Haskell MOOC

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Haskell Mooc, part 2

by Joel Kaasinen (Nitor) and John Lång (University of Helsinki)

Beware! This is a preview of part 2 of the course. It contains lectures 9 to 13. Further lectures will be added before the course is ready. The material presented here is roughly ready, and shouldn’t change too much. However, you should know that:

  • Some exercises might change or be replaced with new exercises for the final release.
  • It won’t be possible to get study credits before the final release.
  • Feedback on this preview is much appreciated! You can help make part 2 better by testing it out. There are feedback links at the end of each section.

Enjoy!

9 Lecture 9: Recap of Part 1

This lecture goes over the basic parts of Haskell introduced in part 1 of the course: types, values, pattern matching, functions and recursion.

9.1 Types

Remember the primitive types of Haskell? Here they are:

Values Type Meaning
True, False Bool Truth values
0, 1, 20, -37, … Int Whole numbers
'A', 'a', '!', … Char Characters
"", "abcd", … String Strings, which are actually just lists of characters, [Char]
0.0, -3.2, 12.3, … Double Floating-point numbers
() () The so called unit type with only one value

It’s possible to combine these primitive types in various ways to form more complex types. Function types, tuple types and list types are examples of types that combine other types.

Values Type Meaning
(1,2), (True,'c'), … (a, b) A pair of a value of type a and a value of type b
(1,2,3), (1,2,'c'), … (a, b, c) A triple of values (of types a, b and c)
[], [1,2,3,4], … [a] List of values of type a
not, reverse, \x -> 1, \x -> x, … a -> b Function from type a to type b

There’s one more powerful mechanism for creating more types: Algebraic datatypes (ADTs). Some examples include:

Values of these types include:

Values Type
True, False Bool
Red, Green, Blue Color
MakeVector 1.5 3.2 Vector2d
Person 13 "Bob" Person
TwoValues 1 3 PairOf Int
Empty, Node 3 (Node 4 Empty) IntList
Nothing, Just 3, Just 4, … Maybe Int
Nothing, Just 'c', Just 'd', … Maybe Char
Left "foo", Right 13, … Either String Int
Nil, Cons True Nil, Const True (Cons False Nil) … List Bool
Leaf 7, Node 1 (Leaf 0) (Leaf 2), … Tree Int
MultiTree 'a' [MultiTree 'b' [], MultiTree 'c' []]], … MultiTree Char

You can combine parameterized types in complex ways, for example with something like Either [String->String] (Maybe String, Int).

The names of concrete types start with capital letters. Lowercase letters are used for type variables which indicate parametric polymorphism: functions and values that can have multiple types. Here are some examples of polymorphic function types:

9.1.1 More About Lists

List literals can be written in using the familiar [x,y,z] syntax. However, lists are actually built up of the list constructors [] and (:). These constructors are also used when pattern matching lists as we’ll see next. Here are some examples of lists:

Abbreviation Full list Type
[1,2,3] 1:2:3:[] [Int]
[[1],[2],[3]] (1:[]):(2:[]):(3:[]):[] [[Int]]
"foo" 'f':'o':'o':[] [Char], also known as String

There’s also a range syntax for lists:

Range Result
['a' .. 'z'] "abcdefghijklmnopqrstuvwxyz"
[0 .. 9] [0,1,2,3,4,5,6,7,8,9]
[0, 5 .. 25] [0,5,10,15,20,25]
[min .. max] everything from min to max
[9 .. 3] []
[max, max-1 .. min] everything from max to min in decreasing order
[9,8 .. 3] [9,8,7,6,5,4,3]

List comprehensions are another way for create lists:

Comprehension Result
[x^3 | x <- [1..3]] [1,8,27]
[x^2 + y^2 | x <- [1..3], y <- [1..2]] [2,5,5,8,10,13]
[y | x <- [1..10], let y = x^2, even x, y<50] [4,16,36]
[c | c <- "Hello, World!", elem c ['a'..'z']] "elloorld"

In general, [f x | x <- xs, p x] is the same as map f (filter p xs). Also, [y | x <- xs, let y = f x] is the same as [f x | x <- xs]. Any combination of <-, let and [f x | ...] is possible.

9.2 Functions

The basic form of function definition is:

For example:

Functions taking multiple arguments are defined in a similar manner. Note how the type of a multi-argument function looks like.

Functions can be polymorphic, can take multiple arguments, and can even take functions as arguments. Here are more examples:

More sophisticated functions can be defined using pattern matching. We can pattern match on the constructors of algebraic datatypes like Nothing, [] and (:).

Yet more sophistication can be achieved with guards. Guards let you define a function case-by-case based on tests of type Bool. Guards are useful in situations where pattern matching can’t be used. Of course, guards can also be combined with pattern matching:

Case expressions let us pattern match inside functions. They are useful in situations where the result of one function depends on the result of another and we want to match the pattern on the output of the other function:

Let-expressions enable local definitions. Where-clauses work similarly to lets. For example:

Lambda expressions are another occasionally useful syntax for defining functions. Lambda expressions represent anonymous (unnamed) functions. They can be used for defining local functions that are typically used only once.

Note that f x = y is the same thing as f = \x -> y.

Finally, binary operators have sections. Sections are partially applied operators. The section of an operator is obtained by writing the operator and one of its arguments in parentheses. For example, (*2) multiplies its argument by 2 from the right, e.g. (*2) 5 ==> 5 * 2. A fractional number (e.g. a Double) can be inverted with the section (1/), e.g. (1/) 2 ==> 0.5.

9.3 Functional Programming

Haskell is a functional programming language, which means that functions can be passed in as arguments and returned from functions. As a programming paradigm, functional programming aims to build programs by combining simple functions together to form larger and larger ones.

The most often presented example of functional programming is functional list manipulation using higher-order functions (functions that take functions as arguments) like map and filter. Here’s one example from part 1:

We also encountered other functional programming patterns in part 1, like partial application:

Also, function composition:

And finally, folds:

9.4 Recursion

To implement a function that uses repetition in Haskell, you need recursion. Haskell has no loops like other programming languages. Here are some simple recursive functions in Haskell:

To consume or produce a list you often need recursion. Here are the implementations of map and filter as examples of recursive list processing:

Sometimes, a recursive helper function is needed in case you need to keep track of multiple pieces of data.

Here’s a final example utilizing guards, pattern matching, a helper function and recursion:

9.5 Type Classes

The following functions are parametrically polymorphic:

Parametrically polymorphic functions always work the same way, no matter what types we’re working with. This means that we can’t define a special implementation of id just for the Int type, or fst for the type (Bool, String).

By contrast, ad hoc polymorphism allows different types to have different implementations of the same function. Ad hoc polymorphism in Haskell can be achieved by defining a type class and then declaring instances of that type class for various types. Ad hoc polymorphism is a handy way for expressing a common set of operations, even when the implementation of the operations depends on the type they’re acting on.

