When any uncaught exceptions occur, Honeybadger will send the data off to the Honeybadger server specified in your environment.
Tested with Python 3.5 - 3.8 against Django latest and LTS releases (1.11.29, 2.2.11, 3.0.4) as well as Flask 1.0 and 1.1.
Honeybadger for Python works out of the box with Django with only a few configuration options required. The following is a basic setup - more advanced setup will be described later.
Install honeybadger with pip.
$ pip install honeybadger
Note: Honeybadger does not report errors in development and test
environments by default. To enable reporting in development environments, see
the force_report_data
setting.
In a Django application, add the Honeybadger Django middleware to the top of your MIDDLEWARE
config variable:
MIDDLEWARE = [
'honeybadger.contrib.DjangoHoneybadgerMiddleware',
...
]
It's important that the Honeybadger middleware is at the top, so that it wraps the entire request process, including all other middlewares.
You'll also need to add a new HONEYBADGER
config variable to your settings.py
to specify your API key:
HONEYBADGER = {
'API_KEY': 'myapikey'
}
To prevent honeybadger from notifying you on certain errors (like an Http404
exception), you can add the full path to the exception you would like to ignore to HONEYBADGER_IGNORED_EXCEPTIONS
in settings:
HONEYBADGER_IGNORED_EXCEPTIONS = ('django.http.response.Http404',)
A Flask extension is available for initializing and configuring Honeybadger: honeybadger.contrib.flask.FlaskHoneybadger
. The extension adds the following information to reported exceptions:
- url: The URL the request was sent to.
- component: The module that the view is defined at. If the view is a class-based view, then the name of the class is also added.
- action: The name of the function called. If the action is defined within a blueprint, then the action name will have the name of the blueprint prefixed.
- params: A dictionary containing query parameters and form data. If a variable is defined in both, then the form data are stored. Params are filtered (see Configuration).
- session: Session data.
- cgi_data: Request headers, filtered (see Configuration) and request method.
In addition, the FlaskHoneybadger
extension:
- Configures Honeybadger using Flask's configuration object.
- (Optionally) Automatically report exceptions raised during views.
- (Optionally) Reset honeybadger context after each request.
Note that
FlaskHoneybadger
uses Flask's signals in order to detect exceptions. In order for the extension to work, you'll have to install theblinker
library as dependency.
FlaskHoneybadger
checks Flask's configuration object for automatically configuring honeybadger. In order to configure it, it checks for the
keys with same name as the environment variables in Configuration section. Note that if a value is also configured as an environment variable,
then the environment variable's value will be used.
Let's see it in action with an example:
from flask import Flask, jsonify, request
from honeybadger.contrib import FlaskHoneybadger
app = Flask(__name__)
app.config['HONEYBADGER_ENVIRONMENT'] = 'development'
app.config['HONEYBADGER_API_KEY'] = '<your key>'
app.config['HONEYBADGER_PARAMS_FILTERS'] = 'password, secret, credit-card'
FlaskHoneybadger(app, report_exceptions=True)
@app.route('/')
def index():
a = int(request.args.get('a'))
b = int(request.args.get('b'))
print('Dividing two numbers {} {}'.format(a, b))
return jsonify({'result': a / b})
[...]
The code above will:
- Initialize honeybadger using Flask's configuration.
- Listen for exceptions.
- Log unhandled exceptions to Honeybadger.
- It will also add
url
,component
,action
,params
,cgi_data
and context (as generated by context generators) to all errors send usinghoneybadger.notify()
.
FlaskHoneybadger
will catch exception raised from a view. Note that calls toabort
method result in an exception being raised. If you don't want this behavior, you can setreport_exceptions
to False and just callhoneybadger.notify
inside your exception handler.
You can also check more examples under directory examples
.
