I wrote data type and instance Monad class. Below is my source code:
data UI a = UI { unUI :: a }
deriving Functor
instance Applicative UI where
pure = UI
m *> k = m >>= \ _ -> k
m <* k = m >>= \ _ -> m
m <*> k = UI $ (unUI m) (unUI k)
instance Monad UI where
m >> k = m >>= \ _ -> k
m >>= k = k $ unUI m
return = UI
But, when i use function like below:
generateUUID :: UI String
generateUUID = do
ruuid <- liftIO $ UV4.nextRandom
return $ UV.toString ruuid
I encounter problems with memoization!
Is it poosible to do something?
By using unsafePerformIO to hide the presence of effects in IO actions you are deliberately (and most probably illegally) entering the realm of pure functions. In that realm it is allowed to memoize / refactor. The compiler will usually try to use the purity to full extent to avoid redundant work in runtime. Is there a real reason why you would hide the impurity, especially in something called UI, where one can expect interaction with outside world (human)? This could indicate bad design. Your UI seems to be the same as Identity which is the simplest functor without any impure effects.
Related
I will preface this by saying that I am a novice Haskell programmer (tinkered with it sporadically over the years) but I have a fair few years on the counter when it comes to OOO and imperative programming. I am currently learning how to use monads & combining them via the use of monad transformers (assuming I've got the correct term).
While I am able to assemble/chain things together, I am finding it hard to build an intuition for what the best way and style is & how best to assemble/write those interactions.
Specifically, I am curious to know what the best practice (or at least your practice) is for using lift/liftIO and any flavour in between & if there is way (and benefit) to hide them as I am finding them rather 'noisy'.
Below is an example snippet which I have put together to illustrate what I mean :
consumeRenderStageGL' :: RenderStage -> StateT RenderStageContext IO ()
consumeRenderStageGL' r = do
pushDebugGroupGL (name r)
liftIO $ consumePrologueGL ( prologue r )
liftIO $ consumeEpilogueGL ( epilogue r )
consumeStreamGL ( stream r )
liftIO $ popDebugGroupGL
Some of the functions it calls make use of the state monad :
pushDebugGroupGL :: String -> StateT RenderStageContext IO ()
pushDebugGroupGL tag = do
currentDebugMessageID <- gets debugMessageID
liftIO $ GL.pushDebugGroup GL.DebugSourceApplication (GL.DebugMessageID currentDebugMessageID) tag
modify (\fc -> fc { debugMessageID = (currentDebugMessageID + 1) })
consumeStreamGL :: Stream -> StateT RenderStageContext IO ()
consumeStreamGL s = do
mapM_ consumeTokenGL s
logGLErrors
While most don't and just live in IO (meaning they have to be lifted):
consumePrologueGL :: Prologue -> IO ()
consumePrologueGL p = do
colourClearFlag <- setupAndReturnClearFlag GL.ColorBuffer ( clearColour p ) (\(Colour4 r g b a) -> GL.clearColor $= (GL.Color4 r g b a))
depthClearFlag <- setupAndReturnClearFlag GL.DepthBuffer ( clearDepth p ) (\d -> GL.clearDepthf $= d)
stencilClearFlag <- setupAndReturnClearFlag GL.StencilBuffer ( clearStencil p ) (\s -> GL.clearStencil $= fromIntegral s)
GL.clear $ catMaybes [colourClearFlag, depthClearFlag, stencilClearFlag]
logGLErrors
where
setupAndReturnClearFlag flag mValue function = case mValue of
Nothing -> return Nothing
Just value -> (function value) >> return (Just flag)
My question is : is there any way to hide the liftIO in consumeRenderStageGL' and more importantly would this be a good or a bad idea?
One way I can think of hiding/getting rid of the liftIO is to bring both my consumePrologueGL & consumeEpilogueGL into my state monad but this seems wrong in the sense that those function do not need (and should not) interact with it ; all this just to reduce code noise.
