coen.bakker
Beyond code that gets it done: code readability. Any advice?
Besides learning Elixir and ‘getting things done’ with it, I hope to also learn how to write code that is highly readable – and therefore also easy to grasp. I still have a lot to learn in that regard, I’m sure. I try not to rush to a new problem to write a solution for and first clean up some of the mess I have left behind. The code might work completely, but it’s never fun to having to come back to my own code a while later and feeling tempted to rewrite the code, rather than to understand what I wrote in the first place because of the mess.
I have noticed that I often find Elixir code more readable than JavaScript code (given being familiar with both). The pipeline pattern in particular. Nevertheless, I am interested in any of your personal advice about writing readable code.
To give you an example. I wrote this function today. I didn’t think it would be this long and involved. I want to clean it up further (I made a tiny start already).
I love pipelines that consists of highly descriptive code. However, in this case that would mean writing a lot of helper function, possibly.
I have also seen people put comments behind pipe elements, to provide description. Often, that results in a lot of info located in one place, though.
alias AppWeb.Component.Helpers
defp histogram_frequencies(responses, variable, bucket_size, start_value, bucket_count) do
end_bucket = start_value + bucket_count * bucket_size
max_value =
responses
|> Enum.map(fn %{^variable => value} -> value end)
|> Enum.max
bucket_number_list = Enum.to_list(1..bucket_count)
zero_list = Enum.map(bucket_number_list, fn b -> {b, 0} end)
sums =
responses
|> Helpers.frequencies(variable)
|> Enum.filter(fn {age, _count} -> age < end_bucket end)
|> Enum.map(fn {age, count} -> {trunc(age/bucket_size), count} end)
|> Kernel.++(zero_list)
|> Enum.group_by(fn {key, _value} -> key end)
|> Enum.map(fn {_key, value} -> Enum.map(value, fn {_x, e} -> e end) end)
|> Enum.map(fn p -> Enum.sum(p) end)
lower_limits_buckets = Helpers.lower_limits_buckets(bucket_size, start_value, bucket_count)
upper_limits_buckets = Helpers.upper_limits_buckets(bucket_size, start_value, bucket_count)
buckets =
[lower_limits_buckets, upper_limits_buckets]
|> Enum.zip_with(fn [x, y] -> "#{x}-#{y}" end)
# If any values greater than range of last bucket,
# put them into the last bucket
# and change that bucket's name accordingly (e.g. "60-70" becomes "60+").
case max_value < end_bucket do
true ->
Enum.zip(buckets, sums)
false ->
last_bucket = "#{start_value + bucket_count * bucket_size}+"
last_sum =
responses
|> Helpers.frequencies(variable)
|> Enum.filter(fn {key, _value} -> key >= end_bucket end)
|> Enum.map(fn {_age, count} -> count end)
|> Enum.sum
sums = sums++[last_sum]
buckets = buckets++[last_bucket]
Enum.zip(buckets, sums)
end
end
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First 10 of 12 Posts
ityonemo
Take those pipelines and make them their own defp functions; the case should probably be an “if”.
There’s general elixir coding guidelines too. Fetch vs fetch! vs get have specific meanings, avoid is_ functions unless they are guards,
Avoid variables called “value”, lol. I have a linter for that to get me out of the habit.
coen.bakker
Thanks. I’ll look into those guidelines.
And now I come to think of it. I guess my concern with readability really also extends to efficient and performant code, really. Since those can potentially also can be optimized after having gotten a initial working solution.
sabiwara
Regarding efficiency and performance, you could improve it by doing some of these successive
Enumoperations in one pass, which should avoid building intermediate lists and walk them twice.filter |> mapcould be re-implemented with a comprehension:could be
map |> mapshould typically be avoided, since you can do it directly in one pass:could be
and even
map |> sumcould be replaced byEnum/reduce/3here (although this one might be slightly less readable):Credohas some checks like MapMap, FilterFilter,MapJoin… to help detect some of these patterns.I didn’t mention
Stream, since it also comes with some overhead and would probably not improve performance here except if you are working with large lists.al2o3cr
Functional question: what is this line intended to calculate? It could return
0forage < bucket_size, which conflicts with the definition ofbucket_number_listas starting at 1.A general rule I find useful: if you see multiple functions with similar prefixes / suffixes, consider if there’s a data structure hiding in the code.
A similar outcome from a different thing: if you see multiple arguments that are always handled together, consider if there’s a data structure hiding in the code.
For instance, a struct called
Buckets:Then the main function can use these higher-level concepts:
There are a lot of advantages to this approach:
Bucketsare easier to read / test / debugBuckets, just sayingcountis sufficient (versusbucket_count)start_valueandbucket_size- which will run, since both are numbers, but produce nonsense output - are avoided by passing around a whole structOne other side-effect of this approach: for large lists in
responseswhere at least one value lands in the “over the limit” bucket, this method will be about twice as fast because it only constructs thefrequenciesmap once!Sebb
In general I think any pure function has the potential to be understandable.
You just have to load the structure of the incoming data into your brain and understand what’s happening in the steps. So this is the main point for me, make it possible for the reader to follow.
Imagine someone telling a story in plain english. If you lose track of the (grammatical) subject, you can’t understand the story. So in a (pure) function, the subject is some data coming in, transformed with the help of some other data structures.
There are two basic techniques already mentioned to make this easier:
While a
defpadds some mental load, it also has the potential to reduce it. It is almost always worth it, when there is a clear transformation of data in the block refactored to adefpthat can be neatly described by the function’s name.If I don’t want to
defpI somethimes just add a comment with an example of the current shape of the data.Sometimes there is so much going on that a new module is of need. Then definitely add a
@specwhich greatly helps with understanding whats going in and out.Reducing the possible structures of data flowing through your functions by using structs (and in the first step, understanding whats there) immensly reduces the mental load of reading a function.
The rest I think is “just” naming things (hard) and using the tools correctly.
coen.bakker
This is honestly a huge help. Thank you.
coen.bakker
My intention was to sum frequencies by bucket (e.g. all respondents between ages 18-27, 28-37, 38-47, etc.). Trunc(age/bucket_size) discriminates ages from different buckets. But all-in-all it takes a lot of steps to sum by bucket in my code (and I should have also subtracted ‘start_value’ from ‘age’ – since otherwise the lower end doesn’t get trimmed properly. Luckily, nobody depends on this code
).
coen.bakker
Love this:
Hope to be able to put this in practice more.
coen.bakker
I think it cleaned up nicely already. Must say: has been very instructive exercise.
ityonemo
Just a minor note, by convention, the functions in a module for a struct should take the struct as a first argument, if that makes sense. Or generally, not just for structs, if the module is a noun, (List, String, Registry, etc)