Functions that use ad hoc polymorphism have a class constraint in their types. Here are some examples:

A type like Num a => a -> a means: for any type X that’s a member of the Num class, this function has type X -> X. In ther words, we can invoke negate on any number type, but not on other types:

Here’s a summary of some useful type classes from the standard library.

  • Comparison
    • Eq is for equality comparison. It contains the == operator
    • Ord is for order comparison. It contains the ordered comparison operators like < and =>, and functions like max and min.
  • Numbers
    • Num is for all number types. It contains +, -, * and negate.
    • Integral is for whole number types. Most notably, it contains integer division div.
    • Fractional is for number types that support division, /
  • Converting values to strings
    • Show contains the function show :: Show a => a -> String that converts values to strings
    • Read contains the function read :: Read a => String -> a that is the inverse of show

Sometimes, multiple class constraints are needed. For instance here:

Now that we’ve seen some classes and types, let’s look at the syntax of declaring classes and instances. Here are two class definitions:

Consider the following data structures:

All of these have sizes that we can count, but we need to perform the operation differently:

We can also easily declare Eq instances for Numbers and IntList:

However, since the Tree datatype is parameterized over the element type a, we need an Eq a instance in order to have an Eq (Tree a) instance. This is achieved by adding a class constraint to the instance declaration. This is called an instance hierarchy.

9.5.1 Deriving

Some standard type classes, most notably Show, Read, Eq and Ord can be derived, that is, you can ask the compiler to generate automatic instances for you. For example we could have derived all of these classes for our earlier Numbers example.

9.6 Quiz

What is the type of ('c',not)

  1. [Char]
  2. [Bool]
  3. (Char,Bool -> Bool)
  4. (Char,Bool)
  5. It is a type error.

What is the type of ['c',not]

  1. [Char]
  2. [Bool]
  3. (Char,Bool -> Bool)
  4. (Char,Bool)
  5. It is a type error.

Which of these is a value of the following type?

  1. X "foo"
  2. Y "foo"
  3. Z (X 1)
  4. X (Z 1)

What is the type of this function?

  1. Maybe a -> a
  2. [Maybe a] -> a
  3. [Maybe a] -> Bool
  4. [Maybe Bool] -> Bool

What is the type of this function?

  1. (Num a, Eq a) => a -> a -> Bool
  2. Num a => a -> a -> Bool
  3. Eq a => a -> a -> Bool
  4. a -> a -> Bool

9.7 Exercises

10 Lecture 10: Reductionism

  • Purity
  • Laziness
  • Haskell evaluation

10.1 Laziness & Purity

Purity and laziness were mentioned as key features of Haskell in the beginning of part 1. Let’s take a closer look at them.

Haskell is a pure functional language. This means that the value f x y is always the same if the values x and y are the same. This property is also called referential transparency.

Purity also means that there are no side effects: you can’t have the evaluation of f x y read a line from the user - the line would be different on different invocations of f and would affect the return value, breaking referential transparency! Obviously you need side effects to actually get something done. We’ll get back to how Haskell handles side effects later.

Haskell is a lazy language. This means that a value is not evaluated if it is not needed. An example illustrates this best. Consider these two functions:

Evaluating f 1 does not halt due to the infinite recursion. However, this works:

Laziness is not a problem because Haskell is pure. Only the result of the function matters, not the side effects. So if the result of a function is not used, we can simply not evaluate it without changing the meaning (semantics) of a program. Well okay, sometimes we get a terminating program instead of one that goes on for ever, but adding laziness never makes a functioning Haskell program break.

If you’re interested in the theory behind this, check out the Church-Rosser theorem or the Haskell Wiki article Lazy vs. non-strict.

10.2 Equational Reasoning

Referential transparency, the feature that an expression always returns the same value for the same inputs, is a very powerful property that we can leverage to reason about programs.

In a C-style language, we might write a procedure that may not always return the same value for the same arguments:

int c = 0;
int funny(int x) {
  return x + c++;
}

The expression c++ increments the value of c and returns the old value of c. The next time it is evaluated, the value of c has increased by one. This means that depending on the current value of c, funny(0) might return 0, 1, 2, or any other integer value. (It might even return negative values if c overflows!)

In some situations this kind of behaviour with side-effects may be useful, but there are also times when it is more important to be able to reason about the code easily. The advantage of pure functions is that they can be analyzed using basic mathematical techniques. Sometimes applying math to our functions can even reveal simplifications or optimisations we otherwise wouldn’t have thought about.

Consider the following expression:

This expression can be simplified to just reverse. We begin by establishing some helpful facts (or lemmas). First, suppose that we know

  1. map id === id
  2. map f . map g === map (f.g)
  3. reverse . map f === map f . reverse

The fourth fact that we’re going to need is the following:

  1. (+1) . (-1) === id

We can prove fact 4 by reasoning about how (+1) . (-1) behaves for an arbitrary input x:

Because we didn’t assume anything about x, we may conclude that the above chain of equations holds for all xs. Thus,

For those who are familiar with the technique of proof by induction, it is a fun exercise to prove the first three facts also. This course doesn’t discuss induction proofs, though, so don’t sweat if you don’t know induction.

Now, from facts 1-4 it follows that

This course won’t go into detail about proving things about programs, but it’s good to know that pure functional programming is very compatible with analysis like this.

10.3 Infinite Lists

The benefits of laziness are best demonstrated with some examples involving infinite lists. Let’s start with repeat 1, which generates an infinite list of 1s. If we try to tell GHCi to print the value repeat 1, it will just keep printing 1s for ever until we interrupt it using Control-C:

However, due to laziness, we can work with infinite lists and write computations that end. We just need to use a finite number of elements from the infinite list. Here are some examples:

An infinite list that just repeats one element can sometimes be necessary, but it’s kind of pointless. Let’s look at some more useful infinite lists next. You can use the [n..] syntax to generate an infinite list of numbers, starting from n:

The function cycle repeats elements from the given list over and over again. It can be useful when dealing with rotations or cycles.

10.3.1 Example: Transaction Numbers

As a more concrete example of how cycle is useful, let’s look at computing the check digit of Finnish bank transfer transaction numbers (viitenumero). A transaction number consists of any number of digits, followed by a single check digit. The check digit is checked by multiplying the digits (from right to left) with the numbers 7, 3, 1, 7, 3, 1 and so on, and summing the results. If the result of the sum plus the check digit is divisible by 10, the number is valid.

Here’s a concrete example. 116127 is a valid transaction number. The computation goes like this:

digits:       1  1  6  1  2
              *  *  *  *  *
multipliers:  3  7  1  3  7
              3+ 7+ 6+ 3+14 = 33
check digit is 7, 33+7=40 is divisible by 10, valid

Here’s the Haskell code for a transaction number checker. Note how we use use the infinite list cycle [7,3,1] for the multipliers.

10.3.2 Example: Finding a Power

Finally, here’s how you would find the first power of 3 that’s larger than 100.

Let’s go through how this works step by step. Note how map and filter are processing the list lazily, one element at a time, as needed. This is similar to how generators or iterators work in languages like Python or Java.

