Tutorial

In this tutorial we’ll walk through building an API for a simple image sharing service. Along the way, we’ll discuss Falcon’s major features and introduce the terminology used by the framework.

First Steps

The first thing we’ll do is install Falcon inside a fresh virtualenv. To that end, let’s create a new project folder called “look”, and set up a virtual environment within it that we can use for the tutorial:

$ mkdir look
$ cd look
$ virtualenv .venv
$ source .venv/bin/activate
$ pip install falcon

It’s customary for the project’s top-level module to be called the same as the project, so let’s create another “look” folder inside the first one and mark it as a python module by creating an empty __init__.py file in it:

$ mkdir look
$ touch look/__init__.py

Next, let’s create a new file that will be the entry point into your app:

$ touch look/app.py

The file hierarchy should now look like this:

look
├── .venv
└── look
    ├── __init__.py
    └── app.py

Now, open app.py in your favorite text editor and add the following lines:

import falcon

api = application = falcon.API()

This code creates your WSGI application and aliases it as api. You can use any variable names you like, but we’ll use application since that is what Gunicorn, by default, expects it to be called (we’ll see how this works in the next section of the tutorial).

Note

A WSGI application is just a callable with a well-defined signature so that you can host the application with any web server that understands the WSGI protocol.

Next let’s take a look at the falcon.API class. Install IPython and fire it up:

$ pip install ipython
$ ipython

Now, type the following to introspect the falcon.API callable:

In [1]: import falcon

In [2]: falcon.API.__call__?

Alternatively, you can use the standard Python help() function:

In [3]: help(falcon.API.__call__)

Note the method signature. env and start_response are standard WSGI params. Falcon adds a thin abstraction on top of these params so you don’t have to interact with them directly.

The Falcon framework contains extensive inline documentation that you can query using the above technique.

Tip

In addition to IPython, the Python community maintains several other super-powered REPLs that you may wish to try, including bpython and ptpython.

Hosting Your App

Now that you have a simple Falcon app, you can take it for a spin with a WSGI server. Python includes a reference server for self-hosting, but let’s use something more robust that you might use in production.

Open a new terminal and run the following:

$ source .venv/bin/activate
$ pip install gunicorn
$ gunicorn --reload look.app

(Note the use of the --reload option to tell Gunicorn to reload the app whenever its code changes.)

If you are a Windows user, Waitress can be used in lieu of Gunicorn, since the latter doesn’t work under Windows:

$ pip install waitress
$ waitress-serve --port=8000 look.app

Now, in a different terminal, try querying the running app with curl:

$ curl -v localhost:8000

You should get a 404. That’s actually OK, because we haven’t specified any routes yet. Falcon includes a default 404 response handler that will fire for any requested path for which a route does not exist.

While curl certainly gets the job done, it can be a bit crufty to use. HTTPie is a modern, user-friendly alternative. Let’s install HTTPie and use it from now on:

$ source .venv/bin/activate
$ pip install httpie
$ http localhost:8000

Creating Resources

Falcon’s design borrows several key concepts from the REST architectural style.

Central to both REST and the Falcon framework is the concept of a “resource”. Resources are simply all the things in your API or application that can be accessed by a URL. For example, an event booking application may have resources such as “ticket” and “venue”, while a video game backend may have resources such as “achievements” and “player”.

URLs provide a way for the client to uniquely identify resources. For example, /players might identify the “list of all players” resource, while /players/45301f54 might identify the “individual player with ID 45301f54”, and /players/45301f54/achievements the “list of all achievements for the player resource with ID 45301f54”.

  POST        /players/45301f54/achievements
└──────┘    └────────────────────────────────┘
 Action            Resource Identifier

In the REST architectural style, the URL only identifies the resource; it does not specify what action to take on that resource. Instead, users choose from a set of standard methods. For HTTP, these are the familiar GET, POST, HEAD, etc. Clients can query a resource to discover which methods it supports.

Note

This is one of the key differences between the REST and RPC architectural styles. REST applies a standard set of verbs across any number of resources, as opposed to having each application define its own unique set of methods.

Depending on the requested action, the server may or may not return a representation to the client. Representations may be encoded in any one of a number of Internet media types, such as JSON and HTML.

Falcon uses Python classes to represent resources. In practice, these classes act as controllers in your application. They convert an incoming request into one or more internal actions, and then compose a response back to the client based on the results of those actions.

