Venusian is a library which allows you to defer the action of decorators. Instead of taking actions when a function, method, or class decorator is executed at import time, you can defer the action until a separate "scan" phase.
This library is most useful for framework authors.
Note
The name "Venusian" is a riff on a library named Martian
(which had its genesis in the Grok
web framework), from which the idea for Venusian was stolen. Venusian is similar to Martian, but it offers less functionality, making it slightly simpler to use.
Offering a decorator that wraps a function, method, or class can be a convenience to your framework's users. But the very purpose of a decorator makes it likely to impede testability of the function or class it decorates: use of a decorator often prevents the function it decorates from being called with the originally passed arguments, or a decorator may modify the return value of the decorated function. Such modifications to behavior are "hidden" in the decorator code itself.
For example, let's suppose your framework defines a decorator function named jsonify
which can wrap a function that returns an arbitrary Python data structure and renders it to a JSON serialization:
import json
def jsonify(wrapped):
def json_wrapper(request):
result = wrapped(request)
dumped = json.dumps(result)
return dumped
return json_wrapper
Let's also suppose a user has written an application using your framework, and he has imported your jsonify decorator function, and uses it to decorate an application function:
from theframework import jsonify
@jsonify
def logged_in(request):
return {'result':'Logged in'}
As a result of an import of the module containing the logged_in
function, a few things happen:
- The user's
logged_in
function is replaced by thejson_wrapper
function. - The only reference left to the original
logged_in
function is inside the frame stack of the call to thejsonify
decorator.
This means, from the perspective of the application developer that the original logged_in
function has effectively "disappeared" when it is decorated with your jsonify
decorator. Without bothersome hackery, it can no longer be imported or retrieved by its original author.
More importantly, it also means that if the developer wants to unit test the logged_in
function, he'll need to do so only indirectly: he'll need to call the json_wrapper
wrapper decorator function and test that the json returned by the function contains the expected values. This will often imply using the json.loads
function to turn the result of the function back into a Python dictionary from the JSON representation serialized by the decorator.
If the developer is a stickler for unit testing, however, he'll want to test only the function he has actually defined, not the wrapper code implied by the decorator your framework has provided. This is the very definition of unit testing (testing a "unit" without any other integration with other code). In this case, it is also more convenient for him to be able to test the function without the decorator: he won't need to use the json.loads
function to turn the result back into a dictionary to make test assertions against. It's likely such a developer will try to find ways to get at the original function for testing purposes.
To do so, he might refactor his code to look like this:
from theframework import jsonify
@jsonify
def logged_in(request):
return _logged_in(request)
def _logged_in(request):
return {'result':'Logged in'}
Then in test code he might import only the _logged_in
function instead of the decorated logged_in
function for purposes of unit testing. In such a scenario, the concentious unit testing app developer has to define two functions for each decorated function. If you're thinking "that looks pretty tedious", you're right.
To give the intrepid tester an "out", you might be tempted as a framework author to leave a reference to the original function around somewhere that the unit tester can import and use only for testing purposes. You might modify the jsonify
decorator like so in order to do that:
import json
def jsonify(wrapped):
def json_wrapper(request):
result = wrapped(request)
dumped = json.dumps(result)
return dumped
json_wrapper.original_function = wrapped
return json_wrapper
The line json_wrapper.original_function = wrapped
is the interesting one above. It means that the application developer has a chance to grab a reference to his original function:
from myapp import logged_in
result = logged_in.original_func(None)
self.assertEqual(result['result'], 'Logged in')
That works. But it's just a little weird. Since the jsonify
decorator function has been imported by the developer from a module in your framework, the developer probably shouldn't really need to know how it works. If he needs to read its code, or understand documentation about how the decorator functions for testing purposes, your framework might be less valuable to him on some level. This is arguable, really. If you use some consistent pattern like this for all your decorators, it might be a perfectly reasonable solution.
However, what if the decorators offered by your framework were passive until activated explicitly? This is the promise of using Venusian within your decorator implementations. You may use Venusian within your decorators to associate a wrapped function, class, or method with a callback. Then you can return the originally wrapped function. Instead of your decorators being "active", the callback associated with the decorator is passive until a "scan" is initiated.
The most basic use of Venusian within a decorator implementation is demonstrated below.
import venusian
def jsonify(wrapped):
def callback(scanner, name, ob):
print 'jsonified'
venusian.attach(wrapped, callback)
return wrapped
As you can see, this decorator actually calls into venusian, but then simply returns the wrapped object. Effectively this means that this decorator is "passive" when the module is imported.
Usage of the decorator:
from theframework import jsonify
@jsonify
def logged_in(request):
return {'result':'Logged in'}
Note that when we import and use the function, the fact that it is decorated with the jsonify
decorator is immaterial. Our decorator doesn't actually change its behavior.
