Mapeach

Many times when using mapcar, I find myself using a complex lambda expression for the function argument. This makes the code difficult to read since it breaks apart the flow. My code winds up looking like the following:

(mapcar (lambda (x)
          ...)
        list)

First you have to read the possibly massive lambda expression, then you finally find out what you are mapping over. As the lambda expression increases in length, it becomes harder and harder to read. A way to fix this is with the macro mapeach. Mapeach is a macro which is meant to be used when the lambda expression that would be passed to mapcar is much longer than the expression for the list. Mapeach works just like mapcar, but instead provides an alternative syntax which makes it easier to read when the lambda expression is complicated. Here is one possible implementation of mapeach:1

(defmacro mapeach (var list &body body)
  `(mapcar (lambda (,var) ,@body) ,list))

Mapeach, does two things to fix the problem. First it hides the lambda, making it easier to find the important parts of the code. Second, it inverts the order of the arguments, putting the simple list expression first and the complex body second. As a simple example of mapeach, here is how one could square each element in a list using it:

(mapeach x '(1 2 3)
  (* x x))

If one wanted to write the above code by using mapcar, it would look something like the following:

(mapcar (lambda (x)
          (* x x))
        '(1 2 3))

Although it doesn’t shine for this simple example, you can tell that mapeach makes the code a bit clearer. As the body for the lambda expression gets longer and longer, mapeach begins to make the code much easier to understand. I find that mapcar is nice to use only when the expression for the function is short. This happens either when you are either using a named function or you are using some sort of reader macro. Mapeach is another one of those macros that makes what seems like an insignificant difference. Even so, I find that it aids a lot in readability since it puts all of the simple parts in one place.


By the way, if you are working on scaling Postgres, I'm currently working on Perfalytics. Perfalytics is a service designed to help teams scale out Postgres by giving them insight into why their queries are slow and how they can go about making their queries faster. If you're interested in learning more about Perfalytics shoot me an email at michaelmalis2@gmail.com.

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

  1. An alternative implementation can expand into loop. That way it will automatically support destructuring over the list elements.

Leave a Reply

Your email address will not be published. Required fields are marked *