Using Dectate

Introduction

Dectate is a configuration system that can help you construct Python frameworks. A framework needs to record some information about the functions and classes that the user supplies. We call this process configuration.

Imagine for instance a framework that supports a certain kind of plugins. The user registers each plugin with a decorator:

from framework import plugin

@plugin(name="foo")
def foo_plugin(...):
   ...

Here the framework registers as a plugin the function foo_plugin under the name foo.

You can implement the plugin decorator as follows:

plugins = {}

class plugin(name):
    def __init__(self, name):
        self.name = name

    def __call__(self, f):
       plugins[self.name] = f

In the user application the user makes sure to import all modules that use the plugin decorator. As a result, the plugins dict contains the names as keys and the functions as values. Your framework can then use this information to do whatever you need to do.

There are a lot of examples of code configuration in frameworks. In a web framework for instance the user can declare routes and assemble middleware.

You may be okay constructing a framework with the simple decorator technique described above. But advanced frameworks need a lot more that the basic decorator system described above cannot offer. You may for instance want to allow the user to reuse configuration, override it, do more advanced error checking, and execute configuration in a particular order.

Dectate supports such advanced use cases. It was extracted from the Morepath web framework.

Features

Here are some features of Dectate:

• Decorator-based configuration – users declare things by using Python decorators on functions and classes: we call these decorators directives, which issue configuration actions.
• Dectate detects conflicts between configuration actions in user code and reports what pieces of code are in conflict.
• Users can easily reuse and extend configuration: it’s just Python class inheritance.
• Users can easily override configurations in subclasses.
• You can compose configuration actions from other, simpler ones.
• You can control the order in which configuration actions are executed. This is unrelated to where the user uses the directives in code. You do this by declaring dependencies between types of configuration actions, and by grouping configuration actions together.
• You can declare exactly what objects are used by a type of configuration action to register the configuration – different types of actions can use different registries.
• Unlike normal decorators, configuration actions aren’t performed immediately when a module is imported. Instead configuration actions are executed only when the user explicitly commits the configuration. This way, all configuration actions are known when they are performed.
• Dectate-based decorators always return the function or class object that is decorated unchanged, which makes the code more predictable for a Python programmer – the user can use the decorated function or class directly in their Python code, just like any other.
• Dectate-based configuration systems are themselves easily extensible with new directives and registries.
• Dectate-based configuration systems can be queried. Dectate also provides the infrastructure to easily construct command-line tools for querying configuration.

Actions

In Dectate, the simple plugins example above looks like this:

import dectate

class PluginAction(dectate.Action):
    config = {
       'plugins': dict
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, plugins):
        return self.name

    def perform(self, obj, plugins):
        plugins[self.name] = obj

We have formulated a configuration action that affects a plugins dict.

App classes

Configuration in Dectate is associated with special classes which derive from dectate.App. We also associate the action with it as a directive:

class PluginApp(dectate.App):
    plugin = dectate.directive(PluginAction)

Let’s use it now:

@PluginApp.plugin('a')
def f():
    pass # do something interesting

@PluginApp.plugin('b')
def g():
    pass # something else interesting

We have registered the function f on PluginApp. The name argument is 'a'. We’ve registered g under 'b'.

We can now commit the configuration for PluginApp:

dectate.commit(PluginApp)

Once the commit has successfully completed, we can take a look at the configuration:

>>> sorted(PluginApp.config.plugins.items())
[('a', <function f at ...>), ('b', <function g at ...>)]

What are the changes between this and the simple plugins example?

The main difference is that the plugin decorator is associated with a class and so is the resulting configuration, which gets stored as the plugins attribute of dectate.App.config. The other difference is that we provide an identifier method in the action definition. These differences support configuration reuse, conflicts, extension, overrides and isolation.

Reuse

You can reuse configuration by simply subclassing PluginApp:

class SubApp(PluginApp):
   pass

We commit both classes:

dectate.commit(PluginApp, SubApp)

SubClass now contains all the configuration declared for PluginApp:

>>> sorted(SubApp.config.plugins.items())
[('a', <function f at ...>), ('b', <function g at ...>)]

So class inheritance lets us reuse configuration, which allows extension and overrides, which we discuss below.

