Showing posts with label Higher-order functions: tags to script comparison series. Show all posts
Showing posts with label Higher-order functions: tags to script comparison series. Show all posts

Thursday, 1 July 2021

One last one! CFML higher-order functions compared to tag-based code: reduceRight


I forgot one!

I've already discussed map, reduce, filter, sort, some, every and each, operations; but recently reduceRight was added to CFML (well: at least in ColdFusion it was; it's not in Lucee yet) as well.

I have to start my day job in 16min, so this will be quick.

reduceRight is the same as reduce, except it starts from the end of the collection, not the beginning:

colours = ["Whero","Karaka","Kowhai","Kakariki","Kikorangi","Poropango","Papura"]

coloursAsList = colours.reduce((all="", colour) => all.listAppend(colour))
coloursAsReversedList = colours.reduceRight((all="", colour) => all.listAppend(colour))

writeOutput("coloursAsList: #coloursAsList#<br>coloursAsReversedList: #coloursAsReversedList#<br>")
coloursAsList: Whero,Karaka,Kowhai,Kakariki,Kikorangi,Poropango,Papura
coloursAsReversedList: Papura,Poropango,Kikorangi,Kakariki,Kowhai,Karaka,Whero

Yes yes Mingo; one would not use reduce to convert an array of strings to a list. That is beside the point. But thanks for letting me know Lucee (but not ColdFusion) has an Array.reverse method, which would be a better way to reverse the list order here: colours.reverse().toList().

And the tags version, just a reversed counting loop does the trick here:

<cfset coloursAsReversedList = "">
<cfloop index="i" from="#arrayLen(colours)#" to="1" step="-1">
    <cfset coloursAsReversedList = listAppend(coloursAsReversedList, colours[i])>

That's it. four minutes to get to work. Fortunately that's just a matter of switching desktops…



CFML higher-order functions compared to tag-based code: some, every and each functions


I'm gonna try to round out this short series today: there's not much to say about the some, every and each methods in the context of comparing their functionality to old-school tag-based code. As a reminder, I've already covered map, reduce, filter and sort operations.


some iterates over the collection, calling a callback on each element, and will exit as soon as the callback returns true for an element. An example might be checking if at least some class members passed (or failed) their test:

examResults = [
    {person="Alex", mark=75},
    {person="Billie", mark=52},
    {person="Charlie", mark=41},
    {person="Daryl", mark=29},
    {person="Evan", mark=53}

somePassed = examResults.some((result) => result.mark >= 50)

writeOutput("Some of the class passed the test? #somePassed#<br><hr>")

someFailed = examResults.some((result) => {
    writeOutput("Called for #result.person#, #result.mark#<br>")
    return result.mark < 50

In the second example there I show a difference between this iteration function and the others we've encountered so far. All the others always iterate through the entire collection, however some and every do not. They exit as soon as they can answer the question. So as soon as some gets a true it exits; as soon as every gets a false it exits. The output of this is:

Some of the class passed the test? true

Called for Alex, 75
Called for Billie, 52
Called for Charlie, 41

In this case it only got as far as the first false result from the callback (because, sadly, Charlie did not make the cut)

The tag-based version of this would be:

<cfset somePassed = false>
<cfloop array="#examResults#" item="result">
    <cfif result.mark GTE 50>
        <cfset somePassed = true>
<cfoutput>Some of the class passed the test? #somePassed#<br><hr></cfoutput>

<cfset someFailed = false>
<cfloop array="#examResults#" item="result">
    <cfoutput>Called for #result.person#, #result.mark#<br></cfoutput>
    <cfif result.mark LT 50>
        <cfset someFailed = true>

Again with the boilerplate code (ref from previous articles).

