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Crystal JSON beyond the basics

lbarasti profile image Lorenzo Barasti Originally published at lbarasti.com ・7 min read

Introduction

When modelling a business domain, you will often find yourself defining custom data types on the top of the language's primitives. If you've been exposed to some functional programming, you're likely to strive for sum types, in particular.

In Crystal, we can represent sum types as composite types inheriting from an abstract one. As a side benefit, this pattern makes it straightforward to encode (and decode) custom types into (and from) JSON, as hinted in the official documentation.

In this article, we'll look at how we can JSON-encode and decode sum types in Crystal using the json module and its powerful macros.

We'll cover:

  • Automatic encoding with JSON::Serializable
  • Type resolution with discriminators
  • Encoding of nested composite data types
  • Considerations on the extensibility of this approach

Case study - a P2P client

Suppose we want to model events related to a peer to peer application. We'll focus on two domain events:

  • Connected event: a connection with a peer is established
  • Started event: a file piece download is started.

A common pattern in this scenario is to represent the various types of event as classes or structs inheriting from a base Event type. Events are inherently immutable, so it makes sense to model them as structs with getters.

alias Peer = String

abstract struct Event
end

struct Connected < Event
  getter peer
  def initialize(@peer : Peer); end
end

struct Started < Event
  getter peer, piece
  def initialize(@peer : Peer, @piece : UInt32); end
end

In the snippet above

  • On line 1, a Peer is represented by a string - its IP address.
  • On line 3, we define an abstract struct Event which serves as base type for all the concrete event types. Mind that the abstract identifier makes it so that Event objects cannot be instantiated - meaning Event.new won't compile.

JSON encoding

As we are contemplating our nicely designed events hierarchy, a requirement comes in saying that we need to persist all the P2P events processed by our application for auditing purposes. After an intense discussion with the team, we decide to go for the JSON format. Let's update our code so that we can turn Event instances into JSON

require "json"

abstract struct Event
  include JSON::Serializable
end

Here we are importing the JSON package (line 1), and then simply mixing the JSON::Serializable module into Event (line 4).

Is that it? Well, let's see...

e0 = Connected.new("0.0.0.0") #=> Connected(@peer="0.0.0.0")
e1 = Started.new("0.0.0.0", 2) #=> Started(@peer="0.0.0.0", @piece=2)

e0.to_json #=> {"peer":"0.0.0.0"}
e1.to_json #=> {"peer":"0.0.0.0","piece":2}

Now, that's impressive! Simply including the JSON::Serializable module into the base type resulted in equipping its subtypes with working #to_json methods. The following works, too:

Connected.from_json(e0.to_json) #=> Connected(@peer="0.0.0.0")
Started.from_json(e1.to_json) #=> Started(@peer="0.0.0.0", @piece=2)

This is nice, e.g. for testing purposes, but mind that the exact type of an event will likely be unknown to us at compile time, so what we'd actually like to run is

Event.from_json(e0.to_json)
# raises "Error: can't instantiate abstract struct Event"

Unfortunately, this raises an error: by default, the deserializer defined within the JSON::Serializable module tries to instantiate an Event object. As we mentioned above, this is not possible, due to the abstract nature of the type. So, where do we go from here?

Discriminators to the rescue

Here is an idea: in order to deserialize a JSON payload to the correct runtime type, we will attach some extra metadata about the event type to the JSON itself. We call this field a discriminator.

Luckily, the json module comes with an aptly named use_json_discriminator macro. This will give us the deserialization capability we are looking for, but it's up to us to make sure that the discriminator field is populated properly at serialization time.

Let's update our code to add support for discriminators.

abstract struct Event
  include JSON::Serializable

  use_json_discriminator "type", {
    connected: Connected,
    started: Started
  }
end

struct Connected < Event
  getter peer
  getter type = "connected"
  def initialize(@peer : Peer); end
end

struct Started < Event
  getter peer, piece
  getter type = "started"
  def initialize(@peer : Peer, @piece : UInt32); end
end

OK, what's going here?

  • On line 4, we call use_json_discriminator by providing a mapping between a discriminator field value and a type. The deserializer expects the discriminator to appear under the "type" field, in this case.
  • lines 12 and 18 ensure that the type field is populated according to the event type name.

You'll notice a correspondence between the value of each type field and the discriminator mapping.

Let's check how this affects our serializer.

e0.to_json #=> {"type":"connected","peer":"0.0.0.0"}
e1.to_json #=> {"type":"started","peer":"0.0.0.0","piece":2}

Notice how the type metadata is now part of the generated JSON. This in turn makes the following work:

Event.from_json(e0.to_json) #=> Connected(@type="connected", @peer="0.0.0.0")
Event.from_json(e1.to_json) #=> Started(@type="started", @peer="0.0.0.0", @piece=2)

Brilliant!