Conflicts

Consider this example:

class ConflictingApp(PluginApp):
    pass

@ConflictingApp.plugin('foo')
def f():
    pass

@ConflictingApp.plugin('foo')
def g():
    pass

Which function should be registered for foo, f or g? We should refuse to guess and instead raise an error that the configuration is in conflict. This is exactly what Dectate does:

>>> dectate.commit(ConflictingApp)
Traceback (most recent call last):
  ...
ConflictError: Conflict between:
 File "...", line 4
   @ConflictingApp.plugin('foo')
 File "...", line 8
   @ConflictingApp.plugin('foo')

As you can see, Dectate reports the lines in which the conflicting configurations occurs.

How does Dectate know that these configurations are in conflict? This is what the identifier method in our action definition did:

def identifier(self, plugins):
    return self.name

We say here that the configuration is uniquely identified by its name attribute. If two configurations exist with the same name, the configuration is considered to be in conflict.

Extension

When you subclass configuration, you can also extend SubApp with additional configuration actions:

@SubApp.plugin('c')
def h():
    pass # do something interesting

dectate.commit(PluginApp, SubApp)

SubApp now has the additional plugin c:

>>> sorted(SubApp.config.plugins.items())
[('a', <function f at ...>), ('b', <function g at ...>), ('c', <function h at ...>)]

But PluginApp is unaffected:

>>> sorted(PluginApp.config.plugins.items())
[('a', <function f at ...>), ('b', <function g at ...>)]

Overrides

What if you wanted to override a piece of configuration? You can do this in SubApp by simply reusing the same name:

@SubApp.plugin('a')
def x():
    pass

dectate.commit(PluginApp, SubApp)

In SubApp we now have changed the configuration for a to register the function x instead of f. If we had done this for MyApp this would have been a conflict, but doing so in a subclass lets you override configuration instead:

>>> sorted(SubApp.config.plugins.items())
[('a', <function x at ...>), ('b', <function g at ...>), ('c', <function h at ...>)]

But PluginApp still uses f:

>>> sorted(PluginApp.config.plugins.items())
[('a', <function f at ...>), ('b', <function g at ...>)]

Isolation

We have already seen in the inheritance and override examples that PluginApp is isolated from configuration extension and overrides done for SubApp. We can in fact entirely isolate configuration from each other.

We first set up a new base class with a directive, independently from everything before:

class PluginAction2(dectate.Action):
    config = {
       'plugins': dict
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, plugins):
        return self.name

    def perform(self, obj, plugins):
        plugins[self.name] = obj

class BaseApp(dectate.App):
    plugin = dectate.directive(PluginAction2)

We don’t set up any configuration for BaseApp; it’s intended to be part of our framework. Now we create two subclasses:

class OneApp(BaseApp):
    pass

class TwoApp(BaseApp):
    pass

As you can see OneApp and TwoApp are completely isolated from each other; the only thing they share is a common BaseApp.

We register a plugin for OneApp:

@OneApp.plugin('a')
def f():
    pass

This won’t affect TwoApp in any way:

dectate.commit(OneApp, TwoApp)

>>> sorted(OneApp.config.plugins.items())
[('a', <function f at ...>)]
>>> sorted(TwoApp.config.plugins.items())
[]

OneApp and TwoApp are isolated, so configurations are independent, and cannot conflict or override.

The Anatomy of an Action

Let’s consider the plugin action in detail:

class PluginAction(dectate.Action):
    config = {
       'plugins': dict
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, plugins):
        return self.name

    def perform(self, obj, plugins):
        plugins[self.name] = obj

What is going on here?

• We implement a custom class called PluginAction that inherits from dectate.Action.
• config (dectate.Action.config) specifies that this directive has a configuration effect on plugins. We declare that plugins is created using the dict factory, so our registry is a plain dictionary. You provide any factory function you like here.
• __init__ specifies the parameters the directive should take and how to store them on the action object. You can use default parameters and such, but otherwise __init__ should be very simple and not do any registration or validation. That logic should be in perform.
• identifier (dectate.Action.identifier()) takes the configuration objects specified by config as keyword arguments. It returns an immutable that is unique for this action. This is used to detect conflicts and determine how configurations override each other.
• perform (dectate.Action.perform()) takes obj, which is the function or class that the decorator is used on, and the arguments specified in config. It should use obj and the information on self to configure the configuration objects. In this case we store obj under the key self.name in the plugins dict.