10.4 How does Haskell Work?

Laziness will probably feel a bit magical to you right now. You might wonder how it can be implemented. Haskell evaluation is remarkably simple, it’s just different than what you might be used to. Let’s dig in.

In most other programming languages (like Java, C or Python), evaluation proceeds inside-out. Arguments to functions are evaluated before the function.

Haskell evaluation proceeds outside-in instead of inside-out. The definition of the outermost function in an expression is applied without evaluating any arguments. Here’s a concrete example with toy functions f and g:

Inside-out (normal) evaluation:

Haskell outside-in evaluation:

Note how the unused calculations 1234*1234 and 1*1000 didn’t get evaluated. This is why laziness is often helpful.

10.4.1 Pattern Matching Drives Evaluation

Let’s look at a more involved example, with pattern matching and more complex data (lists). Pattern matching drives Haskell evaluation in a very concrete way, as we’ll see. Here are some functions that we’ll use. They’re familiar from the Prelude, but I’ll give them simple definitions.

Here’s the inside-out evaluation of an expression:

Here’s how the evaluation proceeds in Haskell. Note how it’s not strictly outside-in, since we sometimes need to evaluate inside arguments to be able to know which pattern is matched.

Note that we didn’t need to evaluate any of the not applications.

Let’s introduce some terminology. We say that pattern matching forces evaluation. When Haskell evaluates something, it evaluates it to something called weak head normal form (WHNF). WHNF basically means a value that can be pattern matched on. An expression is in WHNF if it can’t be evaluated on its top level. This means it either:

  • is a constant, for example: 1
  • has a constructor at the top level, for example: False, Just (1+1), 0:filter f xs
  • is a function, for example: (\x -> 1+x)

The most notable class of expressions that is not in WHNF is function applications. If an expression consists of a function (that is not a constructor), applied to some arguments, it is not in WHNF. We must evaluate it in order to get something pattern matchable.

In the previous example we couldn’t evaluate length (map not (False:[])) at the top level because the argument to length wasn’t in WHNF. When we apply the second equation of map, we get length (not False : map not []), and now the argument to length is in WHNF since there is a constructor, (:), at the top level. This is a bit more evident if we switch from infix to prefix notation and write the argument to length as (:) (not False) (map not []).

In practice, pattern matching is not the only thing that forces evaluation. Primitives like (+) also force their arguments.

Instead of forcing, some sources talk about strictness, we can say for instance that (+) is strict in both arguments.

10.4.2 A Word About Sharing

There’s one more thing about Haskell evaluation. Any time you give a value a name, it gets shared. This means that every occurrence of the name points at the same (potentially unevaluated) expression. When the expression gets evaluated, all occurrences of the name see the result.

Let’s look at a very simple example.

Based on the previous sections, you might imagine evaluation works like the following. The evaluation is first represented textually, and then visually, as an expression tree.

However, what really happens is that the expression 2+2 named by the variable x is only computed once. The result of the evaluation is then shared between the two occurrences of x inside square. So here’s the correct evaluation, first textually, and then visually. Note how now instead of an expression tree, we have an expression graph. This is why Haskell evaluation is sometimes called graph reduction.

As another example, consider the function f below and its evaluation.

Haskell does not compute 1+1 twice because it was named, and the name was used twice. We can contrast this with another function that takes two arguments:

Here we have two different names for equivalent expressions, and Haskell doesn’t magically share them. Automatically sharing equivalent expressions is an optimization called Common Subexpression Elimination (CSE). You can learn a bit more CSE and Haskell here.

You can name things via

  • Function arguments
  • let ... in ...
  • where

Combined with laziness, sharing means that a name gets evaluated at most once.

10.4.3 Further Examples

You’ll find below a slightly contrived recursive definition of the function even. It will illustrate the concepts of forcing and sharing.

Firstly, note that || forces its left argument, but not its right argument. (In other words, || is strict in its left argument.) This is because we only need to evaluate the left argument of || in order to know which equation applies. This means by extension that even forces its first argument:

  • || forces x==0
  • x==0 forces x

Now let’s evaluate the expression even 2 to WHNF.

Note that with this alternate definition even would not have worked. Can you tell why?

Now we can really understand what’s going on in the infinite list example from earlier. Let’s use these definitions:

And here we go:

Whew.

10.5 Working with Infinite Lists

Functions that work with lists often have the best performance when they’re written in such a way that they utilize laziness. One way to try to accomplish this is to write list-handling functions that work well with infinite lists.

To write a function that transforms an infinite list, you need to write a function that only looks at a limited prefix of the input list, then outputs a (:) constructor, and then recurses. Here’s a first example.

A good heuristic for writing functions that work well with infinite lists is: can the head of the result be evaluated cheaply? Here are two examples of functions that don’t work with infinite inputs. In the case of mapTailRecursive, the problem is that it needs to process the whole input before being in WHNF. In the case of myDrop, the problem is that it uses the function length, doesn’t work for infinite lists since it tries to iterate until the end of the list.

Pretty much all the list functions in the standard library are written in this form, for example:

10.6 Interlude: Adding Strictness

Remember foldr from part 1? Let’s have a look at its cousin foldl. Here’s the definition of foldl for lists (it’s actually part of the Foldable typeclass and so works for various other types too). While foldr processes a list right-to-left, foldl processes a list left-to-right. To be a bit more exact, foldr associates to the right while foldl associates to the left. Note the difference in the next example:

Here are the definitions of foldl and foldr:

As foldr f y (x:xs) ==> f x (foldr f y xs), it enables lazy evaluation to focus on f on the second step. Hence, foldr works nicely with lazy or short-circuiting operations:

However foldl needs to process the whole list in order to produce a (WHNF) value. The reason is that foldl remains in the leftmost-outermost position for as long as its list argument remains non-empty. This makes foldl the priority for lazy evaluation. Only after the list becomes empty does the evaluation proceed into simplifying the folded values.

So why use foldl at all? Let’s return to our first fold example again. Now, since + is a strict operation, both types of fold need to build up an expression with lots of +s. The Haskell implementation needs to track this expression in memory, which is why a problem like this is called a space leak.

Now let’s instead look at what happens when we use foldl', a version of foldl that forces its second argument!

Now the work is performed incrementally while scanning the list. No space leak! Sometimes too much laziness can cause space leaks, and a bit of strictness can fix them.

You can find foldl' in the Data.List module, and it works just like this. But how could one implement foldl'? We certainly know by now how to do it for a specific type, say Int. We just add a pattern match on the second argument that doesn’t change the semantics of the function.

To write a generic implementation of foldl' we need to introduce a new built-in function, seq. The call seq a b evaluates to b but forces a into WHNF. Here are some examples of using seq in GHCi. To demonstrate what gets evaluated, we use the special value undefined, which causes an error if something tries to evaluate it into WHNF.

As an example of using seq in a function, here’s a version of head that doesn’t work for infinite lists (since it evaluates the last element of the list):

Let’s play around with it in GHCi:

Finally, here’s a definition for foldl'. Note how we need to introduce sharing of a new variable, z', to be able to make seq evaluate the new value and then use it in a recursive call. The new definition is also used in a more detailed evaluation of foldl' (+) 0 [1,2,3] below.