>>> from theapp import logged_in
>>> logged_in()
{'result':'Logged in'}
>>>
This is the intended result. During unit testing, the original function can be imported and tested despite the fact that it has been wrapped with a decorator.
However, we can cause something to happen when we invoke a scan
.
import venusian
import theapp
scanner = venusian.Scanner()
scanner.scan(theapp)
Above we've imported a module named theapp
. The logged_in
function which we decorated with our jsonify
decorator lives in this module. We've also imported the venusian
module, and we've created an instance of the venusian.Scanner
class. Once we've created the instance of venusian.Scanner
, we invoke its venusian.Scanner.scan
method, passing the theapp
module as an argument to the method.
Here's what happens as a result of invoking the venusian.Scanner.scan
method:
- Every object defined at module scope within the
theapp
Python module will be inspected to see if it has had a Venusian callback attached to it. - For every object that does have an Venusian callback attached to it, the callback is called.
We could have also passed the scan
method a Python package instead of a module. This would recursively import each module in the package (as well as any modules in subpackages), looking for callbacks.
Note
During scan, the only Python files that are processed are Python source (.py
) files. Compiled Python files (.pyc
, .pyo
files) without a corresponding source file are ignored.
In our case, because the callback we defined within the jsonify
decorator function prints jsonified
when it is invoked, which means that the word jsonified
will be printed to the console when we cause venusian.Scanner.scan
to be invoked. How is this useful? It's not! At least not yet. Let's create a more realistic example.
Let's change our jsonify
decorator to perform a more useful action when a scan is invoked by changing the body of its callback.
import venusian
def jsonify(wrapped):
def callback(scanner, name, ob):
def jsonified(request):
result = wrapped(request)
return json.dumps(result)
scanner.registry.add(name, jsonified)
venusian.attach(wrapped, callback)
return wrapped
Now if we invoke a scan, we'll get an error:
import venusian
import theapp
scanner = venusian.Scanner()
scanner.scan(theapp)
AttributeError: Scanner has no attribute 'registry'.
The venusian.Scanner
class constructor accepts any key-value pairs; for each key/value pair passed to the scanner's constructor, an attribute named after the key which points at the value is added to the scanner instance. So when you do:
import venusian
scanner = venusian.Scanner(a=1)
Thereafter, scanner.a
will equal the integer 1.
Any number of key-value pairs can be passed to a scanner. The purpose of being able to pass arbitrary key/value pairs to a scanner is to allow cooperating decorator callbacks to access these values: each callback is passed the scanner
constructed when a scan is invoked.
Let's fix our example by creating an object named registry
that we'll pass to our scanner's constructor:
import venusian
import theapp
class Registry(object):
def __init__(self):
self.registered = []
def add(self, name, ob):
self.registered.append((name, ob))
register = Register()
scanner = venusian.Scanner(registry=registry)
scanner.scan(theapp)
At this point, we have a system which, during a scan, for each object that is wrapped with a Venusian-aware decorator, a tuple will be appended to the registered
attribute of a Registry
object. The first element of the tuple will be the decorated object's name, the second element of the tuple will be a "truly" decorated object. In our case, this will be a jsonify-decorated callable.
Our framework can then use the information in the registry to decide which view function to call when a request comes in.
Venusian callbacks must accept three arguments:
scanner
This will be the instance of the scanner that has had its
scan
method invoked.
name
This is the module-level name of the object being decorated.
ob
This is the object being decorated if it's a function or an instance; if the object being decorated is a method, however, this value will be the class.
If you consider that the decorator and the scanner can cooperate, and can perform arbitrary actions together, you can probably imagine a system where a registry will be populated that informs some higher-level system (such as a web framework) about the available decorated functions.
Because an application may use two separate Venusian-using frameworks, Venusian allows for the concept of "scan categories".
The venusian.attach
function accepts an additional argument named category
.
For example:
import venusian
def jsonify(wrapped):
def callback(scanner, name, ob):
def jsonified(request):
result = wrapped(request)
return json.dumps(result)
scanner.registry.add(name, jsonified)
venusian.attach(wrapped, callback, category='myframework')
return wrapped
Note the category='myframework'
argument in the call to venusian.attach
. This tells Venusian to attach the callback to the wrapped object under the specific scan category myframework
. The default scan category is None
.
Later, during venusian.Scanner.scan
, a user can choose to activate all the decorators associated only with a particular set of scan categories by passing a categories
argument. For example:
import venusian
scanner = venusian.Scanner(a=1)
scanner.scan(theapp, categories=('myframework',))
The default categories
argument is None
, which means activate all Venusian callbacks during a scan regardless of their category.
Venusian is not really a tool that is maximally useful to an application developer. It would be a little silly to use it every time you needed a decorator. Instead, it's most useful for framework authors, in order to be able to say to their users "the frobozz decorator doesn't change the output of your function at all" in documentation. This is a lot easier than telling them how to test methods/functions/classes decorated by each individual decorator offered by your frameworks.
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