Approach Bash Like a Developer  Part 35  Recursion
This is part 35 of a series on how to approach bash programming in a way that’s safer and more structured than your basic script.
See part 1 if you want to catch the series from the start.
Last time, we discussed references and indirection. This time, let’s talk about recursion.
Recursion is a method of structuring a function so that it calls itself with different parameters to solve a smaller version of the same problem. Eventually the problem is small enough to provide a trivial solution which doesn’t require the function to call itself any further, called a basecase.
Recursion relies on the language’s builtin frame stack to manage holding all of the intermediate results without the function needing to manage them explicitly.
Unfortunately, since bash functions don’t return actual values, they don’t have the benefit of a frame stack to manage the return values.
There are a few alternatives:

echo results on stdout and use command substitution for recursive calls

use a global variable for return values

use a reference to return a value
The command substitution method is what you see in most discussions of bash recursion, but it’s very slow. Subshells are expensive, especially if you end up using a lot of them, which is what the recursive approach relies on.
Let’s do the classic fibonacci example using a reference.
The fibonacci sequence starts with the first two numbers 1 and 1. Each term after that is the sum of the prior two terms. The first several terms go: 1, 1, 2, 3, 5, 8, 13…
In our case, we’re going to consider the sequence to start with 0 and 1, for reasons you’ll see in a bit. Starting with 0 and 1 doesn’t change the rest of the sequence, so it’s fine.
shpec/fibonacci_shpec.bash:
I’m going to do a bit of a trick to make things easier. The big idea is to not simply return the requested fibonacci number, it’s to return the entire sequence, from the start. The requested term will be in the matching index of the array.
The first part of the function is the base case. The lowest argument you can give fibonacci is 1, which tells the function to initialize the result with 0 and 1 and return it.
Calling fibonacci with the argument 2 goes down the second code path. Since the result isn’t known yet, we call fibonacci again, just with the argument minus one (which is the base case this time).
The result then has at least two terms (0 and 1), so they can be added together. The result_ array stores the sum in the newest index, which corresponds to the number of the requested term.
There is one additional wrinkle, which is the question of how the nameref return variable is able to be used in the recursive call to fibonacci.
Normally, you would supply the call with the name of the reference variable:
However, that results in the warning: result_: circular name reference. That’s because when called, fibonacci tries to put the name result_ into the following line:
Because namerefs can’t refer to themselves, it fails. Fortunately, we can put the original reference name (the one passed by the caller) into the recursive call instead. It’s still hanging around as $2, which is why the line reads:
Memoization
Since recursion is typically employed to solve computationally complex problems, it is commonly and mistakenly blamed for being slow. Rather, it is typically the problem being solved which is expensive. Recursion is simply one technique for solving such a problem.
Unless it can be shown that the recursive technique is slower than other approaches to solving the same problem, it’s wrong to say recursion is slow.
Nevertheless, once the problem has been solved for a particular input, a common technique for enhancing performance is memoization. Analogous to, but not to be mistaken for “memorization”, memoization is a technique for storing past results of a function so that it doesn’t have to do as much work when called in the future. It’s the same idea as caching in that it’s trading space (in the array) for time (calculating terms).
Memoization and recursion frequently go handinhand. For example, we could update our fibonacci function to test for existing results in the nameref array and to use them if they exist, rather than recursing.
Unfortunately you can’t write unit tests for such a refactoring, since we aren’t changing the interface or what the function returns, just how it accomplishes its task internally. Unit tests treat the function as a black box and don’t see how the function does its job. They test for correctness, not performance.
So we’ll just have to reason it out. Let’s think about whether existing terms can be used to avoid having to recurse.
Here, the last term is checked for before calling fibonacci again. If it exists, then we can be assured that the term before it exists as well, and therefore can add the last two terms, skipping the call.
Conversely, if the last term doesn’t exist, the simplest way to get it is to make the recursive call, at which point we know we can add the terms in the result.
When we want to take advantage of memoization now, all we need to do is call fibonacci with the array that we obtained from a prior fibonacci call. If so, it will not have to recurse for any of the terms which already exist in the array.
That’s it, recursion with memoization. The beautiful thing about recursion is that when done properly, it usually results in simple code. It is a divideandconquer approach that decomposes complex problems into simpler ones which are more easily solved. On problems suited to the approach, it can be very powerful as well as performant. There is nothing inherently slow about it, unless you use a technique such as subshells, which make function calls extremely expensive.
Continue with part 36  functional programming