We won’t dive deeper into this subject on this course, but it’s important that you’re aware that seq exists. You can find more about seq on the Haskell Wiki and learn more about when it is necessary to add strictness in Real World Haskell. Often it’s nicer to use bang patterns instead of seq, as discussed by FPComplete and Real World Haskell.

10.7 Newtype Declarations

Recall lecture 7. Sometimes we need boxed types. There’s a special keyword newtype that can be used instead of data when a boxed type is needed. newtype expects exactly one constructor, with exactly one field. For instance,

However, the following won’t work, you need data:

So what’s the difference? In terms of writing code, nothing. You work with a newtype exactly as you would with a data. However, the memory layout is different. Using data introduces an indirection layer (the constructor), but using newtype doesn’t. The indirection for data is necessary to support multiple constructors and multiple fields. An illustration:

This difference has many repercussions. First of all, newtype is more efficient: the type can be said to “disappear” when compiling. The type is still checked though, so you get type safety without any performance impact. Secondly, newtypes are strict. Concretely, this means that Money x is in weak head normal form only if x is in WHNF. This can be witnessed in GHCi:

So when should you use newtype? In general it’s best to use newtype whenever you have a single-field single-constructor datatype. However nothing will go catastrophically wrong if you always use data. The newtype pattern is also often used when you need to define a different type class instance for a type. Here’s an example that defines a number type with an inverted ordering

10.8 Something Fun: Tying the Knot

Now that we know about sharing and path copying, we can make our own cyclic datastructures. Remember the cycle examples from the list lecture?

This is what it looks like in memory:

Earlier it was said that Haskell data forms directed graphs in memory. This is an example of a directed graph with a cycle.

How can we define structures like this? We just give a value a name, and refer to that name in the value itself. That is, the value is recursive or self-referential. This trick is known as tying the knot. A simple example:

Note how we use the name xs inside the definition of xs. When we make a recursive definition like this, sharing causes it to turn into a cyclic structure in memory.

A more fun example: a simple adventure game where the world is a self-referential structure. Note how the cyclic structure is built with local definitions that refer to each other.

Here’s what the world of the game looks like in memory:

               ,-----------------,
               v                 |
         +-----------------------|-----------------+
meadow-->|Room "It's..." ["Stay" o, "Enter cave" o]|
         +---------------------------------------|-+
                                    ^            v
         +--------------------------|----------------+
cave---->|Room "You are..." ["Exit" o, "Go deeper" o]|
         +-----------------------------------------|-+
                                       ^           v
         +-----------------------------|----------------------------+
tunnel-->|Room "This is..." ["Go back" o, "Go left" o, "Go right" o]|<--------,
         +------------------------------------------|-------------|-+         |
                                                    |             |           |
                     ,------------------------------'             |           |
                     v                                            v           |
         +---------------------+                +-----------------------------|-+
pit----->|Room "You fall..." []|    treasure--->|Room "A green..." ["Go back" o]|
         +---------------------+                +-------------------------------+

We’ve now seen three types of recursion. Recursive functions call themselves. Recursive types allow us to express arbitarily large structures. Recursive values are one way to implement infinite structures.

10.9 Something Fun: Debug.Trace

Even though Haskell is a pure programming language, we can sometimes gain insights by sprinkling in a bit of impurity.

We can use the function trace :: String -> a -> a from the module Debug.Trace to peek into Haskell evaluation. The expression trace "message" x is the same as x, but prints message when it is evaluated (forced). We can use trace to witness the laziness of the || operator:

We can also have a look at when list elements are evaluated. Note how length doesn’t need to evaluate the elements of the list, and sum needs to evaluate all of them. (To be precise, head xs doesn’t actually evaluate the first element of xs, but returns it to GHCi, which evaluates it in order to show it.)

Debug.Trace also offers useful variants of trace. A notable one is traceShowId x which prints show x and evaluates to x. Let’s verify the evaluation of our previous head-filter-map example using traceShowId. Note how even though we map traceShowId over the infinite list [0..], only 6 values are actually evaluated. The last 243 is the returned value, not a trace print.

Debug.Trace is especially useful when you have an infinite recursion bug. Here’s an example:

We can debug this by adding a trace to wrap the whole recursive case.

A ha! The problem is that our recursion base case of sumEverySecond 0 is not enough to stop the recursion.

Finally, a word of caution. Using trace, and especially traceShowId, can cause things that would not otherwise get evaluated to get evaluated. For example:

So feel free to use Debug.Trace when working on the exercises, but try to leave trace calls out of your final answers. Some exercise sets check your imports and disallow Debug.Trace.

We’ll see a more principled way of dealing with side effects in the next lecture!

10.10 Quiz

Which of these statements is true?

  1. reverse . reverse . reverse === reverse
  2. reverse . reverse === reverse
  3. reverse . id === reverse

Which of these is an infinite list that starts with [0,1,2,1,2,1,2...]?

  1. cycle [0,1,2]
  2. 0:repeat [1,2]
  3. 0:cycle [1,2]
  4. 0:[1,2..]

What’s the next step when evaluating this expression?

  1. head (False : True : [])
  2. head (not True)
  3. head (False : map not (False:[]))
  4. head (not True : map not (False:[]))

Which of these values is not in weak head normal form?

  1. map
  2. f 1 : map f (2 : [])
  3. Just (not False)
  4. (\x -> x) True

Which of these statements about the following function is true?

  1. f is strict in its left argument
  2. f is strict in its right argument
  3. f forces both of its arguments
  4. None of the above

Does this function work with infinite lists as input? Why?

  1. No, because it includes a [] case, which is never reached.
  2. No, because it uses map, which evaluates the whole list.
  3. Yes, because it only looks at the first element of the list before producing a WHNF value.
  4. Yes, because it calls map, which works with infinite lists.

What about this one?

  1. No, because it uses map, which evaluates the whole list.
  2. No, because computing the head of the result needs the whole input list.
  3. Yes, because it doesn’t includes a [] case
  4. Yes, because it calls map, which works with infinite lists.

10.11 Exercises

11 Lecture 11: RealWorld -> (a,RealWorld)

11.1 Contents

  • IO

11.2 You’ve Been Fooled!

Forget what we talked about functional programming and purity. Actually, Haskell is the world’s best imperative programming language! Let’s start:

Reading input and writing output was easy enough. We can also read stuff over the network. Here’s a complete Haskell program that fetches a some words from a URL using HTTP and prints them.

You can find this program in the course repository as exercises/Examples/FetchWords.hs, and you can run it like this:

$ cd exercises/Examples
$ stack runhaskell FetchWords.hs
word: haskell
word: for
word: ever

What’s going on here? Let’s look at the types:

A value of type IO a is an operation that produces a value of type a. So getLine is an IO operation that produces a string. The () type is the so called unit type, its only value is (). It’s mostly used when an IO operation doesn’t return anything (but rather just has side effects).

