A blog and website by Peter Bengtsson
22 January 2019 0 comments Python, Django
tl;dr; If you use the django.views.decorators.cache.cache_control decorator, consider this one instead to change the max_age depending on the request.
I had/have a Django view function that looks something like this:
@cache_control(public=True, max_age=60 * 60)
def home(request, oc=None, page=1):
...
But, that number 60 * 60 I really needed it to be different depending on the request parameters. For example, that oc=None, if that's not None
I know the page's Cache-Control header can and should be different.
So I wrote this decorator:
from django.utils.cache import patch_cache_control
def variable_cache_control(**kwargs):
"""Same as django.views.decorators.cache.cache_control except this one will
allow the `max_age` parameter be a callable.
"""
def _cache_controller(viewfunc):
@functools.wraps(viewfunc)
def _cache_controlled(request, *args, **kw):
response = viewfunc(request, *args, **kw)
copied = kwargs
if kwargs.get("max_age") and callable(kwargs["max_age"]):
max_age = kwargs["max_age"](request, *args, **kw)
# Can't re-use, have to create a shallow clone.
copied = dict(kwargs, max_age=max_age)
patch_cache_control(response, **copied)
return response
return _cache_controlled
return _cache_controller
Now, I can do this instead:
def _best_max_age(req, oc=None, **kwargs):
max_age = 60 * 60
if oc:
max_age *= 10
return max_age
@variable_cache_control(public=True, max_age=_best_max_age)
def home(request, oc=None, page=1):
...
I hope it inspires.
15 August 2018 0 comments Django, Web development, Python
tl;dr; I wrote my own extension to django-pipeline that uses Zopfli to create .gz files from static assets collected in Django. Here's the code.
Nginx and Gzip
What I wanted was to continue to use django-pipeline which does a great job of reading a
settings.BUNDLES setting and generating things like /static/js/myapp.min.a206ec6bd8c7.js. It has configurable options to not just make those files but also generate /static/js/myapp.min.a206ec6bd8c7.js.gz which means that with gzip_static in Nginx, Nginx doesn't have to Gzip compress static files on-the-fly but can basically just read it from disk. Nginx doesn't care how the file got there but an immediate advantage of preparing the file on disk is that the compression can be higher (smaller .gz files). That means smaller responses to be sent to the client and less CPU work needed from Nginx. Your job is to set gzip_static on; in your Nginx config (per location) and make sure every compressable file exists on disk with the same name but with the .gz suffix.
In other words, when the client does
GET https://example.com/static/foo.js Nginx quickly does a read on the file system to see if there exists a ROOT/static/foo.js.gz and if so, return that. If the files doesn't exist, and you have gzip on; in your config, Nginx will read the ROOT/static/foo.js into memory, compress it (usually with a lower compression level) and return that. Nginx takes care of figuring out whether to do this, at all, dynamically by reading the Accept-Encoding header from the request.
Zopfli
The best solution today to generate these .gz files is Zopfli. Zopfli is slower than good old regular gzip but the files get smaller. To manually compress a file you can install the zopfli executable (e.g. brew install zopfli or apt install zopfli) and then run zopfli $ROOT/static/foo.js which creates a
$ROOT/static/foo.js.gz file.
So your task is to build some pipelining code that generates .gz version of every static file your Django server creates.
At first I tried django-static-compress which has an extension to regular Django staticfiles storage. The default staticfiles storage is django.contrib.staticfiles.storage.StaticFilesStorage and that's what django-static-compress extends.
But I wanted more. I wanted all the good bits from django-pipeline (minification, hashes in filenames, concatenation, etc.) Also, in django-static-compress you can't control the parameters to zopfli such as the number of iterations. And with django-static-compress you have to install Brotli which I can't use because I don't want to compile my own Nginx.
Solution
So I wrote my own little mashup. I took some ideas from how django-pipeline does regular gzip compression as a post-process step. And in my case, I never want to bother with any of the other files that are put into the settings.STATIC_ROOT directory from the collectstatic command.
Here's my implementation: peterbecom.storage.ZopfliPipelineCachedStorage. Check it out. It's very tailored to my personal preferences and usecase but it works great. To use it, I have this in my settings.py: STATICFILES_STORAGE = "peterbecom.storage.ZopfliPipelineCachedStorage"
I know what you're thinking
Why not try to get this into django-pipeline or
into django-compress-static. The answer is frankly laziness. Hopefully someone else can pick up this task. I have fewer and fewer projects where I use Django to handle static files. These days most of my projects are single-page-apps that are 100% static and using Django for XHR requests to get the data.
14 August 2018 2 comments Redis, Django, Web development, Python
Here's the code. It's quick-n-dirty but it works wonderfully:
import functools
import hashlib
from django.core.cache import cache
from django.utils.encoding import force_bytes
def lock_decorator(key_maker=None):
"""
When you want to lock a function from more than 1 call at a time.