           ┌────────────┐
request  → │            │
           │ Resource   │ ↻ Orchestrate the requested action
           │ Controller │ ↻ Compose the result
response ← │            │
           └────────────┘

A resource in Falcon is just a regular Python class that includes one or more methods representing the standard HTTP verbs supported by that resource. Each requested URL is mapped to a specific resource.

Since we are building an image-sharing API, let’s start by creating an “images” resource. Create a new module, images.py next to app.py, and add the following code to it:

import json

import falcon


class Resource(object):

    def on_get(self, req, resp):
        doc = {
            'images': [
                {
                    'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
                }
            ]
        }

        # Create a JSON representation of the resource
        resp.body = json.dumps(doc, ensure_ascii=False)

        # The following line can be omitted because 200 is the default
        # status returned by the framework, but it is included here to
        # illustrate how this may be overridden as needed.
        resp.status = falcon.HTTP_200

As you can see, Resource is just a regular class. You can name the class anything you like. Falcon uses duck-typing, so you don’t need to inherit from any sort of special base class.

The image resource above defines a single method, on_get(). For any HTTP method you want your resource to support, simply add an on_*() method to the class, where * is any one of the standard HTTP methods, lowercased (e.g., on_get(), on_put(), on_head(), etc.).

We call these well-known methods “responders”. Each responder takes (at least) two params, one representing the HTTP request, and one representing the HTTP response to that request. By convention, these are called req and resp, respectively. Route templates and hooks can inject extra params, as we shall see later on.

Right now, the image resource responds to GET requests with a simple 200 OK and a JSON body. Falcon’s Internet media type defaults to application/json but you can set it to whatever you like. Noteworthy JSON alternatives include YAML and MessagePack.

Next let’s wire up this resource and see it in action. Go back to app.py and modify it so that it looks something like this:

import falcon

from .images import Resource


api = application = falcon.API()

images = Resource()
api.add_route('/images', images)

Now, when a request comes in for /images, Falcon will call the responder on the images resource that corresponds to the requested HTTP method.

Let’s try it. Restart Gunicorn (unless you’re using --reload), and send a GET request to the resource:

$ http localhost:8000/images

You should receive a 200 OK response, including a JSON-encoded representation of the “images” resource.

Note

add_route() expects an instance of the resource class, not the class itself. The same instance is used for all requests. This strategy improves performance and reduces memory usage, but this also means that if you host your application with a threaded web server, resources and their dependencies must be thread-safe.

So far we have only implemented a responder for GET. Let’s see what happens when a different method is requested:

$ http PUT localhost:8000/images

This time you should get back 405 Method Not Allowed, since the resource does not support the PUT method. Note the value of the Allow header:

allow: GET, OPTIONS

This is generated automatically by Falcon based on the set of methods implemented by the target resource. If a resource does not include its own OPTIONS responder, the framework provides a default implementation. Therefore, OPTIONS is always included in the list of allowable methods.

Note

If you have a lot of experience with other Python web frameworks, you may be used to using decorators to set up your routes. Falcon’s particular approach provides the following benefits:

  • The URL structure of the application is centralized. This makes it easier to reason about and maintain the API over time.
  • The use of resource classes maps somewhat naturally to the REST architectural style, in which a URL is used to identify a resource only, not the action to perform on that resource.
  • Resource class methods provide a uniform interface that does not have to be reinvented (and maintained) from class to class and application to application.

Next, just for fun, let’s modify our resource to use MessagePack instead of JSON. Start by installing the relevant package:

$ pip install msgpack-python

Then, update the responder to use the new media type:

import falcon

import msgpack


class Resource(object):

    def on_get(self, req, resp):
        doc = {
            'images': [
                {
                    'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
                }
            ]
        }

        resp.data = msgpack.packb(doc, use_bin_type=True)
        resp.content_type = falcon.MEDIA_MSGPACK
        resp.status = falcon.HTTP_200

Note the use of resp.data in lieu of resp.body. If you assign a bytestring to the latter, Falcon will figure it out, but you can realize a small performance gain by assigning directly to resp.data.

Also note the use of falcon.MEDIA_MSGPACK. The falcon module provides a number of constants for common media types, including falcon.MEDIA_JSON, falcon.MEDIA_MSGPACK, falcon.MEDIA_YAML, falcon.MEDIA_XML, falcon.MEDIA_HTML, falcon.MEDIA_JS, falcon.MEDIA_TEXT, falcon.MEDIA_JPEG, falcon.MEDIA_PNG, and falcon.MEDIA_GIF.

