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 else: return n * factorial(n - 1) >>> factorial(4) 24
The snippet above defines a function which returns the factorial of some number
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
import inspect print inspect.stack() def a(): print inspect.stack() a()
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) 4023872600770937735437024339230039... >>> 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) 40238726007709377354370243392300398... >>> factorial(10000) 28462596809170545189064132121198688...
This function only uses a single frame, and can easily handle values of
times larger than the largest value handled by our recursive version.
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 24
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 else: 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) 24
We see a flat sequence of calls to
factorial(). The state is stored in the
total, not by the interpreter.
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
For more information, I recommend section
1.2.1 of SICP.
The maximum recursion limit can be set in Python with[return]
sys.setrecursionlimit()but it’s generally not advised. Functions which recurse that far down should probably be rewritten to use an iterative process.