"""
def decorator(func):
@functools.wraps(func)
def inner(*args, **kwargs):
if key_maker:
key = key_maker(*args, **kwargs)
else:
key = str(args) + str(kwargs)
lock_key = hashlib.md5(force_bytes(key)).hexdigest()
with cache.lock(lock_key):
return func(*args, **kwargs)
return inner
return decorator
How To Use It
This has saved my bacon more than once. I use it on functions that really need to be made synchronous. For example, suppose you have a function like this:
def fetch_remote_thing(name):
try:
return Thing.objects.get(name=name).result
except Thing.DoesNotExist:
# Need to go out and fetch this
result = some_internet_fetching(name) # Assume this is sloooow
Thing.objects.create(name=name, result=result)
return result
That function is quite dangerous because if executed by two concurrent web requests for example, they will trigger
two "identical" calls to some_internet_fetching and if the database didn't have the name already, it will most likely trigger two calls to Thing.objects.create(name=name, ...) which could lead to integrity errors or if it doesn't the whole function breaks down because it assumes that there is only 1 or 0 of these Thing records.
Easy to solve, just add the lock_decorator:
@lock_decorator()
def fetch_remote_thing(name):
try:
return Thing.objects.get(name=name).result
except Thing.DoesNotExist:
# Need to go out and fetch this
result = some_internet_fetching(name) # Assume this is sloooow
Thing.objects.create(name=name, result=result)
return result
Now, thanks to Redis distributed locks, the function is always allowed to finish before it starts another one. All the hairy locking (in particular, the waiting) is implemented deep down in Redis which is rock solid.
Bonus Usage
Another use that has also saved my bacon is functions that aren't necessarily called with the same input argument but each call is so resource intensive that you want to make sure it only does one of these at a time. Suppose you have a Django view function that does some resource intensive work and you want to stagger the calls so that it only runs it one at a time. Like this for example:
def api_stats_calculations(request, part):
if part == 'users-per-month':
data = _calculate_users_per_month() # expensive
elif part == 'pageviews-per-week':
data = _calculate_pageviews_per_week() # intensive
elif part == 'downloads-per-day':
data = _calculate_download_per_day() # slow
elif you == 'get' and the == 'idea':
...
return http.JsonResponse({'data': data})
If you just put @lock_decorator() on this Django view function, and you have some (almost) concurrent calls to this function, for example from a uWSGI server running with threads and multiple processes, then it will not synchronize the calls.
The solution to this is to write your own function for generating the lock key, like this for example:
@lock_decorator(
key_maker=lamnbda request, part: 'api_stats_calculations'
)
def api_stats_calculations(request, part):
if part == 'users-per-month':
data = _calculate_users_per_month() # expensive
elif part == 'pageviews-per-week':
data = _calculate_pageviews_per_week() # intensive
elif part == 'downloads-per-day':
data = _calculate_download_per_day() # slow
elif you == 'get' and the == 'idea':
...
return http.JsonResponse({'data': data})
Now it works.
How Time-Expensive Is It?
Perhaps you worry that 99% of your calls to the function don't have the problem of calling the function concurrently. How much is this overhead of this lock costing you? I wondered that too and set up a simple stress test where I wrote a really simple Django view function. It looked something like this:
@lock_decorator(key_maker=lambda request: 'samekey')
def sample_view_function(request):
return http.HttpResponse('Ok\n')
I started a Django server with uWSGI with multiple processors and threads enabled. Then I bombarded this function with a simple concurrent stress test and observed the requests per minute. The cost was extremely tiny and almost negligable (compared to not
using the lock decorator). Granted, in this test I used Redis on redis://localhost:6379/0 but generally the conclusion was that the call is extremely fast and not something to worry too much about. But your mileage may vary so do your own experiments for your context.
What's Needed
You need to use django-redis as your Django cache backend. I've blogged before about using django-redis, for example Fastest cache backend possible for Django and Fastest Redis configuration for Django.
13 August 2018 0 comments Django, Web development, Python
django-html-validator is a Django project that can validate your generated HTML. It does so by sending the HTML to https://html5.validator.nu/ or you can start your own Java server locally with vnu.jar from here. The output is that you can have validation errors printed to stdout or you can have them put as .txt
files in a temporary directory. You can also include it in your test suite and make it so that tests fail if invalid HTML is generated during rendering in Django unit tests.
The project seems to have become a lot more popular than I thought it would. It started as a one-evening-hack and because there was interest I wrapped it up in a proper project with "docs" and set up CI for future contributions.
I kinda of forgot the project since almost all my current projects generate JSON on the server and generates the DOM on-the-fly with client-side JavaScript but apparently a lot of issues and PRs were filed related to making it work in Django 2.x. So I took the time last night to tidy up the tox.ini etc. and the necessary compatibility fixes to make it work with but Django 1.8 up to Django 2.1. Pull request here.
Thank you all who contributed! I'll try to make a better job noticing filed issues in the future.
