Tutorial (ASGI)

In this tutorial we’ll walk through building an API for a simple image sharing service. Along the way, we’ll discuss the basic anatomy of an asynchronous Falcon application: responders, routing, middleware, executing synchronous functions in an executor, and more!

Note

This tutorial covers the asynchronous flavor of Falcon using the ASGI protocol.

Synchronous (WSGI) Falcon application development is covered in our WSGI tutorial.

New Falcon users may also want to choose the WSGI flavor to familiarize themselves with Falcon’s basic concepts.

First Steps

Firstly, let’s create a fresh environment and the corresponding project directory structure, along the lines of First Steps from the WSGI tutorial:

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

Note

Installing virtualenv is not needed for recent Python 3.x versions. We can simply create a virtualenv using the venv module from the standard library, for instance:

$ python3.8 -m venv .venv
$ source .venv/bin/activate

However, the above way may be unavailable depending on how Python is packaged and installed in your OS. Even if that is the case, installing virtualenv should still work as usual.

Some of us find it convenient to manage virtualenvs with virtualenvwrapper, particularly when it comes to hopping between several environments.

At the time of writing, ASGI is not yet available in a stable Falcon release. We’ll need to either install a beta release:

$ pip install falcon==3.0.0b1

Or, just check out the latest development version straight from GitHub:

$ pip install git+https://github.com/falconry/falcon

A Falcon ASGI application skeleton (app.py) could look like:

import falcon.asgi

app = falcon.asgi.App()

As in the WSGI tutorial’s introductory part, let’s not forget to mark asgilook as a Python module:

$ touch asgilook/__init__.py

Hosting Our App

For running our async application, we’ll need an ASGI application server. Popular choices include:

• Uvicorn

• Daphne

• Hypercorn

For a simple tutorial application like ours, any of the above should do. Let’s pick the popular uvicorn for now:

$ pip install uvicorn

While at it, it might be handy to also install HTTPie HTTP client:

$ pip install httpie

Now let’s try loading our application:

$ uvicorn asgilook.app:app
INFO:     Started server process [2020]
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Waiting for application startup.
INFO:     Application startup complete.

Let’s verify it works by trying to access the URL provided above by uvicorn:

$ http http://127.0.0.1:8000
HTTP/1.1 404 Not Found
content-length: 0
content-type: application/json
date: Sun, 05 Jul 2020 13:37:01 GMT
server: uvicorn

Woohoo, it works!!!

Well, sort of. Onwards to adding some real functionality!

Configuration

As in the WSGI “Look” tutorial, we are going to configure at least the storage location. There are many approaches to handling application configuration; see also a related discussion in our FAQ: What is the recommended approach for making configuration variables available to multiple resource classes?

In this tutorial, we’ll just pass around a Config instance to resource initializers for easier testing (coming later in this tutorial). Create a new module, config.py next to app.py, and add the following code to it:

import os
import pathlib
import uuid


class Config:
    DEFAULT_CONFIG_PATH = '/tmp/asgilook'
    DEFAULT_UUID_GENERATOR = uuid.uuid4

    def __init__(self):
        self.storage_path = pathlib.Path(
            os.environ.get('ASGI_LOOK_STORAGE_PATH', self.DEFAULT_CONFIG_PATH))
        self.storage_path.mkdir(parents=True, exist_ok=True)

        self.uuid_generator = Config.DEFAULT_UUID_GENERATOR

Image Store

Since we are going to read and write image files, care needs to be taken of making file I/O non-blocking. We’ll give aiofiles a try:

pip install aiofiles

In addition, let’s twist the original WSGI “Look” design a bit, and convert all uploaded images to JPEG. Let’s try the popular Pillow library for that:

pip install Pillow

We can now implement a basic async image store as (save the following code as store.py next to app.py and config.py):

import asyncio
import datetime
import io

import aiofiles
import falcon
import PIL.Image


class Image:

    def __init__(self, config, image_id, size):
        self._config = config

        self.image_id = image_id
        self.size = size
        self.modified = datetime.datetime.utcnow()

