json2swift: a peek under the hood

This article presents a high-level overview of how my json2swift tool works.

Several people have expressed interest in using my code as the foundation for a similar tool, or just want to change the Swift code it generates, and have asked for guidance on how to get started. It’s a modest sized program, so this overview will be straightforward.

Here is a map of the pipeline in which JSON is transformed into Swift.

data_flow

The user specifies a JSON file, represented by the top box in the diagram, when running json2swift on the command line. The JSON file data is read into memory and Apple’s JSONSerialization class tries to convert it into arrays, dictionaries, NSNumber‘s, etc.

Now things get interesting. The JSON objects are analyzed, using a process known as type inference, to create schemas which describe the JSON data entities. The data types that represent a JSON schema are defined here.

Analyzing a JSON document to infer its schema is handled here. However, one of the most useful aspects of json2swift is that it can combine evidence from multiple examples of an attribute, which I refer to as ‘attribute merging’ and have implemented here. An example of this functionality is shown in the Type Inference section of the repository’s README.

At this point the program has done nothing related to Swift whatsoever. If you want to use my JSON type inference engine for a different tool that generates code for languages other than Swift, there won’t be anything Swift-specific getting in your way.

The next step is to translate JSON schemas to data structures that represent the Swift code generated later on. Those abstractions of Swift types are defined in this file. Most of the translation logic is in here.

Last but not least the Swift model is used as a data source for SwiftCodeGenerator, as seen here. This is where you can tweak the tool’s generated Swift code. Once the Swift source code is generated, the tool writes it to a file with a .swift extension in the same directory as the input JSON file.

That’s all folks!

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Swift JSON library vs. code generation

Ever since Swift was first released there has been an endless stream of new libraries to simplify writing Swift code that consumes JSON data. Since this is such a common and tedious aspect of developing apps, it’s an important topic to many Swift developers. In the Swift projects I’ve worked on, we have naturally gravitated toward an implementation pattern quite similar to the approach that Apple advocates. This style does not make use of a third-party library, and is reasonably simple and effective.

Regardless of which approach you take to writing code to consume JSON you still must spend time writing, testing, and fixing code. And let’s face it, writing code to turn JSON into data objects is grunt work. Boring, error-prone, grunt work. Ain’t nobody got time for that!

That’s why I created json2swift; a command line tool that writes your Swift code for you. You provide it with a sample of the JSON data your app consumes, it generates all the Swift code needed to consume that data. You just drop the generated Swift files into your project, rename and rearrange the shiny new code to your heart’s content, and move on to more interesting tasks.

But what about all those sweet JSON libraries that help you write code to turn JSON into Swift objects? Where do they fit in?

A fellow named Vivien recently opened an issue in my json2swift Github repository to ask what I thought about adding support for generating Swift code that uses the popular Unbox JSON library. I understood what he meant, but not why he would ask for such a thing. After a brief discussion, he and I came to an agreement that if you use json2swift there’s no need to continue using a third-party library to turn JSON into Swift data objects. There’s certainly no reason for the tool to generate code that uses a third-party JSON API!

For developers who are not accustomed to working with code generation tools, it might take some getting used to. Think about it this way…

If JSON libraries are a way to make yourself run faster; json2swift is a race car.

runner_driver

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Create Swift data models from JSON

I published a tool that analyzes JSON data and generates Swift data model code, named json2swift. This tool helps eliminate most of the error-prone grunt work involved with consuming JSON data in a Swift app. The repository’s README has more details.

https://github.com/ijoshsmith/json2swift

This has been an interesting project to work on, especially building a type inference engine, as described here.

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Concealment design pattern in Swift

This article introduces a design pattern that I call Concealment, and demonstrates how to use the pattern in Swift code.

Purpose

The Concealment pattern enables types to support novel, related functionality without adding nonessential members to their interface.

Motivation

User-defined types can easily be overused in a codebase, giving more prominence to things than necessary. A common example is having a data model entity be involved with tangentially related concerns; such as validation checks, business rule processing, and display formatting. It is preferable to conceal the augmentation and usage of your types behind an API expressly built for a single responsibility, rather than expose those implementation details to all the types’ consumers.