A comparison with Java (method) types might help:

Haskell type Java type
doIt :: IO () void doIt()
getSomething :: IO Int int getSomething()
force :: a -> b -> IO () void force(a arg0, b arg1)
mogrify :: c -> IO d d mogrify(c arg)

IO operations can be combined into bigger operations using do-notation.

11.2.1 Examples

You can find useful IO operations in the standard library modules Prelude and System.IO

Here’s an IO operation that asks the user for a string, and prints out the length of the string.

The value produced by the last line of a do block is the value produced by the whole block. Note how askForALine has the same type as getLine, IO String:

In addition to IO operations like query you can also run IO operations that produce values, like askForALine, in GHCi. You can use <- to capture the result of the operation into a variable if you want.

If you need to give your operation parameters, you can just make a function that returns an operation. Note how ask has a function type with a ->, just like a normal function. We also use normal function definition syntax to give the parameter the name question.

11.3 The Subtle return

The Haskell function return is named a bit misleadingly. In other languages return is a built-in keyword, but in Haskell it’s just a function. The return :: a -> IO a function takes a value and turns it into an operation, that produces the value.

That doesn’t sound very useful does it? Combined with do-notation it is. Here we return a boolean according to whether the user answered Y or N:

Note! This means that return does not stop execution of an operation (unlike return in Java or C). Remember that in do-blocks, the last line decides which value to produce. This means that this operation produces 2:

Let’s look at this another way. The do notation allows us to cause a sequence of side-effects, and finally to produce a value.

produceThree = do putStrLn "1"   -- side effect, produces (), which is ignored
                  return    2    -- no side effect, produces 2, which is ignored
                  getLine        -- side effect, produces a String, which is ignored
                  return    3    -- no side effect, produces 3, which is passed on
Prelude> final <- produceThree
1
this line is ignored
Prelude> final
3

Also note that these are the same operation:

Since return is a function, you should remember to parenthesize any complex expressions:

11.4 do and Types

Let’s look at the typing of do-notation in more detail. A do-block builds a value of type IO <something>. For example in

The lastOp must be of type IO X (for some X). The type of foo will also be IO X. Let’s look at an example with parameters next:

The lastOp must be of type Y -> IO X (so that lastOp arg has type IO X). The type of bar will be A -> B -> IO X (and inside bar we’ll have x :: A and y :: B).

If we use return:

The function quux will have type A -> IO B, where x :: A and value :: B.

Let’s look at the typing of <- next. If op :: IO X and you have var <- op, var will have type X. We’ve seen this in many GHCi examples.

The last line of a do cannot be foo <- bar. The last line determines what the whole operation produces, so it must be an operation (for example, return something).

Here’s a worked example:

The typing rules guarantee that you can not “escape” IO. Even though <- gives you an X from an IO X, you can only use <- inside do. However a do always means a value of type IO Y. In other words: you can temporarily open the IO box, but you must return into it. “What happens in IO, stays in IO.”

We’ll talk more about what this means later. For now, it’s enough to know that if you have a function with a non-IO type, like for example myFunction :: Int -> [String] -> String, the function can not have IO happening inside it. It is a pure function.

11.5 Control Structures

For the following examples, we’ll need two new operations.

The usual tools of recursion, guards and if-then-else also work in the IO world. Here’s an IO operation that’s defined using a guard:

Here’s an operation that prints all numbers in a list using recursion and pattern matching:

Here are two slightly more complicated examples of recursive IO operations. They use the value produced by the recursive call. The operation readAndSum n reads n numbers from the user and prints their sum. The operation ask questions shows each string in questions to the user, reads a response, and returns a list of all the responses.

Additionally, we have some IO-specific control structures, or rather, functions. These come from the module Control.Monad.

Using these, we can rewrite our earlier examples:

11.6 A Word About do and Indentation

It’s easy to run into weird indentation problems when using do-notation. Here are some rules of thumb to help you get it right.

The most important rule of do and indentation is all operations in a do-block must start in the same column.

Some examples of this rule:

A related rule is when an operation goes over multiple lines, indent the follow-up lines. If you don’t indent, it’ll look like a new operation!

Here’s one more example, with nested do-blocks, and two different valid indentations.

11.7 Let’s Write a Program

After all these short one-off examples, let’s turn to something a bit longer. Let’s write a program to fetch all type annotations from all .hs files. We use IO operations like readFile and listDirectory to read and find files, but also pure code like map and filter to do the actual processing. First off, here’s a recap of the library operations we’re using:

And here’s the program itself. You can also find it in the course repository as exercises/Examples/ReadTypes.hs.

We can run this program by going to the directory exercises/Examples and running:

The exact output will vary according to the contents of the directory, of course.

11.8 What Does It All Mean?

Let’s return to the functional world. How can we reconcile IO operations with Haskell being a pure and lazy language? Something like putStrLn :: String -> IO () is a pure function that returns an operation. How is it pure? putStrLn x is the same when x is the same. In other words: an operation is a pure description of a chain of side effects. Only executing the operation causes those side effects. When a Haskell program is run, only one operation is executed - it’s called main :: IO (). Other operations can be run only by linking them up to main.

When in GHCi, if an expression you type in evaluates to an operation, GHCi runs that operation for you. Here’s a demonstration of the purity of print:

Operations are values just like numbers, lists and functions. We can write code that operates on operations. This function takes two operations, a and b, and returns an operation that asks the user which one he’d like to run.

Using operations specified as parameters lets us write functions like mapM_, which we met earlier. The implementation is a recursive IO operation that takes another IO operation as a parameter. Conceptually complicated, but simple when you read the code:

11.9 One More Thing: IORef

So far the only side-effects we’ve been able to produce in IO have been terminal (getLine, print) and file (readFile, listDirectory) IO. Imperative programs written in Java, Python or C have other types of side effects too that we can’t express in pure Haskell. One of these is mutable (i.e. changeable) state. A pure function can not read mutable state, because otherwise two invocations of the same function might not return the same value.

The Haskell type IORef a from the module Data.IORef is a mutable reference to a value of type a

Here are some examples of using an IORef in GHCi:

Here’s an example of using an IORef to sum the values in a list. Note the similarity with an imperative loop.

Using IORef isn’t necessary most of the time. Haskell style prefers recursion, arguments and return values. However real world programs might need one or two IORefs occasionally.

11.10 Summary of IO

A value of type IO X is an IO operation that produces a value of type X when run. Operations are pure values. Only running the operation causes the side effects.

IO operations can combined together using do-notation:

The return x operation is an operation that always produces value x. When x :: a, return x :: IO a.

Useful IO operations:

11.11 Quiz

What is the type of this IO operation?

  1. String -> IO String
  2. IO Int
  3. String -> IO Int
  4. IO String -> IO Int

Which of these lines could be used in place of ????

  1. q <- getLine
  2. return (y++z)
  3. return [q]
  4. ans <- return [y,z]

What values does blorg [1,2,3] print?