    @property
    def path(self):
        return self._config.storage_path / self.image_id

    @property
    def uri(self):
        return f'/images/{self.image_id}.jpeg'

    def serialize(self):
        return {
            'id': self.image_id,
            'image': self.uri,
            'modified': falcon.dt_to_http(self.modified),
            'size': self.size,
        }


class Store:

    def __init__(self, config):
        self._config = config
        self._images = {}

    def _load_from_bytes(self, data):
        return PIL.Image.open(io.BytesIO(data))

    def _convert(self, image):
        rgb_image = image.convert('RGB')

        converted = io.BytesIO()
        rgb_image.save(converted, 'JPEG')
        return converted.getvalue()

    def get(self, image_id):
        return self._images.get(image_id)

    def list_images(self):
        return sorted(self._images.values(), key=lambda item: item.modified)

    async def save(self, image_id, data):
        loop = asyncio.get_running_loop()
        image = await loop.run_in_executor(None, self._load_from_bytes, data)
        converted = await loop.run_in_executor(None, self._convert, image)

        path = self._config.storage_path / image_id
        async with aiofiles.open(path, 'wb') as output:
            await output.write(converted)

        stored = Image(self._config, image_id, image.size)
        self._images[image_id] = stored
        return stored

Here we store data using aiofiles, and run Pillow image transformation functions in the default ThreadPoolExecutor, hoping that at least some of these image operations release the GIL during processing.

The ProcessPoolExecutor is another alternative for long running tasks that do not release the GIL, such as CPU-bound pure Python code. Note, however, that ProcessPoolExecutor builds upon the multiprocessing module, and thus inherits its caveats: higher synchronization overhead, the requirement for the task and its arguments to be picklable (which also implies that the task must be reachable from the global namespace, i.e., an anonymous lambda simply won’t work).

Images Resource(s)

In the ASGI flavor of Falcon, all responder methods, hooks and middleware methods must be awaitable coroutines. With that in mind, let’s go on to implement the image collection, and the individual image resources (the code below should go into images.py):

import aiofiles
import falcon


class Images:

    def __init__(self, config, store):
        self._config = config
        self._store = store

    async def on_get(self, req, resp):
        resp.media = [image.serialize() for image in self._store.list_images()]

    async def on_get_image(self, req, resp, image_id):
        # NOTE: image_id: UUID is converted back to a string identifier.
        image = self._store.get(str(image_id))
        resp.stream = await aiofiles.open(image.path, 'rb')
        resp.content_type = falcon.MEDIA_JPEG

    async def on_post(self, req, resp):
        data = await req.stream.read()
        image_id = str(self._config.uuid_generator())
        image = await self._store.save(image_id, data)

        resp.location = image.uri
        resp.media = image.serialize()
        resp.status = falcon.HTTP_201

Since the first iteration on the Images class is quite lean, we opted for implementing two resources, image collection (which supports GET for listing the collection, and POST for uploading a new image) and single image (which supports GET for downloading the image), under one class employing responder name suffixes.

If the application continues to grow in complexity, it might get worth to make the code cleaner by splitting classes to strictly represent one RESTful resource per class. See also: What is the recommended way to map related routes to resource classes?

Note

Here, we serve the image by simply assigning an open aiofiles file to resp.stream.

Warning

In production deployment, serving files directly from the web server, rather than through the Falcon ASGI app, will likely be more efficient, and therefore should be preferred. See also: Can Falcon serve static files?

Also worth noting is that the on_get_image responder will be receiving an image_id of type UUID. What is going on here? How will the image_id field, matched from a string path segment, now become a UUID?

Falcon’s default router supports simple validation and transformation using field converters. In this example, we will use the UUIDConverter to validate the image_id input as UUID. Converters are specified using their shorthand identifiers; for instance, the route corresponding to on_get_image will look like (see also the next chapter):

/images/{image_id:uuid}.jpeg

Since our application is still internally centered on string identifiers, feel free to experiment with refactoring the image Store to use UUIDs natively!

(Alternatively, one could implement a custom field converter to use uuid only for validation, but return an unmodified string.)