We then associate the action with a class as a directive:

class PluginApp(dectate.App):
    plugin = dectate.directive(PluginAction)

Once we have declared the directive for our framework we can tell programmers to use it.

Directives have absolutely no effect until commit is called, which we do with dectate.commit. This performs the actions and we can then find the result PluginApp.config (dectate.App.config).

The results are in PluginApp.config.plugins as we set this up with config in our PluginAction.

Depends

In some cases you want to make sure that one type of directive has been executed before the other – the configuration of the second type of directive depends on the former. You can make sure this happens by using the depends (dectate.Action.depends) class attribute.

First we set up a FooAction that registers into a foos dict:

class FooAction(dectate.Action):
    config = {
       'foos': dict
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, foos):
        return self.name

    def perform(self, obj, foos):
        foos[self.name] = obj

Now we create a BarAction directive that depends on FooAction and uses information in the foos dict:

class BarAction(dectate.Action):
    depends = [FooAction]

    config = {
       'foos': dict,  # also use the foos dict
       'bars': list
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, foos, bars):
        return self.name

    def perform(self, obj, foos, bars):
        in_foo = self.name in foos
        bars.append((self.name, obj, in_foo))

In order to use them we need to hook up the actions as directives onto an app class:

class DependsApp(dectate.App):
    foo = dectate.directive(FooAction)
    bar = dectate.directive(BarAction)

Using depends we have ensured that BarAction actions are performed after FooAction action, no matter what order we use them:

@DependsApp.bar('a')
def f():
    pass

@DependsApp.bar('b')
def g():
    pass

@DependsApp.foo('a')
def x():
    pass

dectate.commit(DependsApp)

We expect in_foo to be True for a but to be False for b:

>>> DependsApp.config.bars
[('a', <function f at ...>, True), ('b', <function g at ...>, False)]

config dependencies

In the example above, the items in bars depend on the items in foos and we’ve implemented this dependency in the perform of BarAction.

We can instead make the configuration object for the BarAction depend on foos. This way BarAction does not need to know about foos. You can declare a dependency between config objects with the factory_arguments attribute of the config factory. Any config object that is created in earlier dependencies of this action, or in the action itself, can be listed in factory_arguments. The key and value in factory_arguments have to match the key and value in config of that earlier action.

First we create a FooAction that sets up a foos config item as before:

class FooAction(dectate.Action):
    config = {
       'foos': dict
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, foos):
        return self.name

    def perform(self, obj, foos):
        foos[self.name] = obj

Now we create a Bar class that also depends on the foos dict by listing it in factory_arguments:

class Bar(object):
    factory_arguments = {
        'foos': dict
    }

    def __init__(self, foos):
        self.foos = foos
        self.l = []

    def add(self, name, obj):
        in_foo = name in self.foos
        self.l.append((name, obj, in_foo))

We create a BarAction that depends on the FooAction (so that foos is created first) and that uses the Bar factory:

class BarAction(dectate.Action):
   depends = [FooAction]

   config = {
      'bar': Bar
   }

   def __init__(self, name):
       self.name = name

   def identifier(self, bar):
       return self.name

   def perform(self, obj, bar):
       bar.add(self.name, obj)

And we set them up as directives:

class ConfigDependsApp(dectate.App):
    foo = dectate.directive(FooAction)
    bar = dectate.directive(BarAction)

When we use our directives:

@ConfigDependsApp.bar('a')
def f():
    pass

@ConfigDependsApp.bar('b')
def g():
    pass

@ConfigDependsApp.foo('a')
def x():
    pass

dectate.commit(ConfigDependsApp)

we get the same result as before:

>>> ConfigDependsApp.config.bar.l
[('a', <function f at ...>, True), ('b', <function g at ...>, False)]

app_class_arg

In some cases what you want to configure is not on in the config object (app_class.config), but is associated with the app class in another way. You can get the app class passed in as an argument to dectate.Action.perform(), dectate.Action.identifier(), and so on by setting the special app_class_arg class attribute:

class PluginAction(dectate.Action):
    config = {
       'plugins': dict
    }
    app_class_arg = True

    def __init__(self, name):
        self.name = name

    def identifier(self, plugins, app_class):
        return self.name

    def perform(self, obj, plugins, app_class):
        plugins[self.name] = obj
        app_class.touched = True

class MyApp(dectate.App):
   plugin_with_app_class = dectate.directive(PluginAction)

When we now perform this directive:

@MyApp.plugin_with_app_class('a')
def f():
    pass # do something interesting

dectate.commit(MyApp)

We can see the app class was indeed affected:

>>> MyApp.touched
True

You can also use app_class_arg on a factory so that Dectate passes in the app_class factory argument.

before and after

It can be useful to do some additional setup just before all actions of a certain type are performed, or just afterwards. You can do this using before (dectate.Action.before()) and after (dectate.Action.after()) static methods on the Action class:

class FooAction(dectate.Action):
    config = {
       'foos': list
    }
    def __init__(self, name):
        self.name = name

    @staticmethod
    def before(foos):
        print("before:", foos)

    @staticmethod
    def after(foos):
        print("after:", foos)

    def identifier(self, foos):
        return self.name

    def perform(self, obj, foos):
        foos.append((self.name, obj))

class BeforeAfterApp(dectate.App):
    foo = dectate.directive(FooAction)

@BeforeAfterApp.foo('a')
def f():
    pass

@BeforeAfterApp.foo('b')
def g():
    pass

This executes before just before a and b are configured, and then executes after:

>>> dectate.commit(BeforeAfterApp)
before: []
after: [('a', <function f at ...>), ('b', <function g at ...>)]

grouping actions

Different actions normally don’t conflict with each other. It can be useful to group different actions together in a group so that they do affect each other. You can do this with the group_class (dectate.Action.group_class) class attribute. Grouped classes share their config and their before and after methods.

class FooAction(dectate.Action):
    config = {
       'foos': list
    }
    def __init__(self, name):
        self.name = name

    def identifier(self, foos):
        return self.name

    def perform(self, obj, foos):
        foos.append((self.name, obj))

We now create a BarAction that groups with FooAction:

class BarAction(dectate.Action):
    group_class = FooAction

    def __init__(self, name):
        self.name = name

    def identifier(self, foos):
        return self.name

    def perform(self, obj, foos):
        foos.append((self.name, obj))

class GroupApp(dectate.App):
    foo = dectate.directive(FooAction)
    bar = dectate.directive(BarAction)

It reuses the config from FooAction. This means that foo and bar can be in conflict:

@GroupApp.foo('a')
def f():
    pass

@GroupApp.bar('a')
def g():
    pass

>>> dectate.commit(GroupApp)
Traceback (most recent call last):
  ...
ConflictError: Conflict between:
  File "...", line 8
    @GroupApp.bar('a')

Additional discriminators

In some cases an action should conflict with multiple other actions all at once. You can take care of this with the discriminators (dectate.Action.discriminators()) method on your action:

class FooAction(dectate.Action):
    config = {
       'foos': dict
    }
    def __init__(self, name, extras):
        self.name = name
        self.extras = extras

    def identifier(self, foos):
        return self.name

    def discriminators(self, foos):
        return self.extras

    def perform(self, obj, foos):
        foos[self.name] = obj


class DiscriminatorsApp(dectate.App):
    foo = dectate.directive(FooAction)

An action now conflicts with an action of the same name and with any action that is in the extra list:

# example
@DiscriminatorsApp.foo('a', ['b', 'c'])
def f():
    pass

@DiscriminatorsApp.foo('b', [])
def g():
    pass

And then:

>>> dectate.commit(DiscriminatorsApp)
Traceback (most recent call last):
  ...
ConflictError: Conflict between:
  File "...", line 2:
    @DiscriminatorsApp.foo('a', ['b', 'c'])
  File "...", line 6
    @DiscriminatorsApp.foo('b', [])

Composite actions

When you can define an action entirely in terms of other actions, you can subclass dectate.Composite.

