84 KiB
Lambda the Ultimate Pattern Factory
My first programming languages were Lisp, Scheme, and ML. When I later started to work in OO languages like C++ and Java I noticed that idioms that are standard vocabulary in functional programming (fp) were not so easy to achieve and required sophisticated structures. Books like Design Patterns: Elements of Reusable Object-Oriented Software were a great starting point to reason about those structures. One of my earliest findings was that several of the GoF-Patterns had a stark resemblance of structures that are built into in functional languages: for instance the strategy pattern corresponds to higher order functions in fp (more details see below).
Recently, while re-reading through the Typeclassopedia I thought it would be a good exercise to map the structure of software design-patterns to the concepts found in the Haskell type class library and in functional programming in general.
By searching the web I found some blog entries studying specific patterns, but I did not come across any comprehensive study. As it seemed that nobody did this kind of work yet I found it worthy to spend some time on it and write down all my findings on the subject.
I think this kind of exposition could be helpful if you are either:
- a programmer with an OO background who wants to get a better grip on how to implement complexer designs in functional programming
- a functional programmer who wants to get a deeper intuition for type classes.
This project is still work in progress, so please feel free to contact me with any corrections, adjustments, comments, suggestions and additional ideas you might have. Please use the Issue Tracker to enter your requests.
Directions I'd like to cover in more depths are for instance:
- complete coverage of the GOF set of patterns
- coverage of category theory based patterns (any ideas are welcome!)
- coverage of patterns with a clear FP background, eg. MapReduce, Blockchain, Function-as-a-service
Table of contents
The Patternopedia
The Typeclassopedia is a now classic paper that introduces the Haskell type classes by clarifying their algebraic and category-theoretic background. In particular it explains the relationships among those type classes.
In this section I'm taking a tour through the Typeclassopedia from a design pattern perspective. For each of the Typeclassopedia type classes (at least up to Traversable) I try to explain how it corresponds to structures applied in design patterns.
Strategy → Functor
"The strategy pattern [...] is a behavioral software design pattern that enables selecting an algorithm at runtime. Instead of implementing a single algorithm directly, code receives run-time instructions as to which in a family of algorithms to use"
"In the above UML class diagram, the
Contextclass doesn't implement an algorithm directly. Instead,Contextrefers to theStrategyinterface for performing an algorithm (strategy.algorithm()), which makesContextindependent of how an algorithm is implemented. TheStrategy1andStrategy2classes implement theStrategyinterface, that is, implement (encapsulate) an algorithm." (quoted from Wikipedia
- in C a strategy would be modelled as a function pointer that can be used to dispatch calls to different functions.
- In an OO language like Java a strategy would be modelled as a single strategy-method interface that would be implemented by different strategy classes that provide implementations of the strategy method.
- in functional programming a strategy is just a function that is passed as a parameter to a higher order function.
We are starting with a simplified example working on Numbers. I'm defining Java interfaces for three simple strategies:
public interface StrategySquare {
public double algorithm(double input);
}
public interface StrategyDouble {
public double algorithm(double input);
}
public interface StrategyToString {
public String algorithm(double input);
}
These interface can then be implemented by concrete classes. I'm using anonymous classes to implement the strategies:
static StrategySquare strategySquare = new StrategySquare() {
@Override
public double algorithm(double input) {
return input * input;
}
};
Once I've written this code my Java IDE tells me that this anonymous class could be replaced by a lambda expression. So I can simply implement the strategies as follows:
static StrategySquare strategySquare = input -> input * input;
static StrategyDouble strategyDouble = input -> 2 * input;
static StrategyToString strategyToString = input -> String.valueOf(input);
// now we can use the strategies as follows:
public static void main(String[] args) {
System.out.println(strategySquare.algorithm(4.0));
System.out.println(strategyDouble.algorithm(4.0));
System.out.println(strategyToString.algorithm(strategySquare.algorithm(5)));
}
The interesting point here is that in Java single method interfaces like StrategySquare can be implemented by lambda expressions, that is anonymous functions.
So the conclusion is: a single method interface of a strategy is just the type signature of a function.
That's why in functional programming strategies are implemented as functions passed as arguments to higher order functions. In Haskell our three strategies would be implemented as follows:
-- first we define simple strategies operating on numbers:
strategyDouble :: Num a => a -> a
strategyDouble n = 2*n
strategySquare :: Num a => a -> a
strategySquare n = n*n
strategyToString :: Show a => a -> String
strategyToString = show
These strategies – or rather functions – can then be used to perform operations on numbers, as shown in the following GHCi (The Glasgow Haskell Compiler REPL) session:
ghci> strategySquare 15
225
ghci> strategyDouble 8.0
16.0
ghci> strategyToString 4
"4"
We are using the functions by applying them to some numeric values.
One nice feature of functions is that they can be composed using the (.) operator:
ghci> :type (.)
(.) :: (b -> c) -> (a -> b) -> a -> c
ghci> (strategyToString . strategySquare ) 15
"225"
So far we have been using functions directly and not as a parameter to some higher order function, that is we are using them without a computational context referring to them.
In the next step we will set up such a computational context.
Let's assume we want to be able to apply our strategies defined above not only to single values but to lists of values. We don't want to rewrite our code, but rather reuse the existing functions and use them in a list context.
-- | applyInListContext applies a function of type Num a => a -> b to a list of a's:
applyInListContext :: Num a => (a -> b) -> [a] -> [b]
-- applying f to an empty list returns the empty list
applyInListContext f [] = []
-- applying f to a list with head x returns (f x) 'consed' to a list
-- resulting from applying applyInListContext f to the tail of the list
applyInListContext f (x:xs) = (f x) : applyInListContext f xs
-- HLint, the Haskell linter advices us to use the predefined map function instead of our definition above:
applyInListContext = map
Now we can use the applyInListContext function to apply strategies to lists of numbers:
ghci> applyInListContext strategyDouble [1..10]
[2,4,6,8,10,12,14,16,18,20]
ghci> applyInListContext strategySquare [1..10]
[1,4,9,16,25,36,49,64,81,100]
Using this approach is not limited to lists but we can apply it to any other parametric datatype.
As an example we construct a Context a type with the corresponding higher order function applyInContext.
This function accepts a function of type Num a => (a -> b) and a Context a and returns a Context b.
The return value of type Context b is constructed by applying the function f of type (a -> b) to the value x which has been extracted from the input value Context x by pattern matching:
newtype Context a = Context a deriving (Show, Read)
applyInContext :: Num a => (a -> b) -> Context a -> Context b
applyInContext f (Context x) = Context (f x)
-- using this in ghci:
ghci> applyInContext (strategyToString . strategySquare) (Context 14)
Context "196"
Now imagine we would be asked to implement this way to apply functions within a context for yet another data type. Wouldn't it be great to have a generic tool that would solve this problem for any context, thus avoiding to reinvent the wheel each time?
In Functional Prigramming languages the application of a function in a computational context is generalized with the type class Functor:
class Functor f where
fmap :: (a -> b) -> f a -> f b
By comparing the signature of fmap with our higher order functions applyInListContext and applyIncontext
we notice that they bear the same structure:
fmap :: (a -> b) -> f a -> f b
applyInContext :: Num a => (a -> b) -> Context a -> Context b
applyInListContext :: Num a => (a -> b) -> [a] -> [b]
Actually the function map (which had been suggested as a replacement for applyInContext by the Haskell Linter) is the fmap implementation for the List Functor instance:
instance Functor [] where
fmap = map
In the same way the Functor definition for the Context type defines fmap exactly as the applyInIncontext function:
instance Functor Context where
fmap f (Context a) = Context (f a)
As deriving of Functor instances can be done mechanically for any algebraic data type there is no need to define Functor instances explicitely.
Instead of the the above instance Functor declaration we let the compiler do the work for us by using the DeriveFunctor pragma:
{-# LANGUAGE DeriveFunctor #-}
newtype Context a = Context a deriving (Functor, Show, Read)
composition of functors
In the beginning of this section we have seen that composition of functions using the (.) operator is a very useful tool to construct complex functionality by chaining more simple functions.
