A functional Python web framework that streamlines explicit development practices while eliminating global state.
Clastic is pure-Python, tested on Python 3.7+, and documented, with tutorials.
Clastic is available on PyPI. You can install it by running this command:
easy_install clastic
(pip works, too.)
Getting up and running with Clastic is exceedingly difficult. Just try
and create a file called hello.py with the following
indecipherable runes:
from clastic import Application, render_basic
def hello(name='world'):
return 'Hello, %s!' % name
routes = [('/', hello, render_basic),
('/<name>', hello, render_basic)]
app = Application(routes)
app.serve()If you run python hello.py at the command line and visit
http://localhost:5000 in your browser, you will see the text
Hello, world!. If instead, you visit http://localhost:5000/Ben
then you will see the text Hello, Ben!. Madness.
If we add the request argument to any endpoint function, we get
access to all of the request data, including any GET or POST
parameters or cookies that may have been sent with the request.:
from clastic import Application, render_basic
def fancy(request):
result = ''
# iterate through all of the GET and POST variables
for k in request.values:
result += "Found argument '%s' with value '%s'\n" % (k, request.values[k])
# iterate through all of the cookies
for k in request.cookies:
result += "Found cookie '%s' with value '%s'\n" % (k, request.cookies[k])
return result
routes = [('/fancy', fancy, render_basic)]
app = Application(routes)
app.serve()Since we're being fancy, let's create a curl request which sends a
GET parameter, a POST parameter, and a cookie:
curl -X POST --data "post=posted" --cookie "cookie_crisp=delicious" --url "http://0.0.0.0:5000/fancy?get=gotten"
In response, Clastic sends the following:
Found argument 'post' with value 'posted' Found argument 'get' with value 'gotten' Found cookie 'cookie_crisp' with value 'delicious'
So fancy.
If you're curious how request got there, read past the end of the
Quickstart.
In the previous examples, we have been returning strings from our
endpoints, letting the trusty render_basic handle the rest. If
we want more control, then we can remove render_basic from the
route, opting to instantiate and return our own Response object
directly.
In the following example, we alter the response headers and status code to forward the browser back to the main page:
from clastic import Application, render_basic, Response, redirect
def home():
return 'Home, Sweet Home!'
def return_home():
response = Response()
# Forward the client browser to the home page.
response.headers['Location'] = '/'
response.status_code = 301
return response
def redirect_home():
return redirect('/')
routes = [('/', home, render_basic),
('/return-home', return_home),
('/redirect-home', redirect_home]
app = Application(routes)
app.serve()If you visit the page http://localhost:5000/return-home in your
browser, it will immediately redirect you to the root URL and show the
text Home, Sweet Home!.
The Response object gives you complete control over all HTTP
headers, enabling you to set and delete cookies, play with page
caching, set page encoding, and so forth. If that sort of fine-grained
responsibility sounds daunting or tedious, you're not alone, which is why
the most common operations usually have convenience functions, like
redirect(), which is demonstrated in redirect_home()
above. Clastic also has no-nonsense drop-ins for cookies, HTTP
caching, and more.
While originally built to host a simple train schedule site and a few Wikipedia-related projects, Clastic is also used for both internal and production-grade applications at PayPal.
(If your project or company uses Clastic, feel free to file an issue or submit a pull request to get added to this section.)
Clastic was created to fill the need for a minimalist web framework that does exactly what you tell it to, while eliminating common pitfalls and delays in error discovery. The result is a streamlined and deterministic web development experience.
To put it another way, Clastic is designed such that, by the time your application has loaded, the framework has done all its work and gotten out of the way. It doesn't wait until the first request or the first time a URL is hit to raise an exception.
In a way, every web framework is a systematic answer to the age-old question that has baffled humankind until just a few years ago.
Note
The following is a conceptual introduction, not class reference. Also, don't be fooled by Capital Letters, Clastic really isn't type-heavy.
- Request
- A single incoming communication from a client (to your application). Encapsulates the WSGI environ, which is just Python's representation of an HTTP request.
- Response
- An outgoing reply from your application to the client.
A web application exists to accept Requests and produce Responses. (Clastic knows that every Request has its Response <3):
Request --> [Application] --> Response
- Route
- A regex-like URL pattern, as associated with an endpoint (and optional renderer).