Restart Gunicorn (unless you’re using --reload), and then try sending a GET request to the revised resource:

$ http localhost:8000/images

Testing your application

Fully exercising your code is critical to creating a robust application. Let’s take a moment to write a test for what’s been implemented so far.

First, create a tests directory with __init__.py and a test module (test_app.py) inside it. The project’s structure should now look like this:

look
├── .venv
├── look
│   ├── __init__.py
│   ├── app.py
│   └── images.py
└── tests
    ├── __init__.py
    └── test_app.py

Falcon supports testing its API object by simulating HTTP requests.

Tests can either be written using Python’s standard unittest module, or with any of a number of third-party testing frameworks, such as pytest. For this tutorial we’ll use pytest since it allows for more pythonic test code as compared to the JUnit-inspired unittest module.

Let’s start by installing the pytest package:

$ pip install pytest

Next, edit test_app.py to look like this:

import falcon
from falcon import testing
import msgpack
import pytest

from look.app import api


@pytest.fixture
def client():
    return testing.TestClient(api)


# pytest will inject the object returned by the "client" function
# as an additional parameter.
def test_list_images(client):
    doc = {
        'images': [
            {
                'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
            }
        ]
    }

    response = client.simulate_get('/images')
    result_doc = msgpack.unpackb(response.content, encoding='utf-8')

    assert result_doc == doc
    assert response.status == falcon.HTTP_OK

From the main project directory, exercise your new test by running pytest against the tests directory:

$ pytest tests

If pytest reports any errors, take a moment to fix them up before proceeding to the next section of the tutorial.

Request and Response Objects

Each responder in a resource receives a Request object that can be used to read the headers, query parameters, and body of the request. You can use the standard help() function or IPython’s magic ? function to list the attributes and methods of Falcon’s Request class:

In [1]: import falcon

In [2]: falcon.Request?

Each responder also receives a Response object that can be used for setting the status code, headers, and body of the response:

In [3]: falcon.Response?

This will be useful when creating a POST endpoint in the application that can add new image resources to our collection. We’ll tackle this functionality next.

We’ll use TDD this time around, to demonstrate how to apply this particular testing strategy when developing a Falcon application. Via tests, we’ll first define precisely what we want the application to do, and then code until the tests tell us that we’re done.

Note

To learn more about TDD, you may wish to check out one of the many books on the topic, such as Test Driven Development with Python. The examples in this particular book use the Django framework and even JavaScript, but the author covers a number of testing principles that are widely applicable.

Let’s start by adding an additional import statement to test_app.py. We need to import two modules from unittest.mock if you are using Python 3, or from mock if you are using Python 2.

# Python 3
from unittest.mock import mock_open, call

# Python 2
from mock import mock_open, call

For Python 2, you will also need to install the mock package:

$ pip install mock

Now add the following test:

# "monkeypatch" is a special built-in pytest fixture that can be
# used to install mocks.
def test_posted_image_gets_saved(client, monkeypatch):
    mock_file_open = mock_open()
    monkeypatch.setattr('io.open', mock_file_open)

    fake_uuid = '123e4567-e89b-12d3-a456-426655440000'
    monkeypatch.setattr('uuid.uuid4', lambda: fake_uuid)

    # When the service receives an image through POST...
    fake_image_bytes = b'fake-image-bytes'
    response = client.simulate_post(
        '/images',
        body=fake_image_bytes,
        headers={'content-type': 'image/png'}
    )

    # ...it must return a 201 code, save the file, and return the
    # image's resource location.
    assert response.status == falcon.HTTP_CREATED
    assert call().write(fake_image_bytes) in mock_file_open.mock_calls
    assert response.headers['location'] == '/images/{}.png'.format(fake_uuid)

As you can see, this test relies heavily on mocking, making it somewhat fragile in the face of implementation changes. We’ll revisit this later. For now, run the tests again and watch to make sure they fail. A key step in the TDD workflow is verifying that your tests do not pass before moving on to the implementation:

$ pytest tests

To make the new test pass, we need to add a new method for handling POSTs. Open images.py and add a POST responder to the Resource class as follows:

import io
import os
import uuid
import mimetypes

import falcon
import msgpack


class Resource(object):