The other option I can think of is to simply create the lifted version of the functions and call them in consumeRenderStageGL' - this would reduce the code noise but be identical in execution/evaluation.
The third option, which is how my logGLErrors works is that I have used a type class which has an instance defined for both IO & my state monads.
I look forward to reading what your opinions, advices and practices are.
Thanks in advance!
There are a few solutions. A common one is to make your basic actions MonadIO m => m … instead of IO …:
consumePrologueGL :: (MonadIO m) => Prologue -> m ()
consumePrologueGL p = liftIO $ do
…
Then you can use them in StateT RenderStageContext IO () without wrapping, due to MonadIO m => MonadIO (StateT s m), and of course MonadIO IO where liftIO is the identity function.
You can also abstract over the StateT part using MonadState from mtl, so if you add another transformer above/below it, you won’t have the same issue of lifting from/to StateT.
pushDebugGroupGL
:: (MonadIO m, MonadState RenderStageContext m)
=> String -> m ()
Generally, a concrete stack of transformers types is fine, it just helps to wrap all your basic operations for convenience so that all the lifts are in one place.
mtl helps remove the lift noise from your code altogether, and working in a polymorphic type m means you have to declare which effects a function actually uses, and can substitute different implementations of all the effects (except for MonadIO) for testing. Using monad transformers as an effect system like this is great if you have few types of effects; if you want something finer-grained or more flexible, you’ll start hitting the pain points that make people reach for algebraic effects instead.
It’s also worth assessing whether you need StateT over IO. Typically if you’re in IO, you don’t need the pure state offered by StateT, so instead of StateT MutableState IO you might as well use ReaderT (IORef MutableState) IO.
It’s also possible to make that (or a newtype wrapper for it) an instance of MonadState MutableState, so your code using get/put/modify wouldn’t even need to change:
{-# Language GeneralizedNewtypeDeriving #-}
import Data.Coerce (coerce)
newtype MutT s m a = MutT
{ getMutT :: ReaderT (IORef s) m a }
deriving
( Alternative
, Applicative
, Functor
, Monad
, MonadIO
, MonadTrans
)
evalMutT :: MutT s m a -> IORef s -> m a
evalMutT = coerce
instance (MonadIO m) => MonadState s (MutT s m) where
state f = MutT $ do
r <- ask
liftIO $ do
-- NB: possibly lazier than you want.
(a, s) <- f <$> readIORef r
a <$ writeIORef r s
This combo of ReaderT & IO is a pretty common design pattern.
I have a concern regarding how far the introduction of IO trickles through a program. Say a function deep within my program is altered to include some IO; how do I isolate this change to not have to also change every function in the path to IO as well?
For instance, in a simplified example:
a :: String -> String
a s = (b s) ++ "!"
b :: String -> String
b s = '!':(fetch s)
fetch :: String -> String
fetch s = reverse s
main = putStrLn $ a "hello"
(fetch here could more realistically be reading a value from a static Map to give as its result)
But say if due to some business logic change, I needed to lookup the value returned by fetch in some database (which I can exemplify here with a call to getLine):
fetch :: String -> IO String
fetch s = do
x <- getLine
return $ s ++ x
So my question is, how to prevent having to rewrite every function call in this chain?
a :: String -> IO String
a s = fmap (\x -> x ++ "!") (b s)
b :: String -> IO String
b s = fmap (\x -> '!':x) (fetch s)
fetch :: String -> IO String
fetch s = do
x <- getLine
return $ s ++ x
main = a "hello" >>= putStrLn
I can see that refactoring this would be much simpler if the functions themselves did not depend on each other. That is fine for a simple example:
a :: String -> String
a s = s ++ "!"
b :: String -> String
b s = '!':s
fetch :: String -> IO String
fetch s = do
x <- getLine
return $ s ++ x
doit :: String -> IO String
doit s = fmap (a . b) (fetch s)
main = doit "hello" >>= putStrLn
but I don't know if that is necessarily practical in more complicated programs.