- Endpoint
- The function or callable that is called when an incoming request matches its associated Route. In Django, this is called a view, in most MVC frameworks this is called a controller.
- Renderer
- A function that usually takes a dictionary of values and produces a Response. For a typical website, the content of the response is usually the result of a templating engine, JSON encoder, or file reader.
A web application matches a Request's URL to its Routes' patterns. If there are no matches, it returns a 404 Response. If a matching Route is found, the Route's endpoint is called. If it returns a Response or the Route doesn't have a Renderer, the Response is sent back directly. Otherwise, the endpoint's return value is fed into the Renderer, which produces the actual Response:
Request --> Routes --> Endpoint --> (Renderer) --> Response
A bit of context
It can be useful to think of an application's behavior in terms of overlapping contexts, each with its own lifespan. For instance, a logged-in user's session is a context which can span multiple requests. A database connection has a context, which may be shorter than a Request's context, or longer if your application uses connection pooling.
Application code can introduce dozens of logical contexts, specific to its function, but at the Clastic level, there are two primary contexts to consider:
- The Request context, which begins when the Request is constructed by the framework, and usually ends when the Response has been sent back to the client.
- The Application context, which begins once an Application is successfully constructed at server startup, and ends when the server running the Application shuts down.
Concepts discussed above were more oriented to the Request context, the following items are more Application focused.
- Resources
- A resource is a value that is valid for the lifespan of the Application. An example might be a database connection factory, a logger object, or the path of a configuration file. An Application's resources refers to a map that gives each resource a name.
- Render Factory
A callable which, when called with an argument, returns a suitable renderer. Consider a
TemplateRenderFactory, which, when called with the template filenameindex.html, returns a function that can be passed a dictionary to render the application's home page.A Render Factory is optional. Here are some cases where a Render Factory can be omitted:
- an application's endpoints return Responses directly (as many applications based directly on Werkzeug do)
- render functions are specified explicitly on a per-route basis
- the application is using some fancy middleware to generate Responses
- Middleware
Middleware is a way of splitting up and ordering logic in discrete layers. When installed in an Application, Middleware has access to the Request before and after the endpoint and render steps. In Python itself, decorators could be thought of as a form of function middleware.
There's a lot more to middleware in Clastic, so check out the Middleware section for more information, including diagrams of middleware's role in the request flow.
Armed with this information, it's now possible to define what constitutes a web application, and indeed a Clastic Application:
- Application
- A collection of Resources, list of Routes, and list of Middleware instances, with an optional Render Factory to create the rendering step for each of the routes.
And with any luck this simple Application should be even simpler:
resources = {'start_time': time.time()}
middlewares = [CookieSessionMiddleware()]
render_factory = TemplateRenderFactory('/path/to/templates/')
routes = [('/', hello_world, 'home.html')]
hello_world_app = Application(routes, resources, middlewares, render_factory)hello_world_app is a full-blown WSGI application ready for serving
to any users needing some greeting.
Note
For the record, the Application instantiation seen above is exactly
what is meant by 'constructing' or 'initializing' an
Application. It's just instantiation, nothing more nothing less.
Dynamic binding, or dynamic argument binding, is the process of resolving the arguments and dependencies of endpoints and middlewares to produce a rock-solid application. Basically, if a certain endpoint function takes an argument, Clastic will make sure that argument is available at Application initialization time.
Arguments are simply checked by name. Consider the following "Hello, World!" Application:
from clastic import Application, render_basic
def hello(name='world'):
return 'Hello, %s!' % name
routes = [('/', hello, render_basic),
('/<name>', hello, render_basic)]
app = Application(routes)
app.serve()The hello() function acts as an endpoint for two Routes, one for
the root URL, and one which takes a name as a URL path segment. On
visiting the root URL, one sees Hello, world!, and if a name is
provided, Hello, (whatever-was-in-the-URL).
If the hello() function was changed to read:
def hello(first_name):
return 'Hello, %s!' % first_nameAnd the code was run without other changes, an exception would be
raised, originating from line 9, app = Application(routes):
NameError: unresolved endpoint middleware arguments: set(['first_name'])
Hmm, looks like we've got a bug, but at least we caught it early. In the future we should probably use a message bus or maybe Cassandra?? Actually, let's write a quick test:
def test_hello():
assert hello() == 'Hello, world!'
assert hello('Justin') == 'Hello, Justin!'A nice side-effect of Clastic's argument binding is that endpoints
only take what they need, meaning endpoint functions can have
easy-to-test signatures like hello(name), instead of
hello(request, name). No need for test clients and mock requests
and other contrivances where unnecessary.