BTW, don't get carried away with these higher-order functions if there's another built-in function to do the job. Recently I checked if something was in an array by doing this:

colours = ["Whero","Karaka","Kowhai","Kakariki","Kikorangi","Poropango","Papura"]

containsGreen = colours.some((colour) => colour == "Kakariki")
writeOutput("It contains green: #containsGreen#<br>")

My boss gently pointed out I could just do this:

containsGreen = !!colours.find("Kakariki")

Use the simpler option where possible ;-)


every is the opposite of some: it exits as soon as the callback returns false. Our example here would be to check if everyone passed the exam:

everyonePassed = examResults.every((result) => {
    writeOutput("Called for #result.person#, #result.mark#<br>")
    return result.mark >= 50
writeOutput("Everyone passed the test? #everyonePassed#<br><hr>")
Called for Alex, 75
Called for Billie, 52
Called for Charlie, 41
Everyone passed the test? false

The tag-based equivalent is the usual "mostly boilerplate" thing:

<cfset everyonePassed = true>
<cfloop array="#examResults#" item="result">
    <cfoutput>Called for #result.person#, #result.mark#<br></cfoutput>
    <cfset personPassed =  result.mark GTE 50>
    <cfif NOT personPassed>
        <cfset everyonePassed = false>
<cfoutput>Everyone passed the test? #everyonePassed#<br><hr></cfoutput>


Sometimes it's not a data transformation that one needs when iterating over a collection. If none of the other options do the trick, there's the generic each method:

examResults.each((result) => {
    writeOutput("Name: #result.person#, mark: #result.mark#<br>")

As a general rule never start solving an iteration task with each. Consider if one of the other more situation-specific methods are a better fit. It's seldom that each is the right answer.

And the tag equivalent is pretty much the same, because - really - all the tag version does is "each"; it's down to the inner code block to distinguish between the various iteration possibilities:

<cfloop array="#examResults#" item="result">
    <cfoutput>Name: #result.person#, mark: #result.mark#<br></cfoutput>

OK that's it. Tag-based CFML versions of the more situation-descriptive and less boilerplate iteration higher-order functions. If you need anything else about them explained, let me know.



Wednesday, 30 June 2021

CFML higher-order functions compared to tag-based code: sort function


OK so you've probably got the gist of things with these articles, with my previous treatments of comparing "modern" to "old school" with map, reduce, filter operations. On to sorting now.

I think this is going to involve some awful code.

I don't think I need to explain why we might need to sort a collection, or what "sorting" is. It's really easy using higher-order functions. The need to write the sorting algorithm has been removed, and only a function to compare to elements needs to be provided:

months = [
    {id=1, miSequence=8, mi="Kohi-tātea", anglicised="Hānuere", en="January"}, 
    {id=2, miSequence=9, mi="Hui-tanguru", anglicised="Pēpuere", en="February"}, 
    {id=3, miSequence=10, mi="Poutū-te-rangi", anglicised="Maehe", en="March"}, 
    {id=4, miSequence=11, mi="Paenga-whāwhā", anglicised="Āperira", en="April"}, 
    {id=5, miSequence=12, mi="Haratua", anglicised="Mei", en="May"}, 
    {id=6, miSequence=1, mi="Pipiri", anglicised="Hune", en="June"}, 
    {id=7, miSequence=2, mi="Hōngongoi", anglicised="Hūrae", en="July"}, 
    {id=8, miSequence=3, mi="Here-turi-kōkā", anglicised="Akuhata", en="August"}, 
    {id=9, miSequence=4, mi="Mahuru", anglicised="Hepetema", en="September"}, 
    {id=10, miSequence=5, mi="Whiringa-ā-nuku", anglicised="Oketopa", en="October"}, 
    {id=11, miSequence=6, mi="Whiringa-ā-rangi", anglicised="Noema", en="November"}, 
    {id=12, miSequence=7, mi="Hakihea", anglicised="Tihema", en="December"}
monthsInMaoriOrder = duplicate(months).sort((e1, e2) => e1.miSequence - e2.miSequence)


Here I have a list of the months of the year, ordered according to the Gregorian calendar. The Maori calendar has the same ordering, but the year starts around when the Gregorian calendar considers June. So the exercise here is to re-order the array to respect that ordering. The code for the sorting is just the comparator function.

One thing to note here is that despite appearances given we're assigning the return value of the sorting operation to a new variable, the original array is modified when you call sort on it. I think this is less than ideal, but it's the way it works on both ColdFusion and Lucee. If you want you're original array left alone, then duplicate it first like I have here.

If we're going old school procedural: it's a bit of a nightmare. We need to write our own sorting implementation. Well: we grab one from anyhow. But even then, the original leverages a callback function, so I've modified this to be truly procedural and have that embedded in the implementation.