    _CHUNK_SIZE_BYTES = 4096

    # The resource object must now be initialized with a path used during POST
    def __init__(self, storage_path):
        self._storage_path = storage_path

    # This is the method we implemented before
    def on_get(self, req, resp):
        doc = {
            'images': [
                {
                    'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
                }
            ]
        }

        resp.data = msgpack.packb(doc, use_bin_type=True)
        resp.content_type = falcon.MEDIA_MSGPACK
        resp.status = falcon.HTTP_200

    def on_post(self, req, resp):
        ext = mimetypes.guess_extension(req.content_type)
        name = '{uuid}{ext}'.format(uuid=uuid.uuid4(), ext=ext)
        image_path = os.path.join(self._storage_path, name)

        with io.open(image_path, 'wb') as image_file:
            while True:
                chunk = req.stream.read(self._CHUNK_SIZE_BYTES)
                if not chunk:
                    break

                image_file.write(chunk)

        resp.status = falcon.HTTP_201
        resp.location = '/images/' + name

As you can see, we generate a unique name for the image, and then write it out by reading from req.stream. It’s called stream instead of body to emphasize the fact that you are really reading from an input stream; by default Falcon does not spool or decode request data, instead giving you direct access to the incoming binary stream provided by the WSGI server.

Note the use of falcon.HTTP_201 for setting the response status to “201 Created”. We could have also used the falcon.HTTP_CREATED alias. For a full list of predefined status strings, simply call help() on falcon.status_codes:

In [4]: help(falcon.status_codes)

The last line in the on_post() responder sets the Location header for the newly created resource. (We will create a route for that path in just a minute.) The Request and Response classes contain convent attributes for reading and setting common headers, but you can always access any header by name with the req.get_header() and resp.set_header() methods.

Take a moment to run pytest again to check your progress:

$ pytest tests

You should see a TypeError as a consequence of adding the storage_path parameter to Resource.__init__().

To fix this, simply edit app.py and pass in a path to the initializer. For now, just use the working directory from which you started the service:

images = Resource(storage_path='.')

Try running the tests again. This time, they should pass with flying colors!

$ pytest tests

Finally, restart Gunicorn and then try sending a POST request to the resource from the command line (substituting test.png for a path to any PNG you like.)

$ http POST localhost:8000/images Content-Type:image/png < test.png

Now, if you check your storage directory, it should contain a copy of the image you just POSTed.

Upward and onward!

Refactoring for testability

Earlier we pointed out that our POST test relied heavily on mocking, relying on assumptions that may or may not hold true as the code evolves. To mitigate this problem, we’ll not only have to refactor the tests, but also the application itself.

We’ll start by factoring out the business logic from the resource’s POST responder in images.py so that it can be tested independently. In this case, the resource’s “business logic” is simply the image-saving operation:

import io
import mimetypes
import os
import uuid

import falcon
import msgpack


class Resource(object):

    def __init__(self, image_store):
        self._image_store = image_store

    def on_get(self, req, resp):
        doc = {
            'images': [
                {
                    'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
                }
            ]
        }

        resp.data = msgpack.packb(doc, use_bin_type=True)
        resp.content_type = falcon.MEDIA_MSGPACK
        resp.status = falcon.HTTP_200

    def on_post(self, req, resp):
        name = self._image_store.save(req.stream, req.content_type)
        resp.status = falcon.HTTP_201
        resp.location = '/images/' + name


class ImageStore(object):

    _CHUNK_SIZE_BYTES = 4096

    # Note the use of dependency injection for standard library
    # methods. We'll use these later to avoid monkey-patching.
    def __init__(self, storage_path, uuidgen=uuid.uuid4, fopen=io.open):
        self._storage_path = storage_path
        self._uuidgen = uuidgen
        self._fopen = fopen

    def save(self, image_stream, image_content_type):
        ext = mimetypes.guess_extension(image_content_type)
        name = '{uuid}{ext}'.format(uuid=self._uuidgen(), ext=ext)
        image_path = os.path.join(self._storage_path, name)

        with self._fopen(image_path, 'wb') as image_file:
            while True:
                chunk = image_stream.read(self._CHUNK_SIZE_BYTES)
                if not chunk:
                    break

                image_file.write(chunk)

        return name

Let’s check to see if we broke anything with the changes above:

$ pytest tests

Hmm, it looks like we forgot to update app.py. Let’s do that now:

import falcon

from .images import ImageStore, Resource


api = application = falcon.API()

image_store = ImageStore('.')
images = Resource(image_store)
api.add_route('/images', images)

Let’s try again:

$ pytest tests

Now you should see a failed test assertion regarding mock_file_open. To fix this, we need to switch our strategy from monkey-patching to dependency injection. Return to app.py and modify it to look similar to the following:

import falcon

from .images import ImageStore, Resource


def create_app(image_store):
    image_resource = Resource(image_store)
    api = falcon.API()
    api.add_route('/images', image_resource)
    return api


def get_app():
    image_store = ImageStore('.')
    return create_app(image_store)

As you can see, the bulk of the setup logic has been moved to create_app(), which can be used to obtain an API object either for testing or for hosting in production. get_app() takes care of instantiating additional resources and configuring the application for hosting.

The command to run the application is now:

$ gunicorn --reload 'look.app:get_app()'

Finally, we need to update the test code. Modify test_app.py to look similar to this:

import io

# Python 3
from unittest.mock import call, MagicMock, mock_open

# Python 2
# from mock import call, MagicMock, mock_open

import falcon
from falcon import testing
import msgpack
import pytest

import look.app
import look.images


@pytest.fixture
def mock_store():
    return MagicMock()


@pytest.fixture
def client(mock_store):
    api = look.app.create_app(mock_store)
    return testing.TestClient(api)


def test_list_images(client):
    doc = {
        'images': [
            {
                'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
            }
        ]
    }

    response = client.simulate_get('/images')
    result_doc = msgpack.unpackb(response.content, encoding='utf-8')

    assert result_doc == doc
    assert response.status == falcon.HTTP_OK


# With clever composition of fixtures, we can observe what happens with
# the mock injected into the image resource.
def test_post_image(client, mock_store):
    file_name = 'fake-image-name.xyz'

    # We need to know what ImageStore method will be used
    mock_store.save.return_value = file_name
    image_content_type = 'image/xyz'

    response = client.simulate_post(
        '/images',
        body=b'some-fake-bytes',
        headers={'content-type': image_content_type}
    )

    assert response.status == falcon.HTTP_CREATED
    assert response.headers['location'] == '/images/{}'.format(file_name)
    saver_call = mock_store.save.call_args

    # saver_call is a unittest.mock.call tuple. It's first element is a
    # tuple of positional arguments supplied when calling the mock.
    assert isinstance(saver_call[0][0], falcon.request_helpers.BoundedStream)
    assert saver_call[0][1] == image_content_type

As you can see, we’ve redone the POST. While there are fewer mocks, the assertions have gotten more elaborate to properly check interactions at the interface boundaries.

Let’s check our progress:

$ pytest tests

All green! But since we used a mock, we’re no longer covering the actual saving of the image. Let’s add a test for that:

def test_saving_image(monkeypatch):
    # This still has some mocks, but they are more localized and do not
    # have to be monkey-patched into standard library modules (always a
    # risky business).
    mock_file_open = mock_open()

    fake_uuid = '123e4567-e89b-12d3-a456-426655440000'
    def mock_uuidgen():
        return fake_uuid

    fake_image_bytes = b'fake-image-bytes'
    fake_request_stream = io.BytesIO(fake_image_bytes)
    storage_path = 'fake-storage-path'
    store = look.images.ImageStore(
        storage_path,
        uuidgen=mock_uuidgen,
        fopen=mock_file_open
    )

    assert store.save(fake_request_stream, 'image/png') == fake_uuid + '.png'
    assert call().write(fake_image_bytes) in mock_file_open.mock_calls

Now give it a try:

$ pytest tests -k test_saving_image

Like the former test, this one still uses mocks. But the structure of the code has been improved through the techniques of componentization and dependency inversion, making the application more flexible and testable.

Tip

Checking code coverage would have helped us detect the missing test above; it’s always a good idea to include coverage testing in your workflow to ensure you don’t have any bugs hiding off somewhere in an unexercised code path.

Functional tests

Functional tests define the application’s behavior from the outside. When using TDD, this can be a more natural place to start as opposed to lower-level unit testing, since it is difficult to anticipate what internal interfaces and components are needed in advance of defining the application’s user-facing functionality.

