# Exceptions tutorial from IH book

Published on March 3, 2019

This text originally was written as a chapter for the Intermediate Haskell book. Due to lack of progress with the book in the last year, other authors agreed to let me publish the text as a standalone tutorial so people can benefit at least from this part of our work.

## Motivation for exceptions

It is not uncommon to hear the opinion that exceptions are ugly and hard to work with. Especially in a language like Haskell, with its strong and powerful type system, perfectly capable of expressing exceptional situations in a pure way (i.e. in simplest case as a sum type containing both values that belong to normal results and values that represent adverse outcomes), why should we “contaminate” such a language with the concept of exceptions?

It is a question we must answer first lest the reader be left in a position of doubt as to when it is natural to use exceptions and when a more pure way of organizing an API is better. The topic itself is a bit controversial in the community:

• Some believe that once you start dealing with exceptions, you should go all the way with them and not to pretend that your code is “safe” from exceptions by adding additional MaybeT or ExceptT layers.

• Others are concerned with purity and try to “disinfect” their APIs from exceptions completely (see for example the description of the hasql package, where the exception-free API is apparently perceived by the author as a merit).

• The third opinion is that exceptions, as the name suggests, are for exceptional situations, while explicit encoding of adverse outcomes in the type of returned value is for the situations that are to be quite common, and so an obligatory, explicit handling is desirable.

To see the motivation for introducing exceptions in Haskell, let us consider a simple arithmetic expression:

percent :: Double -> Double -> Double
percent x y = (x / y) * 100


This does look conventional, but the (/) operator is not total! As we all know well, one does not just divide by zero without certain consequences. So the whole percent function has a “morally wrong” type, or at least such a type that does not reflect an important part of percent‘s semantics. We must note that it is still convenient to have percent in this form.

If we went with the approach of purists, we could end up writing programs like this:

percent :: Double -> Double -> Maybe Double
percent x y = (* 100) <$> (x /? y) where a /? 0 = Nothing a /? b = Just (a / b)  While it does not look that bad in this little example, we could quickly find ourselves writing all our code in Maybe or Either monads and corresponding transformers. Either SomeError monad in particular would have to account (in SomeError) for enormous number of things that can go wrong. In a real-world program, there are a lot of failure scenarios that are indeed rather “exceptional” and normally do not happen. SomeError also would need to be extendable somehow because combining code from different libraries A and B would mean building a sum type capable of representing exceptional situations of both A and B. It is somewhat inconvenient to code that way. What to do? Language design is often about balancing different arguments and considerations. In our case, we balance ease of use with correctness. By “correctness” here we mean explicit inclusion of adverse conditions in types and thus forcing handling of those conditions. Precise semantics of exception throwing and handling will be given in the next sections, but it suffices to say for now that intuitively, exceptions (in many programming languages) have the following properties: • They shortcut execution and propagate. This is handy, because once something exceptional has happened, our confidence in success of the program should be realistically very low, so we want to shut it down. • They can be caught. This follows the philosophy make common things easy and more tricky things possible. If an exception can be caught, it becomes not just a way to abort the entire program, it becomes a means to control program’s execution flow opening the possibility to recover from an exceptional situation, not less powerful than explicit handling with something like the ExceptT monad transformer. Thus, whether to return a result wrapped in Maybe, Either, or to throw an exception may be decided on how common the failure in question is and how much attention we want to draw to it. If the failure is common, it makes sense to force the consumer of the API to handle it in all cases for her own good (by wrapping in Maybe, Either, ExceptT, etc.). Otherwise, the exceptional case should be covered by throwing an exception, which works a lot like an implicit short-circuiting monad anyway, except it is neither visible nor the programmer is forced to think about it until he/she needs to. Other reasons to prefer throwing exceptions include: • preserving laziness (as we do not need to check constantly whether we have run into a failure and should short-circuit the execution or not); • more efficient code (the fact that code throws exceptions does not make it run slower). Finally, sometimes it is necessary or convenient to make some function partial and signal an error that would indicate that the programmer made a mistake. We try hard to avoid these cases in Haskell, but sometimes one has to bite the bullet. In that case exceptions allow us to signal the error using functions like error and assert. ## Throwing exceptions Recall from the first example that it should be possible to throw exceptions from pure code. Given that Haskell has non-strict semantics where evaluation happens on demand (i.e. in order that is generally hard to predict), and that throwing an exception actually changes control flow dramatically, we seem to be in a tricky position immediately because of the possible non-determinism. Consider the following example: x :: Integer x = error "foo" + error "bar"  When x is evaluated, what will be thrown, the exception from error "foo" or error "bar"? It is not possible to tell, since the order in which (+) evaluates its arguments is not defined. Programs in Haskell often do not have predictable evaluation order, so the only way to reason about them is to consider values we want to compute. Indeed, what is an exception as not an additional sort of value that can inhabit any type? Let us take a look at the signature of throw function and compare it with bottom, shown here in the form of the undefined function: throw :: Exception e => e -> a undefined :: a  We can see that throw can lift an instance of the Exception type class (more about the type class later) into any value at all, just like undefined—bottom value—is a value found in any lifted type. It is generally well-known that all lifted Haskell values are inhabited by bottom, but in the presence of exceptions, it is also true that exceptions inhabit any type in a sense. ## Catching exceptions If exceptions in Haskell could not be caught, we could consider them bottom values and the non-determinism throw introduces would be not that bad, but once we equip the language with a way to catch exceptions, they can influence behavior of our programs, and if exceptions are non-deterministic, our programs become non-deterministic as well. Not fun! Let us take an example from the paper A semantics for imprecise exceptions: let x = (1/0) + (error "Urk") in getException x == getException x  Here, getException :: a -> Either Exception a is a hypothetical function that allows us to catch exceptions in pure code. What that expression will return, True or False? It would seem natural to state that the result should be True, but it could be False just as well because of the non-determinism (i.e. the two occurrences of x could be replaced by x‘s definition, and (+) could evaluate its left or right argument first). One solution the above-mentioned paper proposes is to return the full set of exceptions in every case, so getException x == getException x evaluates to True reliably. That is not efficient, however, as once an exception is thrown, we would need to force the entire expression to collect all other exceptions no matter what. In reality, there is no good way to make throwing/catching of exceptions in a non-strict language deterministic, so the next best thing we can do is to admit that getException is not an angel, and so we have to put it into IO, where all sorts of sins are known to be witnessed and, in time, forgiven. With getException :: a -> IO (Either Exception a), we can write: main :: IO () main = do let x = (1/0) + (error "Urk") v1 <- getException x v2 <- getException x print (v1 == v2)  This still can print True or False, but now it is nothing to worry about, because we are in the IO monad, which (as we know) depends on the state of RealWorld (including phase of the moon). The Control.Exception module features many functions useful for dealing with exceptions. One such function is throw (which we already know) yet there is no getException. But there is catch: catch :: Exception e => IO a -- ^ The computation to run -> (e -> IO a) -- ^ Handler to invoke if an exception is raised -> IO a  It is quite similar to getException in that it also lives in IO. However, the first argument (the computation to catch exceptions from) also lives in the IO monad. That is because IO a is a more common case as we will see in the next section. ## throwIO While it is somewhat hard to predict when throw will raise an exception in pure code, there is no problem with raising an exception from the IO monad (or any IO-enabled monad stack) at certain point of execution. The IO monad imposes ordering to execution by being a state monad that passes around a magical value that is never looked at, but creates logical dependencies between separate IO actions that are bound together with the bind operator (>>=). In this setting it is possible to have throwIO :: Exception e => e -> IO a, which will throw exactly when it is executed: main :: IO () main = do foo throwIO myException bar  Here myException will be thrown after foo but before bar. We must note the crucial difference between throw e :: a and throwIO e :: IO a, which is exactly the difference between evaluation and execution: evaluation triggers exception in the case of throw e, while execution (when the magical state “goes through” throwIO) triggers exception in the case of throwIO e. This is best demonstrated by the example with seq found in the docs of throwIO: throw e seq x {- => -} throw e -- throwing triggered by evaluation throwIO e seq x {- => -} x -- throwing triggered by execution  e seq x is a function that artificially creates a dependency between e and x, then returns x. Evaluation of x requires evaluation of e as if its value directly depended on it. Evaluation forced by seq does not trigger exception in the case of throwIO, because the expression is not executed. throwIO, being much more predictable (execution order is more predictable than evaluation order in Haskell), is generally preferred in the community and it is a good idea to throw all your exceptions using throwIO instead of throw if at all possible. ## Implementation of throwing and catching A desirable property of exceptions is that computing of a value with an exceptional component does not make program slower or otherwise less-efficient. Unlike explicit bookkeeping with Maybe, Either, or similar, if an exception is not raised, it is the same as if there is no exception at all. Here is how synchronous exceptions are implemented: • getException (or catch) marks the evaluation stack and evaluates its argument to weak head normal form. • When an exception is raised, the stack is trimmed to the top-most getException mark. After that a handler can be run or a value indicating that an exception has happened can be returned. • If evaluation has completed without raising an exception, the computed value can be returned. • If a pure expression throws, its thunk is replaced by the exception because we can be sure that it will throw every time we try to evaluate the thunk. The section is worth ending with the following quote from A semantics for imprecise exceptions: Notice that an exceptional value behaves as a first class value, but it is never explicitly represented as such. When an exception occurs, instead of building a value that represents it, we look for the exception handler right away. The semantic model (exceptional values) is quite different from the implementation (evaluation stacks and stack trimming). The situation is similar to that with lazy evaluation: a value may behave as an infinite list, but it is certainly never explicitly represented as such. ## Defining hierarchy of exceptions Let us consider how Haskell keeps its exceptions extendable. It is natural to think about exceptions as objects in a hierarchy: It is well-known that in object-oriented world the set of operations is fixed, while the set of objects is extendable. In functional programming the situation is reversed: typically data types are fixed, and the set of functions on these objects is extendable. This works pretty well most of the time, and is especially natural in certain domains, like compilers, but now that we would like to build a hierarchy for exceptions, the functional solution is going to look somewhat… stylized. To have an extensible group of data types that share a certain property, Haskell has type classes. We have already seen that throw has the signature with the mysterious Exception constraint: throw :: Exception e => e -> a  Anything that is an instance of Exception can be thrown. Let us take a look at the type class: class (Typeable e, Show e) => Exception e where toException :: e -> SomeException toException = SomeException fromException :: SomeException -> Maybe e fromException (SomeException e) = cast e displayException :: e -> String displayException = show  First of all, an Exception needs to be printable (in case it bubbles to the top level and terminates a program), hence Show is a superclass of Exception. displayException (which was added later) allows us to define a human-readable representation of exception, which is by default show-based. Of course, the interesting part of the definition here is the toException and fromException methods: they provide a way to inject and project an Exception to/from SomeException. And here is how throw itself implemented in base: throw :: Exception e => e -> a throw e = raise# (toException e)  This raise# :: b -> o primitive can in principle throw anything at all, but that usage would lead to a chaos because there would be no way to select a branch of the entire exception hierarchy, or select all possible exceptions. With every exception converted to SomeException before throwing, we know that every exception is always wrapped in SomeException. SomeException, being the root of our hierarchy, defined as: data SomeException = forall e. Exception e => SomeException e  It is an existential wrapper around instances of Exception. It hides information about the type e stored inside SomeException. The only thing we know about e is that it is an instance of Exception. The throwing part should be clear now, but what about catching? We want to be able to say “I want to catch only arithmetic exceptions, like division by zero”. Here is where fromException comes into play. The default implementation does everything we need: it unwraps SomeException, then tries to cast the inner value to the desired type e. If we have got something inside Just, the exception is of the correct type and we can do something with it, otherwise we just re-throw it. This is what happens inside of catch and similar functions that catch exceptions: catch :: Exception e => IO a -- ^ The computation to run -> (e -> IO a) -- ^ Handler to invoke if an exception is raised -> IO a catch (IO io) handler = IO$ catch# io handler'
where
handler' e =
case fromException e of
Just e' -> unIO (handler e')
Nothing -> raiseIO# e


If we try to call catch (or other general exception-catching function) without adding any information about the type of exception to catch, we will get an ambiguous type error.

cast can be used only with instances of Typeable, which is why it is a superclass of the Exception type class. The Typeable type class, derivable by the compiler with the DeriveTypeable language extension (which is not necessary with newer versions of GHC though), allows us to get type of a value at runtime. We will not go into details of that here, but it suffices to say that knowing type of a value we can check if it is the same as the type we want to convert to, so we can go from abstract e to a concrete type.

The fromException method works with SomeException just fine, it never fails (so when we catch SomeException, we catch every exception possible):

instance Exception SomeException where
-- …
fromException = Just


Right now we have a hierarchy with two levels, SomeException as the root, and all instances of Exception as its immediate children. It is however quite simple to add another existential wrapper between SomeException and concrete instance of Exception with the aim of selecting a subset of all exceptions that are wrapped with that wrapper.

