If you have been programming long enough then you know all too well that you can never trust external data.
The first thing you will want to do is validate this data before it becomes too ingrained into your program.
You may have already heard the saying “don’t roll your own crypto”.
Though less complex, the same thing can be said about validation, as you can fall into a variety of traps that library maintainers have already considered extensively and fixed for you.
One of the libraries that does that for you is called joi. Full disclosure, I am the maintainer of that library.
But before we dive in, a little disclaimer. You can use joi as a way to ensure the shape and integrity of data.
Although you could dip a bit into semantic validation (making sure that things actually make sense), I wouldn’t go as far as saying you could check all your business rules with it, so your mileage may vary.
So what does joi look like anyway?
Joi’s usefulness can be separated into two distinct parts.
- in the first part you write what we call schemas, which is a way of formalizing the structure and rules that your objects must follow to be valid,
- in the second part we apply those rules to a specific object.
So let’s illustrate the schema part with a made up example :
At first glance you will notice that it uses a fluent API, and it’s kind of a hit or miss.
If you are one of those people who prefer code over configuration, it is a rather good thing, as is the developer experience that goes with it.
If you’ve ever written pages of JSON configuration and crossed your fingers that it would work when you finished, then you know what I mean ;)
Of course, you then depend on the API that is provided for you.
So far, it seems that people like joi’s, and I am constantly trying to improve it.
You can also see that I split that schema into multiple parts.
An undervalued feature of joi that is important to understand is that schemas are immutable. So you can create schemas, and apply new rules to them without affecting the originals.
By helping you compose the building blocks of your schemas out of the box it also helps with the DRYness of your validations.
Schemas are made of a type and a succession of rules, with or without parameters.
There are simple ones, like required(), which checks that a property is defined, min and max which check the boundaries of a number, and many more.
There are also some joi-specific constructs like Joi.when(), which, as you might have guessed from the looks of it, is the equivalent of a conditional (if/then/else) in your schema. It allows you to adapt the validation that will be applied depending on some conditions of the input you will validate.
Joi also allows you to “try” schemas on all or part of your input until it finds one that matches, or until there are no more schemas to try.
I like to think that it gives you a way to describe 95% of the use cases, leaving you to implement the very few edge cases that are left in extensions.
However if you strongly believe a feature should be in joi core for everyone’s benefit, then you should make your case in joi’s issues!
There’s one thing that you may find strange when using joi, that is not apparent at first. By default it will try to cast any value you give it as best it can and then try to confront it with your rules. This is because it relies on hapi.js, where query strings need to be validated with the same ease, and you probably know that in this world, everything is a string.
If you don’t want to do that, you can happily use joi’s strict mode (on the full schema or only on some parts) which will check types as you have defined them.
Now let’s validate!
“So how are we going to use this?” you ask.
Well there are several ways depending on the context.
If you are using joi as a standalone tool, you can do this:
result will contain two properties, error and value.
If there is no error, the value will be the result of the validation applied to your input, with all the default values set, the values you didn’t want eventually stripped.
It also contains a details property which is an array of all the errors that were used to produce that text as well as their context.
Some modules take advantage of that to rewrite the errors in their own style for translation purposes or for a better wording to the end-users.
validate can receive some options to change its behaviour.
One of the most commonly used is probably abortEarly. By default, joi adopts a fail fast strategy. If there is something wrong, it stops immediately.
You can also set this option to false to try and get all the possible errors it can find.
Of course, if your schema is complex, for example containing many conditionals, you may receive too many errors and have to filter through what’s really relevant to you.
For example, here is the error you would get using the previous schema for the following payload:
As for web servers, hapi supports it out of the box, obviously, so does fastify, and you can also find integration modules for express with celebrate or for restify with restify-joi-middleware.
There are probably other modules out there to do that for your preferred application framework, and if not, I encourage you to create one, it’s easy!
Here is a very simple example of a hapi.js hello world route showing how easy it is to integrate:
This article is only meant to cover the very basics of what joi is, its purpose and usage.
Of course, as with any tool, there is a learning curve to be able to fully express yourself with it, but hopefully not too steep of one.
Now, is it a silver bullet? Obviously not, there’s no such thing.
It’s a constant work in progress to both ensure that the data that comes out of joi is the data you would expect, while still being as approachable and safe as possible for developers.
Sometimes features are missing. The main one right now in my mind being the lack of asynchronous validations (you often don’t need it, but still). But, joi can be extended with custom rules, and when it’s not enough, hey it’s open source, so if you feel motivated, come to me so we can draft a path forward!
Also, if you are interested in hapi.js check out our article on hapi.js performance
Image: bady qb