The following conventions are used for component names:
- When using view functions, the name of the component will be <module name>#<view name>
- For class-based views, the name of the component will be <module name>#<class name>
- When using blueprints, the name of the component will be <module name>#<blueprint name>.<view name>
AWS Lambda environments are auto detected by Honeybadger with no additional configuration. Here's an example lambda function with Honeybadger:
from honeybadger import honeybadger
honeybadger.configure(api_key='myapikey')
def lambda_handler(event, context):
"""
A buggy lambda function that tries to perform a zero division
"""
a = 1
b = 0
return (a/b) #This will be reported
A generic ASGI middleware plugin is available for initializing and configuring Honeybadger: honeybadger.contrib.asgi
.
The general pattern for these cases is wrapping your ASGI application with a middleware:
from honeybadger import contrib
asgi_application = someASGIApplication()
asgi_application = contrib.ASGIHoneybadger(asgi_application)
You can pass configuration parameters (or additional configuration parameters) as keyword arguments at plugin's initialization:
from honeybadger import contrib
asgi_application = someASGIApplication()
asgi_application = contrib.ASGIHoneybadger(asgi_application, api_key="<your-api-key>", params_filters=["sensible_data"])
Or you may want to initialize Honeybadger before your application, and then just registering the plugin/middleware:
from honeybadger import honeybadger, contrib
honeybadger.configure(api_key='<your-api-key>')
some_possibly_failing_function() # you can track errors happening before your plugin initialization.
asgi_application = someASGIApplication()
asgi_application = contrib.ASGIHoneybadger(asgi_application)
FastAPI is based on Starlette, an ASGI application. You use Honeybadger's ASGI middleware on these types of applications.
from fastapi import FastAPI
from honeybadger import contrib
app = FastAPI()
app.add_middleware(contrib.ASGIHoneybadger)
You can pass additional keyword paramters, too:
from fastapi import FastAPI
from honeybadger import honeybadger, contrib
honeybadger.configure(api_key="<your-api-key>")
app = FastAPI()
app.add_middleware(contrib.ASGIHoneybadger, params_filters=["dont-include-this"])
Consuming the request body in an ASGI application's middleware is problematic and discouraged. This is the reason why request body data won't be sent to the web UI.
FastAPI allows overriding the logic used by the Request
and APIRoute
classes, by using custom APIRoute
classes. This gives more control over the request body, and makes it possible to send request body data along with honeybadger notifications.
A custom API Route is available at honeybadger.contrib.fastapi
:
from fastapi import FastAPI, APIRouter
from honeybadger import honeybadger
from honeybadger.contrib.fastapi import HoneybadgerRoute
honeybadger.configure(api_key="<your-api-key>")
app = FastAPI()
app.router.route_class = HoneybadgerRoute
router = APIRouter(route_class=HoneybadgerRoute)
You can configure Honeybadger to work with Starlette just like in any other ASGI framework.
from starlette.applications import Starlette
from starlette.middleware import Middleware
from honeybadger import contrib
app = Starlette()
app.add_middleware(contrib.ASGIHoneybadger)
Django and Flask are the only explicitly supported frameworks at the moment. For other frameworks (tornado, web2py, etc.) or a plain Python script, simply import honeybadger and configure it with your API key. Honeybadger uses a global exception hook to automatically report any uncaught exceptions.
from honeybadger import honeybadger
honeybadger.configure(api_key='myapikey')
raise Exception, "This will get reported!"
That's it! For additional configuration options, keep reading.
Note: By default, honeybadger reports errors in separate threads. For platforms that disallows threading (such as serving a flask/django app with uwsgi and disabling threading), Honeybadger will fail to report errors. You can either enable threading if you have the option, or set force_sync
config option to True
. This causes Honeybadger to report errors in a single thread.
