Haskell is a purely functional language, which means there are no side-effects
and all variables are immutable.
But as you probably know this isn’t
completely true. All variables are indeed immutable, but there
are ways to construct mutable references where we can change what the
reference points to.
Without side effects we wouldn’t be able to do much, which is why Haskell gives us the IO monad. In a similar manner we have many ways to achieve mutable state in Haskell, let’s take a look at them:
- STRef in the ST monad
- TVar in Software Transactional Memory (STM)
We all know that the IO monad allows us to do arbitrary effects in the real
world, so it probably comes as no surprise that it also allows us to create a
mutable reference to an type, called
Data.IORef.) There is not
much complicated about
IORef, as it only takes a single type parameter, which
is the type it’s going to contain.
Before we move into specifics it is important to note here that modifying the
IORef is no a pure operation, which means ever single operation on the
IORef will be inside the
Let’s take a look at some of the functions available for manipulating
data IORef a newIORef :: a -> IO (IORef a) readIORef :: IORef a -> IO a writeIORef :: IORef a -> a -> IO () modifyIORef :: IORef a -> (a -> a) -> IO ()
First thing you’ll probably notice is that in order to create an
need to give it a value. An
IORef must always contain a value of a given
type, it is impossible to create it empty. Here’s a simple example.
import Data.IORef main :: IO () main = do ref <- newIORef (0 :: Int) modifyIORef ref (+1) readIORef ref >>= print
0 :: Int instead of just
0 to make it explicit that we’re using
Ints. If you don’t do that it won’t affect the program but you might get a
warning from the compiler.
There’s not much really happening in this example, we just create a new
IORef, increase it’s value by
1 and then print the result. While this is
nice it doesn’t really show much, so let’s make this more complicated.
A common pattern in Haskell is to take an immutable data structure and put it
inside a mutable reference, which basically gives you a mutable version of that
data structure (let’s ignore the fact that there might be a more efficient way
to do this for now.) This will work because we can take any Haskell type and
put it into an
IORef. Let’s begin by using
Maybe Int to represent a
mutable box for an
Int which can be empty.
magic :: IORef (Maybe Int) -> IO () magic ref = do value <- readIORef ref case value of Just _ -> return () Nothing -> writeIORef ref (Just 42) main :: IO () main = do ref <- newIORef Nothing magic ref readIORef ref >>= print
First we define a function which takes a
IORef (Maybe Int), that is a
mutable reference that maybe contains an
Int and produces some side effects.
The implementation simply reads the
IORef and do nothing if it already has a
value, but if it contains
Nothing it will replace that value with
main function then simply
IORef, which is
In-place bubble sort with
If you’ve read this far there’s a fair chance that you know how bubble sort works. The important thing about it is that it works in-place and modifies the array it is sorting. Here’s a simple implementation in Ruby.
def bubble_sort(list) list.each_index do |i| (list.length - i - 1).times do |j| if list[j] > list[j + 1] list[j], list[j + 1] = list[j + 1], list[j] end end end end
The key part being here is that we’re swapping the elements of the list as we
iterate through it. This is something we can’t do in pure Haskell, but we can
attempt to do this using
We will use a simple Haskell list where each element is
IORef Int, so that we
can move them around. The exact type will be
Disclaimer: I am aware that using a list, which is a linked list, is a
horribly inefficient implementation. The point of this article is however to
IORef can be used, not how to properly sort an array.
Our sorting function will accept a plain list of
Ints, wrap them all in
IORefs, do the sorting in place, and unwrap the
IORefs to return a list of
bubbleSort :: [Int] -> IO [Int] bubbleSort input = do let ln = length input xs <- mapM newIORef input forM_ [0..ln - 1] $ \_ -> do forM_ [0..ln - 2] $ \j -> do let ix = xs !! j let iy = xs !! (j + 1) x <- readIORef ix y <- readIORef iy when (x > y) $ do writeIORef ix y writeIORef iy x mapM readIORef xs
Let’s go through the code one step at a time. First we need to calculate the length of the list being sorted and bind that to a variable.
let ln = length input
Next we wrap all of the items in the list inside an
IORef. This will allow us
to do the sort in-place by swapping around the values of the references.
xs <- mapM newIORef input
Let’s examine the
mapM here a little bit. The
newIORef function has a type
a -> IO (IORef a), if we try to partially apply it with
map, we’ll get
back the following.
λ> :t map newIORef :: [a] -> [IO (IORef a)]
This is not very useful for us, since we need a
[IORef a]. Fortunately
Haskell provides a
sequence :: [IO a] -> IO [a] function which simply pulls
out the monadic effects from a list.