20 June 2018 0 comments Django, Web development, Python
I use this a lot. It has served me very well. The code:
import hashlib
import functools
import markus # optional
from django.core.cache import cache
from django import http
from django.utils.encoding import force_bytes, iri_to_uri
metrics = markus.get_metrics(__name__) # optional
def json_response_cache_page_decorator(seconds):
"""Cache only when there's a healthy http.JsonResponse response."""
def decorator(func):
@functools.wraps(func)
def inner(request, *args, **kwargs):
cache_key = 'json_response_cache:{}:{}'.format(
func.__name__,
hashlib.md5(force_bytes(iri_to_uri(
request.build_absolute_uri()
))).hexdigest()
)
content = cache.get(cache_key)
if content is not None:
# metrics is optional
metrics.incr(
'json_response_cache_hit',
tags=['view:{}'.format(func.__name__)]
)
return http.HttpResponse(
content,
content_type='application/json'
)
response = func(request, *args, **kwargs)
if (
isinstance(response, http.JsonResponse) and
response.status_code in (200, 304)
):
cache.set(cache_key, response.content, seconds)
return response
return inner
return decorator
To use it simply add to Django view functions that might return a http.JsonResponse. For example, something like this:
@json_response_cache_page_decorator(60)
def search(request):
q = request.GET.get('q')
if not q:
return http.HttpResponseBadRequest('no q')
results = search_database(q)
return http.JsonResponse({
'results': results,
})
The reasons I use this instead of django.views.decorators.cache.cache_page() is because of a couple of reasons.
cache_page generates cache keys that don't contain the view function name.cache_page tries to cache the whole http.HttpResponse instance which can't be serialized if you use the msgpack serializer.cache_page also sends Cache-Control headers which is not always what you want.- Not possible to inject your own custom code such as my usage of
metrics.
Disclaimer: This snippet of code comes from a side-project
that has a very specific set of requirements. They're rather unique to that project and I have a full picture of the needs. E.g. I know what specific headers matter and don't matter. Your project might be different. For example, perhaps you don't have markus to handle your metrics. Or perhaps you need to re-write the query string for something to normalize the cache key differently. Point being, take the snippet of code as inspiration when you too find that django.views.decorators.cache.cache_page() isn't good enough for your Django view functions.
11 May 2018 1 comment Django, Python
By default, when you hook up a model to Django REST Framework and run a query in JSON format, what you get is a list. E.g.
For GET localhost:8000/api/mymodel/
[
{"id": 1, "name": "Foo"},
{"id": 2, "name": "Bar"},
{"id": 3, "name": "Baz"}
]
This isn't great because there's no good way to include other auxiliary data points that are relevant to this query. In Elasticsearch you get something like this:
{
"took": 106,
"timed_out": false,
"_shards": {},
"hits": {
"total": 0,
"hits": [],
"max_score": 1
}
}
Another key is that perhaps today
you can't think of any immediate reason why you want to include some additonal meta data about the query, but perhaps some day you will.
The way to solve this in Django REST Framework is to override the list function in your Viewset classes.
Before
# views.py
# views.py
from rest_framework import viewsets
class BlogpostViewSet(viewsets.ModelViewSet):
queryset = Blogpost.objects.all().order_by('date')
serializer_class = serializers.BlogpostSerializer
After
# views.py
from rest_framework import viewsets
class BlogpostViewSet(viewsets.ModelViewSet):
queryset = Blogpost.objects.all().order_by('date')
serializer_class = serializers.BlogpostSerializer
def list(self, request, *args, **kwargs):
response = super().list(request, *args, **kwargs)
# Where the magic happens!
return response
Now, to re-wrap that, the response.data is a OrderedDict which you can change. Here's one way to do it:
# views.py
from rest_framework import viewsets
class BlogpostViewSet(viewsets.ModelViewSet):
queryset = Blogpost.objects.all().order_by('date')
serializer_class = serializers.BlogpostSerializer
def list(self, request, *args, **kwargs):
response = super().list(request, *args, **kwargs)
response.data = {
'hits': response.data,
}
return response
And if you want to do the same the "detail API" where you retrieve a single model instance, you can add an override to the retrieve method:
def retrieve(self, request, *args, **kwargs):
response = super().retrieve(request, *args, **kwargs)
response.data = {
'hit': response.data,
}
return response
That's it. Perhaps it's personal preference but if you, like me, prefers this style, this is how you do it. I like namespacing things instead of dealing with raw lists.
"Namespaces are one honking great idea -- let's do more of those!"
From import this
Note! This works equally when you enable pagination. Enabling pagination immediately changes the main result from a list to a dictionary. I.e. Instead of...
[
{"id": 1, "name": "Foo"},
{"id": 2, "name": "Bar"},
{"id": 3, "name": "Baz"}
]
you now get...
{
"count": 3,
"next": null,
"previous": null,
"items": [
{"id": 1, "name": "Foo"},
{"id": 2, "name": "Bar"},
{"id": 3, "name": "Baz"}
]
}
So if you apply the "trick" mentioned in this blog post you end up with...:
{
"hits": {
"count": 3,
"next": null,
"previous": null,
"items": [
{"id": 1, "name": "Foo"},
{"id": 2, "name": "Bar"},
{"id": 3, "name": "Baz"}
]
}
}