As stated in the Functor laws any Functor instance must ensure that:
fmap (g . h) = (fmap g) . (fmap h)
Let's try to verify this with our two example Functors Context and []:
ghci> (fmap strategyToString . fmap strategySquare) (Context 7)
Context "49"
-- this version is more efficient as we have to pattern match and reconstruct the Context only once:
ghci> fmap (strategyToString . strategySquare) (Context 7)
Context "49"
-- now with a list context:
ghci> (fmap strategyToString . fmap strategySquare) [1..10]
["1","4","9","16","25","36","49","64","81","100"]
-- this version is more efficient as we iterate the list only once:
ghci> fmap (strategyToString . strategySquare) [1..10]
["1","4","9","16","25","36","49","64","81","100"]
But composition doesn't stop here:
ghci> (fmap . fmap) (strategyToString . strategySquare) (Context [6,7])
Context ["36","49"]
As we can see, The two functors [] and Context can be composed and this composition is a new Functor Context []. The composition (fmap . fmap) can be used to apply our strategy functions on the wrapped integers 6 and 7.
conclusion
Although it would be fair to say that the type class Functor captures the essential idea of the strategy pattern – namely the injecting of a function into a computational context and its execution in this context – the usage of higher order functions is of course not limited to Functors – we could use just any higher order function fitting our purpose.
Other type classes like Foldable or Traversable (which is a Foldable Functor) can serve as helpful abstractions when dealing with typical use cases of applying variable strategies within a computational context.
Singleton → Applicative
"The singleton pattern is a software design pattern that restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system." (quoted from Wikipedia
The singleton pattern ensures that multiple requests to a given object always return one and the same singleton instance.
In functional programming this semantics can be achieved by let.
let singleton = someExpensiveComputation
in mainComputation
--or in lambda notation:
(\singleton -> mainComputation) someExpensiveComputation
Via the let-Binding we can thread the singleton through arbitrary code in the in block. All occurences of singleton in the mainComputationwill point to the same instance.
Type classes provide several tools to make this kind of threading more convenient or even to avoid explicit threading of instances.
Using Applicative Functor for threading of singletons
The following code defines a simple expression evaluator:
data Exp e = Var String
| Val e
| Add (Exp e) (Exp e)
| Mul (Exp e) (Exp e)
-- the environment is a list of tupels mapping variable names to values of type e
type Env e = [(String, e)]
-- a simple evaluator reducing expression to numbers
eval :: Num e => Exp e -> Env e -> e
eval (Var x) env = fetch x env
eval (Val i) env = i
eval (Add p q) env = eval p env + eval q env
eval (Mul p q) env = eval p env * eval q env
eval is a classic evaluator function that recursively evaluates sub-expression before applying + or *.
Note how the explicit envparameter is threaded through the recursive eval calls. This is needed to have the
environment avalailable for variable lookup at any recursive call depth.
If we now bind env to a value as in the following snippet it is used as an immutable singleton within the recursive evaluation of eval exp env.
main = do
let exp = Mul (Add (Val 3) (Val 1))
(Mul (Val 2) (Var "pi"))
env = [("pi", pi)]
print $ eval exp env
Experienced Haskellers will notice the "eta-reduction smell" in eval (Var x) env = fetch x env which hints at the possibilty to remove env as an explicit parameter. We can not do this right away as the other equations for eval do not allow eta-reduction. In order to do so we have to apply the combinators of the Applicative Functor:
class Functor f => Applicative f where
pure :: a -> f a
(<*>) :: f (a -> b) -> f a -> f b
instance Applicative ((->) a) where
pure = const
(<*>) f g x = f x (g x)
This Applicative allows us to rewrite eval as follows:
eval :: Num e => Exp e -> Env e -> e
eval (Var x) = fetch x
eval (Val i) = pure i
eval (Add p q) = pure (+) <*> eval p <*> eval q
eval (Mul p q) = pure (*) <*> eval p <*> eval q
Any explicit handling of the variable env is now removed.
(I took this example from the classic paper Applicative programming with effects which details how pure and <*> correspond to the combinatory logic combinators K and S.)
Pipeline → Monad
In software engineering, a pipeline consists of a chain of processing elements (processes, threads, coroutines, functions, etc.), arranged so that the output of each element is the input of the next; the name is by analogy to a physical pipeline. (Quoted from: Wikipedia
The concept of pipes and filters in Unix shell scripts is a typical example of the pipeline architecture pattern.
$ echo "hello world" | wc -w | xargs printf "%d*3\n" | bc -l
6
This works exactly as stated in the wikipedia definition of the pattern: the output of echo "hello world" is used as input for the next command wc -w. The ouptput of this command is then piped as input into xargs printf "%d*3\n" and so on.
On the first glance this might look like ordinary function composition. We could for instance come up with the following approximation in Haskell:
((3 *) . length . words) "hello world"
6
But with this design we missed an important feature of the chain of shell commands: The commands do not work on elementary types like Strings or numbers but on input and output streams that are used to propagate the actual elementary data around. So we can't just send a String into the wc command as in "hello world" | wc -w. Instead we have to use echo to place the string into a stream that we can then use as input to the wc command:
> echo "hello world" | wc -w
So we might say that echo injects the String "hello world" into the stream context.
We can capture this behaviour in a functional program like this:
-- The Stream type is a wrapper around an arbitrary payload type 'a'
newtype Stream a = Stream a deriving (Show)
-- echo injects an item of type 'a' into the Stream context
echo :: a -> Stream a
echo = Stream
-- the 'andThen' operator used for chaining commands
infixl 7 |>
(|>) :: Stream a -> (a -> Stream b) -> Stream b
Stream x |> f = f x
-- echo and |> are used to create the actual pipeline
pipeline :: String -> Stream Int
pipeline str =
echo str |> echo . length . words |> echo . (3 *)
-- now executing the program in ghci repl:
ghci> pipeline "hello world"
Stream 6
The echo function injects any input into the Stream context:
ghci> echo "hello world"
Stream "hello world"
The |> (pronounced as "andThen") does the function chaining:
ghci> echo "hello world" |> echo . words
Stream ["hello","world"]
The result of |> is of type Stream b that's why we cannot just write echo "hello world" |> words. We have to use echo to create a Stream output that can be digested by a subsequent |>.
The interplay of a Context type Stream a and the functions echo and |> is a well known pattern from functional languages: it's the legendary Monad. As the Wikipedia article on the pipeline pattern states:
Pipes and filters can be viewed as a form of functional programming, using byte streams as data objects; more specifically, they can be seen as a particular form of monad for I/O.
There is an interesting paper available elaborating on the monadic nature of Unix pipes: Monadic Shell.
Here is the definition of the Monad type class in Haskell:
class Applicative m => Monad m where
-- | Sequentially compose two actions, passing any value produced
-- by the first as an argument to the second.
(>>=) :: m a -> (a -> m b) -> m b
-- | Inject a value into the monadic type.
return :: a -> m a
return = pure
By looking at the types of >>= and return it's easy to see the direct correspondence to |> and echo in the pipeline example above:
(|>) :: Stream a -> (a -> Stream b) -> Stream b
echo :: a -> Stream a
Mhh, this is nice, but still looks a lot like ordinary composition of functions, just with the addition of a wrapper.
In this simplified example that's true, because we have designed the |> operator to simply unwrap a value from the Stream and bind it to the formal parameter of the subsequent function:
Stream x |> f = f x
But we are free to implement the andThen operator in any way that we seem fit as long we maintain the type signature and the monad laws.
So we could for instance change the semantics of >>= to keep a log along the execution pipeline:
-- The DeriveFunctor Language Pragma provides automatic derivation of Functor instances
{-# LANGUAGE DeriveFunctor #-}
-- a Log is just a list of Strings
type Log = [String]
-- the Stream type is extended by a Log that keeps track of any logged messages
newtype LoggerStream a = LoggerStream (a, Log) deriving (Show, Functor)
instance Applicative LoggerStream where
pure = return
LoggerStream (f, _) <*> r = fmap f r
-- our definition of the Logging Stream Monad:
instance Monad LoggerStream where
-- returns a Stream wrapping a tuple of the actual payload and an empty Log
return a = LoggerStream (a, [])
-- we define (>>=) to return a tuple (composed functions, concatenated logs)
m1 >>= m2 = let LoggerStream(f1, l1) = m1
LoggerStream(f2, l2) = m2 f1
in LoggerStream(f2, l1 ++ l2)
-- compute length of a String and provide a log message
logLength :: String -> LoggerStream Int
logLength str = let l = length(words str)
in LoggerStream (l, ["length(" ++ str ++ ") = " ++ show l])
-- multiply x with 3 and provide a log message
logMultiply :: Int -> LoggerStream Int
logMultiply x = let z = x * 3
in LoggerStream (z, ["multiply(" ++ show x ++ ", 3" ++") = " ++ show z])
-- the logging version of the pipeline
logPipeline :: String -> LoggerStream Int
logPipeline str =
return str >>= logLength >>= logMultiply
-- and then in Ghci:
> logPipeline "hello logging world"
LoggerStream (9,["length(hello logging world) = 3","multiply(3, 3) = 9"])
What's noteworthy here is that Monads allow to make the mechanism of chaining functions explicit. We can define what andThen should mean in our pipeline by choosing a different Monad implementation.