The only way I've found thus far to really isolate an IO addition like this is to use unsafePerformIO, but, by its very name, I don't want to do that if I can help it. Is there some other way to isolate this change? If the refactoring is substantial, I would start to feel inclined to avoid having to do it (especially under deadlines, etc).
Thanks for any advice!
Here are a few methods I use.
Reduce dependencies on effects by inverting control. (One of the methods you described in your question.) That is, execute the effects outside and pass the results (or functions with those results partially applied) into pure code. Instead of having main → a → b → fetch, have main → fetch and then main → a → b:
a :: String -> String
a f = b f ++ "!"
b :: String -> String
b f = '!' : f
fetch :: String -> IO String
fetch s = do
x <- getLine
return $ s ++ x
main = do
f <- fetch "hello"
putStrLn $ a f
For more complex cases of this, where you need to thread an argument to do this sort of “dependency injection” through many levels, Reader/ReaderT lets you abstract over the boilerplate.
Write pure code that you expect might need effects in monadic style from the start. (Polymorphic over the choice of monad.) Then if you do eventually need effects in that code, you don’t need to change the implementation, only the signature.
a :: (Monad m) => String -> m String
a s = (++ "!") <$> b s
b :: (Monad m) => String -> m String
b s = ('!' :) <$> fetch s
fetch :: (Monad m) => String -> m String
fetch s = pure (reverse s)
Since this code works for any m with a Monad instance (or in fact just Applicative), you can run it directly in IO, or purely with the “dummy” monad Identity:
main = putStrLn =<< a "hello"
main = putStrLn $ runIdentity $ a "hello"
Then as you need more effects, you can use “mtl style” (as #dfeuer’s answer describes) to enable effects on an as-needed basis, or if you’re using the same monad stack everywhere, just replace m with that concrete type, e.g.:
newtype Fetch a = Fetch { unFetch :: IO a }
deriving (Applicative, Functor, Monad, MonadIO)
a :: String -> Fetch String
a s = pure (b s ++ "!")
b :: String -> Fetch String
b s = ('!' :) <$> fetch s
fetch :: String -> Fetch String
fetch s = do
x <- liftIO getLine
return $ s ++ x
main = putStrLn =<< unFetch (a "hello")
The advantage of mtl style is that you can have multiple different implementations of your effects. That makes things like testing & mocking easy, since you can reuse the logic but run it with different “handlers” for production & testing. In fact, you can get even more flexibility (at the cost of some runtime performance) using an algebraic effects library such as freer-effects, which not only lets the caller change how each effect is handled, but also the order in which they’re handled.
Roll up your sleeves and do the refactoring. The compiler will tell you everywhere that needs to be updated anyway. After enough times doing this, you’ll naturally end up recognising when you’re writing code that will require this refactoring later, so you’ll consider effects from the beginning and not run into the problem.
You’re quite right to doubt unsafePerformIO! It’s not just unsafe because it breaks referential transparency, it’s unsafe because it can break type, memory, and concurrency safety as well—you can use it to coerce any type to any other, cause a segfault, or cause deadlocks and concurrency errors that would ordinarily be impossible. You’re telling the compiler that some code is pure, so it’s going to assume it can do all the transformations it does with pure code—such as duplicating, reordering, or even dropping it, which may completely change the correctness and performance of your code.
The main legitimate use cases for unsafePerformIO are things like using the FFI to wrap foreign code (that you know is pure), or doing GHC-specific performance hacks; stay away from it otherwise, since it’s not meant as an “escape hatch” for ordinary code.
First off, the refactoring doesn't tend to be as bad as you might imagine. Once you make the first change, the type checker will point you to the next few, and so on. But suppose you have a reason to suspect from the start that you might need some extra capability to make a function go. A common way to do this (called mtl-style, after the monad transformer library) is to express your needs in a constraint.
class Monad m => MonadFetch m where
fetch :: String -> m String
a :: MonadFetch m => String -> m String
a s = fmap (\x -> x ++ "!") (b s)
b :: MonadFetch m => String -> m String
b s = fmap (\x -> '!':x) (fetch s)
instance MonadFetch IO where
-- fetch :: String -> IO String
fetch s = do
x <- getLine
return $ s ++ x
instance MonadFetch Identity where
-- fetch :: String -> Identity String
fetch = Identity . reverse
You're no longer tied to a particular monad: you just need one that can fetch. Code operating on an arbitrary MonadFetch instance is pure, except that it can fetch.