Note

In contrast to asynchronous building blocks (responders, middleware, hooks etc) of a Falcon ASGI application, field converters are simple synchronous data transformation functions that are not expected to perform any I/O.

Running Our Application

Let’s refactor our app.py to allow create_app()ing whenever we need it, be it tests or the ASGI application module:

import falcon.asgi

from .config import Config
from .images import Images
from .store import Store


def create_app(config=None):
    config = config or Config()
    store = Store(config)
    images = Images(config, store)

    app = falcon.asgi.App()
    app.add_route('/images', images)
    app.add_route('/images/{image_id:uuid}.jpeg', images, suffix='image')

    return app

But how about route suffixes for the Images class? Here, we have to remember to map the single image resource to the '/images/{image_id:uuid}.jpeg' URI template using the 'image' suffix in the respective add_route call, as well as specify the uuid field converter as discussed in the previous chapter.

The ASGI application now resides in asgi.py:

from .app import create_app

app = create_app()

Running the application is not too dissimilar from the previous command line:

$ uvicorn asgilook.asgi:app

Provided uvicorn is started as per the above command line, let’s try uploading some images:

$ http POST localhost:8000/images @/home/user/Pictures/test.png

HTTP/1.1 201 Created
content-length: 173
content-type: application/json
date: Tue, 24 Dec 2019 17:32:18 GMT
location: /images/5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c.jpeg
server: uvicorn

{
    "id": "5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c",
    "image": "/images/5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c.jpeg",
    "modified": "Tue, 24 Dec 2019 17:32:19 GMT",
    "size": [
        462,
        462
    ]
}

Accessing the newly uploaded image:

$ http localhost:8000/images/5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c.jpeg

HTTP/1.1 200 OK
content-type: image/jpeg
date: Tue, 24 Dec 2019 17:34:53 GMT
server: uvicorn
transfer-encoding: chunked

+-----------------------------------------+
| NOTE: binary data not shown in terminal |
+-----------------------------------------+

We could also open the link in the web browser to verify the converted JPEG image looks as intended.

Let’s check the image collection now:

$ http localhost:8000/images

HTTP/1.1 200 OK
content-length: 175
content-type: application/json
date: Tue, 24 Dec 2019 17:36:31 GMT
server: uvicorn

[
    {
        "id": "5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c",
        "image": "/images/5cfd9fb6-259a-4c72-b8b0-5f4c35edcd3c.jpeg",
        "modified": "Tue, 24 Dec 2019 17:32:19 GMT",
        "size": [
            462,
            462
        ]
    }
]

The application file layout should now look like:

asgilook
├── .venv
└── asgilook
    ├── __init__.py
    ├── app.py
    ├── asgi.py
    ├── config.py
    ├── images.py
    └── store.py

Dynamic Thumbnails

Let’s pretend our image service customers want to render images in multiple resolutions, for instance, as srcset for responsive HTML images or other purposes.

Let’s add a new method Store.make_thumbnail() to perform scaling on the fly:

async def make_thumbnail(self, image, size):
    async with aiofiles.open(image.path, 'rb') as img_file:
        data = await img_file.read()

    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(None, self._resize, data, size)

As well as an internal helper to run the Pillow thumbnail operation that is offloaded to a threadpool executor, again, in hoping that Pillow can release the GIL for some operations:

def _resize(self, data, size):
    image = PIL.Image.open(io.BytesIO(data))
    image.thumbnail(size)

    resized = io.BytesIO()
    image.save(resized, 'JPEG')
    return resized.getvalue()

The store.Image class can be extended to also return URIs to thumbnails:

def thumbnails(self):
    def reductions(size, min_size):
        width, height = size
        factor = 2
        while width // factor >= min_size and height // factor >= min_size:
            yield (width // factor, height // factor)
            factor *= 2

    return [
        f'/thumbnails/{self.image_id}/{width}x{height}.jpeg'
        for width, height in reductions(
            self.size, self._config.min_thumb_size)]

Here, we are refererring to downsized resolutions in advance, and the actual scaling will happen on the fly upon requesting these URIs.