So in a sense Monads could be called programmable semicolons
To make this statement a bit clearer we will have a closer look at the internal workings of the Maybe Monad in the next section.
NullObject → Maybe Monad
[...] a null object is an object with no referenced value or with defined neutral ("null") behavior. The null object design pattern describes the uses of such objects and their behavior (or lack thereof). Quoted from Wikipedia
In functional programming the null object pattern is typically formalized with option types:
[...] an option type or maybe type is a polymorphic type that represents encapsulation of an optional value; e.g., it is used as the return type of functions which may or may not return a meaningful value when they are applied. It consists of a constructor which either is empty (named None or
Nothing), or which encapsulates the original data typeA(writtenJust Aor Some A). Quoted from Wikipedia
(See also: Null Object as Identity)
In Haskell the most simple option type is Maybe. Let's directly dive into an example. We define a reverse index, mapping songs to album titles.
If we now lookup up a song title we may either be lucky and find the respective album or not so lucky when there is no album matching our song:
import Data.Map (Map, fromList)
import qualified Data.Map as Map (lookup) -- avoid clash with Prelude.lookup
-- type aliases for Songs and Albums
type Song = String
type Album = String
-- the simplified reverse song index
songMap :: Map Song Album
songMap = fromList
[("Baby Satellite","Microgravity")
,("An Ending", "Apollo: Atmospheres and Soundtracks")]
We can lookup this map by using the function Map.lookup :: Ord k => k -> Map k a -> Maybe a.
If no match is found it will return Nothing if a match is found it will return Just match:
ghci> Map.lookup "Baby Satellite" songMap
Just "Microgravity"
ghci> Map.lookup "The Fairy Tale" songMap
Nothing
Actually the Maybe type is defined as:
data Maybe a = Nothing | Just a
deriving (Eq, Ord)
All code using the Map.lookup function will never be confronted with any kind of Exceptions, null pointers or other nasty things. Even in case of errors a lookup will always return a properly typed Maybe instance. By pattern matching for Nothing or Just a client code can react on failing matches or positive results:
case Map.lookup "Ancient Campfire" songMap of
Nothing -> print "sorry, could not find your song"
Just a -> print a
Let's try to apply this to an extension of our simple song lookup. Let's assume that our music database has much more information available. Apart from a reverse index from songs to albums, there might also be an index mapping album titles to artists. And we might also have an index mapping artist names to their websites:
type Song = String
type Album = String
type Artist = String
type URL = String
songMap :: Map Song Album
songMap = fromList
[("Baby Satellite","Microgravity")
,("An Ending", "Apollo: Atmospheres and Soundtracks")]
albumMap :: Map Album Artist
albumMap = fromList
[("Microgravity","Biosphere")
,("Apollo: Atmospheres and Soundtracks", "Brian Eno")]
artistMap :: Map Artist URL
artistMap = fromList
[("Biosphere","http://www.biosphere.no//")
,("Brian Eno", "http://www.brian-eno.net")]
lookup' :: Ord a => Map a b -> a -> Maybe b
lookup' = flip Map.lookup
findAlbum :: Song -> Maybe Album
findAlbum = lookup' songMap
findArtist :: Album -> Maybe Artist
findArtist = lookup' albumMap
findWebSite :: Artist -> Maybe URL
findWebSite = lookup' artistMap
With all this information at hand we want to write a function that has an input parameter of type Song and returns a Maybe URL by going from song to album to artist to website url:
findUrlFromSong :: Song -> Maybe URL
findUrlFromSong song =
case findAlbum song of
Nothing -> Nothing
Just album ->
case findArtist album of
Nothing -> Nothing
Just artist ->
case findWebSite artist of
Nothing -> Nothing
Just url -> Just url
This code makes use of the pattern matching logic described before. It's worth to note that there is some nice circuit breaking happening in case of a Nothing. In this case Nothing is directly returned as result of the function and the rest of the case-ladder is not executed.
What's not so nice is "the dreaded ladder of code marching off the right of the screen" (quoted from Real World Haskell).
For each find function we have to repeat the same ceremony of pattern matching on the result and either return Nothing or proceed with the next nested level.
The good news is that it is possible to avoid this ladder.
We can rewrite our search by applying the andThen operator >>= as Maybe is an instance of Monad:
findUrlFromSong' :: Song -> Maybe URL
findUrlFromSong' song =
findAlbum song >>= \album ->
findArtist album >>= \artist ->
findWebSite artist
or even shorter as we can eliminate the lambda expressions by applying eta-conversion:
findUrlFromSong'' :: Song -> Maybe URL
findUrlFromSong'' song =
findAlbum song >>= findArtist >>= findWebSite
Using it in GHCi:
ghci> findUrlFromSong'' "All you need is love"
Nothing
ghci> findUrlFromSong'' "An Ending"
Just "http://www.brian-eno.net"
The expression findAlbum song >>= findArtist >>= findWebSite and the sequencing of actions in the pipeline example return str >>= return . length . words >>= return . (3 *) have a similar structure.
But the behaviour of both chains is quite different: In the Maybe Monad a >>= b does not evaluate b if a == Nothing but stops the whole chain of actions by simply returning Nothing.
The pattern matching and 'short-circuiting' is directly coded into the definition of (>>=) in the Monad implementation of Maybe:
instance Monad Maybe where
(Just x) >>= k = k x
Nothing >>= _ = Nothing
This elegant feature of (>>=) in the Maybe Monad allows us to avoid ugly and repetetive coding.
Avoiding partial functions by using Maybe
Maybe is often used to avoid the exposure of partial functions to client code. Take for example division by zero or computing the square root of negative numbers which are undefined (at least for real numbers).
Here come safe – that is total – definitions of these functions that return Nothing for undefined cases:
safeRoot :: Double -> Maybe Double
safeRoot x
| x >= 0 = Just (sqrt x)
| otherwise = Nothing
safeReciprocal :: Double -> Maybe Double
safeReciprocal x
| x /= 0 = Just (1/x)
| otherwise = Nothing
As we have already learned the monadic >>= operator allows to chain such function as in the following example:
safeRootReciprocal :: Double -> Maybe Double
safeRootReciprocal x = return x >>= safeReciprocal >>= safeRoot
This can even written more terse as:
safeRootReciprocal :: Double -> Maybe Double
safeRootReciprocal = safeReciprocal >=> safeRoot
The use of the Kleisli operator >=> makes it more evident that we are actually aiming at a composition of the monadic functions safeReciprocal and safeRoot.
There are many predefined Monads available in the Haskell curated libraries and it's also possible to combine their effects by making use of MonadTransformers. But that's a different story...
Composite → SemiGroup → Monoid
In software engineering, the composite pattern is a partitioning design pattern. The composite pattern describes a group of objects that is treated the same way as a single instance of the same type of object. The intent of a composite is to "compose" objects into tree structures to represent part-whole hierarchies. Implementing the composite pattern lets clients treat individual objects and compositions uniformly. (Quoted from Wikipedia)
A typical example for the composite pattern is the hierarchical grouping of test cases to TestSuites in a testing framework. Take for instance the following class diagram from the JUnit cooks tour which shows how JUnit applies the Composite pattern to group TestCases to TestSuites while both of them implement the Test interface:
In Haskell we could model this kind of hierachy with an algebraic data type (ADT):
-- the composite data structure: a Test can be either a single TestCase
-- or a TestSuite holding a list of Tests
data Test = TestCase TestCase
| TestSuite [Test]
-- a test case produces a boolean when executed
type TestCase = () -> Bool
The function run as defined below can either execute a single TestCase or a composite TestSuite:
-- execution of a Test.
run :: Test -> Bool
run (TestCase t) = t () -- evaluating the TestCase by applying t to ()
run (TestSuite l) = all (True ==) (map run l) -- running all tests in l and return True if all tests pass
-- a few most simple test cases
t1 :: Test
t1 = TestCase (\() -> True)
t2 :: Test
t2 = TestCase (\() -> True)
t3 :: Test
t3 = TestCase (\() -> False)
-- collecting all test cases in a TestSuite
ts = TestSuite [t1,t2,t3]
As run is of type run :: Test -> Bool we can use it to execute single TestCases or complete TestSuites.