Let us pick the SomeAsyncException from base as an example of such a wrapper. It is defined in the same fashion as SomeException:

data SomeAsyncException = forall e. Exception e => SomeAsyncException e


There is nothing unusual in SomeAsyncException‘s Exception instance, it uses the default definitions. If we take a look how Exception instance is defined for AsyncException—data type for async-specific exception like stack overflow—we can see the following:

instance Exception AsyncException where
toException = toException . SomeAsyncException
fromException x = do
SomeAsyncException a <- fromException x
cast a


toException simply wraps AsyncException in SomeAsyncException before calling toException again (now the Exception instance of SomeAsyncException is used), which in turn wraps the whole thing in SomeException.

fromException first unwraps x :: SomeException with fromException (again the instance of SomeAsyncException is at work here), the result is returned in Maybe, so the do block is in the Maybe monad here. If we were lucky enough to extract SomeAsyncException, we can try to cast it to get to the ArithException type.

If we define another exception data type with a similar instance definition, we will be able to catch it and AsyncException at the same time by specifying just SomeAsyncException as the type of exception to catch. We have a working hierarchy for our exceptions now.

This system of exception organization was proposed in the paper An extensible Dynamically-Typed Hierarchy of Exceptions.

## Asynchronous exceptions

So far we have been talking about synchronous exceptions, that is, exceptions that affect the same thread where evaluation of exceptional value takes place. Asynchronous exceptions are initiated outside of the thread they affect. Haskell supports fully-asynchronous exceptions that allow us to:

• interrupt the program from the outside (for example when the user presses Ctrl+C in a terminal);

• receive signals about exceptional conditions that relate to resource consumption: stack overflow, reaching of allocations limits, heap overflow, etc.;

• kill threads that are recognized as permanently blocked by the Haskell’s run time system;

• control execution in a multi-threaded environment allowing one thread to throw exceptions to another thread to terminate the latter (used to implement timeouts and other multi-thread control idioms).

I mentioned the phrase “fully-asynchronous exceptions”, but what exactly does it mean?

There are different ways to implement asynchronous exceptions. One way (preferred in the imperative world), is to employ semi-asynchronous techniques where the thread that initiates an exception sets a flag, which is then periodically checked by the thread that is to receive the exception. This however cannot work in Haskell, as we should be able to interrupt evaluation of pure code, but polling of a global flag would certainly be a side-effect.

In the volatile, mutable world of imperative programming, it is just too dangerous to allow interruption at arbitrary moment in time. In contrast to that, there is absolutely no danger in terminating evaluation of pure code, because it does not perform any mutations or side-effects. That means that all pure code works fine with fully-asynchronous exceptions without change. We should note that this model also has higher modularity because the target thread does not need to do anything in order to be able to receive asynchronous exceptions.

Speaking of semantics of values being evaluated in the presence of asynchronous exceptions, we can say that since the exceptions can strike at any time during evaluation of any value in our program, we cannot consider asynchronous exceptions as part of semantics of value that is being evaluated when such an exception happens.

Oftentimes it is necessary to ensure a certain level of atomicity so that asynchronous exceptions do not strike in the middle of a composite operation leading to partially updated, inconsistent state of program.

To illustrate this, consider a toy example—the updateVar function, which applies a function to the contents of a mutable variable:

updateVar :: MVar a -> (a -> IO a) -> IO ()
updateVar m f = do
x  <- takeMVar m -- (1)
x' <- f x        -- (2)
putMVar m x'     -- (3)


A quick reminder, MVars work like this:

• MVar a can be either empty or have a value of type a inside.

• takeMVar extracts a value from given mutable location if it is full, otherwise it blocks waiting for the value to be placed there.

• putMVar places a value in the specified mutable location. If it is already full, it blocks waiting for the location to become empty.

In the current form, updateVar is not an atomic operation, because asynchronous exception can strike between (1) and (3), during evaluation of f x (2) leaving m empty. To prevent asynchronous exceptions from interrupting execution of updateVar, we can use mask:

--      | blocking callback              |
mask :: ((forall a. IO a -> IO a) -> IO b) -> IO b
--       | unblocking callback  |


It probably looks a bit confusing, so let me explain. The argument of mask is the function to run with asynchronous exceptions disabled or “masked” (or rather delayed). That function in turn receives another callback that allows us to unmask asynchronous exceptions.

On a lower level, there are wrappers like block :: IO a -> IO a and unblock :: IO a -> IO a that modify the masking state of thread while the inner code in executed. When block and unblock are nested, only the innermost layer matters:

-- async exceptions:
block   (block io)   -- io blocked
block   (unblock io) -- io unblocked
unblock (unblock io) -- io unblocked
unblock (block io)   -- io blocked


Masking state of the current thread can be looked up with the getMaskingState :: IO MaskingState function. The MaskingState type is the enumeration of all possible masking states:

getMaskingState :: IO MaskingState



mask is defined like this:

mask :: ((forall a. IO a -> IO a) -> IO b) -> IO b
case b of
Unmasked              -> block $io unblock MaskedInterruptible -> io block MaskedUninterruptible -> io blockUninterruptible  The definition gives us a hint about behavior of nested masks. We can see that the inner “unblocking” callback does not necessarily unmask asynchronous exceptions, but rather returns masking state to what it was outside of current mask. Ignore blockUninterruptible for now, we will get to it soon. Let us continue with the examples: mask (\_ -> mask (\_ -> io)) -- io blocked mask (\_ -> mask (\restore -> restore io)) -- io blocked  Here the first mask layer blocks asynchronous exceptions and the second mask layer can not unblock with restore because that callback only returns masking state to what it was outside of the respective mask call. The behavior is exactly what we usually want: when we enclose a region of code with mask, we want to be sure async exceptions are masked, no matter what happens inside. The guarantee extends even further: mask affects all code that is lexically enclosed by it, even if we fork with forkIO and forkOS (the new thread inherits the parental masking state). This is important because otherwise there would be no way to prevent exceptions happening between (1) and (2) in the following example, and so they could leak: x <- takeMVar m forkIO$               -- (1)
mask $\unmask -> do -- (2) x' <- catch (unmask (…)) (\e -> putMVar m x >> throw (e :: SomeException)) putMVar m x'  There is also mask_ in Control.Exception for the cases when we only want to block: mask_ :: IO a -> IO a mask_ io = mask$ \_ -> io


