apply-iterator-pattern

Apply Iterator Pattern

Provide a way to sequentially access elements of a collection without exposing its underlying structure.

Why This Is Best Practice

Adopted by: Python's iteration protocol (__iter__/__next__ — every for loop uses it, making it the most-invoked pattern in the language), Java's Iterable/ Iterator (every Java collection implements it — for-each loops, streams, and collectors all depend on it), C++ STL iterators (the foundation of the entire Standard Template Library algorithm set), and Ruby's Enumerable module. Impact: Python's iterator protocol is cited in the language reference as the reason generators, comprehensions, and the for statement all share one protocol. The unifying effect: any object implementing __iter__ works with for, zip, map, list(), and every standard library function — without those functions knowing the collection's type. Why best: The alternative — index-based traversal — exposes the collection type (list[i] doesn't work on a linked list or a file stream). Iterator abstracts over any sequential structure, enabling algorithms that work on lists, trees, streams, database cursors, and network responses uniformly.

Sources: Gamma et al. (1994) pp. 257–271; Python iterator protocol documentation; Java Iterable specification

Steps

Step 1: Implement Python's iterator protocol on your collection

class NumberRange:
    def __init__(self, start: int, end: int, step: int = 1):
        self._start = start
        self._end = end
        self._step = step

    def __iter__(self):
        current = self._start
        while current < self._end:
            yield current
            current += self._step

Using yield creates a generator iterator — the simplest correct implementation.

Step 2: For stateful iterators with external control, use __next__ explicitly

class NumberRangeIterator:
    def __init__(self, start: int, end: int, step: int):
        self._current = start
        self._end = end
        self._step = step

    def __iter__(self):
        return self

    def __next__(self):
        if self._current >= self._end:
            raise StopIteration
        value = self._current
        self._current += self._step
        return value

Use explicit __next__ when the iterator must be paused and resumed externally (e.g., paginated API responses loaded one page at a time).

Step 3: Keep the iterator separate from the collection for multiple simultaneous traversals

class BookShelf:
    def __init__(self):
        self._books: list[str] = []

    def add(self, book: str):
        self._books.append(book)

    def __iter__(self):
        return iter(self._books)   # each call creates a fresh iterator

iter(self._books) creates a new list iterator each time, so two simultaneous for loops over the same BookShelf don't interfere.

Step 4: Use standard library iteration — don't reinvent next(), zip(), enumerate()

shelf = BookShelf()
shelf.add("Refactoring")
shelf.add("Clean Code")

for i, book in enumerate(shelf):       # enumerate works — shelf is iterable
    print(f"{i+1}. {book}")

paired = list(zip(shelf, shelf))       # multiple iterators — independent

Step 5: For lazy or infinite sequences, use generators

def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

for n in fibonacci():                  # infinite iterator
    if n > 100:
        break
    print(n)

When NOT to Use

• Random access collections where index-based access is the primary use — if callers need collection[i] more than sequential traversal, a list interface is clearer.
• When only one traversal order exists and the collection is simple — Python lists already have full iterator support; wrapping them adds nothing.

Common Mistakes

Modifying the collection during iteration. Deleting or inserting elements while iterating over them produces undefined behavior in most languages. Collect items to remove, then remove after the loop.

Making the collection and iterator the same object. If BookShelf.__iter__ returns self and BookShelf.__next__ manages position, two for loops share state and conflict. Return a new iterator object.

Forgetting StopIteration in __next__. An iterator that never raises StopIteration produces an infinite loop in a for loop. Always raise it when exhausted.