<cffunction name="monthsSortedByMaoriSequence" returntype="array" output="false">
    <cfargument name="arrayToCompare" type="array" required="true">

    <cfset var lesserArray = arrayNew(1)>
    <cfset var greaterArray = arrayNew(1)>
    <cfset var pivotArray = arrayNew(1)>
    <cfset var examine = 2>
    <cfset var comparison = 0>
    <cfset pivotArray[1] = arrayToCompare[1]>

    <cfif  arrayLen(arrayToCompare) LT 2>
        <cfreturn arrayToCompare>

    <cfset arrayDeleteAt(arrayToCompare, 1)>
    <cfloop array="#arrayToCompare#" item="element">
        <cfset comparison = element.miSequence - pivotArray[1].miSequence>

        <cfswitch expression="#sgn(comparison)#">
            <cfcase value="-1">
                <cfset arrayAppend(lesserArray, element)>
            <cfcase value="0">
                <cfset arrayAppend(pivotArray, element)>
            <cfcase value="1">
                <cfset arrayAppend(greaterArray, element)>

    <cfif arrayLen(lesserArray)>
        <cfset lesserArray = monthsSortedByMaoriSequence(lesserArray)>
        <cfset lesserArray = arrayNew(1)>

    <cfif arrayLen(greaterArray)>
        <cfset greaterArray = monthsSortedByMaoriSequence(greaterArray)>
        <cfset greaterArray = arrayNew(1)>

    <cfset arrayAppend(lesserArray, pivotArray, true)>
    <cfset arrayAppend(lesserArray, greaterArray, true)>

    <cfreturn lesserArray>
<cfset sorted = monthsSortedByMaoriSequence(months)>

It's hard to see the bit that the modern implementation needs, but it's buried here.

Note: to an clever clogs who spot the odd shortcoming in that implementation of quicksort: you're missing the point of the article, and also yer talking to the wrong person because I didn't write it. But - yes yes - you're very clever.

The point is: that's awful. Writing old-school tag-based procedural code one needs to re-implement (and re-test!) the sorting function every time you need one. This is an extreme example and only a lunatic would not use the callback approach even with tag based code:

<cffunction name="comparator">
    <cfargument name="e1">
    <cfargument name="e2">
    <cfreturn e1.miSequence - e2.miSequence>

<cfset sorted = duplicate(months)>
<cfset arraySort(sorted, comparator)>

But still: it's just better to get with the programme (or the decade) and use the modern version for this.



Tuesday, 29 June 2021

CFML higher-order functions compared to tag-based code: filter function


This one will be pretty short I think. It's the next effort in going over how these higher-order functions work compared to writing procedural code in CFML tags. I've previous covered map and reduce. There's less intricacy to filter, so I won't have so much to say.

Yesterday I showed an example of how not to remove records from a collection using reduce

numbers = [1,2,3,4,5,6,7,8,9,10]
evens = numbers.reduce((evens=[], number) => number MOD 2 ? evens : evens.append(number))

This works, but it's not how one ought to do it. It's putting a square peg in a round hole, and it's gonna cause a small amount of FUD when someone comes back to review the code later ("why are they using reduce here? What am I missing?"). So… use the correct tool for the job. The idiomatic way to filter our elements from a collection is with a filter operation. Here's the equivalent operation using filter:

evens = numbers.filter((number) => number MOD 2 == 0)

Filter's callback receive the value of the collection element (and additionally its index/key, as well as the whole collection as additional parameters, if you need to use those too). If the logic in the callback returns true? The element is preserved in the result collection. if it's false? It's filtered out. That's it. The callback logic can be a one-liner like it is here, or as convoluted as it needs to be. As long as it boils down to a true or a false, you'll get your filtered collection. As with the other collection higher-order functions: it does not change the original collection; it returns a new one.

The tag-based equivalent is simple:

<cfset evens = []>
<cfloop array="#numbers#" item="number">
    <cfif number MOD 2 EQ 0>
        <cfset arrayAppend(evens, number)>

Just slightly more verbose, and it's mostly boilerplate.

The concept here is simple, and the object of the exercise for these articles is to just show the difference between using the higher-order functions and using a procedural approach with tags, and that's pretty much it.



Monday, 28 June 2021

CFML higher-order functions compared to tag-based code: reduce function


Here's the next effort in going over how these higher-order functions work compared to writing procedural code in CFML tags. The previous one was "CFML higher-order functions compared to tag-based code: map function". Today I'm looking at the reduce method. As per yesterday, I've discussed this before in ColdFusion 11: .map() and .reduce().