Let's try it in GHCI:
ghci> run t1
True
ghci> run ts
False
In order to aggregate TestComponents we follow the design of JUnit and define a function addTest. Adding two atomic Tests will result in a TestSuite holding a list with the two Tests. If a Test is added to a TestSuite, the test is added to the list of tests of the suite. Adding TestSuites will merge them.
-- adding Tests
addTest :: Test -> Test -> Test
addTest t1@(TestCase _) t2@(TestCase _) = TestSuite [t1,t2]
addTest t1@(TestCase _) (TestSuite list) = TestSuite ([t1] ++ list)
addTest (TestSuite list) t2@(TestCase _) = TestSuite (list ++ [t2])
addTest (TestSuite l1) (TestSuite l2) = TestSuite (l1 ++ l2)
If we take a closer look at addTest we will see that it is a associative binary operation on the set of Tests.
In mathemathics a set with an associative binary operation is a Semigroup.
We can thus make our type Test an instance of the type class Semigroup with the following declaration:
instance Semigroup Test where
(<>) = addTest
What's not visible from the JUnit class diagram is how typical object oriented implementations will have to deal with null-references. That is the implementations would have to make sure that the methods run and addTest will handle empty references correctly.
With Haskells algebraic data types we would rather make this explicit with a dedicated Empty element.
Here are the changes we have to add to our code:
-- the composite data structure: a Test can be Empty, a single TestCase
-- or a TestSuite holding a list of Tests
data Test = Empty
| TestCase TestCase
| TestSuite [Test]
-- execution of a Test.
run :: Test -> Bool
run Empty = True -- empty tests will pass
run (TestCase t) = t () -- evaluating the TestCase by applying t to ()
--run (TestSuite l) = foldr ((&&) . run) True l
run (TestSuite l) = all (True ==) (map run l) -- running all tests in l and return True if all tests pass
-- addTesting Tests
addTest :: Test -> Test -> Test
addTest Empty t = t
addTest t Empty = t
addTest t1@(TestCase _) t2@(TestCase _) = TestSuite [t1,t2]
addTest t1@(TestCase _) (TestSuite list) = TestSuite ([t1] ++ list)
addTest (TestSuite list) t2@(TestCase _) = TestSuite (list ++ [t2])
addTest (TestSuite l1) (TestSuite l2) = TestSuite (l1 ++ l2)
From our additions it's obvious that Empty is the identity element of the addTest function. In Algebra a Semigroup with an identity element is called Monoid:
In abstract algebra, [...] a monoid is an algebraic structure with a single associative binary operation and an identity element. Quoted from Wikipedia
With haskell we can declare Test as an instance of the Monoid type class by defining:
instance Monoid Test where
mempty = Empty
We can now use all functions provided by the Monoid type class to work with our Test:
compositeDemo = do
print $ run $ t1 <> t2
print $ run $ t1 <> t2 <> t3
We can also use the function mconcat :: Monoid a => [a] -> a on a list of Tests: mconcat composes a list of Tests into a single Test. That's exactly the mechanism of forming a TestSuite from atomic TestCases.
compositeDemo = do
print $ run $ mconcat [t1,t2]
print $ run $ mconcat [t1,t2,t3]
This particular feature of mconcat :: Monoid a => [a] -> a to condense a list of Monoids to a single Monoid can be used to drastically simplify the design of our test framework.
We need just one more hint from our mathematician friends:
Functions are monoids if they return monoids Quoted from blog.ploeh.dk
Currently our TestCases are defined as functions yielding boolean values:
type TestCase = () -> Bool
If Bool was a Monoid we could use mconcat to form test suite aggregates. Bool in itself is not a Monoid; but together with a binary associative operation like (&&) or (||) it will form a Monoid.
The intuitive semantics of a TestSuite is that a whole Suite is "green" only when all enclosed TestCases succeed. That is the conjunction of all TestCases must return True.
So we are looking for the Monoid of boolean values under conjunction (&&). In Haskell this Monoid is called All):
-- | Boolean monoid under conjunction ('&&').
-- >>> getAll (All True <> mempty <> All False)
-- False
-- >>> getAll (mconcat (map (\x -> All (even x)) [2,4,6,7,8]))
-- False
newtype All = All { getAll :: Bool }
instance Semigroup All where
(<>) = coerce (&&)
instance Monoid All where
mempty = All True
Making use of All our improved definition of TestCases is as follows:
type SmartTestCase = () -> All
Now our test cases do not directly return a boolean value but an All wrapper, which allows automatic conjunction of test results to a single value.
Here are our redefined TestCases:
tc1 :: SmartTestCase
tc1 () = All True
tc2 :: SmartTestCase
tc2 () = All True
tc3 :: SmartTestCase
tc3 () = All False
We now implement a new evaluation function run' which evaluates a SmartTestCase (which may be either an atomic TestCase or a TestSuite assembled by mconcat) to a single boolean result.
run' :: SmartTestCase -> Bool
run' tc = getAll $ tc ()
This version of run is much simpler than the original and we can completely avoid the rather laborious addTest function. We also don't need any composite type Test.
By just sticking to the Haskell built-in type classes we achieve cleanly designed functionality with just a few lines of code.
compositeDemo = do
-- execute a single test case
print $ run' tc1
--- execute a complex test suite
print $ run' $ mconcat [tc1,tc2]
print $ run' $ mconcat [tc1,tc2,tc3]
For more details on Composite as a Monoid please refer to the following blog: Composite as Monoid
Visitor → Foldable
[...] the visitor design pattern is a way of separating an algorithm from an object structure on which it operates. A practical result of this separation is the ability to add new operations to existent object structures without modifying the structures. It is one way to follow the open/closed principle. (Quoted from Wikipedia)
In functional languages - and Haskell in particular - we have a whole armada of tools serving this purpose:
- higher order functions like map, fold, filter and all their variants allow to "visit" lists
- The Haskell type classes
Functor,Foldable,Traversable, etc. provide a generic framework to allow visiting any algebraic datatype by just deriving one of these type classes.
Using Foldable
-- we are re-using the Exp data type from the Singleton example
-- and transform it into a Foldable type:
instance Foldable Exp where
foldMap f (Val x) = f x
foldMap f (Add x y) = foldMap f x `mappend` foldMap f y
foldMap f (Mul x y) = foldMap f x `mappend` foldMap f y
filterF :: Foldable f => (a -> Bool) -> f a -> [a]
filterF p = foldMap (\a -> if p a then [a] else [])
visitorDemo = do
let exp = Mul (Add (Val 3) (Val 2))
(Mul (Val 4) (Val 6))
putStr "size of exp: "
print $ length exp
putStrLn "filter even numbers from tree"
print $ filterF even exp
By virtue of the instance declaration Exp becomes a Foldable instance an can be used with arbitrary functions defined on Foldable like length in the example.
foldMap can for example be used to write a filtering function filterFthat collects all elements matching a predicate into a list.
Alternative approaches
Iterator → Traversable
[...] the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled. Quoted from Wikipedia
Iterating over a Tree
The most generic type class enabling iteration over algebraic data types is Traversable as it allows combinations of map and fold operations.