First we define a normal SubAction to use in the composite action later:

class SubAction(dectate.Action):
    config = {
        'my': list
    }

    def __init__(self, name):
        self.name = name

    def identifier(self, my):
        return self.name

    def perform(self, obj, my):
        my.append((self.name, obj))

Now we can define a special dectate.Composite subclass that uses SubAction in an actions (dectate.Composite.actions()) method:

class CompositeAction(dectate.Composite):
    def __init__(self, names):
        self.names = names

    def actions(self, obj):
        return [(SubAction(name), obj) for name in self.names]

class CompositeApp(dectate.App):
    _sub = dectate.directive(SubAction)
    composite = dectate.directive(CompositeAction)

Note that even though _sub is not intended to be a public part of the API we still need to include it in our dectate.App subclass, as Dectate does need to know it exists.

We can now use it:

@CompositeApp.composite(['a', 'b', 'c'])
def f():
    pass

dectate.commit(CompositeApp)

And SubAction is performed three times as a result:

>>> CompositeApp.config.my
[('a', <function f at ...>), ('b', <function f at ...>), ('c', <function f at ...>)]

with statement

Sometimes you want to issue a lot of similar actions at once. You can use the with statement to do so with less repetition:

class FooAction(dectate.Action):
    config = {
        'my': list
    }

    def __init__(self, a, b):
        self.a = a
        self.b = b

    def identifier(self, my):
        return (self.a, self.b)

    def perform(self, obj, my):
        my.append((self.a, self.b, obj))


class WithApp(dectate.App):
    foo = dectate.directive(FooAction)

Instead of this:

class VerboseWithApp(WithApp):
    pass

@VerboseWithApp.foo('a', 'x')
def f():
   pass

@VerboseWithApp.foo('a', 'y')
def g():
   pass

@VerboseWithApp.foo('a', 'z')
def h():
   pass

You can instead write:

class SuccinctWithApp(WithApp):
    pass

with SuccinctWithApp.foo('a') as foo:
    @foo('x')
    def f():
        pass

    @foo('y')
    def g():
        pass

    @foo('z')
    def h():
        pass

And this has the same configuration effect:

>>> dectate.commit(VerboseWithApp, SuccinctWithApp)
>>> VerboseWithApp.config.my
[('a', 'x', <function f at ...>), ('a', 'y', <function g at ...>), ('a', 'z', <function h at ...>)]
>>> SuccinctWithApp.config.my
[('a', 'x', <function f at ...>), ('a', 'y', <function g at ...>), ('a', 'z', <function h at ...>)]

importing recursively

When you use dectate-based decorators across a package, it can be useful to just import all modules in it at once. This way the user cannot forget to import a module with decorators in it.

Dectate itself does not offer this facility, but you can use the importscan library to do this recursive import. Simply do something like:

import my_package

importscan.scan(my_package, ignore=['.tests'])

This imports every module in my_package, except for the tests sub package.

logging

Dectate logs information about the performed actions as debug log messages. By default this goes to the dectate.directive.<directive_name> log. You can use the standard Python logging module function to make this information go to a log file.

If you want to override the name of the log you can set logger_name (dectate.App.logger_name) on the app class:

class MorepathApp(dectate.App):
   logger_name = 'morepath.directive'

querying

Dectate keeps a database of committed actions that can be queried by using dectate.Query.

Here is an example of a query for all the plugin actions on PluginApp:

q = dectate.Query('plugin')

We can now run the query:

>>> list(q(PluginApp))
[(<PluginAction ...>, <function f ...>),
 (<PluginAction ...>, <function g ...>)]

We can also filter the query for attributes of the action:

>>> list(q.filter(name='a')(PluginApp))
[(<PluginAction object ...>, <function f ...>)]

Sometimes the attribute on the action is not the same as the name you may want to use in the filter. You can use dectate.Action.filter_name to create a mapping to the correct attribute.

By default the filter does an equality comparison. You can define your own comparison function for an attribute using dectate.Action.filter_compare.