λ> :t sequence . map newIORef :: [a] -> IO [IORef a]
mapM is simply defined a shorthand for as
sequence . map. There also exists
forM which is exactly like
mapM, but the arguments are swapped around.
λ> :t mapM mapM :: Monad m => (a -> m b) -> [a] -> m [b] λ> :t forM forM :: Monad m => [a] -> (a -> m b) -> m [b]
One last variant is
forM_, which the same as
only their return value is discarded.
λ> :t mapM_ mapM_ :: Monad m => (a -> m b) -> [a] -> m () λ> :t forM_ forM_ :: Monad m => [a] -> (a -> m b) -> m ()
forM because the function we pass in as an argument is quite long
and it just ends up being syntactically more pleasing, and because we only care
about the effects produced by the function we apply.
[0..ln - 2] simply
allows us to call the function
length - 2 number of times.
forM_ [0..ln - 2] $ \_ -> do forM_ [0..ln - 2] $ \j -> do
Next we extract two items from the list, note that these have the type
let ix = xs !! j let iy = xs !! (j + 1)
We need to read the values from the
IORefs in order to be able to compare them
x <- readIORef ix y <- readIORef iy
and then simply swap the contents if
x > y
when (x > y) $ do writeIORef ix y writeIORef iy x
The last step is to unwrap the
mapM readIORef xs
Now that we went through each of the steps, let’s test our bubble sort implementation.
λ> bubbleSort [1,2,3,4] [1,2,3,4] λ> bubbleSort [4,3,2,1] [1,2,3,4] λ> bubbleSort [4,99,23,93,17] [4,17,23,93,99]
It works! Keep in mind that this implementation is horribly slow. If you’re interested in fast arrays in Haskell check out the vector library.
You’ve probably noticed that the only reason why we need to perform our sorting
algorithm in the
IO monad is to have mutable references, which is not ideal
since we’re not really doing any
Luckily for us there is a solution called the state thread monad. I won’t be
going on into great detail since the API for
STRef is almost
exactly the same.
data STRef s a newSTRef :: a -> ST s (STRef s a) readSTRef :: STRef s a -> ST s a writeSTRef :: STRef s a -> a -> ST s () modifySTRef :: STRef s a -> (a -> a) -> ST s ()
The key difference is that while we can’t ever escape from the
IO monad, we
do have the ability to escape from the
ST monad with the
runST :: ST s a ->
a function, making the computation pure.
import Control.Monad.ST import Data.STRef magic :: Int -> Int magic x = runST $ do ref <- newSTRef x modifySTRef ref (+1) readSTRef ref
The only thing worth mentioning here compared to the
IORef example is that
the type of the function
magic is just
Int -> Int, because we’re able to
ST monad using a call to
If you’re not sure why this is useful, think of the sorting algorithm we
developed earlier. There are many algorithms which require mutation, but which
are also pure in their nature. If the way to achieve mutation was using the
IO monad, we wouldn’t be able to implement such algorithm in pure code.
The next type we’re going to take a look at is a little bit more complicated
IORef, it’s called an
MVar. As usual most of the API is similar, but
there is one huge difference. While an
IORef must always have a value,
can be empty.
We have two ways of constructing an
newMVar :: a -> IO (MVar a) newEmptyMVar :: IO (MVar a)
We also have an additional operation
takeMVar :: MVar a -> IO a which takes a
value out of an
MVar and leaves it empty. Now comes the important part, if
we try to do
takeMVar from an empty
MVar, it will block the thread until
someone else puts a value into the
MVar. The same thing happens when you
putMVar into an
MVar that already has a value, it will block until
someone takes that value out.
Try compiling and running the following program.
import Control.Concurrent main :: IO () main = do a <- newEmptyMVar takeMVar a
After a second or so you’ll get an exception and the program will crash.
*** Exception: thread blocked indefinitely in an MVar operation
The reason for this is that there are no other threads that could possibly
MVar, so the runtime kills the thread. If we modify the program to
first put a value into the
MVar it will work correctly.
main :: IO () main = do a <- newEmptyMVar putMVar a "hello" takeMVar a >>= print
Now you might be thinking, how does the runtime know that there are no other
threads that could put a value into that
MVar? Using garbage collection!
MVar knows which threads are currently blocked on it. If a thread that
is currently blocked on an
MVar is not accessible from any other running
thread, it will get killed since there is no way it to become unblocked.
Synchronizing threads using
One of the great benefits of
MVars is that they can be be used to serve as
synchronization primitives for communication between threads.