We choose to provide a series of thumbnail images, where each image is approximately twice (four times area-wise) smaller than the previous one, similarly to how mipmapping works in computer graphics. You may want to tune this resolution distribution to better match the sizes that are common in your application.

Furthermore, it is practical to impose a minimum resolution, as any potential benefit from switching between very small thumbnails (a few kilobytes each) is likely to be overshadowed by the request overhead. As you may have noticed in the above snippet, we are referencing this lower size limit as self._config.min_thumb_size. The revised configuration with support for min_thumb_size (by default initialized to 64 pixels) reads:

import os
import pathlib
import uuid


class Config:
    DEFAULT_CONFIG_PATH = '/tmp/asgilook'
    DEFAULT_MIN_THUMB_SIZE = 64
    DEFAULT_UUID_GENERATOR = uuid.uuid4

    def __init__(self):
        self.storage_path = pathlib.Path(
            os.environ.get('ASGI_LOOK_STORAGE_PATH', self.DEFAULT_CONFIG_PATH))
        self.storage_path.mkdir(parents=True, exist_ok=True)

        self.uuid_generator = Config.DEFAULT_UUID_GENERATOR
        self.min_thumb_size = self.DEFAULT_MIN_THUMB_SIZE

The updated store.py should now look like:

import asyncio
import datetime
import io

import aiofiles
import falcon
import PIL.Image


class Image:

    def __init__(self, config, image_id, size):
        self._config = config

        self.image_id = image_id
        self.size = size
        self.modified = datetime.datetime.utcnow()

    @property
    def path(self):
        return self._config.storage_path / self.image_id

    @property
    def uri(self):
        return f'/images/{self.image_id}.jpeg'

    def serialize(self):
        return {
            'id': self.image_id,
            'image': self.uri,
            'modified': falcon.dt_to_http(self.modified),
            'size': self.size,
            'thumbnails': self.thumbnails(),
        }

    def thumbnails(self):
        def reductions(size, min_size):
            width, height = size
            factor = 2
            while width // factor >= min_size and height // factor >= min_size:
                yield (width // factor, height // factor)
                factor *= 2

        return [
            f'/thumbnails/{self.image_id}/{width}x{height}.jpeg'
            for width, height in reductions(
                self.size, self._config.min_thumb_size)]


class Store:

    def __init__(self, config):
        self._config = config
        self._images = {}

    def _load_from_bytes(self, data):
        return PIL.Image.open(io.BytesIO(data))

    def _convert(self, image):
        rgb_image = image.convert('RGB')

        converted = io.BytesIO()
        rgb_image.save(converted, 'JPEG')
        return converted.getvalue()

    def _resize(self, data, size):
        image = PIL.Image.open(io.BytesIO(data))
        image.thumbnail(size)

        resized = io.BytesIO()
        image.save(resized, 'JPEG')
        return resized.getvalue()

    def get(self, image_id):
        return self._images.get(image_id)

    def list_images(self):
        return sorted(self._images.values(), key=lambda item: item.modified)

    async def make_thumbnail(self, image, size):
        async with aiofiles.open(image.path, 'rb') as img_file:
            data = await img_file.read()

        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(None, self._resize, data, size)

    async def save(self, image_id, data):
        loop = asyncio.get_running_loop()
        image = await loop.run_in_executor(None, self._load_from_bytes, data)
        converted = await loop.run_in_executor(None, self._convert, image)

        path = self._config.storage_path / image_id
        async with aiofiles.open(path, 'wb') as output:
            await output.write(converted)

        stored = Image(self._config, image_id, image.size)
        self._images[image_id] = stored
        return stored

