In computer science, a generator is a special routine that can be used to control the iteration behaviour of a loop. A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values. However, instead of building an array containing all the values and returning them all at once, a generator yields the values one at a time, which requires less memory and allows the caller to get started processing the first few values immediately. In short, a generator looks like a function but behaves like an iterator.
Generators first appeared in CLU (1975) [1] and are now available in Python and C#. (In CLU and C#, generators are called iterators.)
Generators are usually invoked inside loops. The first time that a generator invocation is reached in a loop, an iterator object is created that encapsulates the state of the generator routine at its beginning, with arguments bound to the corresponding parameters. The generator's body is then executed in the context of that iterator until a special yield action is encountered; at that time, the value provided with the yield action is used as the value of the invocation expression. The next time the same generator invocation is reached in a subsequent iteration, the execution of the generator's body is resumed after the yield action, until yet another yield action is encountered. In addition to the yield action, execution of the generator body can also be terminated by a finish action, at which time the innermost loop enclosing the generator invocation is terminated.
In the presence of generators, loop constructs of a language can be reduced into a single loop ... end loop construct; all the usual loop constructs can then be comfortably simulated by using suitable generators in the right way.
An example Python generator:
def sums(n, min=1):
if n == 0:
yield []
elif n >= min:
for i in range(min, n+1):
for sol in sums(n-min, i):
yield [i] + sol
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever the iterator's next() method is called, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
Because generators compute their yielded values only on demand, they are useful for representing sequences that are expensive to compute, or even infinite.
References
- Stephan Murer, Stephen Omohundro, David Stoutamire and Clemens Szyperski: Iteration abstraction in Sather. ACM Transactions on Programming Languages and Systems, 18(1):1-15 (1996) [2]
- PEPs (Python Enhancement Proposals):