If you want to allow a query on a Composite action you need to give it some help by defining xs:attr:dectate.Composite.query_classes.

query tool

Dectate also includes a command-line tool that lets you issue queries. You need to configure it for your application. For instance, in the module main.py of your project:

import dectate

def query_tool():
    # make sure to scan or import everything needed at this point
    dectate.query_tool(SomeApp.commit())

In this function you should commit any dectate.App subclasses your application normally uses, and then provide an iterable of them to dectate.query_tool(). These are the applications that are queried by default if you don’t specify --app. We do it all in one here as we can get the app class that were committed from the result of App.commit().

Then in setup.py of your project:

entry_points={
    'console_scripts': [
        'decq = query.main:query_tool',
    ]
},

When you re-install this project you have a command-line tool called decq that lets you issues queries. For instance, this query returns all uses of directive foo in the apps you provided to query_tool:

$ decq foo
App: <class 'query.a.App'>
  File ".../query/b.py", line 4
  @App.foo(name='alpha')

  File ".../query/b.py", line 9
  @App.foo(name='beta')

  File ".../query/b.py", line 14
  @App.foo(name='gamma')

  File ".../query/c.py", line 4
  @App.foo(name='lah')

App: <class 'query.a.Other'>
  File ".../query/b.py", line 19
  @Other.foo(name='alpha')

And this query filters by name:

$ decq foo name=alpha
App: <class 'query.a.App'>
  File ".../query/b.py", line 4
  @App.foo(name='alpha')

App: <class 'query.a.Other'>
  File ".../query/b.py", line 19
  @Other.foo(name='alpha')

You can also explicit provide the app classes to query with the --app option; the default list of app classes is ignored in this case:

$ bin/decq --app query.a.App foo name=alpha
App: <class 'query.a.App'>
  File ".../query/b.py", line 4
  @App.foo(name='alpha')

You need to give --app a dotted name of the dectate.App subclass to query. You can repeat the --app option to query multiple apps.

Not all things you would wish to query on are string attributes. You can provide a conversion function that takes the string input and converts it to the underlying object you want to compare to using dectate.Action.filter_convert.

A working example is in scenarios/query of the Dectate project.

Sphinx Extension

If you use Sphinx to document your project and you use the sphinx.ext.autodoc extension to document your API, you need to install a Sphinx extension so that directives are documented properly. In your Sphinx conf.py add 'dectate.sphinxext' to the extensions list.

__main__ and conflicts

Import-time side effects are evil

This scenario is based on the one described in Application programmers don’t control the module-scope codepath in the Pyramid design defense document. If you’re curious, look under scenarios/main_module in the Dectate project for a Dectate version.

Dectate makes a different compromise than Venusian – it reports an error if a directive is executed because of a double import, so it won’t get you into trouble. But since Dectate’s directives cause registrations to happen immediately (but defer configuration), you can dynamically generate them inside Python function, which won’t work with with Venusian.

In certain scenarios where you run your code like this:

$ python app.py

and you use __name__ == '__main__' to determine whether the module should run:

if __name__ == '__main__':
    import another_module
    dectate.commit(App)

you might get a ConflictError from Dectate that looks somewhat like this:

Traceback (most recent call last):
 ...
dectate.error.ConflictError: Conflict between:
  File "/path/to/app.py", line 6
    @App.foo(name='a')
  File "app.py", line 6
    @App.foo(name='a')

The same line shows up on both sides of the configuration conflict, but the path is absolute on one side and relative on the other.

This happens because in some scenarios involving __main__, Python imports a module twice (more about this). Dectate refuses to operate in this case until you change your imports so that this doesn’t happen anymore.

How to avoid this scenario? If you use setuptools automatic script creation this problem is avoided entirely.

Fooling Dectate after all

It is possible to fool Dectate into accepting a double import without conflicts, but you’d need to work hard. You need to use a global variable that gets modified during import time and then use it as a directive argument. If you want to dynamically generate directives then don’t do that in module-scope – do it in a function.

If you want to use the if __name__ == '__main__' system, keep your main module tiny and just import the main function you want to run from elsewhere.

So, Dectate warns you if you do it wrong, so don’t worry about it.