In the case of the refactoring work from the last section, we could have inadvertently introduced a functional bug into the application that our unit tests would not have caught. This can happen when a bug is a result of an unexpected interaction between multiple units, between the application and the web server, or between the application and any external services it depends on.

With test helpers such as simulate_get() and simulate_post(), we can create tests that span multiple units. But we can also go one step further and run the application as a normal, separate process (e.g. with Gunicorn). We can then write tests that interact with the running process through HTTP, behaving like a normal client.

Let’s see this in action. Create a new test module, tests/test_integration.py with the following contents:

import os

import requests


def test_posted_image_gets_saved():
    file_save_prefix = '/tmp/'
    location_prefix = '/images/'
    fake_image_bytes = b'fake-image-bytes'

    response = requests.post(
        'http://localhost:8000/images',
        data=fake_image_bytes,
        headers={'content-type': 'image/png'}
    )

    assert response.status_code == 201
    location = response.headers['location']
    assert location.startswith(location_prefix)
    image_name = location.replace(location_prefix, '')

    file_path = file_save_prefix + image_name
    with open(file_path, 'rb') as image_file:
        assert image_file.read() == fake_image_bytes

    os.remove(file_path)

Next, install the requests package (as required by the new test) and make sure Gunicorn is up and running:

$ pip install requests
$ gunicorn 'look.app:get_app()'

Then, in another terminal, try running the new test:

$ pytest tests -k test_posted_image_gets_saved

The test will fail since it expects the image file to reside under /tmp. To fix this, modify app.py to add the ability to configure the image storage directory with an environment variable:

import os

import falcon

from .images import ImageStore, Resource


def create_app(image_store):
    image_resource = Resource(image_store)
    api = falcon.API()
    api.add_route('/images', image_resource)
    return api


def get_app():
    storage_path = os.environ.get('LOOK_STORAGE_PATH', '.')
    image_store = ImageStore(storage_path)
    return create_app(image_store)

Now you can re-run the app against the desired storage directory:

$ LOOK_STORAGE_PATH=/tmp gunicorn --reload 'look.app:get_app()'

You should now be able to re-run the test and see it succeed:

$ pytest tests -k test_posted_image_gets_saved

Note

The above process of starting, testing, stopping, and cleaning up after each test run can (and really should be) automated. Depending on your needs, you can develop your own automation fixtures, or use a library such as mountepy.

Many developers choose to write tests like the above to sanity-check their application’s primary functionality, while leaving the bulk of testing to simulated requests and unit tests. These latter types of tests generally execute much faster and facilitate more fine-grained test assertions as compared to higher-level functional and system tests. That being said, testing strategies vary widely and you should choose the one that best suits your needs.

At this point, you should have a good grip on how to apply common testing strategies to your Falcon application. For the sake of brevity we’ll omit further testing instructions from the following sections, focusing instead on showcasing more of Falcon’s features.

Serving Images

Now that we have a way of getting images into the service, we of course need a way to get them back out. What we want to do is return an image when it is requested, using the path that came back in the Location header.

Try executing the following:

$ http localhost:8000/images/db79e518-c8d3-4a87-93fe-38b620f9d410.png

In response, you should get a 404 Not Found. This is the default response given by Falcon when it can not find a resource that matches the requested URL path.

Let’s address this by creating a separate class to represent a single image resource. We will then add an on_get() method to respond to the path above.

Go ahead and edit your images.py file to look something like this:

import io
import os
import re
import uuid
import mimetypes

import falcon
import msgpack


class Collection(object):

    def __init__(self, image_store):
        self._image_store = image_store

    def on_get(self, req, resp):
        # TODO: Modify this to return a list of href's based on
        # what images are actually available.
        doc = {
            'images': [
                {
                    'href': '/images/1eaf6ef1-7f2d-4ecc-a8d5-6e8adba7cc0e.png'
                }
            ]
        }

        resp.data = msgpack.packb(doc, use_bin_type=True)
        resp.content_type = falcon.MEDIA_MSGPACK
        resp.status = falcon.HTTP_200

    def on_post(self, req, resp):
        name = self._image_store.save(req.stream, req.content_type)
        resp.status = falcon.HTTP_201
        resp.location = '/images/' + name


class Item(object):

    def __init__(self, image_store):
        self._image_store = image_store

    def on_get(self, req, resp, name):
        resp.content_type = mimetypes.guess_type(name)[0]
        resp.stream, resp.stream_len = self._image_store.open(name)


class ImageStore(object):