The "Hello, World!" example used an argument bound in from the URL, one of the four sources for arguments:
- Route URL pattern
- Application resources - As mentioned above, arguments which are valid for the lifespan of the Application.
- Middleware provides - Arguments provided by an Application's middleware. See Middleware for more information.
- Clastic built-ins - Special arguments that are always made available by Clastic. These arguments are also reserved, and conflicting names will raise an exception. A list of these arguments and their meanings is below.
Clastic provides a small, but powerful set of six built-in arguments for every occasion. These arguments are reserved by Clastic, so know them well.
request- Probably the most commonly used built-in,
requestis the currentRequestobject being handled by the Application. It has the URL arguments, POST parameters, cookies, user agent, other HTTP headers, and everything from the WSGI environ. nextnextis only for use by Middleware, and represents the next function in the execution chain. It is called with the arguments the middleware class declared that it would provide. If the middleware does not provide any arguments, then it is called with no arguments.nextallows a middleware to not worry about what middleware or function comes after it in the chain. All the middleware knows is that the result of (or exception raised by) thenextfunction is the Response that a client would receive.Middleware functions must accept
nextas the first argument. If a middleware function does not accept thenextargument, or if a non-middleware function accepts thenextargument, an exception is raised at Application initialization.contextcontextis the output of the endpoint side of the middleware chain. By convention, it is almost always a dictionary of values meant to be used in templating or other sorts of Response serialization.Accepting the
contextbuilt-in outside of the render branch of middleware will cause an exception to be raised at Application initialization.
The following built-ins are considered primarily for internal and advanced usage, and are thus prefixed with an underscore.
_application- The
Applicationinstance in which this middleware or endpoint is currently embedded. The Application has access to all routes, endpoints, middlewares, and other fun stuff, which makes_applicationuseful for introspective activities, like those provided by Clastic's built-inMetaApplication. _route- The Route which was matched by the URL and is currently being
executed. Also mostly introspective in nature.
_routehas a lot of useful attributes, such asendpoint, which can be used to shortcut execution in an extreme case.
And, that's it! All other argument names are unreserved and yours for the binding.
Clastic's dynamic binding system makes for concise, testable web applications, free of global state and whole classes of common bugs, but there are a couple implications.
This means that Clastic does not support functions which use *args
or **kwargs as part of a Route's function chain. In practice, such
signatures reduce testability, introspectability, and debuggability,
while providing little benefit to endpoints and middlewares. As a
result, Clastic actively discourages their use; currently the presence
of such functions does not raise an exception, but this behavior may
change.
There is one substantial exception to this assertion, which is that of
function decorators, which make extensive use of *args and
**kwargs, and of which Clastic is a close cousin. To use
decorators, simply import clastic_decorator and decorate your
decorator, like so:
from clastic.decorators import clastic_decorator
cl_my_deco = clastic_decorator(my_deco)clastic_decorator simply wraps another decorator in a way that
lifts the eventually decorated function's signature so that it remains
visible to the rest of the Clastic system.
As a corallary to the above, all parameters in the URL pattern are
required to be named, which in practice, makes for a cleaner and more
testable application. For the few Routes that might actually use such
URLs, simply use a path converter to capture arbitrarily long
segments and split it in middleware or the endpoint itself.
Almost every system has the potential for naming conflicts and Clastic is no exception. The good news is that Clastic actively checks for such conflicts at Application initialization. This early-warning system means naming conflicts are only ever encountered during development, circumventing the much worse and much more common scenario of accidental overriding in production.
Because each Route is independent, and there is no global state, there's no way for one Route's URL parameters to get intermingled with one another, but it is possible for a URL parameter to conflict with an Application's resources or middleware-provided arguments. in the event of such a conflict an error like the following would be raised at Application initialization:
NameError: found conflicting provides: [('name', (u'url', u'resources'))]
Which means that name was provided by both the Route's URL and the
Application's resources.
