Custom logic in the middle — dependency injection

What happens if we have code that is largely common, but want to do something different “in the middle”?

We are getting into more advanced territory now, so this page is heavier than the ones that have come before, but the techniques here are also very powerful and widely applicable.

Continuing our example of two different views both featuring lists of products, let’s add a new requirement, imitating the kind of complexity you will likely encounter in real projects.

Instead of using Django’s QuerySets as the basis for our list of products, we have to use a different API. Maybe it is a third party HTTP-based service, or our own service, but our entry point is a function that doesn’t take a QuerySet as an input. Perhaps like this:

def product_search(filters, page=1):
    ...
    return product_list

filters is a dictionary that contains product filtering info, with allowable keys defined elsewhere. Our display_product_list now needs to convert query string parameters from request.GET to something that can be passed as filters.

(For the sake of simplicity, we’re doing a much more basic kind of paging in this example, in contrast to what Paginator gives you with page counts etc.)

For special offers, however, we have been provided with a different function to use:

def special_product_search(filters, special_offer, page=1):
    ...
    return product_list

In addition, we have a further requirement: for our special offer page, after retrieving the list of products that will be displayed, we need to do some database logging to record the user, the special offer and the products that were displayed.

The point of all this is to set up a common requirement — something that applies to many programming situations, not just view functions:

How can we execute some custom logic in the middle of some common logic?

We can think of this is as just another example of parameterisation. We need a parameter that will capture “what we need to do in the middle”.

Let’s start with the easier case — just the product_list view, factored out as before into the main view and the display_product_list function it delegates to. The latter now needs changing:

  1. It no longer takes a queryset parameter, but a searcher parameter.

  2. It has to be adapted to use this searcher parameter instead of manipulating a passed in QuerySet.

Something like this:

from somewhere import product_search

def product_list(request):
    return display_product_list(
        request,
        searcher=product_search,
        template_name='shop/product_list.html',
    )

def display_product_list(request, *, context=None, searcher, template_name):
    if context is None:
        context = {}
    filters = collect_filtering_parameters(request)
    try:
        page = int(request.GET['page'])
    except (KeyError, ValueError):
        page = 1
    context['products'] = searcher(filters, page=page)
    return TemplateResponse(request, template_name, context)

To explain a little: here we passed the product_search function into display_product_list as the parameter searcher. This feature is called “first class functions” — just like you can pass around any other data as a parameter, you can pass around functions too. That is the heart of the technique here, allowing us to insert our custom logic into the middle of the common logic.

But what about the special_offer_detail view? If we pass searcher=special_product_search, inside display_product_list we’ll have a problem. Our passed in function gets called like this:

searcher(filters, page=page)

But that doesn’t match the signature of special_product_search, which has an extra parameter. How can we get that parameter passed?

You might be tempted to make display_product_list accept the additional parameters needed, but this is clunky — we’ll have to pass these parameters that it doesn’t care about, just so that it can pass them on to somewhere else. Plus it is unnecessary.

Instead, what we do is make special_offer_detail provide a wrapper function that matches the signature that display_product_list expects for searcher. Inside the wrapper function, we’ll call the special_product_search function the way it needs to be called. While we’re at it, we can do our additional requirements too.

It looks like this, assuming we’ve written log_special_offer_product_view function for the extra logging:

from somewhere import special_product_search

def special_offer_detail(request, slug):
    special_offer = get_object_or_404(SpecialOffer.objects.all(), slug=slug)

    def special_product_search_wrapper(filters, page=1):
        products = special_product_search(filters, special_offer, page=page)
        log_special_offer_product_view(request.user, special_offer, products)
        return products

    return display_product_list(
        request,
        context={
            'special_offer': special_offer,
        },
        searcher=special_product_search_wrapper,
        template_name='products/special_offer_detail.html',
    })

There are some important things to note about this:

  • We defined our wrapper function special_product_search_wrapper inside the body of the main view. This is important for the functionality that follows. (There are other ways to do it but this is the simplest.)

  • We made its signature match the one expected by display_product_list.

  • Our wrapper function has access to the special_offer object from the enclosing scope, and also request. These objects “stay with it” when the wrapper function gets passed to display_product_list, so they are able to use them despite not having been passed them as a normal arguments.

    Functions that behave in this way are called “closures” — they capture variables from their enclosing scope.

This powerful technique has lots of great advantages. For one, display_product_list never needs to be concerned with all of this. We don’t have to modify its signature, nor the signature of the searcher parameter it expects. Also, this works really well with static analysis, like the linters that are built-in to many IDEs which can point out undefined names and so on.

Closures are a concept that some find intimidating, but they are extremely useful in a wide variety of programming situations. If you found the above confusing, have a look at this Python closures primer and then come back to the more complex example here.

In our theme of re-using logic, I want to cover Preconditions, but before that we’re going to go back to some basics, the first of which is Redirects and then Forms.

Note — terminology

In OO languages, the standard solution to this problem is the “strategy pattern”. That involves creating an object which can encapsulate the action you need to take.

In Python, functions are “first class objects“ i.e. objects that you can pass around just like every other type of value. So we can just use “functions” where we need “the strategy pattern”, particularly if our strategy has only one part to it. If you have more than one entry point that you need to bundle together, a class can be helpful.

A slightly more general concept is “dependency injection”. If you have some code that needs to do something, i.e. it has a dependency on some other code, instead of depending directly, the dependency gets injected from the outside. If our dependency is a just a single function call, we can simply accept a function as a parameter. If our dependency is a set of related function calls, we might want an object with methods as the parameter.