We are re-using the Exp type from earlier examples to show what's needed for enabling iteration in functional languages.
instance Functor Exp where
fmap f (Var x) = Var x
fmap f (Val a) = Val $ f a
fmap f (Add x y) = Add (fmap f x) (fmap f y)
fmap f (Mul x y) = Mul (fmap f x) (fmap f y)
instance Traversable Exp where
traverse g (Var x) = pure $ Var x
traverse g (Val x) = Val <$> g x
traverse g (Add x y) = Add <$> traverse g x <*> traverse g y
traverse g (Mul x y) = Mul <$> traverse g x <*> traverse g y
With this declaration we can traverse an Exp tree:
iteratorDemo = do
putStrLn "Iterator -> Traversable"
let exp = Mul (Add (Val 3) (Val 1))
(Mul (Val 2) (Var "pi"))
env = [("pi", pi)]
print $ traverse (\x c -> if even x then [x] else [2*x]) exp 0
In this example we are touching all (nested) Val elements and multiply all odd values by 2.
Combining traversal operations
Compared with Foldable or Functor the declaration of a Traversable instance looks a bit intimidating. In particular the type declaration for traverse:
traverse :: (Traversable t, Applicative f) => (a -> f b) -> t a -> f (t b)
looks like quite a bit of over-engineering for simple traversals as in the above example.
In oder to explain the real power of the Traversable type class we will look at a more sophisticated example in this section.
The Unix utility wc is a good example for a traversal operation that performs several different tasks while traversing its input:
echo "counting lines, words and characters in one traversal" | wc
1 8 54
The output simply means that our input has 1 line, 8 words and a total of 54 characters.
Obviously an efficients implementation of wc will accumulate the three counters for lines, words and characters in a single pass of the input and will not run three iterations to compute the three counters separately.
Here is a Java implementation:
private static int[] wordCount(String str) {
int nl=0, nw=0, nc=0; // number of lines, number of words, number of characters
boolean readingWord = false; // state information for "parsing" words
for (Character c : asList(str)) {
nc++; // count just any character
if (c == '\n') {
nl++; // count only newlines
}
if (c == ' ' || c == '\n' || c == '\t') {
readingWord = false; // when detecting white space, signal end of word
} else if (readingWord == false) {
readingWord = true; // when switching from white space to characters, signal new word
nw++; // increase the word counter only once while in a word
}
}
return new int[]{nl,nw,nc};
}
private static List<Character> asList(String str) {
return str.chars().mapToObj(c -> (char) c).collect(Collectors.toList());
}
Please note that the for (Character c : asList(str)) {...} notation is just syntactic sugar for
for (Iterator<Character> iter = asList(str).iterator(); iter.hasNext();) {
Character c = iter.next();
...
}
For efficiency reasons this solution may be okay, but from a design perspective the solution lacks clarity as the required logic for accumulating the three counters is heavily entangled within one code block. Just imagine how the complexity of the for-loop will increase once we have to add new features like counting bytes, counting white space or counting maximum line width.
So we would like to be able to isolate the different counting algorithms (separation of concerns) and be able to combine them in a way that provides efficient one-time traversal.
We start with the simple task of character counting:
type Count = Const (Sum Integer)
count :: a -> Count b
count _ = Const 1
cciBody :: Char -> Count a
cciBody = count
cci :: String -> Count [a]
cci = traverse cciBody
-- and then in ghci:
> cci "hello world"
Const (Sum {getSum = 11})
For each character we just emit a Const 1 which are elements of type Const (Sum Integer).
As (Sum Integer) is the monoid of Integers under addition, this design allows automatic summation over all collected Const values.
The next step of counting newlines looks similar:
-- return (Sum 1) if true, else (Sum 0)
test :: Bool -> Sum Integer
test b = Sum $ if b then 1 else 0
-- use the test function to emit (Sum 1) only when a newline char is detected
lciBody :: Char -> Count a
lciBody c = Const $ test (c == '\n')
-- define the linecount using traverse
lci :: String -> Count [a]
lci = traverse lciBody
-- and the in ghci:
> lci "hello \n world"
Const (Sum {getSum = 1})
Now let's try to combine character counting and line counting.
In order to match the type declaration for traverse:
traverse :: (Traversable t, Applicative f) => (a -> f b) -> t a -> f (t b)
We had to define cciBody and lciBody so that their return types are Applicative Functors.
The good news is that the product of two Applicatives is again an Applicative (the same holds true for Composition of Applicatives).
With this knowledge we can now use traverse to use the product of cciBody and lciBody:
import Data.Functor.Product -- Product of Functors
-- define infix operator for building a Functor Product
(<#>) :: (Functor m, Functor n) => (a -> m b) -> (a -> n b) -> (a -> Product m n b)
(f <#> g) y = Pair (f y) (g y)
-- use a single traverse to apply the Product of cciBody and lciBody
clci :: String -> Product Count Count [a]
clci = traverse (cciBody <#> lciBody)
-- and then in ghci:
> clci "hello \n world"
Pair (Const (Sum {getSum = 13})) (Const (Sum {getSum = 1}))
So we have achieved our aim of separating line counting and character counting in separate functions while still being able to apply them in only one traversal.
The only piece missing is the word counting. This is a bit tricky as it involves dealing with a state monad and wrapping it as an Applicative Functor:
import Data.Functor.Compose -- Composition of Functors
import Data.Functor.Const -- Const Functor
import Data.Functor.Identity -- Identity Functor (needed for coercion)
import Data.Monoid (Sum (..), getSum) -- Sum Monoid for Integers
import Control.Monad.State.Lazy -- State Monad
import Control.Applicative -- WrappedMonad (wrapping a Monad as Applicative Functor)
import Data.Coerce (coerce) -- Coercion (forcing types to match, when
-- their underlying representations are equal)
-- we use a (State Bool) monad to carry the 'readingWord' state through all invocations
-- WrappedMonad is used to use the monad as an Applicative Functor
-- This Applicative is then Composed with the actual Count a
wciBody :: Char -> Compose (WrappedMonad (State Bool)) Count a
wciBody c = coerce (updateState c) where
updateState :: Char -> Bool -> (Sum Integer, Bool)
updateState c w = let s = not(isSpace c) in (test (not w && s), s)
isSpace :: Char -> Bool
isSpace c = c == ' ' || c == '\n' || c == '\t'
-- using traverse to count words in a String
wci :: String -> Compose (WrappedMonad (State Bool)) Count [a]
wci = traverse wciBody
-- Forming the Product of character counting, line counting and word counting
-- and performing a one go traversal using this Functor product
clwci :: String -> (Product (Product Count Count) (Compose (WrappedMonad (State Bool)) Count)) [a]
clwci = traverse (cciBody <#> lciBody <#> wciBody)
-- the actual wordcount implementation.
-- for any String a triple of line count, word count, character count is returned
wc :: String -> (Integer, Integer, Integer)
wc str =
let raw = clwci str
cc = coerce $ pfst (pfst raw)
lc = coerce $ psnd (pfst raw)
wc = coerce $ evalState (unwrapMonad (getCompose (psnd raw))) False
in (lc,wc,cc)
-- and then in ghci:
> wc "hello \n world"
(1,2,13)
This example has been implemented according to ideas presented in the paper The Essence of the Iterator Pattern.
The Pattern behind the Patterns → Category
TBD
Arrow & Co
TBD
Beyond type class patterns
TBD:
-
Chain of Responsibility: ADT + pattern matching the ADT (at least the distpatch variant)
-
Partial application
-
Blockchain as Monadic chain of Actions
Dependency Injection → Parameter Binding
[...] Dependency injection is a technique whereby one object (or static method) supplies the dependencies of another object. A dependency is an object that can be used (a service). An injection is the passing of a dependency to a dependent object (a client) that would use it. The service is made part of the client's state. Passing the service to the client, rather than allowing a client to build or find the service, is the fundamental requirement of the pattern.
This fundamental requirement means that using values (services) produced within the class from new or static methods is prohibited. The client should accept values passed in from outside. This allows the client to make acquiring dependencies someone else's problem. (Quoted from Wikipedia)
In functional languages this is achieved by binding the formal parameters of a function to values.
