A discussion on how we can design better software with Node.js
This blog post was originally posted at https://blog.octo.com/en/clean-node-part-1 on March 3, 2018.
I have been working with Node.js for 6 years now (started back in 2012 with 0.6.10). During these years, co-workers have been asking me the same question over and over again: “What does your application look like?”
Coming from Java, Python or Ruby, the question might sound irrelevant since frameworks such as Spring, Django or Rails and patterns like layered architectures or clean architectures offered answers some time ago. However, if you have worked with Node.js, you might be able to relate.
Indeed, the Node.js ecosystem has thrived these past years thanks to npm, its package manager (among other things, of course). And even though npm did a really good job at making small pieces of code available to all, the community has never really communicated on how these pieces should be orchestrated together. As a proof, take a look at https://github.com/sindresorhus/awesome-nodejs that gathers useful Node.js resources. This repository has 20k stars on GitHub and is an awesome list of cool and well-maintained libraries. Yet, I challenge you to find a single link discussing how these pieces can be assembled together in order to build a consistent and maintainable product.
Funnily enough, answers to this question exist and have been discussed for years: SOLID principles, Clean Architecture, Hexagonal Architecture, Clean Code, eXtreme Programming, TDD, BDD, DDD, etc. All these techniques and principles help us design our code better. What I think is definitely lacking, though, is the application of these in the Node.js realm.
This article is first of a three-part series discussing this exact topic and suggesting potential solutions. Each article will use a test-first approach, either unit or end-to-end:
- This first article will set a common ground: quick and dirty version with end to end tests and discussing common mistakes, general opinions and misconceptions;
- The second article will refactor the “quick and dirty” version with unit tests and a layered architecture;
- The third article will refactor the code using the Clean Architecture and unit tests.
Ideas and implementations are 100% open for discussions. Comment, issues and PRs are very welcome: https://github.com/simonrenoult/nodejs-application-architecture
Gears and features
In order to get started, we need a few tools and materials we can play with. To keep things mainstream and easily applicable to your context, we’ll stick to widespread technologies:
- API built with the Express framework
- Relational database with SQLite
- Sequelize as our ORM
- Bunyan for logging
- Mocha and Chai as testing and assertion libraries
We now need a use-case. Again, nothing fancy but some business rules to be realistic:
Shopping API with products, orders and bills.
- Can be created and listed
- Have an identifier, a name, a price and a weight
- Products can be sorted by name, price or weight
- Can be created and listed
- Have a status, a product list, a shipment amount, a total amount and a weight
- Orders status can be one of pending, paid or canceled
- Are offered 5% discount when the price exceeds 1000€
- Shipment costs 25€ for every 50kg (50€ for 100kg, 75€ for 150kg, etc.)
- Can be listed
- Have an amount and a creation date
- Are automatically generated when an order status is set to paid
Down the rabbit hole…
Quick and dirty
Most Node.js tools and frameworks embrace the first rule of the UNIX philosophy: “do one thing and do it well”. What has been missing however is the translation of the cooperation rules. Which means that in Node.js, the look of your application architecture mostly boils down to your software engineering experience.
To address this issue, we’ll start with a “quick and dirty” version in order to reveal recurrent design flaws Node.js developers can make. Please keep in mind that this code base is a combination of several common mistakes and might look silly to most of you.
We’ll start with a server using Express (note: we separated the server starting instructions in bin/start to ease testing):
We now add the body parser and the database connection which requires some refactoring in order to use async/await and a configuration file:
We’re now set to start the real work!
A bit of methodology
First I will start by choosing which set of features (that I call “epic”) I want my API to offer: product creation. In order to achieve this epic, I need to fulfil the following requirements:
- API returns HTTP code 201 when it succeeds
- API returns the resource location in the header Location
- API returns HTTP code 400 when product data is invalid
- API returns all the keys in error
We want our product to be fully functionally tested. Why? Because writing specifications as automated tests is future-proof: it prevents code updates and refactoring to break any production behavior. This implies the translation of our specifications (listed above) into end-to-end tests.
This practice is a derivative from ATDD where we start with product behavior specifications written as tests and only then implement these behaviors. This practice is also great to create discussions with product owners and business experts in order to tackle unexpected cases that might arise while discussing features. I say “derivative” because in this context there is no business expert.
The first feature
I like to start with error cases in order to get rid of the noise they create once we start thinking about the actual feature. Hence writing a test to check the HTTP code returned when product payload is invalid:
The matching implementation:
The second feature
We now test that error keys can be found in the response body:
The matching implementation:
The third feature
We can now check the success cases, starting with the server HTTP code response:
The matching implementation:
The fourth feature
We will now test that the API returns the resource location:
The matching implementation:
A pattern emerges…
Can you see where this practice leads us? We start adding blobs and blobs of code within the same anonymous function responsible of the product creation. And why would we do otherwise? It makes perfect sense from our functional test suite perspective. We could try to refactor but we have no incentive to go a different way we just did and no idea which way to go.
This code base has a few qualities which will help us refactor it:
- Relatively short: 200 lines
- Super low global complexity
- End to end tests
- Linter to maintain code style
- Variables are correctly named
- Magic strings and number are extracted
Where did we end up? We have added a few hundred lines of code with tests in total good faith. However, as the code grew, we kind of felt like something was going wrong, but what is it?