By default, Honeybadger uses the logging.NullHandler
for logging so it doesn't make any assumptions about your logging setup. In Django, add a honeybadger
section to your LOGGING
config to enable Honeybadger logging. For example:
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'handlers': {
'file': {
'level': 'DEBUG',
'class': 'logging.FileHandler',
'filename': '/path/to/django/debug.log',
},
},
'loggers': {
'honeybadger': {
'handlers': ['file'],
'level': 'DEBUG',
'propagate': True,
},
},
}
For other frameworks or a plain Python script, you can use logging.dictConfig
or explicitly configure it like so:
import logging
logging.getLogger('honeybadger').addHandler(logging.StreamHandler())
To set configuration options, use the honeybadger.configure
method, like so:
honeybadger.configure(api_key='your api key', environment='production')
All of Honeybadger's configuration options can also be set via environment variables with the HONEYBADGER
prefix (12-factor style). For example, the api_key
option can be set via the HONEYBADGER_API_KEY
environment variable.
The following options are available to you:
Name | Type | Default | Example | Environment variable |
---|---|---|---|---|
api_key | str |
"" |
"badgers" |
HONEYBADGER_API_KEY |
project_root | str |
The current working directory | "/path/to/project" |
HONEYBADGER_PROJECT_ROOT |
environment | str |
"production" |
"staging" |
HONEYBADGER_ENVIRONMENT |
hostname | str |
The hostname of the current server. | "badger01" |
HONEYBADGER_HOSTNAME |
endpoint | str |
"https://api.honeybadger.io" |
"https://honeybadger.example.com/" |
HONEYBADGER_ENDPOINT |
params_filters | list |
['password', 'password_confirmation', 'credit_card'] |
['super', 'secret', 'keys'] |
HONEYBADGER_PARAMS_FILTERS |
force_report_data | bool |
False |
True |
HONEYBADGER_FORCE_REPORT_DATA |
force_sync | bool |
False |
True |
HONEYBADGER_FORCE_SYNC |
This method allows you to send additional information to the Honeybadger API to assist in debugging. This method sets global context data and is additive - eg. every time you call it, it adds to the existing set unless you call reset_context
, documented below.
from honeybadger import honeybadger
honeybadger.set_context(my_data='my_value')
This method clears the global context dictionary.
from honeybadger import honeybadger
honeybadger.reset_context()
What if you don't want to set global context data? You can use Python context managers to set case-specific contextual information.
# from a Django view
from honeybadger import honeybadger
def my_view(request):
with honeybadger.context(user_email=request.POST.get('user_email', None)):
form = UserForm(request.POST)
...
Allows you to configure honeybadger within your code. Accepts any of the above-listed configuration options as keyword arguments.
honeybadger.configure(api_key='myapikey', project_root='/home/dave/crywolf-django')
In cases where you'd like to manually send error notices to Honeybadger, this is what you're looking for. You can either pass it an exception as the first argument, or an error_class
/error_message
pair of keyword arguments. You can also pass it a custom context dictionary which will get merged with the global context.
# with an exception
mydict = dict(a=1)
try:
print mydict['b']
except KeyError, exc:
honeybadger.notify(exc, context={'foo': 'bar'})
# with custom arguments
honeybadger.notify(error_class='ValueError', error_message='Something bad happened!')
After cloning the repo, run:
python setup.py develop
To run the unit tests:
python setup.py test
If you're adding a new feature, please submit an issue as a preliminary step; that way you can be (moderately) sure that your pull request will be accepted.
- Fork it.
- Create a topic branch
git checkout -b my_branch
- Commit your changes
git commit -am "Boom"
- Push to your branch
git push origin my_branch
- Send a pull request
See https://github.com/honeybadger-io/honeybadger-python/blob/master/CHANGELOG.md
- Ensure the latest version of twine is installed with
pip install --upgrade twine wheel
- Update the version in honeybadger/version.py
- Update unreleased heading in CHANGELOG.md
- Commit changes with "Release [version]", i.e.: "Release 0.3.0" (example commit)
- Tag changes:
git tag v[version]
, i.e.:git tag v0.3.0
- Push changes to GitHub:
git push origin master --tags
- Clean out the existing dist dir with
rm -rf dist/
- Run
python setup.py bdist_wheel
which will build the python2 package in dist/ - Run
python3 setup.py bdist_wheel
which will build the python3 package in dist/ - Run
twine upload dist/*
to upload the release to PyPI
This project is MIT licensed. See the LICENSE file in this repository for details.