  1. It prints 1, 2, 3
  2. It prints 1, 2, 3, 6
  3. It prints 3, 2, 1
  4. It prints 3, 2, 1, 6

Which of these can a function of type Int -> IO Int do?

  1. No function of this type can be defined.
  2. Return a constant value.
  3. Run the IO operation it has been given and return its value.
  4. Query the user for a number and return it.

Which of these can a function of type IO Int -> Int do?

  1. No function of this type can be defined.
  2. Return a constant value.
  3. Run the IO operation it has been given and return its value.
  4. Query the user for a number and return it.

11.12 Exercises

12 Lecture 12: fmap fmap fmap

12.1 Contents

  • Functors

12.2 Functors

12.2.1 Preserving Structure

Remember the map function for lists? Here’s the definition again:

It applies a function g :: a -> b to each element of a list of type [a], returning a list of type [b]. Another way to express the type of map would be (a -> b) -> ([a] -> [b]). This is the same type because -> associates to right. The extra parentheses emphasize the fact that map converts the function g :: a -> b into a function map g :: [a] -> [b]. This means that map is a higher-order function that transforms functions to functions.

As map is parametrically polymorphic, its definition doesn’t depend on the type of values stored in the list. Thus, every function of type a -> b is converted into a function of type [a] -> [b] using exactly the same logic. Using the definition above, we can see that:

What’s notable here is that map preserves the structure of a list. The length of the list and the relative positions of the elements are the same. The general idea is demonstrated in the picture below.

Mapping a function to a list
Mapping a function to a list

Let’s see if we can find other similar functions. A value of type Maybe a is kind of like a list of length at most 1. Let’s map over a Maybe! Can you see the similarity with the definition of map?

Here too, the structure of the value is preserved. A Nothing turns into a Nothing, and a Just turns into a Just. Here too, we can think of the type as (a -> b) -> (Maybe a -> Maybe b), converting (or “lifting”) a normal function into a function that works on Maybes.

One more example: consider binary trees.

A binary tree might look like this:

A binary tree
A binary tree

After mapTree f the tree would looke like this:

A binary tree after mapping
A binary tree after mapping

12.2.2 The Functor Class

Now we have three different structure-preserving mapping functions. Three similar operations over different types. Could we write a type class to capture this similarity?

A naive attempt at writing a type class runs into problems. If we try to abstract over Maybe c, we can’t seem to write the right type for the map operation. We’d need to be able to change the type parameter c somehow.

Luckily Haskell type classes have a feature we haven’t covered before. You can write classes for type constructors in addition to types. What does this mean? Let’s just have a look at the standard type class Functor that does what we tried to do with our Mappable.

Note how the type parameter f is a type constructor: it’s being passed a and b arguments in different parts of the type of fmap. Now let’s see the instance for Maybe.

Now fmap has the right type and we can implement it like mapMaybe! Note how we’ve declared instance Functor Maybe instead of instance Functor (Maybe a). The type Maybe a isn’t a functor, the type constructor Maybe is.

The type constructor for lists is written []. It’s special syntax, just like other list syntax. However if the type [a] was written List a, the type constructor [] would mean List.

Here’s the final of our examples, as a Functor instance.

Sidenote: the term functor comes originally from a branch of mathematics called category theory. However, to work with Haskell you don’t need to know any category theory. When you see functor, you can just think “something I can map over”, or perhaps “a container”.

Let’s zoom out a bit. When we have an instance Functor MyFun we know that we can map a type X into a new type MyFun X (since MyFun is a type constructor), but also that we can lift a function f that takes an X argument into a function fmap f that takes a MyFun X argument! So you could say we’re mapping both on the type level and the value level.

Oh right, one more thing. Once you’ve gotten the hang of fmap you might find yourself using it quite a bit. For code that uses fmap heavily it can be nice to use its infix alias, <$>. Consider the symmetry between $ and <$> in these examples:

12.3 Lawful Instances

What is this “preserving of the structure” that was mentioned above exactly? The following two functor laws are expected to hold for any Functor instance f (though unfortunately Haskell compilers can’t enforce them):

  1. fmap id === id
  2. fmap (f . g) === fmap f . fmap g

Don’t worry if that sounded abstract! The first law says that a functor maps id :: a -> a into id :: f a -> f a. (id is the identity function, meaning that id x = x.) Let’s be concrete and see how it works for the list [1,2,3]:

On the other hand,

Hence, the result of fmap id [1,2,3] was the same as the result of id [1,2,3], so the first functor law holds in this case. It’s not hard to show that the first functor law holds for any list whatsoever.

The first functor law is really a very simple proposition if you think about it. It just says that you can either fmap id or just apply id directly without a noticeable difference.

How about the second functor law? For lists, consider what happens if we fmap the function negate.(*2) (remember, negate maps x to -x and (*2) multiplies its argument by 2):

Let’s consider the right-hand side of the second functor law in this case:

The second functor law turns out to hold in this particular case. In fact, it holds in all cases (exercise!).

12.4 Sidenote: Kinds

Remember that Functor was a class for type constructors. If we try to define an instance of Functor for a type, we get an error:

The error message talks about kinds. Kinds are types of types. A type like Int, Bool or Maybe Int that can contain values has kind *. A type constructor has a kind that looks like a function, for example, Maybe has kind * -> *. This means that the Maybe type constructor must be applied to a type of kind * to get a type of kind *.

We can ask GHCi for the kinds of types:

If we ask GHCi for info about the Functor class, it tells us that instances of Functor must have kind * -> *:

Here are some examples of even more complex kinds.

You won’t bump into kinds that much in Haskell programming, but sometimes you’ll see error messages that talk about kinds, so it’s good to know what they are.

12.5 Foldable, Again

We briefly covered the class Foldable, which occurs in many type signatures of basic functions, in part 1. For example:

From these type signatures we can see that Foldable, just like Functor, is a class for type constructors (things of kind * -> *). The essence of Foldable is to be a class for things you can fold over. The class definition could be as simple as

However, for performance reasons, the class contains many methods (you can see them yourself by checking :info Foldable in GHCi!), but when we’re defining an instance for Foldable it’s enought to define just foldr.

Another way of thinking of the Foldable class is processing elements left-to-right, in other words, if Functor was the class for containers, Foldable is the class for ordered containers.

As an example, let’s implement Functor and Foldable for our own pair type.

Now, we can use Pair almost wherever we can use a list:

Other types we’ve met that are Foldable include Data.Map and Data.Array.

12.6 Recap

So, to summarize, a functor is a type constructor f and the corresponding Functor f instance such that fmap satisfies the two functor laws. These laws assert that fmap must preserve the identity function and distribute over function composition. More informally, fmap lifts a function g :: a -> b operating on values to one operating on containers: fmap g :: f a -> f b. Basically all well-behaving data structures in Haskell are functors.

12.7 Quiz

What’s the type of fmap?

  1. a -> b -> f a -> f b
  2. (a -> b) -> f a -> f b
  3. Functor f => a -> b -> f a -> f b
  4. Functor f => (a -> b) -> f a -> f b

Which code snippet completes the next Functor instance?