So what does reduce to? It helps if we compare it to map. Remember how I said this yesterday:

A mapping operation takes one collection and remaps the values for each key into a different value. The keys and the overall size and order (if it has a sense of order) of the collection is preserved. Also the original collection is not altered; an entirely new collection is returned.

A reduce operation is used to return a different data structure. It doesn't mean "reduce" in the sense of "make smaller"; the resultant data structure might be "bigger" (for some definition of bigger). Or it might be the same length, but a different type.

An example of returning the same length but different type would be similar to yesterday's example of mapping an array of records to an array of objects:

records = [
    {id=1, mi="whero", en="red"},
    {id=2, mi="kakariki", en="green"},
    {id=3, mi="kikorangi", en="blue"}
objects = => new Colour(, record.mi, record.en))

A more likely scenario in CFML is for the records to be a query. But one still wants to pass an array of objects back from the storage tier to the application, so we use reduce to make the type conversion:

records = queryNew(
        [1, "whero", "red"],
        [2, "kakariki", "green"],
        [3, "kikorangi", "blue"]
objects = records.reduce((objects=[], record) => objects.append(new Colour(, record.mi, record.en)))

Note the way reduce works. The first argument is an "accumulator" that is passed into every iteration, and is ultimately returned to the calling code. One builds the return value iteration at a time into that. Here I'm appending to the array of objects each iteration. Whatever is returned from each iteration is the first argument of the next iteration. So as I iterate over the query, I start with an empty array. I append the first object to it, and that one-element array is then passed into the accumulator of the second call to the callback in the next iteration; and so on for all iterations so ultimately I have an array that I've appended three objects to. Some pseudo-code might make this more clear. Let's consider the iterations as they progress:

1: objects argument=[]; append Red; return value=[Red]
2: objects argument=[Red]; append Green; return value=[Red, Green]
3: objects argument=[Red, Green]; append Blue; return value=[Red, Green, Blue]
result: [Red, Green, Blue]

We start empty, we append red, we append green, we append blue.

After that first argument, the subsequent arguments follow the same pattern as with map: the second argument is a row of the query (passed as a struct). The callback can also receive the current index / key (or currentRow equivalent to a query loop in this case), and the last argument is the entire query. I don't need these here, so do not mention them in the callback's function signature.

The tag version of this is actually round about the same amount of code (109 bytes vs 112 bytes it seems):

<cfset objects = []>
<cfloop query="records">
    <cfset arrayAppend(objects, new Colour(id, mi, en))>

Another case is shown here:

transactions = [
    {id=1, amount=.1},
    {id=2, amount=2.2},
    {id=3, amount=33.3},
    {id=4, amount=44.44}

sum = transactions.reduce((sum=0, transaction) => sum += transaction.amount)

We're summing the transactions. We are reducing the collection to a single value, I guess.

Oh one thing maybe work making very clear: it's complete coincidence that the final variable is called sum, and the accumulator parameter is called sum. They don't need to be, it just makes sense to me to match them up given we're kinda building the end result in that accumulator argument, and accordingly it's going to be the same sort of values, so makes sense it's called the same thing.

The tag-based version for this is simple again:

<cfset sum = 0>
<cfloop array="#transactions#" item="transaction">
    <cfset sum = sum += transaction.amount>

Another more complicated example of script-vs-tags when reducing is in yesterday's article "CFML: tag-based versions of some script-based code". There I am reducing a query to a struct, then reducing that struct into another query. Both CFScript and tag versions of the code are in that.

One thing to not use reduce for is to actually reduce the size of a collection by removing records from it, eg:

numbers = [1,2,3,4,5,6,7,8,9,10]
evens = numbers.reduce((evens=[], number) => number MOD 2 ? evens : evens.append(number))

One would not use reduce for that. One would use filter. I guess I'll get to that tomorrow.



CFML higher-order functions compared to tag-based code: map function


As I mentioned yesterday ("CFML: tag-based versions of some script-based code") I've been asked by a couple of people to show the tag-based version of the script-based CFML code. This has ben particularly in reference to my typical approach of using higher-order functions to perform data transformation operations on iterable objects (eg: arrays, structs, lists, etc). Here I will briefly do that for some examples of using mapping functions. The process is the same each time, so I'll not dwell on it too much.

