connection engine can be observed by using a custom connection engine from metrics-python.

DATABASES = {
    "default": {
        "ENGINE": 'metrics_python.django.postgres_engine',
        ...
    }
}

Celery

To setup Celery monitoring, import and execute setup_celery_metrics as early as possible in your application to connect Celery signals. This is usually done in the settings.py file in Django applications.

from metrics_python.celery import setup_celery_metrics

setup_celery_metrics()

django-api-decorator

To measure request durations to views served by django-api-decorator, add the DjangoAPIDecoratorMetricsMiddleware.

MIDDLEWARE = [
    ...
    "metrics_python.django_api_decorator.DjangoAPIDecoratorMetricsMiddleware",
]

GraphQL

Strawberry

The Prometheus extension needs to be added to the schema to instrument GraphQL operations.

import strawberry
from metrics_python.graphql.strawberry import PrometheusExtension

schema = strawberry.Schema(
    Query,
    extensions=[
        PrometheusExtension,
    ],
)

Graphene

metrics-python has a Graphene middleware to instrument GraphQL operations. Add the middleware to Graphene by changing the GRAPHENE config in settings.py.

GRAPHENE = {
    ...
    "MIDDLEWARE": ["metrics_python.graphql.graphene.MetricsMiddleware"],
}

Gunicorn

To setup Gunicorn monitoring, add the Prometheus logger (to measure request durations) and add the worker state signals to the gunicorn config.

from metrics_python.generics.workers import export_worker_busy_state

logger_class = "metrics_python.gunicorn.Prometheus"

def pre_request(worker: Any, req: Any) -> None:
    export_worker_busy_state(worker_type="gunicorn", busy=True)


def post_request(worker: Any, req: Any, environ: Any, resp: Any) -> None:
    export_worker_busy_state(worker_type="gunicorn", busy=False)


def post_fork(server: Any, worker: Any) -> None:
    export_worker_busy_state(worker_type="gunicorn", busy=False)

Release new version

We use release-please from Google to relese new versions, this is done automatically.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metrics_python-0.0.20.tar.gz (52.0 kB view details)

Uploaded Source

Built Distribution

metrics_python-0.0.20-py3-none-any.whl (34.7 kB view details)

Uploaded Python 3

File details

Details for the file metrics_python-0.0.20.tar.gz.

File metadata

  • Download URL: metrics_python-0.0.20.tar.gz
  • Upload date:
  • Size: 52.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.1 CPython/3.10.12 Linux/6.5.0-1023-azure

File hashes

Hashes for metrics_python-0.0.20.tar.gz
Algorithm Hash digest
SHA256 e30313b0c211978b9ec17b2bb10b270fe4d0b824b51942683aa9552fa8aea73f
MD5 f5830c271c652d85fa219533f0b34657
BLAKE2b-256 2d35cca2b8c9fc071f479b105a0626d3d073d98e3e8dea131c67d3a7a5cbf37b

See more details on using hashes here.

File details

Details for the file metrics_python-0.0.20-py3-none-any.whl.

File metadata

  • Download URL: metrics_python-0.0.20-py3-none-any.whl
  • Upload date:
  • Size: 34.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.7.1 CPython/3.10.12 Linux/6.5.0-1023-azure

File hashes

Hashes for metrics_python-0.0.20-py3-none-any.whl
Algorithm Hash digest
SHA256 2a1614d37b840a63e5cc4aaf3227173285cee1ac07fab894023f6c7baa84b956
MD5 9677fa40c33a7223b7d67889fa6abe77
BLAKE2b-256 5f00e46db372f76e3ea7e59db195911a8226e88bdf10c06c4b2ade604c0fa252

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page