Let's see how this works in a real world example. Say we have been building a renderer that allows to produce a markdown representation of a data type that represents the table of contents of a document:
-- | a table of contents consists of a heading and a list of entries
data TableOfContents = Section Heading [TocEntry]
-- | a ToC entry can be a heading or a sub-table of contents
data TocEntry = Head Heading | Sub TableOfContents
-- | a heading can be just a title string or an url with a title and the actual link
data Heading = Title String | Url String String
-- | render a ToC entry as a Markdown String with the proper indentation
teToMd :: Int -> TocEntry -> String
teToMd depth (Head head) = headToMd depth head
teToMd depth (Sub toc) = tocToMd depth toc
-- | render a heading as a Markdown String with the proper indentation
headToMd :: Int -> Heading -> String
headToMd depth (Title str) = indent depth ++ "* " ++ str ++ "\n"
headToMd depth (Url title url) = indent depth ++ "* [" ++ title ++ "](" ++ url ++ ")\n"
-- | convert a ToC to Markdown String. The parameter depth is used for proper indentation.
tocToMd :: Int -> TableOfContents -> String
tocToMd depth (Section heading entries) = headToMd depth heading ++ concatMap (teToMd (depth+2)) entries
-- | produce a String of length n, consisting only of blanks
indent :: Int -> String
indent n = replicate n ' '
-- | render a ToC as a Text (consisting of properly indented Markdown)
tocToMDText :: TableOfContents -> T.Text
tocToMDText = T.pack . tocToMd 0
We can use these definitions to create a table of contents data structure and to render it to markdown syntax:
demoDI = do
let toc = Section (Title "Chapter 1")
[ Sub $ Section (Title "Section a")
[Head $ Title "First Heading",
Head $ Url "Second Heading" "http://the.url"]
, Sub $ Section (Url "Section b" "http://the.section.b.url")
[ Sub $ Section (Title "UnderSection b1")
[Head $ Title "First", Head $ Title "Second"]]]
putStrLn $ T.unpack $ tocToMDText toc
-- and the in ghci:
ghci > demoDI
* Chapter 1
* Section a
* First Heading
* [Second Heading](http://the.url)
* [Section b](http://the.section.b.url)
* UnderSection b1
* First
* Second
So far so good. But of course we also want to be able to render our TableOfContent to HTML.
As we don't want to repeat all the coding work for HTML we think about using an existing Markdown library.
But we don't want any hard coded dependencies to a specific library in our code.
With these design ideas in mind we specify a rendering processor:
-- | render a ToC as a Text with html markup.
-- we specify this function as a chain of parse and rendering functions
-- which must be provided externally
tocToHtmlText :: (TableOfContents -> T.Text) -- 1. a renderer function from ToC to Text with markdown markups
-> (T.Text -> MarkDown) -- 2. a parser function from Text to a MarkDown document
-> (MarkDown -> HTML) -- 3. a renderer function from MarkDown to an HTML document
-> (HTML -> T.Text) -- 4. a renderer function from HTML to Text
-> TableOfContents -- the actual ToC to be rendered
-> T.Text -- the Text output (containing html markup)
tocToHtmlText tocToMdText textToMd mdToHtml htmlToText =
tocToMdText >>> -- 1. render a ToC as a Text (consisting of properly indented Markdown)
textToMd >>> -- 2. parse text with Markdown to a MarkDown data structure
mdToHtml >>> -- 3. convert the MarkDown data to an HTML data structure
htmlToText -- 4. render the HTML data to a Text with hmtl markup
The idea is simple:
- We render our
TableOfContentsto a MarkdownText(e.g. using our already definedtocToMDTextfunction). - This text is then parsed into a
MarkDowndata structure. - The
Markdowndocument is rendered into anHTMLdata structure, - which is then rendered to a
Textcontaining html markup.
To notate the chaining of functions in their natural order I have used the >>> operator from Control.Arrow which is defined as follows:
f >>> g = g . f
So >>> is just left to right composition of functions which makes reading of longer composition chains much easier to read (at least for people trained to read from left to right).
Please note that at this point we have not defined the types HTML and Markdown. They are just abstract placeholders and we just expect them to be provided externally.
In the same way we just specified that there must be functions available that can be bound to the formal parameters
tocToText, textToMd, mdToHtml and htmlToText.
If such functions are avaliable we can inject them (or rather bind them to the formal parameters) as in the following definition:
-- | a default implementation of a ToC to html Text renderer.
-- this function is constructed by partially applying `tocToHtmlText` to four functions
-- matching the signature of `tocToHtmlText`.
defaultTocToHtmlText :: TableOfContents -> T.Text
defaultTocToHtmlText =
tocToHtmlText
tocToMDText -- the ToC to markdown Text renderer as defined above
textToMarkDown -- a MarkDown parser, externally provided via import
markDownToHtml -- a MarkDown to HTML renderer, externally provided via import
htmlToText -- a HTML to Text with html markup, externally provided via import
This definition assumes that apart from tocToMDText which has already been defined the functions textToMarkDown, markDownToHtml and htmlToText are also present in the current scope.
This is achieved by the following import statement:
import CheapskateRenderer (HTML, MarkDown, textToMarkDown, markDownToHtml, htmlToText)
The implementation in file CheapskateRenderer.hs then looks like follows:
module CheapskateRenderer where
import qualified Cheapskate as C
import qualified Data.Text as T
import qualified Text.Blaze.Html as H
import qualified Text.Blaze.Html.Renderer.Pretty as R
-- | a type synonym that hides the Cheapskate internal Doc type
type MarkDown = C.Doc
-- | a type synonym the hides the Blaze.Html internal Html type
type HTML = H.Html
-- | parse Markdown from a Text (with markdown markup). Using the Cheapskate library.
textToMarkDown :: T.Text -> MarkDown
textToMarkDown = C.markdown C.def
-- | convert MarkDown to HTML by using the Blaze.Html library
markDownToHtml :: MarkDown -> HTML
markDownToHtml = H.toHtml
-- | rendering a Text with html markup from HTML. Using Blaze again.
htmlToText :: HTML -> T.Text
htmlToText = T.pack . R.renderHtml
Now let's try it out:
demoDI = do
let toc = Section (Title "Chapter 1")
[ Sub $ Section (Title "Section a")
[Head $ Title "First Heading",
Head $ Url "Second Heading" "http://the.url"]
, Sub $ Section (Url "Section b" "http://the.section.b.url")
[ Sub $ Section (Title "UnderSection b1")
[Head $ Title "First", Head $ Title "Second"]]]
putStrLn $ T.unpack $ tocToMDText toc
putStrLn $ T.unpack $ defaultTocToHtmlText toc
-- using this in ghci:
ghci > demoDI
* Chapter 1
* Section a
* First Heading
* [Second Heading](http://the.url)
* [Section b](http://the.section.b.url)
* UnderSection b1
* First
* Second
<ul>
<li>Chapter 1
<ul>
<li>Section a
<ul>
<li>First Heading</li>
<li><a href="http://the.url">Second Heading</a></li>
</ul></li>
<li><a href="http://the.section.b.url">Section b</a>
<ul>
<li>UnderSection b1
<ul>
<li>First</li>
<li>Second</li>
</ul></li>
</ul></li>
</ul></li>
</ul>
By inlining this output into the present Markdown document we can see that Markdown and HTML rendering produce the same structure:
- Chapter 1
- Section a
- First Heading
- Second Heading
- Section b
- UnderSection b1
- First
- Second
- Chapter 1
- Section a
- First Heading
- Second Heading
- Section b
- UnderSection b1
- First
- Second
Adapter → Function Composition
"The adapter pattern is a software design pattern (also known as wrapper, an alternative naming shared with the decorator pattern) that allows the interface of an existing class to be used as another interface. It is often used to make existing classes work with others without modifying their source code." (Quoted from Wikipedia
An example is an adapter that converts the interface of a Document Object Model of an XML document into a tree structure that can be displayed.
What does an adapter do? It translates a call to the adapter into a call of the adapted backend code. Which may also involve translation of the argument data.
Say we have some backend function that we want to provide with an adapter. we assume that backend has type c -> d:
backend :: c -> d
Our adapter should be of type a -> b:
adapter :: a -> b
In order to write this adapter we have to write two function. The first is:
marshal :: a -> c
which translated the input argument of adapter into the correct type c that can be digested by the backend.
And the second function is:
unmarshal :: d -> b
which translates the result of the backendfunction into the correct return type of adapter.
adapter will then look like follows:
adapter :: a -> b
adapter = unmarshal . backend . marshal
So in essence the Adapter Patterns is just function composition.
Here is a simple example. Say we have a backend that understands only 24 hour arithmetics (eg. 23:50 + 0:20 = 0:10).