Let’s also add a new Thumbnails resource to expose the new functionality. The final version of images.py reads:

import aiofiles
import falcon


class Images:

    def __init__(self, config, store):
        self._config = config
        self._store = store

    async def on_get(self, req, resp):
        resp.media = [image.serialize() for image in self._store.list_images()]

    async def on_get_image(self, req, resp, image_id):
        # NOTE: image_id: UUID is converted back to a string identifier.
        image = self._store.get(str(image_id))
        if not image:
            raise falcon.HTTPNotFound

        resp.stream = await aiofiles.open(image.path, 'rb')
        resp.content_type = falcon.MEDIA_JPEG

    async def on_post(self, req, resp):
        data = await req.stream.read()
        image_id = str(self._config.uuid_generator())
        image = await self._store.save(image_id, data)

        resp.location = image.uri
        resp.media = image.serialize()
        resp.status = falcon.HTTP_201


class Thumbnails:

    def __init__(self, store):
        self._store = store

    async def on_get(self, req, resp, image_id, width, height):
        image = self._store.get(str(image_id))
        if not image:
            raise falcon.HTTPNotFound
        if req.path not in image.thumbnails():
            raise falcon.HTTPNotFound

        resp.content_type = falcon.MEDIA_JPEG
        resp.data = await self._store.make_thumbnail(image, (width, height))

Adding a new thumbnails route in app.py is left as an exercise for the reader.

Tip

Draw inspiration from the thumbnail URI formatting string:

f'/thumbnails/{self.image_id}/{width}x{height}.jpeg'

The actual URI template for the thumbnails route should look quite similar to the above.

Remember that we want to use the uuid converter for the image_id field, and image dimensions (width and height) should ideally be converted to ints.

(If you get stuck, see the final version of app.py later in this tutorial.)

Note

If you tried to access a non-existent route (e.g., if you forgot to add an intended route, or simply misspelled the URI), the framework would automatically render an HTTP 404 Not Found response by raising an instance of HTTPNotFound (unless that exception is intercepted by a custom error handler, or if the path matches a sink prefix).

Conversely, if a route was matched, but there is no responder for the HTTP method in question, Falcon would render HTTP 405 Method Not Allowed via HTTPMethodNotAllowed.

The new thumbnails end-point should now render thumbnails on-the-fly:

$ http POST localhost:8000/images @/home/user/Pictures/test.png

HTTP/1.1 201 Created
content-length: 319
content-type: application/json
date: Tue, 24 Dec 2019 18:58:20 GMT
location: /images/f2375273-8049-4b10-b17e-8851db9ac7af.jpeg
server: uvicorn

{
    "id": "f2375273-8049-4b10-b17e-8851db9ac7af",
    "image": "/images/f2375273-8049-4b10-b17e-8851db9ac7af.jpeg",
    "modified": "Tue, 24 Dec 2019 18:58:21 GMT",
    "size": [
        462,
        462
    ],
    "thumbnails": [
        "/thumbnails/f2375273-8049-4b10-b17e-8851db9ac7af/231x231.jpeg",
        "/thumbnails/f2375273-8049-4b10-b17e-8851db9ac7af/115x115.jpeg"
    ]
}


$ http localhost:8000/thumbnails/f2375273-8049-4b10-b17e-8851db9ac7af/115x115.jpeg

HTTP/1.1 200 OK
content-length: 2985
content-type: image/jpeg
date: Tue, 24 Dec 2019 19:00:14 GMT
server: uvicorn

+-----------------------------------------+
| NOTE: binary data not shown in terminal |
+-----------------------------------------+

Again, we could also verify thumbnail URIs in the browser or image viewer that supports HTTP input.

Caching Responses

Although scaling thumbnails on-the-fly sounds cool, and we also avoid many pesky small files littering our storage, it consumes CPU resources, and we would soon find our application crumbling under load.

Let’s thus implement response caching in Redis, utilizing aioredis for async support:

pip install aioredis

We will also need to serialize response data (the Content-Type header and the body in the first version); msgpack should do:

pip install msgpack

Our application will obviously need access to a Redis server. Apart from just installing Redis server on your machine, one could also:

• Spin up Redis in Docker, eg:

  docker run -p 6379:6379 redis
  

• Assuming Redis is installed on the machine, one could also try pifpaf for spinning up Redis just temporarily for uvicorn:

  pifpaf run redis -- uvicorn asgilook.asgi:app
  

We are going to perform caching in Falcon Middleware. Again, note that all middleware methods must be asynchronous; even initializing the Redis connection must be awaited. How to achieve that in the __init__() method?