Let us use mask in our example to make it more correct in the presence of asynchronous exceptions:

updateVar :: MVar a -> (a -> IO a) -> IO ()
updateVar m f = mask $\unmask -> do x <- takeMVar m -- (1) x' <- unmask (f x) -- (2) putMVar m x' -- (3)  This is a bit better in the sense that asynchronous exceptions can not strike between (1) and (2), (2) and (3), but they still can happen on the line (2), while f x is being evaluated. There are two possible ways to fix it: • Run f x without unmasking, wrapping the whole thing with mask_. This solution has a flaw however: if f x takes a very long time to compute or worse yet, hangs, there is no way to abort the thread—we are stuck. • Add catch to handle exceptions thrown from f x and so re-fill m with the old value should we be forced to abort the computation. Let us explore the solution involving catch: updateVar :: MVar a -> (a -> IO a) -> IO () updateVar m f = mask$ \unmask -> do
x <- takeMVar m
x' <- catch (unmask (f x))
(\e -> putMVar m x >> throw (e :: SomeException))
putMVar m x'


This code behaves as expected: the value inside m is guaranteed to be updated and if an asynchronous exception strikes while we are in the unmasked state old value will be preserved.

Now here comes a subtle detail about masking. It is explained in papers and in the Simon Marlow’s book Parallel and Concurrent Programming in Haskell, but we will try to re-iterate it here as concisely and clearly as possible (because it is easy to get confused):

• Blocking (for example while waiting for a value to be put into m, as in takeMVar m) with asynchronous exceptions disabled is a bad thing. This increases the probability of deadlock that cannot be interrupted (normally some deadlocks are detected by the Haskell’s runtime system and an exception such as BlockedIndefinitelyOnMVar is thrown to blocking threads to shut them down).

• To deal with that, certain operations like takeMVar were made interruptible, that is, the mask can be “pierced” and an asynchronous exception can get through it. But they are only interruptible when they actually block, for example when takeMVar m is waiting for m to be filled with a value (otherwise mask would be meaningless).

Let us consider various scenarios of what might happen:

• When takeMVar m is blocking, we can throw an asynchronous exception to the thread we are working in and it will be treated in fact as a synchronous exception happening exactly before takeMVar m. The thread will be killed. Asynchronous exceptions become synchronous inside mask, because it is known which operations are interruptible and so the exception becomes synchronized with respect to other operations in the thread.

• putMVar is also interruptible, because it can block (waiting for its argument to become empty). Does it mean that an asynchronous exception can interrupt either of our putMVars from the example above? No, it can not. We can see from the example that m is guaranteed to be empty after the last takeMVar invocation, so putMVars cannot be interrupted in that case, as it does not need to wait for m to become empty.

As we can see, the tricky part in behavior of so-called “interruptible” operations is that they only become interruptible when they actually block. All operations that may block indefinitely are designated as interruptible.

Now that we know that masking asynchronous exceptions with mask can be pierced, we also need to discuss a way to mask in an uninterruptible fashion and when it is useful.

Similar to mask, there is uninterruptibleMask, which uses blockUninterruptible instead of block. It should be noted that the ability to interrupt operations is there for a good reason: if your program hangs inside of an uninterruptible mask, it will become unresponsive and there will be no way to kill it.

That said, sometimes uninterruptible mask is useful. To show an example of that, let us first introduce the bracket function:

bracket
:: IO a         -- ^ Computation to run first (acquire resource)
-> (a -> IO b)  -- ^ Computation to run last (release resource)
-> (a -> IO c)  -- ^ Computation to run in-between
-> IO c         -- ^ Returns the value from the in-between computation
bracket before after thing =
mask $\restore -> do a <- before r <- restore (thing a) onException after a _ <- after a return r  Everything in bracket happens with asynchronous exceptions masked, except for thing a. onException :: IO a -> IO b -> IO a is another useful function from the Control.Exception module. f onException g tries to run f, but if it throws an exception, onException runs g and re-throws: onException :: IO a -> IO b -> IO a onException f g = f catch \e -> do _ <- g throwIO (e :: SomeException)  bracket allows us to guarantee that some resource will be released no matter what. However, there are a number of things that can invalidate this promise. For now we will consider one particular use case—working with temporary directories: withTempDirectory :: FilePath -- ^ Directory in which to create temp directory -> String -- ^ Name pattern for the temp directory -> (FilePath -> IO a) -- ^ Callback that receives the name of temp directory -> IO a -- ^ Result returned from the callback withTempDirectory targetDir template = bracket (createTempDirectory targetDir template) (ignoringIOErrors . removeDirectoryRecursive)  The failure scenario is taken from this blog post by Roman Cheplyaka: • createTempDirectory targetDir template completes successfully. • User’s action completes successfully creating some files in the temporary directory (the third argument of withTempDirectory, it is not bound explicitly because of eta-reduction). • Clean up removeDirectoryRecursive begins, but while it is working an asynchronous exception is received. Even though it is inside mask (it is after a in the definition of bracket), individual file deletions are interruptible (because they may block indefinitely), so the mask gets pierced. Result: the guarantees of withTempDirectory are broken. One solution is to use something like this: withTempDirectory' :: FilePath -> String -> (FilePath -> IO a) -> IO a withTempDirectory targetDir template action = uninterruptibleMask$ \restore -> do
tdir <- createTempDirectory targetDir template
let after = ignoringIOErrors . removeDirectoryRecursive
r <- restore (action tdir) onException after tdir
after tdir
return r


uninterruptibleMask makes sure that creation of temporary directory and its deletion will not be interrupted.

Use uninterruptibleMask only when you know for sure that its inner computation will not ever block for a long period of time. Chances are, most of the time you will not need uninterruptibleMask, but it is good to know about it and have it in your programming toolbox.

The functions that guarantee release of resources in case of exception, such as bracket, may also let you down for reasons that are not related to asynchronous exceptions. It is important to remember that when the main thread of program finishes, the program exits instantly, without sending asynchronous exceptions to child threads and so without giving them a chance to clean up properly. If the program in question uses bracket (or similar function) to manipulate internal data (such as an MVar), it is OK. However, if it manipulates an object from the outside (for example, creates a temporary directory and then deletes it), it is only guaranteed to clean up properly if it is run in the main thread. The most natural solution to this is not to fork manually, but with withAsync from the async package. That function will ensure that the forked thread is killed when the inner computation returns or throws.