Often you will hear the term “dependency injection” being used for things that go one step further, and inject dependencies automatically in some way. I call these “dependency injection frameworks/containers”. Outside of pytest’s fixtures I have not yet found a need or desire for these in Python.

So, we can call this pattern “first class functions”, or “callbacks”, “strategy pattern” or “dependency injection”. But dependency injection is clearly the coolest sounding, so I used that in the title.

Discussion: DI vs inheritance

In contrast to the pattern I’m suggesting here (dependency injection / strategy / first class functions), Django’s CBVs opt for inheritance as the basic method of customisation, resulting in the need for class attributes and method overrides.

Inheritance brings with it the problems we’ve discussed under helpers vs mixins.

To make it more concrete, suppose we had solved the above custom-logic-in-the-middle problem by using inheritance and the template method pattern, in which we have a base class that calls an abstract do_product_search method, and two subclasses which each implement that method. The base class might look something like this:

class ProductSearchBase(TemplateView):
    def get_context_data(self, **kwargs):
        context = super().get_context_data(**kwargs)
        filters = collect_filtering_parameters(self.request)
        try:
            page = int(self.request.GET['page'])
        except (KeyError, ValueError):
            page = 1
        context['products'] = self.product_search(filters, page=page)
        return context

    def product_search(self, filters, page=1):
        raise NotImplementedError()

Now, how do we implement product_search for our “special offer“ subclass? To call special_product_search, we need access to the special_offer object that we already looked up in a different method. Note that we’ve got the same problem as before — in both cases we need some way to adapt our common code to call functions with two different signatures.

We could solve this by saving the object onto self, something like this:

class SpecialOfferDetail(ProductSearchBase):
    template_name = 'shop/special_offer_detail.html'

    def get(self, request, *args, **kwargs):
        special_offer = get_object_or_404(SpecialOffer.objects.all(), slug=kwargs['slug'])
        self.special_offer = special_offer
        return super().get(request, **kwargs)

    def product_search(self, filters, page=1):
        products = special_product_search(filters, self.special_offer, page=page)
        log_special_offer_product_view(self.request.user, self.special_offer, products)
        return products

    def get_context_data(self, **kwargs):
        context = super().get_context_data(**kwargs)
        context['special_offer'] = self.special_offer
        return context

In this solution, we have separate methods that are forced to communicate with each other by setting data on self. This is hacky and difficult to follow or reason about. Your product_search method now has some hidden inputs that could easily be missing. To be sure of correctness, you need to know the order in which your different methods are going to get called. When you are forced to use self like this, it’s worth reflecting on the objects are a poor man’s closures koan.

This kind of code is not uncommon with CBVs. For example, a lot of code that uses DetailView will need to use the fact that get_object method stores its result in self.object.

I recently refactored some CBV views that demonstrated exactly this issue into the FBV pattern I recommend above. The initial CBV views had a significant advantage over most CBVs you’ll find — I was using my own custom CBV base class, that I had specifically designed to avoid what I consider to be the worst features of Django’s offering.

Despite this advantage, rewriting as FBVs yielded immediate improvements. There was a noticeable reduction in length (542 tokens vs 631). But far more important and impressive was the fact that I completed the task without any errors — the new code had no bugs and passed all the tests first time.

Was this because I’m some kind of super-programmer? No, it was simply that my linter was pointing out every single mistake I made while I was moving code around. Once I had fixed all the “undefined name” and “unused variable” errors, I was done. The reason for this is that static analysis has a much easier time with code written using functions and closures.

The same static analysis is almost impossible with the CBV version. Half of the local variables become instance variables, and not set up in __init__ either. This means the analyser has to trace all the methods to see if any of them create the instance variables. Really, it then needs to check the order in which methods are called, to check whether they get set up before they are used. Most static analysis tools will not get very far with this, if they even attempt it, and it will be almost impossible to get past this line.

However, the static analysis tools we use are simply automating what you can do as a human. The fact that they fail with the CBV and succeed with the FBV is just pointing out to you the much greater complexity of the former, which has implications for any human maintainer of the code, as well as for tools.

I’m not using anything fancy in terms of linters, by the way — just flake8 integrated into my editor. If you want to go further and add type hints and use mypy, you will find it very easy to do with the approach I’ve outlined above, and make it possible to automatically verify even more things. On the other hand, if your CBV self object is a rag-bag of stuff as above it will be very hard for even the most advanced tools to help you.

pylint gets further than flake8 in trying to detect typos in instance variables, and does a pretty good job. However it cannot detect the ordering issue mentioned, and it also complains about us setting instance variables outside of __init__ (W0201 attribute-defined-outside-init) — a complaint which has some solid reasons, and is essentially recommending that you don’t structure our code like this. If you follow its recommendations you’ll (eventually) get yourself to the FBV.

When I had finished this refactoring, which in the end completely removed my custom CBV base class, I confess I had a little twinge of sadness — my final code seemed just a little bit… plain. I now had just a bunch of simple functions and a few closures, and fewer OOP hierarchies and clever tricks to feel smug about. But this is misplaced sadness. If you are into smugness-driven development, nothing can beat the feeling you get when you come back to some code 3 months or 3 years later and find it’s so straightforward to work with that, after doing git praise, you feel the need to give yourself a little hug.