    _CHUNK_SIZE_BYTES = 4096
    _IMAGE_NAME_PATTERN = re.compile(
        '[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}\.[a-z]{2,4}$'
    )

    def __init__(self, storage_path, uuidgen=uuid.uuid4, fopen=io.open):
        self._storage_path = storage_path
        self._uuidgen = uuidgen
        self._fopen = fopen

    def save(self, image_stream, image_content_type):
        ext = mimetypes.guess_extension(image_content_type)
        name = '{uuid}{ext}'.format(uuid=self._uuidgen(), ext=ext)
        image_path = os.path.join(self._storage_path, name)

        with self._fopen(image_path, 'wb') as image_file:
            while True:
                chunk = image_stream.read(self._CHUNK_SIZE_BYTES)
                if not chunk:
                    break

                image_file.write(chunk)

        return name

    def open(self, name):
        # Always validate untrusted input!
        if not self._IMAGE_NAME_PATTERN.match(name):
            raise IOError('File not found')

        image_path = os.path.join(self._storage_path, name)
        stream = self._fopen(image_path, 'rb')
        stream_len = os.path.getsize(image_path)

        return stream, stream_len

As you can see, we renamed Resource to Collection and added a new Item class to represent a single image resource. Also, note the name parameter for the on_get() responder. Any URI parameters that you specify in your routes will be turned into corresponding kwargs and passed into the target responder as such. We’ll see how to specify URI parameters in a moment.

Inside the on_get() responder, we set the Content-Type header based on the filename extension, and then stream out the image directly from an open file handle. Note the use of resp.stream_len. Whenever using resp.stream instead of resp.body or resp.data, you typically also specify the expected length of the stream so that the web client knows how much data to read from the response.

Note

If you do not know the size of the stream in advance, you can work around that by using chunked encoding, but that’s beyond the scope of this tutorial.

If resp.status is not set explicitly, it defaults to 200 OK, which is exactly what we want on_get() to do.

Now let’s wire everything up and give it a try. Edit app.py to look similar to the following:

import os

import falcon

import images


def create_app(image_store):
    api = falcon.API()
    api.add_route('/images', images.Collection(image_store))
    api.add_route('/images/{name}', images.Item(image_store))
    return api


def get_app():
    storage_path = os.environ.get('LOOK_STORAGE_PATH', '.')
    image_store = images.ImageStore(storage_path)
    return create_app(image_store)

As you can see, we specified a new route, /images/{name}. This causes Falcon to expect all associated responders to accept a name argument.

Note

Falcon also supports more complex parameterized path segments that contain multiple values. For example, a version control API might use the following route template for diffing two code branches:

/repos/{org}/{repo}/compare/{usr0}:{branch0}...{usr1}:{branch1}

Now re-run your app and try to POST another picture:

$ http POST localhost:8000/images Content-Type:image/png < test.png

Make a note of the path returned in the Location header, and use it to GET the image:

$ http localhost:8000/images/dddff30e-d2a6-4b57-be6a-b985ee67fa87.png

HTTPie won’t display the image, but you can see that the response headers were set correctly. Just for fun, go ahead and paste the above URI into your browser. The image should display correctly.

Introducing Hooks

At this point you should have a pretty good understanding of the basic parts that make up a Falcon-based API. Before we finish up, let’s just take a few minutes to clean up the code and add some error handling.

First, let’s check the incoming media type when something is posted to make sure it is a common image type. We’ll implement this with a before hook.

Start by defining a list of media types the service will accept. Place this constant near the top, just after the import statements in images.py:

ALLOWED_IMAGE_TYPES = (
    'image/gif',
    'image/jpeg',
    'image/png',
)

The idea here is to only accept GIF, JPEG, and PNG images. You can add others to the list if you like.

Next, let’s create a hook that will run before each request to post a message. Add this method below the definition of ALLOWED_IMAGE_TYPES:

def validate_image_type(req, resp, resource, params):
    if req.content_type not in ALLOWED_IMAGE_TYPES:
        msg = 'Image type not allowed. Must be PNG, JPEG, or GIF'
        raise falcon.HTTPBadRequest('Bad request', msg)

And then attach the hook to the on_post() responder:

@falcon.before(validate_image_type)
def on_post(self, req, resp):
    # ...