In practice, Clastic naming conflicts are rare and easily resolvable. Resolution leads to less ambiguous, more maintainable code, and the application developer lives to see another day.
Middleware can be a very useful way to provide separation of horizontal concerns from the actual application logic. Common uses include logging, caching, request serialization/deserialization, performance profiling, and even compression. Including these functions in all endpoint functions would be bad design, not to mention a downright tedious task.
One of Clastic's most defining features may well be its interpretation of middleware. As opposed to simple pre- and post-request hooks, Clastic middlewares use real function-nesting scope. Furthermore, middlewares are dependency-checked to minimize breakage caused by ordering or accidental omission.
A request flows from the client, to the server, through the middlewares, to the endpoint/render functions, which produce a response. The response then travels back through the middlewares, in reverse order, to the server, which relays it to the client.
Middleware is often described using an onion analogy, wherein the first middleware gets first say on the request and last say on the response. For example, given middlewares "A" and "B":
--Request--> A --> B --> Endpoint --> B --> A --Response-->
Within each individual middleware class (e.g., "A"), there are three functions which Clastic will look for and call:
request()- most commonly usedendpoint()- post-routing, pre-logicrender()- post-logic, pre-response, when applicable (e.g., template context processing)
Those are terse descriptions, but that's ok, because all you need to remember is: "Dial 'M' for Middleware":
(endpoint) (render)
|\ /|
| \ / |
mw.endpoint() | \ / | mw.render()
^ \ / v
| \ / |
-- -- --|-- --*-- --|-- -- --
| |
mw.request() ^ v mw.request()
| |
| |
(Request) (Response)
To summarize, if a middleware has a request function, it will be
called such that it wraps both endpoint and render steps, whereas
endpoint and render functions only wrap their respective
domains. A middleware class can implement all or none of these
functions.
Because Clastic middlewares use nested function scopes, Clastic's middleware system is essentially a dynamic and specialized decorator system. Middleware effectively provides hooks for decorating many endpoints at once.
Note
The * at the center vertex of the 'M' represents a checkpoint
of sorts: If the return value of the endpoint + endpoint
middlewares is a Response object, it will be returned directly,
skipping the render vertex of the M completely, but still
executing the outgoing request middlewares.
In any framework, all but the simplest middlewares serve some stateful
purpose. Even a simple timer middleware needs to associate a request
with a response to calculate how much time elapsed in between. In
other middleware paradigms, this state usually ends up attached to the
request object, or worse, somewhere in global state:
class DjangoTimingMiddleware(object):
# Django-like, might be somewhat simplified
def process_request(self, request):
request.start_time = time.time()
def process_response(self, request, response):
total_time = time.time() - request.start_time
return response
def process_exception(self, request, exception):
... # TODO: exception handlingIn Clastic, this would look like:
class TimingMiddleware(Middleware):
def request(self, next):
start_time = time.time()
try:
ret = next()
except:
raise # TODO: exception handling
total_time = time.time() - start_time
return retIn this case, local function scope suffices for our calculation, no need to mutate the request. However, if the middleware did want to provide something new, it could use the provides system to do so.
Often, well-intentioned middlewares want to give a little something back. Clastic let's them do this with provides. For an example of this, here's an ever-so-slightly simplified version of Clastic's basic built-in cookie session middleware:
class CookieSessionMiddleware(Middleware):
provides = ('session',)
def __init__(self, cookie_name='clastic_session', secret_key=None):
self.cookie_name = cookie_name
self.secret_key = secret_key or os.urandom(20)
def request(self, next, request):
session = load_cookie(request, self.cookie_name, self.secret_key)
response = next(session=session)
session.save_cookie(response, key=self.cookie_name)
return responseNotice how the provides class variable, and how the next()
function is called with the session keyword argument. The endpoint
and nested middlewares now have access to the session, should they
need it, while middlewares before CookieSessionMiddleware do not.
Middleware provides vs. resources
Should a value come from middleware or from the resources? Reading the conceptual overview should make this distinction much easier: provides are for the lifetime of the request, whereas resources are for the lifetime of an application. A session-store connection factory is a good resource, but the session retrieved is best provided by middleware (if not in the application logic).
Developers using Clastic to its fullest can use middleware to
drastically increase the reusability of their code. Middlewares can be
used to extract variables from the request and any other complex
objects, then provided to endpoints with much more reusable and
testable usage patterns.