I have already written about the nuts and bolts of mapping higher-order functions in CFML back in 2014 in "ColdFusion 11: .map() and .reduce()". I also looked at how to implement arrayMap in older versions of CFML: "arrayMap(): a reverse CFML history".

In short, these collection-iteration higher order functions work on the premise that most looping operations exist solely to perform data transformation, and it makes sense to encapsulate that into a function, rather than having to hand-crank it. Obviously every data transformation is specific to its circumstance, so the collection-iteration functions take a callback as an argument (thus making them higher-order functions), where the callback defines the data transformation operation. Taking this approach makes the code clearer as to what the intent of the transformation is, and also encapsuates the implementation in its own functions, so its variables are all well encapsulated and don't impact the rest of the calling code. It's just a tider way of doing data transformation.

A mapping operation takes one collection and remaps the values for each key into a different value. The keys and the overall size and order (if it has a sense of order) of the collection is preserved. Also the original collection is not altered; an entirely new collection is returned.

That's enough of an explanation. This article is about comparing code styles. Here goes.

keys = ["ONE", "TWO", "THREE", "FOUR"]

translationLookup = {
    "ONE" = {mi = "tahi", jp = "一"},
    "TWO" = {mi = "rua", jp = "二"},
    "THREE" = {mi = "toru", jp = "三"},
    "FOUR" = {mi = "wha", jp = "四"}

maori = => translationLookup[key].mi)


Here we have a one-liner that takes an array of translation keys and maps them to their actual translations.

Equivalent tag-based code is a bit more effort. We need to hand-crank our array construction:

<cfset japanese = []>
<cfloop array="#keys#" item="key">
    <cfset arrayAppend(japanese, translationLookup[key].jp)>
<cfdump var="#japanese#">

In the next example I am being less literal about the "key mapping" idea, in case one got a sense that that sort of thing is inate to a mapping operation. I'm doubling each element in the array:

values = [1, 22, 333, 4444]
doubled = => n*2)

And the tags version (although here I'm halvig the values, for the hell of it). Same as the previous exercise really: just a wee bit clunkier than using the dedicated mapping function:

<cfset halved = []>
<cfloop array="#values#" item="value">
    <cfset arrayAppend(halved, value / 2)>
<cfdump var="#halved#">

A more real-world example would be when yer getting an array of raw data values back from some sort of data-retrieval operation, and you want to properly model those as objects before returning them to your business logic:

records = [
    {id=1, mi="whero", en="red"},
    {id=2, mi="kakariki", en="green"},
    {id=3, mi="kikorangi", en="blue"}
objects = => new Colour(, record.mi, record.en))


<cfset objects = []>
<cfloop array="#records#" item="record">
    <cfset arrayAppend(objects, new Colour(, record.mi, record.en))>
<cfdump var="#objects#">

You get the idea.

To show how strings can be remapped too, I knocked-together a quick example of, but then remembered Lucee does not support yet, so needed to use a list instead:

s = "The Quick Brown Fox Jumps Over The Lazy Dog"

a = asc("a")
z = asc("z")

rot13 = s.listToArray("").map((c) => {
    var checkCode = asc(lcase(c))

    if (checkCode < a || checkCode > z) {
        return c
    var offset = (checkCode + 13) <= z ? 13 : -13

    return chr(asc(c) + offset)

And I tested this by feeding the result back into a tag-based version of the operation, to make sure it returned to the original string:

<cfset a = asc("a")>
<cfset z = asc("z")>

<cfset s2 = "">
<cfloop array="#listToArray(rot13, "")#" item="c">
    <cfset checkCode = asc(lcase(c))>

    <cfif checkCode LT a OR checkCode GT z>
        <cfset s2 &= c>
    <cfset offset = 13>
    <cfif checkCode + 13 GT z>
        <cfset offset = -13>
    <cfset s2 &= chr(asc(c) + offset)>

All in all using the specific iteration function is slightly clearer as to what sort of transformation is taking place, plus it saves you from having to write the looping and assignment scaffolding that a tags-based / hand-cranked version might. Often remappings are one-liners, and it's just more readable to do it as a simple assignment epression than having to hand-crank the boilerplate looping code.

The code for this article is all munged together in public/nonWheelsTests/higherOrderFunctionsDemonstration.

I'll have a look at how reduce operations work, tomorrow.