Generators are just functions which, when invoked, will give you a single value from a list of values. They’re stateful functions which return the next value in a list of values for each successive invocation.
Usually, programming languages will give you a
yield keyword which works basically the same way as
return, but when the function is invoked again, execution resumes after the last
yield. For example, below is a generator in Python.
yield statement, execution is suspended at that line and exeuction resumes at the line right after until the next
yield or until the function terminates.
Note: normally, when you invoke a function, a stack frame gets allocated on the stack section of memory. When you invoke a generator function, the ‘stack’ frame actually gets allocated in the heap instead (at least in CPython) and so they persist separately from the regular function call stack.
# Generators & Iterator
All generators are iterators. When you invoke a generator function, it returns an iterator which you can loop through by invoking some function/method like
next on them (in the case of Python).
# Why Use Generators?
Normally, when you need a list of values of some kind, you’d call a function which returns that entire list of values back to you. Generators are lazy, so they only return one value of a stream of values at a time. In other words, you get values from a stream of values on-demand rather than getting all values upfront. This is great when you don’t know how many values from a stream of values you might need. If you really needed the first 10 values, but you loaded all 1000 values upfront, for example, you’re hogging an unnecessarily large amount of memory. With generators, you only really hold the memory for a single value of the list, so it’s a really common way to optimise for memory usage.
Generators are also a great way to represent streams of infinite values. For example, it might make sense to write a prime numbers generator function only get the next prime number, on-demand.