But in our frontend we don't want to see this ugly arithmetics and want to be able to add minutes to a time representation in minutes (eg. 100 + 200 = 300).
We solve this by using the above mentioned function composition of unmarshal . backend . marshal:
-- a 24:00 hour clock representation of time
newtype WallTime = WallTime (Int, Int) deriving (Show)
-- this is our backend. It can add minutes to a WallTime representation
addMinutesToWallTime :: Int -> WallTime -> WallTime
addMinutesToWallTime x (WallTime (h, m)) =
let (hAdd, mAdd) = x `quotRem` 60
hNew = h + hAdd
mNew = m + mAdd
in if mNew >= 60
then
let (dnew, hnew') = (hNew + 1) `quotRem` 24
in WallTime (24*dnew + hnew', mNew-60)
else WallTime (hNew, mNew)
-- this is our time representation in Minutes that we want to use in the frontend
newtype Minute = Minute Int deriving (Show)
-- convert a Minute value into a WallTime representation
marshalMW :: Minute -> WallTime
marshalMW (Minute x) =
let (h,m) = x `quotRem` 60
in WallTime (h `rem` 24, m)
-- convert a WallTime value back to Minutes
unmarshalWM :: WallTime -> Minute
unmarshalWM (WallTime (h,m)) = Minute $ 60 * h + m
-- this is our frontend that add Minutes to a time of a day
-- measured in minutes
addMinutesAdapter :: Int -> Minute -> Minute
addMinutesAdapter x = unmarshalWM . addMinutesToWallTime x . marshalMW
adapterDemo = do
putStrLn "Adapter vs. function composition"
print $ addMinutesAdapter 100 $ Minute 400
putStrLn ""
Template Method → type class default functions
In software engineering, the template method pattern is a behavioral design pattern that defines the program skeleton of an algorithm in an operation, deferring some steps to subclasses. It lets one redefine certain steps of an algorithm without changing the algorithm's structure. Quoted from Wikipedia
The TemplateMethod pattern is quite similar to the StrategyPattern. The main difference is the level of granularity. In Strategy a complete block of functionality - the Strategy - can be replaced. In TemplateMethod the overall layout of an algorithm is predefined and only specific parts of it may be replaced.
In functional programming the answer to this kind of problem is again the usage of higher order functions.
In the following example we come back to the example for the Adapter.
The function addMinutesAdapter lays out a structure for interfacing to some kind of backend:
- marshalling the arguments into the backend format
- apply the backend logic to the marshalled arguments
- unmarshal the backend result data into the frontend format
addMinutesAdapter :: Int -> Minute -> Minute
addMinutesAdapter x = unmarshalWM . addMinutesToWallTime x . marshalMW
In this code the backend functionality - addMinutesToWallTime - is a hardcoded part of the overall structure.
Let's assume we want to use different kind of backend implementations - for instance a mock replacement. In this case we would like to keep the overall structure - the template - and would just make a specific part of it flexible. This sounds like an ideal candidate for the TemplateMethod pattern:
addMinutesTemplate :: (Int -> WallTime -> WallTime) -> Int -> Minute -> Minute
addMinutesTemplate f x =
unmarshalWM .
f x .
marshalMW
addMinutesTemplate has an additional parameter f of type (Int -> WallTime -> WallTime). This parameter may be bound to addMinutesToWallTime or alternative implementations:
-- implements linear addition (the normal case) even for values > 1440
linearTimeAdd :: Int -> Minute -> Minute
linearTimeAdd = addMinutesTemplate addMinutesToWallTime
-- implements cyclic addition, respecting a 24 hour (1440 Min) cycle
cyclicTimeAdd :: Int -> Minute -> Minute
cyclicTimeAdd = addMinutesTemplate addMinutesToWallTime'
where addMinutesToWallTime' implements a silly 24 hour cyclic addition:
-- a 24 hour (1440 min) cyclic version of addition: 1400 + 100 = 60
addMinutesToWallTime' :: Int -> WallTime -> WallTime
addMinutesToWallTime' x (WallTime (h, m)) =
let (hAdd, mAdd) = x `quotRem` 60
hNew = h + hAdd
mNew = m + mAdd
in if mNew >= 60
then WallTime ((hNew + 1) `rem` 24, mNew-60)
else WallTime (hNew, mNew)
And here is how we use it to do actual computations:
templateMethodDemo = do
putStrLn $ "linear time: " ++ (show $ linearTimeAdd 100 (Minute 1400))
putStrLn $ "cyclic time: " ++ (show $ cyclicTimeAdd 100 (Minute 1400))
type class minimal implementations as template method
The template method is used in frameworks, where each implements the invariant parts of a domain's architecture, leaving "placeholders" for customization options. This is an example of inversion of control. Quoted from Wikipedia
The type classes in Haskells base library apply this template approach frequently to reduce the effort for implementing type class instances and to provide a predefined structure with specific 'customization options'.
As an example let's extend the type WallTime by an associative binary operation addWallTimes to form an instance of the Monoid type class:
addWallTimes :: WallTime -> WallTime -> WallTime
addWallTimes a@(WallTime (h,m)) b =
let aMin = h*60 + m
in addMinutesToWallTime aMin b
instance Semigroup WallTime where
(<>) = addWallTimes
instance Monoid WallTime where
mempty = WallTime (0,0)
Even though we specified only mempty and (<>) we can now use the functions mappend :: Monoid a => a -> a -> a and mconcat :: Monoid a => [a] -> a on WallTime instances:
templateMethodDemo = do
let a = WallTime (3,20)
print $ mappend a a
print $ mconcat [a,a,a,a,a,a,a,a,a]
By looking at the definition of the Monoid type class we can see how this 'magic' is made possible:
class Semigroup a => Monoid a where
-- | Identity of 'mappend'
mempty :: a
-- | An associative operation
mappend :: a -> a -> a
mappend = (<>)
-- | Fold a list using the monoid.
mconcat :: [a] -> a
mconcat = foldr mappend mempty
For mempty only a type requirement but no definition is given.
But for mappend and mconcat default implementations are provided.
So the Monoid type class definition forms a template where the default implementations define the 'invariant parts' of the type class and the part specified by us form the 'customization options'.
(please note that it's generally possible to override the default implementations)
Creational Patterns
Abstract Factory → functions as data type values
The abstract factory pattern provides a way to encapsulate a group of individual factories that have a common theme without specifying their concrete classes. In normal usage, the client software creates a concrete implementation of the abstract factory and then uses the generic interface of the factory to create the concrete objects that are part of the theme. The client doesn't know (or care) which concrete objects it gets from each of these internal factories, since it uses only the generic interfaces of their products. This pattern separates the details of implementation of a set of objects from their general usage and relies on object composition, as object creation is implemented in methods exposed in the factory interface. Quoted from Wikipedia
There is a classic example that demonstrates the application of this pattern in the context of a typical problem in object oriented software design:
The example revolves around a small GUI framework that needs different implementations to render Buttons for different OS Platforms (called WIN and OSX in this example). A client of the GUI API should work with a uniform API that hides the specifics of the different platforms. The problem then is: how can the client be provided with a platform specific implementation without explicitely asking for a given implementation and how can we maintain a uniform API that hides the implementation specifics.
In OO languages like Java the abstract factory pattern would be the canonical answer to this problem:
- The client calls an abstract factory
GUIFactoryinterface to create aButtonby callingcreateButton() : Buttonthat somehow chooses (typically by some kind of configuration) which concrete factory has to be used to create concreteButtoninstances. - The concrete classes
WinButtonandOSXButtonimplement the interfaceButtonand provide platform specific implementations ofpaint () : void. - As the client uses only the interface methods
createButton()andpaint()it does not have to deal with any platform specific code.
The following diagram depicts the structure of interfaces and classes in this scenario:
In a functional language this kind of problem would be solved quite differently. In FP functions are first class citizens and thus it is much easier to treat function that represent platform specific actions as "normal" values that can be reached around.