### The throwTo function

throwTo allows us to raise an arbitrary exception in a thread with known ThreadId:

throwTo :: Exception e => ThreadId -> e -> IO ()


The docs for throwTo are exceptionally good, so let us just quote them here with some clarifications:

• Exception delivery synchronizes between the source and target thread: throwTo does not return until the exception has been raised in the target thread. The calling thread can thus be certain that the target thread has received the exception. Exception delivery is also atomic with respect to other exceptions. Atomicity is a useful property to have when dealing with race conditions: e.g. if there are two threads that can kill each other, it is guaranteed that only one of the threads will get to kill the other.

• This means in particular that use of mask can block the thread that attempts to throw an exception to a thread in masked state. Like any blocking operation, throwTo is therefore interruptible, but unlike other interruptible operations, however, throwTo is always interruptible, even if it does not actually block.

• If the target thread is currently making a foreign call, then the exception will not be raised (and hence throwTo will not return) until the call has completed. This is the case regardless of whether the call is inside a mask or not. However, in GHC a foreign call can be annotated as interruptible, in which case a throwTo will cause the RTS to attempt to cause the call to return.

• There is no guarantee that the exception will be delivered promptly, although the runtime will endeavour to ensure that arbitrary delays do not occur. In GHC, an exception can only be raised when a thread reaches a safe point, where a safe point is where memory allocation occurs. Some loops do not perform any memory allocation inside the loop and therefore cannot be interrupted by a throwTo.

• Whatever work the target thread was doing when the exception was raised is not lost: the computation is suspended until required by another thread. This is best understood if we imagine an expensive pure computation that is interrupted by an asynchronous exceptio—what we have evaluated so far is not lost.

• If the target of throwTo is the calling thread, then the behaviour is the same as throwIO, except that the exception is thrown as an asynchronous exception. This means that if there is an enclosing pure computation, which would be the case if the current IO operation is inside unsafePerformIO or unsafeInterleaveIO, that computation is not permanently replaced by the exception, but is suspended as if it had received an asynchronous exception.

• Note that if throwTo is called with the current thread as the target, the exception will be thrown even if the thread is currently inside mask or uninterruptibleMask.

This are certainly a lot of subtle points to keep in mind. The main takeaway is that throwTo is synchronized with the thread it throws to (i.e. it does not return till the exception has been raised) and so it may block if the target thread is in mask or doing a foreign call.

### How asynchronous exceptions are implemented

Information on implementation of asynchronous exceptions can be found in the paper Asynchronous exceptions in Haskell. Here we just briefly enumerate the main points for the curious readers:

1. Every thread has a data block associated with it to store thread-specific data. The data includes the masking state we have discussed and a queue of asynchronous exceptions pending delivery.

2. When thread is not in masked state, the queue of asynchronous exceptions is checked at regular intervals and if there are exceptions pending, they are delivered.

4. When getException or catch marks the evaluation stack, it also saves current masking state so it can be restored after handling an exception.

5. Two more marks (or “frames” in the terminology of the above-mentioned paper) are necessary: one for block and another one for unblock. When execution passes either of these, masking state changes accordingly. There are also some rules for the purpose of keeping the evaluation stack from growing unnecessarily, but we will not include them here.

6. throwTo simply places an exception in the queue of target thread then blocks till the exception is delivered.

## Lifting exception-related functionality

The functions from the Control.Exception module (which we advise to examine on your own too, it is well documented) cover all the needs we might have dealing with exceptions. However, they only work in the IO monad. That is a sane choice for the module from the base package as lifting of these functions into arbitrary monad stacks is not always straightforward, and base cannot depend on transformers.

In this section, we are going to look at the exceptions package first. Then we will consider a somewhat more flexible but lower-level alternative in the form of the monad-control package. Finally, we will examine the newer unliftio package.

### Lifting with exceptions

The exceptions package provides three principal type classes:

• MonadThrow for monads that support an analogue of throwIO.

• MonadCatch for monads that provide lifted catch.

• MonadMask for monads that provide lifted mask and uninterruptibleMask functions.

throwM of MonadThrow is defined just as lifted throwIO, except for pure monads, for which it just returns an “empty” value (which in monadic setting also shortcuts execution):

class Monad m => MonadThrow m where
throwM :: Exception e => e -> m a

throwM _ = []

throwM _ = Nothing

throwM = Control.Exception.throwIO

throwM = lift . throwM

-- etc.


Indeed, there is generally no problem with throwing exceptions, so it is rather trivial to define such a class. Arguably, the ability to throw in pure setting is a good thing, although the practice shows that industrial users are mainly interested in lifting throwing functionality through monadic transformers.

Next, here goes MonadCatch:

class MonadThrow m => MonadCatch m where
catch :: Exception e => m a -> (e -> m a) -> m a

catch = Control.Exception.catch

catch = liftCatch catch

-- etc.


Not every monad that implements MonadThrow is an instance of MonadCatch. For example Maybe throws away the information about what went wrong, so there cannot be a MonadCatch instance for it.

On the other hand, if we try to convert something we caught to the expected type and fail (fromException returns Nothing), we need to re-throw. This is why MonadThrow is a superclass of MonadCatch.

What about the implementations? Well, the case of IO looks trivial, as do most others. The instance definitions for StateT and other transformers are more interesting though. We re-use here catch defined for m monad, but lift it using liftCatch, which is:

-- | Lift a @catchE@ operation to the new monad.
liftCatch :: Catch e m (a,s) -> Catch e (StateT s m) a
liftCatch catchE m h =
StateT $\s -> runStateT m s catchE \e -> runStateT (h e) s  Where Catch is just a type synonym: type Catch e m a = m a -> (e -> m a) -> m a  From that, we can see that liftCatch turns a catching function that catches e in monad m which returns a value of type (a,s) into a catching function that catches the same exception e in monad StateT s m and returns just a. This is the sort of code that is type-driven, meaning we can write the implementation mostly by just following the types. If we replace catchE by catch from Control.Exception for simplicity and assume m fixed to IO, the function starts to look more understandable: -- | Lift a @catchE@ operation to the new monad. liftCatch :: Exception e => StateT s IO a -- ^ m, action to run -> (e -> StateT s IO a) -- ^ h, exception handler -> StateT s IO a liftCatch m h = StateT$ \s -> runStateT m s catch \e -> runStateT (h e) s


The result is in StateT, so we start by putting its constructor in place, and so we have the state s. Both arguments of “vanilla” catch must be in plain IO, so we have to run m and h in order to unwrap them and get to the IO monad.

