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15-212: Principles of Programming |
Previously, within the purely functional part of ML, we saw that all values were persistent. At worst, a binding might shadow a previous binding. As a result our queues and dictionaries were persistent data structures. Adding an element to a queue did not change the old queue; instead it created a new queue, possibly sharing values with the old queue, but not modifying the old queue in any way.
Now that we are able to create cells and modify their contents we can create ephemeral data structures. These are data structures that change over time. The main advantage of such data structures is their ability to maintain state as a shared resource among many routines. Another advantage in some caes is the ability to write code that is more time-efficient than purely functional code. The disadvantages are error and complexity: our routines may accidentally and irreversibly change the contents of a datastructure; variables may be aliases for each other. As a result it is much more difficult to prove the correctness of code involving ephemeral data structures. As always, it is a good idea to keep mutation to a minimum and to be careful about enforcing invariants.
We present two examples. First, we consider a standard implementation of hash tables. We use arrays to implement generic hash tables as a functor parameterized by an abstract hashable equality type. Second, we revisit the queue data structure, now defining an ephemeral queue. The queue signature clearly indicates that internal state is maintained. Our implementation uses a pair of reference cells containing mutable lists, and highlights some of the subtleties involved when reasoning about references.
We end the lecture with a few words about ML's value restriction. The value restriction is enforced by the ML compiler in order to avoid runtime type errors. All expressions must have well-defined lexically-determined static types.
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