I'm trying to work out if it's possible to write an abstraction for the following situation. Suppose I have a type a with function a -> m Bool e.g. MVar Bool and readMVar. To abstract this concept out I create a newtype wrapper for the type and its function:
newtype MPredicate m a = MPredicate (a,a -> m Bool)
I can define a fairly simple operation like so:
doUnless :: (Monad m) => Predicate m a -> m () -> m ()
doUnless (MPredicate (a,mg)) g = mg a >>= \b -> unless b g
main = do
b <- newMVar False
let mpred = MPredicate (b,readMVar)
doUnless mpred (print "foo")
In this case doUnless would print "foo". Aside: I'm not sure whether a type class might be more appropriate to use instead of a newtype.
Now take the code below, which outputs an incrementing number then waits a second and repeats. It does this until it receives a "turn off" instruction via the MVar.
foobar :: MVar Bool -> IO ()
foobar mvb = foobar' 0
where
foobar' :: Int -> IO ()
foobar' x = readMVar mvb >>= \b -> unless b $ do
let x' = x + 1
print x'
threadDelay 1000000
foobar' x'
goTillEnter :: MVar Bool -> IO ()
goTillEnter mv = do
_ <- getLine
_ <- takeMVar mv
putMVar mv True
main = do
mvb <- newMVar False
forkIO $ foobar mvb
goTillEnter mvb
Is it possible to refactor foobar so that it uses MPredicate and doUnless?
Ignoring the actual implementation of foobar' I can think of a simplistic way of doing something similar:
cycleUnless :: x -> (x -> x) -> MPredicate m a -> m ()
cycleUnless x g mp = let g' x' = doUnless mp (g' $ g x')
in g' $ g x
Aside: I feel like fix could be used to make the above neater, though I still have trouble working out how to use it
But cycleUnless won't work on foobar because the type of foobar' is actually Int -> IO () (from the use of print x').
I'd also like to take this abstraction further, so that it can work threading around a Monad. With stateful Monads it becomes even harder. E.g.
-- EDIT: Updated the below to show an example of how the code is used
{- ^^ some parent function which has the MVar ^^ -}
cycleST :: (forall s. ST s (STArray s Int Int)) -> IO ()
cycleST sta = readMVar mvb >>= \b -> unless b $ do
n <- readMVar someMVar
i <- readMVar someOtherMVar
let sta' = do
arr <- sta
x <- readArray arr n
writeArray arr n (x + i)
return arr
y = runSTArray sta'
print y
cycleST sta'
I have something similar to the above working with RankNTypes. Now there's the additional problem of trying to thread through the existential s, which is not likely to type check if threaded around through an abstraction the likes of cycleUnless.
Additionally, this is simplified to make the question easier to answer. I also use a set of semaphores built from MVar [MVar ()] similar to the skip channel example in the MVar module. If I can solve the above problem I plan to generalize the semaphores as well.
Ultimately this isn't some blocking problem. I have 3 components of the application operating in a cycle off the same MVar Bool but doing fairly different asynchronous tasks. In each one I have written a custom function that performs the appropriate cycle.
I'm trying to learn the "don't write large programs" approach. What I'd like to do is refactor chunks of code into their own mini libraries so that I'm not building a large program but assembling lots of small ones. But so far this particular abstraction is escaping me.
Any thoughts on how I might go about this are very much appreciated!
You want to cleanly combine a stateful action having side effects, a delay, and an independent stopping condition.
The iterative monad transformer from the free package can be useful in these cases.