This shows that it is quite feasible to lift catch into most monadic stacks. Let us see about MonadMask.

class MonadCatch m => MonadMask m where
mask                :: ((forall a. m a -> m a) -> m b) -> m b
uninterruptibleMask :: ((forall a. m a -> m a) -> m b) -> m b

mask a = StateT $\s -> mask$ \u -> runStateT (a $q u) s where q :: (m (a, s) -> m (a, s)) -> StateT s m a -> StateT s m a q u (StateT b) = StateT (u . b) uninterruptibleMask a = StateT$ \s -> uninterruptibleMask $\u -> runStateT (a$ q u) s
where
q :: (m (a, s) -> m (a, s)) -> StateT s m a -> StateT s m a
q u (LazyS.StateT b) = StateT (u . b)

-- etc.


The ability to receive/catch exceptions is a logical prerequisite for masking of asynchronous exceptions, hence the MonadCatch (and by implication MonadThrow) is a superclass of MonadMask.

Let us take a look at the case of StateT s m again. mask receives the a :: (forall a. m a -> m a) -> m b argument which expects to get this forall a. m a -> m a—unmasking callback working in the StateT s m monad. Let us see how we provide that. First of all, the result of mask must be in StateT s m, so we put the StateT constructor in place and so we get the state s. Inside the lambda that binds s we use mask of underlying monad m, which we require to be an instance of MonadMask. That mask in turn receives unmasking callback u (remember that the \u -> … lambda is in masked state, so its argument u happens to be the unmasking callback) which works in the inner monad m, not StateT s m. To feed this unmasking callback into a we lift it from m (a, s) -> m (a, s) to StateT s m a -> StateT s m a with q which is rather trivial. Finally, a applied to q u is of the type StateT s m, but the inner mask expects something in m, so we run it. The entire mask $\u -> runStateT (a$ q u) s thing thus ends up being of the right type m (a, s) to be put into StateT $\s -> …. Again, this is an example of type-driven programming. uninterruptibleMask works exactly the same. Having these there basic concepts (throwing, catching, and masking async exceptions) abstracted in that way, the exceptions package seems to be well-equipped to define lifted versions of most everything. For example, here is how bracket is defined: bracket :: MonadMask m => m a -> (a -> m b) -> (a -> m c) -> m c bracket acquire release use = mask$ \unmasked -> do
resource <- acquire
result <- unmasked (use resource) onException release resource -- (1)
_ <- release resource                                            -- (2)
return result


It does make sense, although the generality of the lifting technique until recently did not extend quite long enough to support all useful monads. Also, when we deal with stateful monadic stacks, there may be several different implementations of bracket that differ in how the state is passed around, and depending on your use-case, you may want one or another:

• State from successful computation use affects release. When use fails, release runs with the same state as were passed to use.
• release always runs with the same state as use.
• acquire can/cannot affect state that is passed to use.
• Etc.

As an exercise, figure out how bracket will pass around state in the case of StateT s m monad transformer. Definitions of MonadCatch and MonadMask instances for StateT s m and bracket are shown above. Hint: concentrate on the difference between the lines (1) and (2). You will also need the definition of lifted onException from exceptions:

onException :: MonadCatch m => m a -> m b -> m a
onException action handler = action catchAll \e -> handler >> throwM e
where
catchAll :: MonadCatch m => m a -> (SomeException -> m a) -> m a
catchAll = catch -- specialization of catch that catches all exceptions


Next, let us consider ExceptT e m as our monad. This monad transformer is short-circuiting:

newtype ExceptT e m a = ExceptT (m (Either e a))

return a = ExceptT $return (Right a) m >>= k = ExceptT$ do
a <- runExceptT m
case a of
Left  e -> return (Left e)
Right x -> runExceptT (k x)


In English, if at some point (>>=) gets m with inner value inside Left, we finish immediately ignoring the computation k altogether. This is how monadic bind works for this transformer. Applying this to the definition of bracket we can see that if acquire or use resource happen to return something inside Left, the later actions (including release) have no chance to run—guarantees of bracket are broken.

Until recently MonadMask had to rely on the guarantee that MonadCatch catches all possible exceptions and there is no other way for the computation to exit early—therefore, although there was a possible instance of MonadCatch for ExceptT, it was not considered valid and so there was no MonadMask instance for ExceptT.

Since version 0.9.0 of the exceptions package, the problem is solved by introducing a new method of the MonadMask type class:

generalBracket
:: m a
-- ^ Acquire some resource
-> (a -> ExitCase b -> m c)
-- ^ Release the resource, observing the outcome of the inner action
-> (a -> m b)
-- ^ Inner action to perform with the resource
-> m (b, c)


where ExitCase is:

data ExitCase a
= ExitCaseSuccess a -- ^ Success
| ExitCaseException SomeException -- ^ Exception thrown
| ExitCaseAbort -- ^ Aborting without exceptions, e.g. with Left
deriving Show


The solution is simple: for monads like ExceptT we explicitly handle the case when the inner computation exited by “aborting” computation (e.g. with Left result), not by throwing an exception. Then in the release handler we can clean up in that case as well.

Functions like bracket and finally were previously defined using the mask method of MonadMask, now they are defined via generalBracket which allows them to work in monads like ExceptT properly.

To learn how to write instances see the documentation for generalBracket on Hackage. The documentation is so good that it would be pure duplication to try to add an explanation here as well.

### Lifting with monad-control

The approach used in the monad-control package is to temporarily “unlift” complex monadic stack to some base monad such as IO so we can use the existing functions like catch and bracket from base, then “restore” the monadic stack when we are done.

To understand why we need to do unlifting instead of following a more familiar path—lifting IO computation into a more complex IO-enabled monadic stack—consider the catch function:

catch :: Exception e
=> IO a              -- ^ The computation to run
-> (e -> IO a)       -- ^ Handler to invoke if an exception is raised
-> IO a


Even though we can use liftIO for lifting after applying arguments to catch, it also expects IO a as argument, and here liftIO cannot help, in fact, we need something opposite.

We need a way to unlift to IO a. The good news is that there is a way to unlift without losing information so we can re-construct virtually any monadic stack built from the familiar transformers.

To understand why it is so, look at the definitions of some monad transformers:

newtype ReaderT r m a = ReaderT { runReaderT :: r -> m a }
newtype StateT  s m a = StateT  { runStateT  :: s -> m (a, s) }
newtype WriterT w m a = WriterT { runWriterT :: m (a, w) }


You probably have noticed the common structure inside the wrappers: m containing what we will call state—information that can be used to recreate the transformer. ReaderT is stateless—its state is just the monadic value a (in ReaderT r m a), while StateT carries state s, and so its state is (a, s), similar to WriterT. (The lambdas are not of any concern to us here, as we can wrap anything with a lambda, it is not part of the state.)