We can use them as a simple 1 item channel, where we fork a thread that forever
loops trying to read from the
MVar and print the result, and in the main
thread we read input from the user and put it into the same
import Control.Monad import Control.Concurrent main :: IO () main = do a <- newEmptyMVar forkIO $ forever $ takeMVar a >>= putStrLn forever $ do text <- getLine putMVar a text
Everything will work as expected since
takeMVar will block until we put
something into the
One important thing to note here is that when
main returns the runtime
automatically kills all of the other running threads. It doesn’t wait for them
to finish. Let’s see a simple example.
import Control.Monad import Control.Concurrent main :: IO () main = do forkIO $ do threadDelay 2000000 putStrLn "Hello World" putStrLn "Game over!"
If you run this using
runhaskell or by compiling and running the binary
you’ll only see the output of
Game over!. The second thread will never print
Hello World, because by the time it starts waiting the
main function will
return and the runtime will kill the other thread.
We can fix this by using an
MVar to make the
main function wait for the
other thread to finish.
import Control.Monad import Control.Concurrent main :: IO () main = do a <- newEmptyMVar forkIO $ do threadDelay 2000000 putStrLn "Hello World" putMVar a () takeMVar a putStrLn "Game over!"
The main thread first tried to take a value out of the
MVar, which will block
because there’s nothing in there yet, and then the second thread will sleep for
2 seconds, print
Hello World and put a
() into the
MVar. This causes
main to continue, print
Game over! and exit the program. We could also do
this the other way around by using
putMVar on a full
MVar in order to
block, but the end result is the same.
main :: IO () main = do a <- newMVar () forkIO $ do threadDelay 2000000 putStrLn "Hello World" takeMVar a putStrLn "Game over!" putMVar a ()
There are many more things to cover with respect to
MVar, but I’m not going
to go more in depth here, since there already are other great resources on the
- Parallel & Concurrent Programming in Haskell - Chapter 7. Basic Concurrency: Threads and MVars
- Real World Haskell - Chapter 24. Concurrent and multicore programming
Software Transactional Memory - STM
Last on our list is Software Transactional Memory. Much like we had
MVar, STM gives us
TVar, which stands for transaction variable. The way
that STM works is that it builds up a log of actions that are to be performed
atomically. We won’t be covering STM itself as a method for managing
concurrency, since it’s a rather lengthy topic. Instead we’ll just examine the
options for achieving mutable state using STM using a
Every STM operation happens inside the
STM monad, which already tells us that
we can chain multiple
STM operations into one (since the monad instance
provides us with
>>=.) In order to run the actual
STM transaction we must
use the function
atomically :: STM a -> IO a, which takes any
and performs it in a single atomic step.
The API for creating
TVars is almost the same as for
data TVar a newTVar :: a -> STM (TVar a) readTVar :: TVar a -> STM a writeTVar :: TVar a -> a -> STM () modifyTVar :: TVar a -> (a -> a) -> STM ()
There are also alternatives that work in the
newTVarIO :: a -> IO (TVar a) readTVarIO :: TVar a -> IO a
Note that these are just convenience functions that we could have implemented
newTVarIO :: a -> IO (TVar a) newTVarIO = atomically . newTVar readTVarIO :: TVar a -> IO a readTVarIO = atomically . readTVar
Now let’s move onto mutations. We’ll use the same example as we did with
IORef, but implement it using a
TVar. We have many ways to approach it,
either by building one big transaction with all the steps, or by doing this in
many small ones.
First let’s do one big
atomically with all the steps.
bigTransaction :: IO () bigTransaction = do value <- atomically $ do var <- newTVar (0 :: Int) modifyTVar var (+1) readTVar var print value
There’s not much interesting going on in here, so let’s split it into smaller
chunks. Even though
modifyTVar is the perfect function for our use case, we
can use a combination or
writeTVar to achieve the same,
atomically will make sure those two happen in a single step.
atomicReadWrite :: IO () atomicReadWrite = do var <- newTVarIO (0 :: Int) atomically $ do value <- readTVar var writeTVar var (value + 1) readTVarIO var >>= print
STM is a monad, we can also make this more interesting by combining two
STM operations together and running those atomically.
f :: TVar Int -> STM () f var = modifyTVar var (+1) twoCombined :: IO () twoCombined = do var <- newTVarIO (0 :: Int) atomically $ do f var f var readTVarIO var >>= print
There’s a lot more to
STM than just
TVars which is why I’d encourage you,
dear reader, to take a look at the following resources. You might find that it
will change the way you think about concurrent programming completely.
- Lock Free Data Structures using STM in Haskell
- Parallel and Concurrent Programming in Haskell - Chapter 10. Software Transactional Memory
- Real World Haskell - Chapter 28. Software transactional memory
- Lens Tutorial - Introduction (part 1)
- Using Phantom Types in Haskell for Extra Safety - Part 2
- Using Phantom Types for Extra Safety
- Yesod is Fun
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