  1. fmap (Things x ys) = Things (f x) [f x]
  2. fmap (Things x ys) = Things (f x) (map f ys)
  3. fmap (Things x ys) = Things (f x) ys
  4. fmap (Things x ys) = f (Things x ys)

What’s the kind of [a]?

  1. *
  2. * -> *
  3. [a]

What’s the kind of Foo?

  1. *
  2. * -> *
  3. Foo

What is the value of foldr (-) 1 (Just 2)?

  1. -1
  2. 1
  3. 2
  4. 3

Which code snippet completes the next Functor instance?

  1. foldr f z (Things x ys) = f x z
  2. foldr f z (Things x ys) = foldr f x ys
  3. foldr f z (Things x ys) = f x (foldr f z ys)
  4. foldr f z (Things x ys) = foldr f z (x:ys)

12.8 Exercises

13 Lecture 13: A Monoid in the Category of Problems

  • Monads

In this lecture we’ll build up to the concept of a monad using a number of examples. By now you should be familiar with all the Haskell features needed for understanding monads.

Monads are a famously hard topic in programming, which is partly due to weird terminology, partly due to bad tutorials, and partly due to trying to understand monads too early when learning Haskell. Monads are introduced this late in the course in an attempt to make understanding them easier.

If you find this lecture hard, don’t despair, many others have found the topic hard as well. There are many many productive Haskell programmers who have managed to understand monads, so the task is not hopeless.

One final word of caution: monads, like functors, are a concept originally from a branch of mathematics called Category Theory. However, and I can’t stress this enough, you do not need to know anything or even care about category theory to understand monads in Haskell programming. Just like one can work with object-oriented programming or functional programming without knowing the theory of objects or functions, one can work with monads without understanding the math associated with them.

13.1 Example 1: Maybes

When working with many Maybe values, the code tends to become a bit messy. Let’s look at some examples. First, we combine some functions returning Maybe String. Note the nested case we need in stealSecret: not fun to write.

Next up, we modify a list of pairs. We use the Maybe-returning function lookup from the Prelude. Here we have an if inside a case instead of a nested case.

This type of code is pretty common, and usually repeats the same pattern: if any intermediate result is Nothing, the whole result is Nothing. Let’s try to make writing code like this easier by defining a chaining operator ?>. The chaining operator takes a result and the next step of computation, and only runs the next step if the result was a Just value.

The chaining operator streamlines our examples nicely. Note how we can define simple helper functions that take care of one step of the computation instead of writing one big expression.

Here’s another example: safe list indexing built from safeHead and safeTail:

PS. note that ?> associates to the left as is the default in Haskell. That means that op ?> f ?> g means (op ?> f) ?> g. The alternative, op ?> (f ?> g) would not even type check!

13.2 Example 2: Logging

Let’s explore the concept of chaining with another example: logging. The type Logger represents a value plus a list of log messages (produced by the computation that produced the value).

Here’s a login function that logs some details about the usernames and passwords it processes. Note how we run into complicated code in login when we need to handle multiple Logger values.

Let’s try to streamline this code by defining a chaining operator for Logger. The important thing when doing multiple Logger operations is to preserve all the logs. Here’s a chaining operator, #>, and an example of how it can be used to log some arithmetic computations.

We can streamline login quite a bit by using #>. Note how we don’t need to worry about combining logs together. Also note how we use a lambda expression instead of defining a helper function.

To ramp things up a bit, let’s use Logger in a recursive list processing function. Here’s a logging version of filter. Note how the code chains a log message before the recursive call in order to keep the order of log entries nice.

13.3 Example 3: Keeping State

In the previous example we just wrote some state (the log). Sometimes we need computations that change some sort of shared state. Let’s look at accounts in a small bank. We’ll first define a datatype for the state of the bank: the balances of all accounts, as a map from account name to balance.

Here’s how we can deposit some money to an account. We use the function adjust from Data.Map to modify the map.

Withdrawing money is a bit more complicated, since we want to handle some special cases like the account not existing, or the account not having enough money. We use the library function findWithDefault to help us along.

Finally, let’s write a function that takes at most 100 money from one account, splits the money in half, and deposits it in two accounts. Pay attention to how we need to carefully thread the different versions of the bank, bank, bank1, bank2 and bank3 to make sure all transactions happen in the right order.

Code like this turns up often in Haskell when you’re doing serial updates to one value, while also performing some other computations on the side. It’s easy to make a mistake, and the type system won’t help you if you e.g. reuse the bank1 value. Let’s rewrite share so that we don’t need to refer to the bank itself. We can again use the same chaining idea to accomplish this.

Let’s see how chaining works with these bank operations.

Note how a value of type BankOp represents a process that transforms the bank. The initial state of the bank must be supplied using runBankOp. This makes sense because BankOp transformations can be composed, unlike Bank states. Having to use runBankOp makes the distinction between defining operations and executing them clearer.

Now that we’re familiar with manipulating BankOp values, we can implement share as a BankOp. We implement a helper distributeOp to make the code a bit neater.

That was pretty clean wasn’t it? We don’t need to mention the bank at all, we can almost program as if in an imperative language while staying completely pure.

You can find all of this code in the course repository under exercises/Examples/Bank.hs.

13.4 Finally: The Monad Type Class

We’ve now seen three different types with a chaining operation:

Just like previously with map and Functor, there is a type class that captures this pattern. Note that Monad is a class for type constructors, just like Functor.

There are some additional operations in Monad too:

Recall that the Functor class was about a generic map operation. Similarly, the Monad class is just about a generic chaining operation.

The expression operation >>= next takes a monadic operation operation :: m a, and does some further computation with the value that it produces using next :: a -> m b. If this feels too abstract, just recall how chaining works for Maybe:

13.5 Maybe is a Monad!

Here’s the full Monad instance for Maybe and some examples.

Here are the stealSecret and increase examples rewritten with monad operations. The changes are ?> to >>= and Just to return.

13.6 The Return of do

Here’s an example of what a complex monad operation might look like.

Let’s see what happens when we transform this code a bit. First off, let’s inline the definitions.

Due to lambda expressions continuing to the end of the expression, we can omit the parentheses. Let’s also indent differently.

Now we can notice the similarity with do notation. The do block below is actually the same code!

To clarify, do notation is just a nicer syntax for the monad operations (>>= and >>) and lambdas. Here’s how do notation gets transformed into monad operations. Note! the definition is recursive.

Here’s safeNth using do notation:

Here is increase one last time, now with do notation

13.7 Logger is a Monad!

We should be able to write a Monad instance for Logger ourselves, by setting >>= to #>. However, due to recent changes in the Haskell language we must implement Functor and Applicative instances to be allowed to implement the Monad instance. Functor we’ve already met, but what’s Applicative? We’ll find out later. Let’s implement the instances:

We don’t need the nomsg operation any more since it’s just return. We can also reimplement the annotate operation using monad operations.