Well, it mostly boils down to two major design flaws:
- Tight coupling
- Multiple responsibility
See for yourself, the index.js file does almost everything:
- Server initialization
- Database connection
- Environment support
- Route declaration
- HTTP deserialization
- Configuration logic
- Business logic
- Database bindings
- HTTP serialization
What’s more, business and infrastructure logic depend on each other. For instance, in order to create an order, which is a real-life business use-case, we must extract information from the request body (infrastructure), then apply some format validation in order to work on consistent data (infrastructure), then calculate prices and discounts (business) and finally send the appropriate HTTP code (infrastructure).
But why would we like our code to be different? What changes would that make?
Dealing with the consequences
Software development is the encounter of business and crafting skills. In order to achieve this marriage with the highest outcome possible: as many features in the shortest time possible, the current code is kind of a pain to deal with for many reasons we list below.
Heavy cognitive load
One has to keep many things in mind to comprehend how the code works at the very top level of our application. How does the API achieve an order creation? A product listing? This should be visible in one glance. As you can see, it is not.
Also, the code lacks expressiveness. We do things but we never give these things a name. Instead, we have added comments that, while bringing meanings and explanations, also bring a lot of noise.
These are all technical details that hide both developer and business intentions.
How does one know how to create an order? Well, looking at the code we can see this:
This anonymous function is 70 lines long and does many things. However, what creating an order really boils down to is (from both technical and business perspectives):
- Validate what comes in the API
- Send HTTP error when something goes wrong
- Retrieve the product list from the order
- Calculate the shipment price, total weight and amount
- Save the order in the database
- Return the appropriate HTTP code
An appropriate code design should reflect these steps and hide the implementation details using higher levels of abstraction. By doing so, we are going to make tradeoffs, because that’s what design is: decrease the local complexity and increase the global one.
I started writing this application with functional tests first then implementing the requirements expressed in these tests. This is a great start in order to preserve API features across refactoring and code updates but it does not address the inner application design.
As revealed by our current implementation, we designed the code with a single application layer, hitting all the infrastructure layers: HTTP and database.
Doing a single (functional) test loop does not enforce any unit test driven development initiative. Such an approach would have helped us make the application design emerge off the unit test suite.
No code reuse
Our code base does not scale and is error-prone.
The piece of code below contains the error mapping logic and is repeated twice (lines 85 and 126). Duplicating this code each time we want the appropriate error message means that modifications have to be made at several locations in our code, implying more occasions to forget things or make mistakes.
Functional tests take time to run
Our functional test suite hits both network and database layers, this means four (de)serialization steps:
- HTTP to application
- Application to database
- Database to application
- Application to HTTP
Plus the database response delay.
If we consider that making a read operation on our database takes 50ms and a write operation takes 100ms, the test suite will take 22s to execute (as shown in the commit a877fc), which is awfully long for a feedback loop. And that’s just on our 200 lines application.
No way to test at the unit level
What if the business comes to me and ask to change the discount logic? Currently, I have to update my functional tests which, as mentioned before, are already in charge of ensuring many steps of the application logic.
Why should I bother with infrastructure details when all I want to ensure is the business logic my product owner asked me to implement? How do I get quick feedback on whether I broke an already existing rule?
Well, at the moment, we literally can’t: our design does not allow it. Here is the code we would like to test:
And this code is located between instructions doing completely different things! Above these are a few lines calculating the total amount and the shipment weight and prices, which are a completely unrelated set of business rules:
And below these lines is the creation of the client response body, which has more to do with infrastructure details than business logic:
Generally speaking, we have no way to test specific parts of our application. This leads to inappropriate test practices made of longer feedback loop (because tests will take more time to execute) and a black box approach since our test practice does not allow us to drive our implementation and make it de facto modular.
Misconceptions regarding software quality
Linting is the level 0 of software quality
Linting fills unanswered questions of the language, time consuming questions people love to debate: semicolons? Tabs or spaces? 80 columns? Etc.
Don’t get me wrong though: linting is mandatory. It gets rid of all the unnecessary noise and helps developers focus on what really matters: developing product features while preserving software quality.
Stick to a standard and never worry about it again.
Functional testing has nothing to do with software quality
Whether it is unit, integration, functional or something else, testing is always a good practice. But different testing practices mean different costs and purposes.
What functional testing ensures is non-regression and feature documentation. What it costs in terms of development time is up to your team experience and choices. What it brings to your software design is nothing.
Sum it up!
What went well:
- It was tested!
- Collaboration is a bit easier with the use of a linter
What went wrong:
- No clear responsibility: who does what?
- Code Duplication
- Hard to grasp features in a single glance
OK, so where does this leave us?
Well, we observed many flaws, most of them related to responsibility, readability, coupling and testing practices. In order to solve these issues, I am going to refactor the current code with two well-known approaches:
- TDD using unit tests
- Layered architecture
What I am not saying though, is that these techniques will solve all of our problems. Rather, I bet it will offer an other perspective on how you can write and organize your Node.js code base.
Hope you liked it and see you next month!