Since we cannot escape IO, there is no IOT monad transformer, and so if there is IO in monadic stack, it is always at the bottom. If we remove the newtypes, unlifting a complex monad transformer, we always get a value that produces something like IO st where st is the state we mentioned earlier. For example:

type MyStack r s w a = ReaderT r (StateT s (WriterT w IO)) a

{- isomorphic to -}

r -> s -> IO ((a, s), w)


Simply put, when transformers are stacked, their states are combined. With the example shown, if we are currently in MyStack, we have r and s to apply and get IO ((a, s), w), which we can pass to a function such as catch.

Result of catch, being of the type IO ((a, s), w) can be wrapped back into lambdas and newtypes to the effect that we restore MyStack monad back. This is the idea behind monad-control.

Now that the idea should be clear, let us see what form it takes in the actual library.

Meet the MonadBaseControl b m type class which allows us to lift functions that work in monad b (like catch, b ~ IO) into a more complex monadic stack m:

class MonadBase b m => MonadBaseControl b m | m -> b where -- (1)
type StM m a :: *                                        -- (2)
liftBaseWith :: (RunInBase m b -> b a) -> m a            -- (3)
restoreM :: StM m a -> m a                               -- (4)

type RunInBase m b = forall a. m a -> b (StM m a)          -- (5)

1. MonadBase is a superclass of MonadBaseControl. MonadBase b m is best understood as a generalization of MonadIO where we can lift arbitrary base monad b (not just IO) into m. MonadBase is a fairly trivial tool and will not be of any interest in this chapter. Note that MonadBaseControl is a multi-parameter type class which has the functional dependency m -> b. The functional dependency helps the compiler resolve type ambiguity. It says: if you know what m instantiated to, then you can learn b by searching existing instances for an instance with matching m. It is guaranteed by the compiler that b is uniquely identified by choice of m.

2. StM m a is an associated type of the type class MonadBaseControl. This feature is enabled by the -XTypeFamilies language extension. This is the type of state we have talked about.

3. liftBaseWith is a function that takes another function RunInBase m b -> b a, inside which we generate a value in base monad b. The RunInBase m b argument is the “running” or “unlifting” callback. Look at the definition (5), for a value m a this function strips all monadic layers till the desired base monad b inside which we get the state StM m a.

4. restoreM allows us to restore/replace state in the monad m from a value of the type StM m a.

5. Type-synonym for the unlifiting callback, as explained in (3).

It is beneficial to learn how various instances of MonadBaseControl are defined. Let us start from MonadBaseControl IO IO:

instance MonadBaseControl IO IO where
type StM IO a  = a
liftBaseWith f = f id
restoreM       = return


This should make sense, to unlift IO a we do not need to do anything. Similarly it is not difficult to restore this monad’s state because it has none.

ReaderT is a bit more interesting:

instance MonadBaseControl b m => MonadBaseControl b (ReaderT r m) where
type StM (ReaderT r m) a = StM m (StT t a)
liftBaseWith = defaultLiftBaseWith
restoreM     = defaultRestoreM


Here we step into the territory of monad transformers. The definition builds on the assumption that the inner monad m in ReaderT r m is also an instance of MonadBaseControl b m. To use that instance though, we first need to go from ReaderT r m to m, i.e. we need to unlift a monad transformer, ReaderT. How do we do that?

The answer is: using the second important type class of monad-controlMonadTransControl:

class MonadTrans t => MonadTransControl t where
type StT t a :: *
liftWith :: Monad m => (Run t -> m a) -> t m a -- like liftBaseWith
restoreT :: Monad m => m (StT t a) -> t m a    -- like restoreM

type Run t = forall n b. Monad n => t n b -> n (StT t b)


MonadTransControl to MonadTrans is the same as MonadBaseControl to MonadBase. MonadTrans establishes a “connection” between monad transformer t m and its inner monad m allowing to lift m into t m with lift. Note that lift lifts through only one monadic layer. MonadTransControl also allows us to unlift through one monadic layer. MonadBase, as we have mentioned already, allows us to lift through many layers with a single liftBase call, similarly liftBaseWith lifts through all layers till we reach the base monad.

Knowing this, let us quickly go through the definitions of defaultLiftBaseWith and defaultRestoreM:

defaultLiftBaseWith
=> (RunInBaseDefault t m b -> b a)
-> t m a
defaultLiftBaseWith f = liftWith $\run -> liftBaseWith$ \runInBase ->
f $runInBase . run defaultRestoreM :: (MonadTransControl t, MonadBaseControl b m) => ComposeSt t m a -> t m a defaultRestoreM = restoreT . restoreM  defaultLiftBaseWith unlifts through one monadic layer with liftWith, then we again call liftBaseWith recursively which either happens to use a “terminal” instance like MonadBaseControl IO IO or another instance which unlifts through next monadic layer in exactly the same manner. defaultRestoreM restores state in base monad with restoreM than lifts base monad through one layer with restoreT. defaultLiftBaseWith and defaultRestoreM are used to implement not only ReaderT instance, but also StateT and most other instances because actual transformer-specific logic is defined in MonadTransControl instances: instance MonadTransControl (ReaderT r) where type StT (ReaderT r) a = a liftWith f = ReaderT$ \r ->
f $\t -> runReaderT t r restoreT = ReaderT . const instance MonadTransControl (StateT s) where type StT (StateT s) a = (a, s) liftWith f = StateT$ \s ->
liftM (\x -> (x, s)) (f $\t -> runStateT t s) restoreT = StateT . const  A common idiom with monad-control is to return monadic state from the function passed to liftBaseWith: RunInBase m b -> b (StM m a)  And then use restoreM to immediately restore monadic state. This is captured by the control helper: control :: MonadBaseControl b m => (RunInBase m b -> b (StM m a)) -> m a control f = liftBaseWith f >>= restoreM  Finally, let us show how we can use bracket from Control.Exception with a complex monadic stack: liftedBracket :: StateT s IO a -- ^ Computation to run first (acquire resource) -> (a -> StateT s IO b) -- ^ Computation to run last (release resource) -> (a -> StateT s IO c) -- ^ Computation to run in-between -> StateT s IO c liftedBracket acquire release use = control$ \runInBase ->
bracket
(fst <$> runInBase acquire) -- we dicard state s from (a, s) (runInBase . release) (runInBase . use)  Note that acquire and release cannot modify state s, it is restored from the state returned from runInBase . use. With a bit more effort we could “fix” that: liftedBracket' :: StateT s IO a -- ^ Computation to run first (acquire resource) -> (a -> StateT s IO b) -- ^ Computation to run last (release resource) -> (a -> StateT s IO c) -- ^ Computation to run in-between -> StateT s IO c liftedBracket' acquire release use = control$ \runInBase ->
bracket
(runInBase acquire) -- returns (a, s)
(\(a, s) -> runInBase (put s >> release a))
(\(a, s) -> runInBase (put s >> use a))


State modifications made in acquire now influence both restore and use. State from use is restored, state from restore is discarded (because there is no way to pick that b value from the top level signature). This shows that not only monad-control allows us to do this sort of lifting, it also allows us to control precisely what happens to monadic state.