So we could represent a Button type as a data type with a label (holding the text to display on the button) and an IO () action that represents the platform specific rendering:
-- | representation of a Button UI widget
data Button = Button
{ label :: String -- the text label of the button
, render :: Button -> IO () -- a platform specific rendering action
}
Platform specific actions to render a Button would look like follows:
-- | rendering a Button for the WIN platform (we just simulate it by printing the label)
winPaint :: Button -> IO ()
winPaint btn = putStrLn $ "winButton: " ++ label btn
-- | rendering a Button for the OSX platform
osxPaint :: Button -> IO ()
osxPaint btn = putStrLn $ "osxButton: " ++ label btn
-- | paint a button by using the Buttons render function
paint :: Button -> IO ()
paint btn@(Button _ render) = render btn
(Of course a real implementation would be quite more complex, but we don't care about the nitty gritty details here.)
With this code we can now create and use concrete Buttons like so:
ghci> button = Button "Okay" winPaint
ghci> :type button
button :: Button
ghci> paint button
winButton: Okay
We created a button with Button "Okay" winPaint. The field render of that button instance now holds the function winPaint.
The function paint now applies this render function -- i.e. winPaint -- to draw the Button.
Applying this scheme it is now very simple to create buttons with different render implementations:
-- | a representation of the operating system platform
data Platform = OSX | WIN | NIX | Other
-- | determine Platform by inspecting System.Info.os string
platform :: Platform
platform =
case os of
"darwin" -> OSX
"mingw32" -> WIN
"linux" -> NIX
_ -> Other
-- | create a button for os platform with label lbl
createButton :: String -> Button
createButton lbl =
case platform of
OSX -> Button lbl osxPaint
WIN -> Button lbl winPaint
NIX -> Button lbl (\btn -> putStrLn $ "nixButton: " ++ label btn)
Other -> Button lbl (\btn -> putStrLn $ "otherButton: " ++ label btn)
The function createButton determines the actual execution environment and accordingly creates platform specific buttons.
Now we have an API that hides all implementation specifics from the client and allows him to use only createButton and paint to work with Buttons for different OS platforms:
abstractFactoryDemo = do
putStrLn "AbstractFactory -> functions as data type values"
let exit = createButton "Exit" -- using the "abstract" API to create buttons
let ok = createButton "OK"
paint ok -- using the "abstract" API to paint buttons
paint exit
paint $ Button "Apple" osxPaint -- paint a platform specific button
paint $ Button "Pi" -- paint a user-defined button
(\btn -> putStrLn $ "raspberryButton: " ++ label btn)
Builder → record syntax, smart constructor
The Builder is a design pattern designed to provide a flexible solution to various object creation problems in object-oriented programming. The intent of the Builder design pattern is to separate the construction of a complex object from its representation.
Quoted from Wikipedia
The Builder patterns is frequently used to ease the construction of complex objects by providing a safe and convenient API to client code.
In the following Java example we define a POJO Class BankAccount:
public class BankAccount {
private int accountNo;
private String name;
private String branch;
private double balance;
private double interestRate;
BankAccount(int accountNo, String name, String branch, double balance, double interestRate) {
this.accountNo = accountNo;
this.name = name;
this.branch = branch;
this.balance = balance;
this.interestRate = interestRate;
}
@Override
public String toString() {
return "BankAccount {accountNo = " + accountNo + ", name = \"" + name
+ "\", branch = \"" + branch + "\", balance = " + balance + ", interestRate = " + interestRate + "}";
}
}
The class provides a package private constructor that takes 5 arguments that are used to fill the instance attributes. Using constructors with so many arguments is often considered inconvenient and potentially unsafe as certain constraints on the arguments might not be maintained by client code invoking this constructor.
The typical solution is to provide a Builder class that is responsible for maintaining internal data constraints and providing a robust and convenient API. In the following example the Builder ensures that a BankAccount must have an accountNo and that non null values are provided for the String attributes:
public class BankAccountBuilder {
private int accountNo;
private String name;
private String branch;
private double balance;
private double interestRate;
public BankAccountBuilder(int accountNo) {
this.accountNo = accountNo;
this.name = "Dummy Customer";
this.branch = "London";
this.balance = 0;
this.interestRate = 0;
}
public BankAccountBuilder withAccountNo(int accountNo) {
this.accountNo = accountNo;
return this;
}
public BankAccountBuilder withName(String name) {
this.name = name;
return this;
}
public BankAccountBuilder withBranch(String branch) {
this.branch = branch;
return this;
}
public BankAccountBuilder withBalance(double balance) {
this.balance = balance;
return this;
}
public BankAccountBuilder withInterestRate(double interestRate) {
this.interestRate = interestRate;
return this;
}
public BankAccount build() {
return new BankAccount(this.accountNo, this.name, this.branch, this.balance, this.interestRate);
}
}
Next comes an example of how the builder is used in client code:
public class BankAccountTest {
public static void main(String[] args) {
new BankAccountTest().testAccount();
}
public void testAccount() {
BankAccountBuilder builder = new BankAccountBuilder(1234);
// the builder can provide a dummy instance, that might be used for testing
BankAccount account = builder.build();
System.out.println(account);
// the builder provides a fluent API to construct regular instances
BankAccount account1 =
builder.withName("Marjin Mejer")
.withBranch("Paris")
.withBalance(10000)
.withInterestRate(2)
.build();
System.out.println(account1);
}
}
As we see the Builder can be either used to create dummy instaces that are still safe to use (e.g. for test cases) or by using the withXxx methods to populate all attributes:
BankAccount {accountNo = 1234, name = "Dummy Customer", branch = "London", balance = 0.0, interestRate = 0.0}
BankAccount {accountNo = 1234, name = "Marjin Mejer", branch = "Paris", balance = 10000.0, interestRate = 2.0}
From an API client perspective the Builder pattern can help to provide safe and convenient object construction which is not provided by the Java core language. As the Builder code is quite a redundant (e.g. having all attributes of the actual instance class) Builders are typically generated (e.g. with Lombok).
In functional languages there is usually no need for the Builder pattern as the languages already provide the necessary infrastructure.
The following example shows how the above example would be solved in Haskell:
data BankAccount = BankAccount {
accountNo :: Int
, name :: String
, branch :: String
, balance :: Double
, interestRate :: Double
} deriving (Show)
-- a "smart constructor" that just needs a unique int to construct a BankAccount
buildAccount :: Int -> BankAccount
buildAccount i = BankAccount i "Dummy Customer" "London" 0 0
builderDemo = do
-- construct a dummmy instance
let account = buildAccount 1234
print account
-- use record syntax to create a modified clone of the dummy instance
let account1 = account {name="Marjin Mejer", branch="Paris", balance=10000, interestRate=2}
print account1
-- directly using record syntax to create an instance
let account2 = BankAccount {
accountNo = 5678
, name = "Marjin"
, branch = "Reikjavik"
, balance = 1000
, interestRate = 2.5
}
print account2
-- and then in Ghci:
ghci> builderDemo
BankAccount {accountNo = 1234, name = "Dummy Customer", branch = "London", balance = 0.0, interestRate = 0.0}
BankAccount {accountNo = 1234, name = "Marjin Mejer", branch = "Paris", balance = 10000.0, interestRate = 2.0}
BankAccount {accountNo = 5678, name = "Marjin Mejer", branch = "Reikjavik", balance = 1000.0, interestRate = 2.5}
Conclusions
Design patterns are reusable abstractions in object-oriented software. However, using current mainstream programming languages, these elements can only be expressed extra-linguistically: as prose,pictures, and prototypes. We believe that this is not inherent in the patterns themselves, but evidence of a lack of expressivity in the languages of today. We expect that, in the languages of the future, the code parts of design patterns will be expressible as reusable library components. Indeed, we claim that the languages of tomorrow will suffice; the future is not far away. All that is needed, in addition to commonly-available features, are higher-order and datatype-generic constructs; these features are already or nearly available now.
Quoted from Design Patterns as Higher-Order Datatype-Generic ProgramsCrystallizing design patterns
To end with FP benefits, there is this curious thing called Curry–Howard correspondence which is a direct analogy between mathematical concepts and computational calculus (which is what we do, programmers).
This correspondence means that a lot of useful stuff discovered and proven for decades in Math can then be transposed to programming, opening a way for a lot of extremely robust constructs for free.
In OOP, Design patterns are used a lot and could be defined as idiomatic ways to solve a given problems, in specific contexts but their existences won’t save you from having to apply and write them again and again each time you encounter the problems they solve.
Functional programming constructs, some directly coming from category theory (mathematics), solve directly what you would have tried to solve with design patterns.
Quoted from Geekocephale