Now, before every call to that responder, Falcon will first invoke validate_image_type(). There isn’t anything special about this function, other than it must accept four arguments. Every hook takes, as its first two arguments, a reference to the same req and resp objects that are passed into responders. The resource argument is a Resource instance associated with the request. The fourth argument, named params by convention, is a reference to the kwarg dictionary Falcon creates for each request. params will contain the route’s URI template params and their values, if any.

As you can see in the example above, you can use req to get information about the incoming request. However, you can also use resp to play with the HTTP response as needed, and you can even use hooks to inject extra kwargs:

def extract_project_id(req, resp, resource, params):
    """Adds `project_id` to the list of params for all responders.

    Meant to be used as a `before` hook.
    """
    params['project_id'] = req.get_header('X-PROJECT-ID')

Now, you might imagine that such a hook should apply to all responders for a resource. In fact, hooks can be applied to an entire resource by simply decorating the class:

@falcon.before(extract_project_id)
class Message(object):

    # ...

Similar logic can be applied globally with middleware. (See also: falcon.middleware)

Now that you’ve added a hook to validate the media type, you can see it in action by attempting to POST something nefarious:

$ http POST localhost:8000/images Content-Type:image/jpx

You should get back a 400 Bad Request status and a nicely structured error body.

Tip

When something goes wrong, you usually want to give your users some info to help them resolve the issue. The exception to this rule is when an error occurs because the user is requested something they are not authorized to access. In that case, you may wish to simply return 404 Not Found with an empty body, in case a malicious user is fishing for information that will help them crack your app.

Check out the hooks reference to learn more.

Error Handling

Generally speaking, Falcon assumes that resource responders (on_get(), on_post(), etc.) will, for the most part, do the right thing. In other words, Falcon doesn’t try very hard to protect responder code from itself.

This approach reduces the number of (often) extraneous checks that Falcon would otherwise have to perform, making the framework more efficient. With that in mind, writing a high-quality API based on Falcon requires that:

  1. Resource responders set response variables to sane values.
  2. Untrusted input (i.e., input from an external client or service) is validated.
  3. Your code is well-tested, with high code coverage.
  4. Errors are anticipated, detected, logged, and handled appropriately within each responder or by global error handling hooks.

When it comes to error handling, you can always directly set the error status, appropriate response headers, and error body using the resp object. However, Falcon tries to make things a little easier by providing a set of error classes you can raise when something goes wrong. Falcon will convert any instance or subclass of falcon.HTTPError raised by a responder, hook, or middleware component into an appropriate HTTP response.

You may raise an instance of falcon.HTTPError directly, or use any one of a number of predefined errors that are designed to set the response headers and body appropriately for each error type.

Tip

Falcon will re-raise errors that do not inherit from falcon.HTTPError unless you have registered a custom error handler for that type.

Error handlers may be registered for any type, including HTTPError. This feature provides a central location for logging and otherwise handling exceptions raised by responders, hooks, and middleware components.

See also: add_error_handler().

Let’s see a quick example of how this works. Try requesting an invalid image name from your application:

$ http localhost:8000/images/voltron.png

As you can see, the result isn’t exactly graceful. To fix this, we’ll need to add some exception handling. Modify your Item class as follows:

class Item(object):

    def __init__(self, image_store):
        self._image_store = image_store

    def on_get(self, req, resp, name):
        resp.content_type = mimetypes.guess_type(name)[0]

        try:
            resp.stream, resp.stream_len = self._image_store.open(name)
        except IOError:
            # Normally you would also log the error.
            raise falcon.HTTPNotFound()

Now let’s try that request again:

$ http localhost:8000/images/voltron.png

Additional information about error handling is available in the error handling reference.

What Now?

Our friendly community is available to answer your questions and help you work through sticky problems. See also: Getting Help.

As mentioned previously, Falcon’s docstrings are quite extensive, and so you can learn a lot just by poking around Falcon’s modules from a Python REPL, such as IPython or bpython.

Also, don’t be shy about pulling up Falcon’s source code on GitHub or in your favorite text editor. The team has tried to make the code as straightforward and readable as possible; where other documentation may fall short, the code basically can’t be wrong.

A number of Falcon add-ons, templates, and complementary packages are available for use in your projects. We’ve listed several of these on the Falcon wiki as a starting point, but you may also wish to search PyPI for additional resources.