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I’ve previously blogged about the premise behind JSON#. For a full explanation of the theory behind the code, check out this post.

Now, let’s dive into an example…

Let’s say that we have a JSON object that represents a real-world object, like a classroom full of students. It might look something like this:

{
    "classroom": {
        "teachers": [
            {
                "name": "Pablo",
                "age": 33
            },
            {
                "name": "John",
                "age": 28
            }
        ],
        "blackboard": {
            "madeOf": "wood",
            "height": "100",
            "width": "500"
        }
    }
}

This doesn’t look like it presents any great challenge to parse on any platform. But what if we expand it further to describe a school:

{
    "school": {
        "classrooms": [
            {
                "name": "Room #1",
                "teachers": [
                    {
                        "name": "Pablo",
                        "age": 33
                    },
                    {
                        "name": "John",
                        "age": 28
                    }
                ],
                "blackboard": {
                    "madeOf": "wood",
                    "height": "100",
                    "width": "500"
                }
            },
            {
                "name": "Room #2",
                "teachers": [
                    {
                        "name": "David",
                        "age": 33
                    },
                    {
                        "name": "Mary",
                        "age": 28
                    }
                ],
                "blackboard": {
                    "madeOf": "metal",
                    "height": "200",
                    "width": "600"
                }
            }
        ]
    }
}

Notice that our school object contains 2 classrooms, each of which contain similar objects, such as “blackboard”. Imagine that our school object needs to represent every school in the country. For argument’s sake, let’s say that we need to retrieve details about the blackboard in every classroom of every school. How would we go about that?

Well, we could refer to one of numerous JSON-parsing tools available. But how do these tools actually operate? Firstly, our massive JSON object will likely end up a large object. I’ve mentioned in previous blogs that objects greater than 85KB can significantly impact performance. So, immediately we’re potentially in trouble.

We can always cache the JSON object as a Stream, and read from it byte-by-byte. Tools like JSON.net offer capabilities like this, using components such as the JsonTextReader. So we’ve overcome the performance overhead associated with storing big strings in memory. But now we have another problem – we’re drilling into a massive JSON file, and searching for metadata that’s spread widely. We’re going to have to implement a lot of logic in order to draw the “blackboard” objects out.

What if requirements change, and we no longer need the “blackboard” objects? Now we just need the height of each blackboard. Well, we’ll have to throw out a lot of code, which is essentially wasted effort. Requirements change again, and we no longer need “blackboard” objects at all – now we need “teacher” objects. We need to rewrite all of our logic. Not the most flexible solution.

Let’s do this instead:

First, download the JSON# library from Github (MIT license).

Now let’s get those “blackboard” objects:

    const string schoolMetadata = @"{ "school": {...";
    var jsonParser = new JsonObjectParser();

    using (var stream = new MemoryStream(Encoding.UTF8.GetBytes(schoolMetadata))) {
        Json.Parse(jsonParser, stream, "blackboard");
    }

This will return all “blackboard” objects from the JSON file. Let’s say that our requirements change. Now we need all “teacher” objects instead. Simply change the code as follows:

    const string schoolMetadata = @"{ "school": {...";
    var jsonParser = new JsonObjectParser();

    using (var stream = new MemoryStream(Encoding.UTF8.GetBytes(schoolMetadata))) {
        Json.Parse(jsonParser, stream, "teachers");
    }

Such a change would have required significant effort, had we implemented our own custom logic using JSON.net’s JsonTextReader, or a similar component. Using JSON#, we achieve this by changing a single word. Now we’ve:

• Optimised performance
• Reduced our application’s memory-footprint
• Avoided the Large Object Heap
• Reduced development-time

The next tutorial outlines how to serialise objects using JSON#.

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