Objects in Memory

We often talk about the class as a blueprint for an object. This is because classes define what properties and methods an object should have, in the form of a constructor. Consider this class representing a planet:

public class Planet{
    
    private double mass;
    public double getMass(){
        return this.mass;
    }
    
    private double radius;
    public double getRadius(){
        return this.radius;
    }
    
    public Planet(double mass, double radius){
        this.mass = mass;
        this.radius = radius;
    }
}

class Planet

    @property
    def mass(self) -> float:
        return self.__mass
    
    @property
    def radius(self) -> float:
        return self.__radius
    
    def __init__(self, mass: float, radius: float) -> None:
        self.__mass = mass
        self.__radius = radius

It describes a planet as having a mass and a radius, which will be stored as the ratio of this planet’s attribute compared to Earth. We can create a specific planet by invoking its constructor, i.e.:

Planet earth = new Planet(1.0, 1.0);

earth: Planet = Planet(1.0, 1.0)

In this example, earth is an instance of the class Planet. We can create other instances, i.e.

Planet mars = new Planet(0.107, 0.53);

mars: Planet = Planet(0.107, 0.53)

We can even create a Planet instance to represent one of the exoplanets discovered by NASA’s TESS:

Planet hd21749b = new Planet(23.20, 2.836);

hd21749b: Planet = Planet(23.20, 2.836)

Let’s think more deeply about the idea of a class as a blueprint. A blueprint for what, exactly? For one thing, it serves to describe the state of the object, and helps us label that state. If we were to check the radius of our variable mars, we would access the getter for the radius field:

mars.getRadius()

mars.radius

But a class does more than just labeling the properties and fields and providing methods to mutate the state they contain. It also specifies how memory needs to be allocated to hold those values as the program runs.

Looking at our Planet class again, we can see it contains two floating point values. So, when we run the constructor for that class, our computer will know that it needs to allocate enough space in memory for those two values (8 bytes each in Java, and 24 bytes each in Python).

State and memory are clearly related - the current state is what data is stored in memory. It is possible to take that memory’s current state, write it to persistent storage (like the hard drive), and then read it back out at a later point in time and restore the program to exactly the state we left it with. This is actually what your operating system does when you put it into hibernation mode.

The process of writing out the state is known as serialization, and it’s a topic we’ll revisit later.

Static Modifier

You might have wondered how the static modifier plays into objects. Essentially, the static keyword indicates the field or method it modifies exists in only one memory location. I.e. a static field references the same memory location for all objects that possess it.

Consider this simple example class:

public class Simple:
    public static int x;
    public int y;
    
    public Simple(int x, int y){
        this.x = x;
        this.y = y;
    }
}

class Simple:
    
    x: int = 0
        
    def __init__(self, x: int, y: int) -> None:
        Simple.x = x
        self.y = y

Notice that the Java language uses the static keyword for fields, whereas in Python the field is simply defined outside of the constructor, and only attached to the class name and not self.

We can also create a couple of instances:

Simple first = new Simple(10, 12);
Simple second = new Simple(8, 5);

first: Simple = Simple(10, 12)
second: Simple = Simple(8, 5)

Once we’ve created both instances, the value of first.x would be 8 - because first.x and second.x reference the same memory location (a static unchanging location), and second.x was set after first.x. If we changed it again:

first.x = 3

Then both first.x and second.x would have the value 3, as they share the same memory location. first.y would still be 12, and second.y would still be 5.

Another way to think about static is that it means the field or method we are modifying belongs to the class and not the individual object. Hence, each object shares a static variable, because it belongs to their class.

Used on a method, the static keyword in Java or the @staticmethod decorator in Python indicates that the method belongs to the class definition, not the object instance. Hence, we must invoke it from the class, not an object instance: i.e. Math.pow().

Finally, when used with a class in Java, static indicates we can’t create objects from the class - the class definition exists on its own. Hence, the Math m = new Math(); is actually an error, as the Math class is declared static. Python does not directly support the static keyword for classes themselves, but classes which only contain static attributes and methods could be considered static classes.