ASGI application lifespan events come to the rescue. An ASGI application server emits these events upon application startup and shutdown. Let’s implement the process_startup handler in our middleware to execute code upon our application startup:

async def process_startup(self, scope, event):
    self.redis = await self._config.create_redis_pool(
        self._config.redis_host)

Warning

The Lifespan Protocol is an optional extention; please check if your ASGI server of choice implements it.

uvicorn (that we picked for this tutorial) supports Lifespan.

At minimum, our middleware will need to know the Redis host(s) to connect to. In addition, we are also going to make our Redis connection factory configurable in order to afford injecting different Redis client implementations for production and testing.

Assuming we call our new configuration items redis_host and create_redis_pool(), respectively, the final version of config.py now reads:

import os
import pathlib
import uuid

import aioredis


class Config:
    DEFAULT_CONFIG_PATH = '/tmp/asgilook'
    DEFAULT_MIN_THUMB_SIZE = 64
    DEFAULT_REDIS_HOST = 'redis://localhost'
    DEFAULT_REDIS_POOL = aioredis.create_redis_pool
    DEFAULT_UUID_GENERATOR = uuid.uuid4

    def __init__(self):
        self.storage_path = pathlib.Path(
            os.environ.get('ASGI_LOOK_STORAGE_PATH', self.DEFAULT_CONFIG_PATH))
        self.storage_path.mkdir(parents=True, exist_ok=True)

        self.create_redis_pool = Config.DEFAULT_REDIS_POOL
        self.min_thumb_size = self.DEFAULT_MIN_THUMB_SIZE
        self.redis_host = self.DEFAULT_REDIS_HOST
        self.uuid_generator = Config.DEFAULT_UUID_GENERATOR

A complete Redis cache component (cache.py) could look like:

import msgpack


class RedisCache:
    PREFIX = 'asgilook:'
    INVALIDATE_ON = frozenset({'DELETE', 'POST', 'PUT'})
    CACHE_HEADER = 'X-ASGILook-Cache'
    TTL = 3600

    def __init__(self, config):
        self._config = config

        # NOTE(vytas): To be initialized upon application startup (see the
        #   method below).
        self._redis = None

    async def _serialize_response(self, resp):
        data = await resp.render_body()
        return msgpack.packb([resp.content_type, data], use_bin_type=True)

    def _deserialize_response(self, resp, data):
        resp.content_type, resp.data = msgpack.unpackb(data, raw=False)
        resp.complete = True
        resp.context.cached = True

    async def process_startup(self, scope, event):
        if self._redis is None:
            self._redis = await self._config.create_redis_pool(
                self._config.redis_host)

    async def process_request(self, req, resp):
        resp.context.cached = False

        if req.method in self.INVALIDATE_ON:
            return

        key = f'{self.PREFIX}/{req.path}'
        data = await self._redis.get(key)
        if data is not None:
            self._deserialize_response(resp, data)
            resp.set_header(self.CACHE_HEADER, 'Hit')
        else:
            resp.set_header(self.CACHE_HEADER, 'Miss')

    async def process_response(self, req, resp, resource, req_succeeded):
        if not req_succeeded:
            return

        key = f'{self.PREFIX}/{req.path}'

        if req.method in self.INVALIDATE_ON:
            await self._redis.delete(key)
        elif not resp.context.cached:
            data = await self._serialize_response(resp)
            await self._redis.set(key, data, expire=self.TTL)

For caching to come into effect, we also need to add the RedisCache component to our application’s middleware list. The final definition of all components in app.py now is:

import falcon.asgi

from .cache import RedisCache
from .config import Config
from .images import Images, Thumbnails
from .store import Store


def create_app(config=None):
    config = config or Config()
    cache = RedisCache(config)
    store = Store(config)
    images = Images(config, store)
    thumbnails = Thumbnails(store)

    app = falcon.asgi.App(middleware=[cache])
    app.add_route('/images', images)
    app.add_route('/images/{image_id:uuid}.jpeg', images, suffix='image')
    app.add_route('/thumbnails/{image_id:uuid}/{width:int}x{height:int}.jpeg',
                  thumbnails)

    return app

Now, subsequent access to /thumbnails should be cached, as indicated by the x-asgilook-cache header:

$ http localhost:8000/thumbnails/167308e4-e444-4ad9-88b2-c8751a4e37d4/115x115.jpeg

HTTP/1.1 200 OK
content-length: 2985
content-type: image/jpeg
date: Tue, 24 Dec 2019 19:46:51 GMT
server: uvicorn
x-asgilook-cache: Hit

+-----------------------------------------+
| NOTE: binary data not shown in terminal |
+-----------------------------------------+

Note

Left as another exercise for the reader: individual images are streamed directly from aiofiles instances, and caching therefore does not work for them at the moment.

The project’s structure should now look like this:

asgilook
├── .venv
└── asgilook
    ├── __init__.py
    ├── app.py
    ├── asgi.py
    ├── cache.py
    ├── config.py
    ├── images.py
    └── store.py

Testing Our Application

So far, so good? We have only tested our application by sending a handful of requests manually. Have we tested all code paths? Have we covered typical user inputs to the application?

Having a comprehensive test suite is vital not only for verifying that application is correctly behaving at the moment, but also limiting the impact of future regressions that will be eventually introduced into the codebase.

In order to implement actual tests, we’ll need to revise our dependencies and decide which abstraction level we are after:

• Will we run a real Redis server?

• Will we store “real” files on a filesystem or just provide a fixture for aiofiles?

• Will we use mocks and monkey patching, or would we inject dependencies?

There is no right and wrong here, as different testing strategies (or a combination thereof) have their own advantages in terms of test running time, how easy it is to implement new tests, how close tests are to the “real” service, and so on.

Another thing to choose is a testing framework. Just as in the WSGI tutorial, let’s use pytest. This is a matter of taste; if you prefer xUnit/JUnit-style layout, you’ll feel at home with the stdlib’s unittest.

In order to deliver something working faster, we’ll allow our tests to access the real filesystem. As pytest offers various temporary directory out of the box, Let’s create a simple storage_path fixture shared among all tests in the whole suite (in the pytest parlance, a “session”-scoped fixture).

More in-depth documentation of pytest fixtures can be found here: pytest fixtures: explicit, modular, scalable.

As mentioned in the previous section, there are many ways to spin up a temporary or permanent Redis server; or mock it altogether. For our tests, we’ll try fakeredis, a pure Python implementation tailored specifically for writing unit tests.

pytest and fakeredis can be installed as:

$ pip install fakeredis pytest

While at it, we’ll also initialize the tests directory structure:

$ mkdir -p tests
$ touch tests/__init__.py

Let’s now write fixtures to replace uuid and aioredis, and inject them into our tests via conftest.py (place it in the newly created tests directory):

import io
import random
import uuid

import fakeredis.aioredis
import falcon.asgi
import falcon.testing
import PIL.Image
import PIL.ImageDraw
import pytest

from asgilook.app import create_app
from asgilook.config import Config


@pytest.fixture()
def predictable_uuid():
    fixtures = (
        uuid.UUID('36562622-48e5-4a61-be67-e426b11821ed'),
        uuid.UUID('3bc731ac-8cd8-4f39-b6fe-1a195d3b4e74'),
        uuid.UUID('ba1c4951-73bc-45a4-a1f6-aa2b958dafa4'),
    )

    def uuid_func():
        try:
            return next(fixtures_it)
        except StopIteration:
            return uuid.uuid4()

    fixtures_it = iter(fixtures)
    return uuid_func


@pytest.fixture(scope='session')
def storage_path(tmpdir_factory):
    return tmpdir_factory.mktemp('asgilook')


@pytest.fixture
def client(predictable_uuid, storage_path):
    config = Config()
    config.create_redis_pool = fakeredis.aioredis.create_redis_pool
    config.redis_host = None
    config.storage_path = storage_path
    config.uuid_generator = predictable_uuid

    app = create_app(config)
    return falcon.testing.TestClient(app)