Composing composite types

The above works all right with composite types where fields are primitive types, but what if we were to define composite types on the top of other composite types? 😱

Let's expand the definition of Peer to check this out:

struct Peer
  getter address : String
  getter port : Int32

  def initialize(@address, @port)
  end
end

e0 = Connected.new(Peer.new("0.0.0.0", 8020))
e1 = Started.new(Peer.new("0.0.0.0", 8020), 2)

Now the following fails

e0.to_json
# raises "Error: no overload matches 'Peer#to_json' with type JSON::Builder"

The compiler is pretty explicit here: it does not know how to turn a Peer object into JSON.

🤔 I know this one! Let's include JSON::Serializable into Peer.

struct Peer
  include JSON::Serializable

  getter address : String
  getter port : Int32

  def initialize(@address, @port)
  end
end

And now try

e0.to_json #=> {"type":"connected","peer":{"address":"0.0.0.0","port":8020}}
e1.to_json #=> {"type":"started","peer":{"address":"0.0.0.0","port":8020},"piece":2}

Success! What about deserialization?

Event.from_json(s0) #=> Connected(@type="connected", @peer=Peer(@address="0.0.0.0", @port=8020))
Event.from_json(s1) #=> Started(@type="started", @peer=Peer(@address="0.0.0.0", @port=8020), @piece=2)

Excellent! This is really all there is to it. Let's wrap up with an interesting trick and some more considerations on the extensibility of this method.

Adding Event subtypes

At present, both the base event type and its implementation are JSON-aware, meaning the code in both includes bits related to the JSON support.

This is not an issue in itself, but it feels like the JSON logic is leaking implementation details into the Event subtypes definition. Could we make it so that an Event implementer does not have to know about the type field? After all, the type getter returns a value that can be computed programmatically - in this case, a downcase version of the type name.

It turns out we can:

abstract struct Event
  include JSON::Serializable

  use_json_discriminator "type", {connected: Connected, started: Started}

  macro inherited
    getter type : String = {{@type.stringify.downcase}}
  end
end

Wait, what is this macro inherited on line 6 about? It's a special macro hook that injects the code in its body into any type inheriting from Event. This is exactly what we need, as it gives us the opportunity to inject the type getter into each implementation of Event and set it to the type name, stringified and downcased. On line 7, note that occurrences of @type in a macro resolve to the name of the instantiating type.

Now the rest of the code looks like this:

struct Connected < Event
  getter peer
  def initialize(@peer : Peer); end
end

struct Started < Event
  getter peer, piece
  def initialize(@peer : Peer, @piece : UInt32); end
end

No trace of JSON logic 🎉

Let's introduce a new Event type to demonstrate this.

# An event indicating the completion of a file piece.
struct Completed < Event
  getter peer, piece
  def initialize(@peer : Peer, @piece : UInt32); end
end

No extra logic on the implementer side, as expected, but mind that we still need to update the discriminator mapping:

abstract struct Event
  use_json_discriminator "type", {
    connected: Connected,
    started: Started,
    completed: Completed
  }
  # ...
end

Challenge. Can we avoid having to manually update the mapping?

Hint: the following macro generates exactly the NamedTupleLiteral we're looking for:

abstract struct Event
  macro subclasses
    {
      {% for name in @type.subclasses %}
      {{ name.stringify.downcase.id }}: {{ name.id }},
      {% end %}
    }
  end
end

Event.subclasses # => {connected: Connected, started: Started, completed: Completed}

Unfortunately, the following does not work.

use_json_discriminator "type", Event.subclasses
#=> raises Error: mapping argument must be a HashLiteral or a NamedTupleLiteral, not Call

This is because at the time when the use_json_discriminator macro is expanded, Event.subclasses hasn't been expanded, yet. I've seen this kind of issues arising often when working with macros: they can save you from writing a lot of code, but composing them can be frustratingly complicated.

Here is my recommendation:

when working with macros, keep it simple. If something feels too complicated, it probably is.

Anyhow, leave a comment in the section below, if you'd like to share your hack solution.

Further reading

  • This article was inspired by my experience writing a BitTorrent client in Crystal.
  • If you'd like to find out more about Algebraic Data Types, I recommend this article by James Sinclair.
  • You can find the official json module documentation here
  • To read more about Crystal's macro hooks, check out the official Crystal reference

I hope you enjoyed this JSON-themed article and learned something new about Crystal. If you have any question or learning in the JSON-serialization space, then I'd love to read about it in the comments section below.

If you'd like to stay in touch, you can subscribe or follow me on Twitter. You can also find me live coding on Twitch, sometimes 📺

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