Here are the compute and filterLog examples rewritten using do-notation. Note how nice filterLog is with do-notation.

13.8 The State Monad

Haskell’s State monad is a generalized version of our BankOp type. The State type is parameterized by two types, the first being the type of the state, and the second the type of the value produced. State Bank a would be equivalent to our BankOp a. You can find the State monad in the module Control.Monad.Trans.State of the transformers package. Here’s a simplified implementation of State.

Note how we declare an instance Monad (State s). We’re using a partially-applied type constructor because instances of Monad can only be declared for type constructors that take one more type parameter. This might be a bit clearer if you look at how m, Maybe and State occur in the type of >>= below.

class Monad m where
  (>>=) :: m a -> (a -> m b) -> m b

instance Monad Maybe where
  (>>=) :: Maybe a -> (a -> Maybe b) -> Maybe b

instance Monad (State s) where
  (>>=) :: State s a -> (a -> State s b) -> State s b

Let’s look at some examples of working with State. To start off, let’s consider computations of type State Int a, which represent working with a single counter.

Note how a value of type State s a represents a process that transforms the state (just like BankOp). The initial state must be supplied using runState. Again, having to use runState makes the distinction between defining operations and executing them clearer.

A state can replace an accumulator parameter when processing a list. Here are two examples: finding the largest element of a list, and finding values in a list that occur directly after a 0.

13.9 The Return of mapM

The control structures from the IO lecture work in all monads. Here are their real types.

As we can see here, we can use mapM over all of the monads we’ve met so far:

Some more examples:

Here’s filter reimplemented using the State monad:

We can write our own operations that work for all monads. This is made possible by type classes, as we’ve seen before. If you only use monad operations like return and do-notation, the type system will infer a generic type for your function.

We can use these generic operations in each of our example monads:

13.10 Monads are Functors

One useful operation hasn’t yet been introduced: liftM.

The liftM operation makes it easy to write code with pure and monadic parts.

Does the type of liftM look familiar? It’s just like the type of fmap! In fact, it’s easy to define a functor instance for a monad: just set fmap = liftM. Since every Monad needs to be a Functor these days, modern Haskell style prefers fmap over liftM.

13.11 One More Monad

The list monad (that is, the Monad instance for []) represents computations with multiple return values. It’s useful for searching through alternatives. Here’s a first example. For every x we produce both x and -x:

We can filter out unsuitable values by produing an empty list:

If we’re using do-notation the list monad starts to look more like a looping construct:

More interesting example: find all the pairs in a list that sum to k

A final, more complex example. We find all palindromes from a string using the list monad, and then find the longest one.

Here’s the surprisingly simple implementation of the list monad:

We’ve actually seen the list monad previously in the guise of list comprehensions. Compare this reimplementation of findSum to the earlier one that uses do-notation.

13.12 Oh Right, IO

As you’ve probably guessed by now, IO is a monad. However the implementations of the IO type and instance Monad IO are compiler built-ins. You couldn’t implement the IO monad just using standard Haskell, unlike Maybe monad, State monad and other monads we’ve seen.

However, true side effects fit the monad pattern just like State and Maybe. Just like with other monads, we’re separating the pure definitions of operations from the process of running the operations. As a bonus, you can use all the generic monad operations (mapM and friends) with IO.

Here are some examples of writing IO using monad operations.

13.13 Monads in Other Languages

Once you’ve gotten familiar with the concept of a monad, you’ll start seeing monadlike things in other languages too. The most well-known examples of this are Option types, Java Streams and JavaScript promises . If you know these languages or concepts from before, you might find this section illuminating. If you don’t, feel free to skip this.

13.13.1 Optionals

Many langages have an option type. This type is called Optional<T> in Java, std::optional<T> in C++, Nullable<T> in C#, and so on. These types often have behaviour resembling the Haskell Maybe monad, for example:

  • In Java, Optional.flatMap corresponds to >>=: it lets you apply a Function<T,<Optional<U>> to an Optional<T> and get an Optional<U>.
  • In C#, binary operations get automatically lifted to Nullable types. For example, a + null becomes null.

13.13.2 Streams

Java Streams have a monadlike API too. Streams are about producing many values incrementally. Just like with Optional, the method Stream.flatMap lets us take a Stream<T>, combine it with a Function<T,Stream<U>> and get a Stream<U>.

As an example, if lines is a Stream<String>, words takes a String and returns a Stream<String> and readInt takes a String and returns an Integer, we can write:

This corresponds to the following Haskell list monad code:

13.13.3 Promises

There is much disagreement about whether Promises in JavaScript really are monads or not. However, some similarities are obvious.

First, consider the similarities between Promise.then and >>=. Both take an operation (promise or monadic operation), and combine it with a function that returns a new operation.

Next, let’s consider the similarities between async/await and do-notation. Both are nicer syntaxes for working with the raw Promise.then or >>= mechanisms. We reimplement concatPromises using async/await, and concatMonadic using do-notation. Their behaviour stays the same.

13.14 Monads: Wrap-up

  • The Monad type class is a way to represent different ways of executing recipes
    • failure (Maybe)
    • logging
    • state
    • nondeterminism (the list monad)
    • IO
  • You can write monad code in two equivalent ways:
    • Using the Monad class operations (>>=, >>) directly
    • Using do-notation
  • When M is a monad, values of type M a are operations that produce a result of type a
  • Monads are a design pattern and a library (mapM etc)
    • Using common abstractions makes it easier to understand code
    • Reading a State operation is easier than deciphering a complicated recursion with state
  • Everything you can do with monads, you can also do without them
    • Exception: IO
    • Using a monad often simplifies code
  • Warning: the internet is full of tutorials trying to explain monads using a simple analogy
    • In my experience, this doesn’t work
    • What works is using different monads and slowly getting used to the concept

13.15 Sidenote: Standard Haskell

This and the previous lecture have covered many parts where the GHC version of Haskell differs from standard Haskell 2010. Here’s a short list of the changes GHC has made, just so you know:

  • length, sum, foldr etc. generalized to work on Foldable instead of just lists
  • Functor and Applicative are superclasses of Monad
  • The fail method has been moved from the Monad type class to its own MonadFail class

13.16 Quiz

What is the expression equivalent to the following do block?

  1. z >> \y -> s y >> return (f y)
  2. z >>= \y -> s y >> return (f y)
  3. z >> \y -> s y >>= return (f y)

What is the type of \x xs -> return (x : xs)?

  1. Monad m => a -> [a] -> m [a]
  2. Monad m => a -> [m a] -> [m a]
  3. a -> [a] -> Monad [a]
  4. None of the above

What is the type of \x xs -> return x : xs?

  1. Monad m => a -> [a] -> m [a]
  2. Monad m => a -> [m a] -> [m a]
  3. a -> [a] -> Monad [a]
  4. None of the above

What is the type of (\x xs -> return x) : xs?

  1. Monad m => a -> [a] -> m [a]
  2. Monad m => a -> [m a] -> [m a]
  3. a -> [a] -> Monad [a]
  4. None of the above

13.17 Exercises