Other frameworks require request to be passed in as an argument,
even when the endpoint doesn't need it. Still other frameworks provide
request as a threadlocal (thread-global anyone?), but this
still makes for harder-to-test code when an endpoint actually does use
a resource provided by request.
Clastic lets you lift nearly anything into a wrapping middleware, so
it's even possible to make Routes that use builtins like abs() and
dict() as endpoints.
Clastic is intentionally much less comprehensive of a web development suite. Django can be great for beginners or prototypes, and can be made to work for larger projects, but experienced developers know what works for them, and Django can get in the way. (Fun Fact: function-based view deprecation was the straw that led to Clastic)
Here are some Clastic features that might appeal to fellow veteran Djangonauts:
For an example of the aggressive checking Clastic provides, consider the following Django URL route:
(r'^articles/(?P<year>\d{4})/$', 'news.views.year_archive')And view function:
def year_archive(year, month):
passThe URL routing rule arguments and view function signature don't match, but a Django application will happily start up without complaints, only to 500 on the first access of that URL.
In Clastic, this sort of mismatch will raise an exception when the Application is constructed.
In Django, applications and middleware have no way to detect when they are fully loaded by the server. Django's lazy loading means middleware aren't even initialized until the first request. For more information, see this Django bug report which led to corrected Django documentation.
Clastic is all about middleware. Middleware provides modularity with nesting semantics. Clastic takes the most literal approach to this possible, using actual function nesting, while Django attempts to mimic this with a set of hooks. During the context of a request, middleware calls are not actually nested, and there is no middleware scope, which usually results in the request object becoming a dumping ground for middleware context.
There are also certain conditions under which the Django framework itself may cause an error or reraise an exception in such a way that a middleware's exception hook is called without having its process_request hook called. Not only does this make tracking down a particular bug difficult, but unless middleware is built extremely conservatively (i.e., assuming nothing; doing an excess of checks), middleware errors can mask the original exception.
Django is beyond dependent on global state. One need look no further
than settings.py; while allegedly modular, Django's ORM and
templating systems cannot be used independently without a settings
module, sometimes an environment variable. Furthermore, it's not
possible or safe to have more than one Django project in one
process. The settings and models would overwrite one another.
This makes Django much less flexible for highly-concurrent or programmatic usage, but to be fair, other than settings.py filling up with loggers and other globals, Django's global state isn't the direct concern of most developers.
That said, Clastic was built 100% free of global state, and provides a model for application developers to do the same. In addition, Clastic's model offers some neat functional features, such as application composability, the ability to embed an application within another, and dependency checking.
Django has an ORM. Clastic is ORM-agnostic.
There is an excess of discussion on the pros and cons of ORMs, so suffice to say that a large portion of experienced engineers find ORM usage to be detrimental in larger projects. The usual reasoning is that ORMs make CRUD operations easy, but eventually get in the way of constructing and tuning more advanced queries.
Portability is a common concern, but very rarely does a real project switch their RDBMS, if they use relational storage at all. There are exceptions, but practically speaking, a project runs one of MySQL, Oracle, or Postgres in production and that or SQLite in staging/test/local. In fact, for every sizable project that eventually migrates from MySQL to PostgreSQL, there are at least two which would benefit from learning and using proprietary features specific to their chosen database.
Without getting too deep into the dangers of lazy query execution, let's just say that ORMs, while handy for the short-term and alluring in the long-term, can make some things appear too easy, resulting in a template accidentally issuing thousands of queries. It's because of the obvious nuances that Clastic is not anti-ORM, per se, but doesn't consider an ORM to be a feature. Every developer has an opinion, and every project has its needs, so feel free to use Clastic with straight SQL, SQLAlchemy, your non-relational backend of choice, or even Django's ORM.
For as many claims as its docs make to being standard Python, Django makes WSGI slightly choreful, which is a shame, because WSGI has blessed Python with so many neat servers that work with any WSGI application.
Clastic applications are themselves WSGI applications. There's no need for special one-off modules or imports.
Thanks to the following folks for helping make Clastic:
- Kurt Rose - Design review and implementation
- Justin van Winkle - Inspiration
- Pocoo and the Werkzeug team - For a very great WSGI toolkit
And thanks to you for making it this far in the docs!