### Lifting with unliftio

The unliftio package is a newer, simpler, and safer alternative to monad-control. It uses the same idea of unlifting monadic stacks but it takes a bit different form:

class MonadIO m => MonadUnliftIO m where

-- | Capture the current monadic context, providing the ability to
-- run monadic actions in 'IO'.

askUnliftIO = withRunInIO (\run -> return (UnliftIO run))

-- | Convenience function for capturing the monadic context and running an 'IO'
-- action with a runner function. The runner function is used to run a monadic
-- action @m@ in @IO@.

withRunInIO :: ((forall a. m a -> IO a) -> IO b) -> m b
withRunInIO inner = askUnliftIO >>= \u -> liftIO (inner (unliftIO u))

-- | The ability to run any monadic action @m a@ as @IO a@.
--
-- This is more precisely a natural transformation. We need to new
-- datatype (instead of simply using a @forall@) due to lack of
-- support in GHC for impredicative types.

newtype UnliftIO m = UnliftIO { unliftIO :: forall a. m a -> IO a }


withRunInIO is essentially the same thing as liftBaseWith from monad-control, only specialized to IO as base monad (the most common use case, if not the only). The UnliftIO newtype is necessary to be able to return function that has universally quantified arguments (introduced with foralls) in its signature (this is “impredicative polymorphism” the documentation mentions). We only need this in askUnliftIO. The method is rather unique to unliftio, it provides running function forall a. m a -> IO a that one can pass around freely and apply several times, to different a types.

The second difference between the library and monad-control is that it only defines instances of MonadUnliftIO for stateless monadic stacks which are isomorphic to ReaderT over IO. This way the question how to combine state from different branches of computation does not arise.

## How to avoid catching asynchronous exceptions

The last (but not least) issue we have to consider is the inability to tell whether an exception we have caught was synchronous or asynchronous. When we enclose code with a function like catch, it catches everything that matches the exception type:

import Control.Concurrent
import Control.Concurrent.Async
import Control.Exception

catchErrorCall :: IO () -> IO ()
catchErrorCall m = catch m h
where
h :: ErrorCall -> IO ()
h e = putStrLn ("Caught: " ++ displayException e)

synchronous :: IO ()
synchronous = catchErrorCall $throwIO (ErrorCallWithLocation "Synchronously thrown." "") asynchronous :: IO () asynchronous = withAsync (catchErrorCall (threadDelay 1000000))$ \a ->
throwTo (asyncThreadId a) (ErrorCallWithLocation "Asynchronously thrown." "")

λ> synchronous
Caught: Synchronously thrown.
λ> asynchronous
Caught: Asynchronously thrown.


It is often not an issue because normally we prefer to be very specific about type of exception we want to catch. For example, if we want to catch HttpException (assume that it is an exception that an HTTP client library throws when something goes wrong), it is very unlikely that someone will throw it to our thread asynchronously. And even if he/she does, I would argue that it is not a problem with our code.

Things start to get worse when we want to catch all exceptions, that is, we specify SomeException as the type of exception to catch. As we have learned, asynchronous exceptions, like all other exceptions are converted to SomeException before being thrown, so by catching SomeException we catch both asynchronous and synchronous exceptions. This may lead to quite unexpected results, because we probably want to safeguard against issues that happen synchronously inside the code enclosed by catch, not to catch things like ThreadKilled (which should just kill our thread).

One simple way to solve the problem is to assume that the exceptions that are typically thrown asynchronously are wrapped in SomeAsyncException (which is the case for all exceptions from Control.Exception). Here is how this could be done:

import Control.Concurrent
import Control.Concurrent.Async
import Control.Exception

catchOnlySync :: Exception e => IO a -> (e -> IO a) -> IO a
catchOnlySync = catchJust $\e -> -- catchJust from Control.Exception case fromException e of Nothing -> fromException e Just (SomeAsyncException _) -> Nothing catchAllSync :: IO () -> IO () catchAllSync m = catchOnlySync m h where h :: SomeException -> IO () h e = putStrLn ("Caught: " ++ displayException e) synchronous :: IO () synchronous = catchAllSync$
throwIO (ErrorCallWithLocation "A synchronous exception." "")

asynchronous :: IO ()
asynchronous = withAsync (catchAllSync action) \$ \a ->
where
action = do
print "Boo!"

λ> synchronous
Caught: A synchronous exception.
λ> asynchronous -- ThreadKilled wasn't caught


This relies on a convention, not on something the compiler can check or enforce. SomeAsyncException can be thrown synchronously, ErrorCall could be thrown asynchronously—nothing prevents that. If, however, we trust that every exception to be thrown asynchronously has a correctly defined Exception instance that wraps it with SomeAsyncException, we are safe.

Alternative solution is the following: we run the action to catch exceptions from in a separate thread forked with e.g. withAsync (we will refer to it as worker thread) and setup exception handler that catches all exceptions in that thread. When a synchronous exception is thrown there we catch it, pack result in Either SomeException a and return to the main thread, where we can do whatever we like with it. If an asynchronous exception strikes in the main thread, it propagates to the worker thread and so shuts it down.

### safe-exceptions

It would be an omission not to mention the safe-exceptions package which solves the problem of asynchronous vs synchronous exceptions in a systematic way. The library defines functions like catch which only catch synchronous exceptions by testing the type of exception using the SomeAsyncException wrapper.

Next, it follows the following logic (taken from readme of the package):

• If the user is trying to install a cleanup function (such as with bracket or finally), we don’t care if the exception is synchronous or asynchronous: call the cleanup function and then re-throw the exception.

• If the user is trying to catch an exception and recover from it, only catch sync exceptions and immediately rethrow async exceptions.

It should be noted that unliftio also provides exception-handling functions with the same behavior as the ones in safe-exceptions. The functions are lifted with MonadUnliftIO instead of being defined in terms of classes from exceptions. We recommend just using the UnliftIO.Exception module from unliftio.