@pytest.fixture(scope='session')
def png_image():
    image = PIL.Image.new('RGBA', (640, 360), color='black')

    draw = PIL.ImageDraw.Draw(image)
    for _ in range(32):
        x0 = random.randint(20, 620)
        y0 = random.randint(20, 340)
        x1 = random.randint(20, 620)
        y1 = random.randint(20, 340)
        if x0 > x1:
            x0, x1 = x1, x0
        if y0 > y1:
            y0, y1 = y1, y0
        draw.ellipse([(x0, y0), (x1, y1)], fill='yellow', outline='red')

    output = io.BytesIO()
    image.save(output, 'PNG')
    return output.getvalue()


@pytest.fixture(scope='session')
def image_size():
    def report_size(data):
        image = PIL.Image.open(io.BytesIO(data))
        return image.size

    return report_size

Note

In the png_image fixture above, we are drawing random images that will look different every time the tests are run.

If your testing flow affords that, it is often a great idea to introduce some unpredictability in your test inputs. This will provide more confidence that your application can handle a broader range of inputs than just 2-3 test cases crafted specifically for that sole purpose.

On the other hand, random inputs can make assertions less stringent and harder to formulate, so judge according to what is the most important for your application. You can also try to combine the best of both worlds by using a healthy mix of rigid fixtures and fuzz testing.

Note

More information on conftest.py's anatomy and pytest configuration can be found in the latter’s documentation: conftest.py: local per-directory plugins.

With the groundwork in place, we can write a simple test (called tests/test_images.py) that will attempt to simulate access our /images end-point:

def test_list_images(client):
    resp = client.simulate_get('/images')

    assert resp.status_code == 200
    assert resp.json == []

test_images.py can be run as:

$ pytest tests/test_images.py

========================= test session starts ==========================
platform linux -- Python 3.8.0, pytest-6.2.1, py-1.10.0, pluggy-0.13.1
rootdir: /falcon/tutorials/asgilook
collected 1 item

tests/test_images.py .                                           [100%]

========================== 1 passed in 0.01s ===========================

Success! 🎉

At this point, our project structure (containing the asgilook and test modules) should look like:

asgilook
├── .venv
├── asgilook
│   ├── __init__.py
│   ├── app.py
│   ├── asgi.py
│   ├── cache.py
│   ├── config.py
│   ├── images.py
│   └── store.py
└── tests
    ├── __init__.py
    ├── conftest.py
    └── test_images.py

Now, we need more tests!

Feel free to try writing some yourself. Otherwise, check out examples/asgilook/tests in the Falcon repository.

Code Coverage

How much of our asgilook code is covered by these tests?

An easy way to get a coverage report is using the pytest-cov plugin (available on PyPi).

The updated pytest command line to use this plugin reads:

$ pytest --cov=asgilook --cov-report=term-missing tests/

Oh, wow! We do happen to have full line coverage (except asgilook/asgi.py which is meant for the application server). We can instruct coverage to omit this file by listing it in the omit section of a .coveragerc file.

What is more, we could turn the current coverage into a future requirement by adding --cov-fail-under=100 (or any other percent threshold) to our pytest command.

Note

The pytest-cov plugin is quite simplistic; more advanced testing strategies such as blending different type of tests and/or running the same tests in multiple environments would most probably involve running coverage directly, and combining results.

What Now?

Congratulations, you have successfully completed the Falcon ASGI tutorial!

Needless to say, our first Falcon+ASGI application could still be improved in numerous ways:

• Make the image store persistent and reusable across worker processes. Maybe by using a database?

• Improve error handling for malformed images.

• Check how and when Pillow releases the GIL, and tune what is offloaded to a threadpool executor.

• Test Pillow-SIMD to boost performance.

• Publish image upload events via SSE or WebSockets.

• …And much more (patches welcome, as they say)!

Compared to the sync version, asynchronous code can at times be harder to design and reason about. Should you run into any issues, our friendly community is available to answer your questions and help you work through these sticky problems. See also: Getting Help.