Tail recursion for imperative programmers

At the Recurse Center, I’ve been working my way through The Structure and Interpretation of Computer Programs (SICP) book. It’s an introductory programming book written for an MIT course in 1985. It teaches programming using the language Scheme, a LISP dialect. Scheme is functional, and I’ve been enjoying learning new functional concepts.

This article aims to explain tail recursion to programmers without experience in functional languages or concepts.

Before looking at tail recursion, let’s look at recursion in an imperative language, Python.

An issue with recursion

>>> def factorial(n):
        if n == 1:
            return 1
            return n * factorial(n - 1)
>>> factorial(4)

The snippet above defines a function which returns the factorial of some number n. factorial(n) = n * n - 1 * ... * 2 * 1. For n = 4, we expect the result to be 4 * 3 * 2 * 1 = 24, which we do get.

What happens behind the scenes when we run a recursive function? When any function call is made, a frame containing data associated with that function is added to the stack. We can see this happening using the inspect package in Python:

import inspect

print inspect.stack()

def a():
    print inspect.stack()

Running this script gives:

    (<frame object at 0x1042a9c20>, ...)  # output truncated
    (<frame object at 0x7fa702d2a000>, ...), 
    (<frame object at 0x1042a9c20>, ...)  

We can see that when inspect.stack() is called the first time, a single frame is on the stack. When it is called again, there are two.

Frames take up memory, and a Python process is allocated a limited amount of memory. If a stack contains too many frames, the process can run out of memory, or the stack may expand into memory not allocated to its process, causing a stack overflow. To stop this from happening, the interpreter sets a maximum recursion limit, which can be found with sys.getrecursionlimit(). On my computer, this limit is set to 1000 1.

For each call to factorial(), a new frame is added to the stack. If too many frames are added, we’ll excede the maximum recursion limit and the interpreter will throw an exception:

>>> factorial(999)
>>> factorial(1000)
RuntimeError: maximum recursion depth exceeded

Recursion in imperative languages can be memory intensive, due to the frame overhead. Compare it to a function which finds a factorial iteratively:

>>> def factorial_iter(n):
        total = 1
        for i in range(1, n + 1):
            total *= i
        return total
>>> factorial(1000)
>>> factorial(10000)

This function only uses a single frame, and can easily handle values of n ten times larger than the largest value handled by our recursive version.

Iterative recursion

Consider what happens when the interpreter executes factorial(4).

4 * factorial(3)
4 * 3 * factorial(2)
4 * 3 * 2 * factorial(1)
4 * 3 * 2 * 1

We see that a chain of deferred operations builds up. The total isn’t calculated until the base case of n = 1 is hit. The interpreter must keep track of operations which must be performed later.

If we reformulate the factorial function:

>>> def factorial_new(n, total):
        if n == 1:
            return total
            return factorial_new(n - 1, n * total)
>>> factorial(4, 1)  # initial total = 1

And reconsider what the interpreter does:

factorial_new(4, 1)
factorial_new(3, 4)
factorial_new(2, 12)
factorial_new(1, 24)

We see a flat sequence of calls to factorial(). The state is stored in the variable total, not by the interpreter.

Tail recursion

In tail-recursive languages, recursive procedures defined in the second way are interpreted as iterative processes, and do not exhibit the downsides of recursive processes. You get the performance benefits of an iterative process, with the elegance of a recursive procedure. The interpreter works out that no more work needs to be done on the stack frame, and throws it away.

Unfortunately, Python is not a tail-recursive language, so factorial_new(1000) still throws RuntimeError: maximum recursion depth exceeded.

For more information, I recommend section 1.2.1 of SICP.

  1. The maximum recursion limit can be set in Python with sys.setrecursionlimit() but it’s generally not advised. Functions which recurse that far down should probably be rewritten to use an iterative process.