Swift implementation advice

Create a new type that represents the task to be performed. In the new type’s file, add a private extension for each of your pre-existing types whose participation in the task is being concealed. Pass an instance of the new type into the private extension methods, as a form of context, if necessary.

Swift Tip: private type members can be accessed from anywhere in the same file.

Example

This demo project is available on GitHub. It is an iOS app that converts text to Morse code and plays it out loud, inspired by a programming challenge described here.

The Morse code data model is simple, and I wanted it to stay this way.

The app converts the user’s text input into instances of this abstraction. It must then transform that abstraction into two consumable formats:

  1. A string filled with dots, dashes, and separators so that the user can view the message they typed in as Morse code.
  2. A sequence of on/off states with relative durations so that the Morse code message can be played to the user.

It can be tempting to add methods or properties to the data model types to support such transformations, but doing so would reduce the clarity and simplicity of the data model. Instead, let’s conceal the data model’s participation in these transformation tasks, treating their involvement in the task as a mere implementation detail.

Here is how an EncodedMessage is transformed to a string filled with Morse code dots and dashes.

Similarly, this is how the same EncodedMessage becomes a sequence of on/off states suitable for transmission (a.k.a. playback).

The MorseTransformation<T> struct is responsible for projecting a Morse encoded message into another format. However, as seen below, it merely contains the parameters for a transformation and passes itself to the data model, which does the heavy lifting.

Since all of the data model extensions are private, the rest of the codebase does not know about or have access to the methods added to support transformations. Usage of the data model to support this novel functionality is an implementation detail.


The Concealment pattern is only applicable in languages that support private type extensions, or something equivalent. Thanks to Swift’s thoughtful design, it is easy for Swift developers to apply this pattern and keep their types simple.

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Analyzing a dependency graph in Swift

I published my solution to Dave Thomas’s Transitive Dependencies programming exercise, known as a kata, to GitHub:

https://github.com/ijoshsmith/transitive-dependencies-kata

The Challenge

This exercise involves analyzing a graph data structure which contains nodes that “depend on” other nodes. A graph is represented as direct dependencies between nodes:

direct-deps

The objective is to find all of a node’s dependencies, both direct and indirect, like so:

resolved-deps

An interesting complication is that a graph might contain cyclical dependencies, where a node indirectly depends upon itself. A proper solution to this problem must be able to cope with a graph like this:

graph-problem

If this programming challenge interests you, stop reading now and solve the puzzle for yourself! Check out the kata for more information.


My Solution

My solution relies on three things:

  1. An instance of Swift’s Set<T> collection keeps track of which nodes have been visited.
  2. The higher-order function reduce accumulates nodes into a Set as they are visited.
  3. Recursion is used to visit dependencies from the reduce method’s combine logic.

Here’s how I solved this little puzzle:

dependency-resolver

You can download the Xcode project here.

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DRY Fusion data munging kata in Swift

I worked through one of Dave Thomas’s programming exercises, called a kata, and pushed my Swift 2.2 code to GitHub. The code shows how to apply the Don’t Repeat Yourself (DRY) principle to two similar but different data processing programs.

DRY

The end result of this exercise is a satisfyingly readable higher-order function, which is similar to a template method but does not require subclassing to supply context-specific logic.

Here is the kata:

http://codekata.com/kata/kata04-data-munging/

And here is my implementation of the kata’s third challenge:

https://github.com/ijoshsmith/dry-munging-kata

This was a great learning experience!

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Creating an iOS Wizard UI in Swift

I published a reusable framework, written in Swift, for implementing the Wizard UI design pattern, named Wizardry. It simplifies creating a user interface composed of multiple view controllers that enables the user to complete a task. This is a particularly important UI design pattern for mobile apps due to the small screen size.

signup_wizard

Over the years I have built wizards for several applications, on various of platforms and with different languages. The design approach seen in Wizardry is the culmination of these efforts, with a simple, clean API that I hope developers find intuitive and complete.

Wizardry’s README file explains how to use it to build your own wizard. For a complete example, refer to the demo project.

https://github.com/ijoshsmith/Wizardry

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