This monad transformer lets you describe a (possibly nonending) computation as a series of discrete steps. And what's better, it let's you interleave "stepped" computations using mplus. The combined computation stops when any of the individual computations stops.
Some preliminary imports:
import Data.Bool
import Control.Monad
import Control.Monad.Trans
import Control.Monad.Trans.Iter (delay,untilJust,IterT,retract,cutoff)
import Control.Concurrent
Your foobar function could be understood as a "sum" of three things:
A computation that does nothing but reading from the MVar at each step, and finishes when the Mvar is True.
untilTrue :: (MonadIO m) => MVar Bool -> IterT m ()
untilTrue = untilJust . liftM guard . liftIO . readMVar
An infinite computation that takes a delay at each step.
delays :: (MonadIO m) => Int -> IterT m a
delays = forever . delay . liftIO . threadDelay
An infinite computation that prints an increasing series of numbers.
foobar' :: (MonadIO m) => Int -> IterT m a
foobar' x = do
let x' = x + 1
liftIO (print x')
delay (foobar' x')
With this in place, we can write foobar as:
foobar :: (MonadIO m) => MVar Bool -> m ()
foobar v = retract (delays 1000000 `mplus` untilTrue v `mplus` foobar' 0)
The neat thing about this is that you can change or remove the "stopping condition" and the delay very easily.
Some clarifications:
The delay function is not a delay in IO, it just tells the iterative monad transformer to "put the argument in a separate step".
retract brings you back from the iterative monad transformer to the base monad. It's like saying "I don't care about the steps, just run the computation". You can combine retract with cutoff if you want to limit the maximum number of iterations.
untilJustconverts a value m (Maybe a) of the base monad into a IterT m a by retrying in each step until a Just is returned. Of course, this risks non-termination!
MPredicate is rather superfluous here; m Bool can be used instead. The monad-loops package contains plenty of control structures with m Bool conditions. whileM_ in particular is applicable here, although we need to include a State monad for the Int that we're threading around:
import Control.Monad.State
import Control.Monad.Loops
import Control.Applicative
foobar :: MVar Bool -> IO ()
foobar mvb = (`evalStateT` (0 :: Int)) $
whileM_ (not <$> lift (readMVar mvb)) $ do
modify (+1)
lift . print =<< get
lift $ threadDelay 1000000
Alternatively, we can use a monadic version of unless. For some reason monad-loops doesn't export such a function, so let's write it:
unlessM :: Monad m => m Bool -> m () -> m ()
unlessM mb action = do
b <- mb
unless b action
It's somewhat more convenient and more modular in a monadic setting, since we can always go from a pure Bool to m Bool, but not vice versa.
foobar :: MVar Bool -> IO ()
foobar mvb = go 0
where
go :: Int -> IO ()
go x = unlessM (readMVar mvb) $ do
let x' = x + 1
print x'
threadDelay 1000000
go x'
You mentioned fix; sometimes people indeed use it for ad-hoc monadic loops, for example:
printUntil0 :: IO ()
printUntil0 =
putStrLn "hello"
fix $ \loop -> do
n <- fmap read getLine :: IO Int
print n
when (n /= 0) loop
putStrLn "bye"
With some juggling it's possible to use fix with multi-argument functions. In the case of foobar:
foobar :: MVar Bool -> IO ()
foobar mvb = ($(0 :: Int)) $ fix $ \loop x -> do
unlessM (readMVar mvb) $ do
let x' = x + 1
print x'
threadDelay 1000000
loop x'
I'm not sure what's your MPredicate is doing.
First, instead of newtyping a tuple, it's probably better to use a normal algebric data type
data MPredicate a m = MPredicate a (a -> m Bool)
Second, the way you use it, MPredicate is equivalent to m Bool.
Haskell is lazzy, therefore there is no need to pass, a function and it's argument (even though
it's usefull with strict languages). Just pass the result, and the function will be called when needed.
I mean, instead of passing (x, f) around, just pass f x
Of course, if you are not trying to delay the evaluation and really need at some point, the argument or the function as well as the result, a tuple is fine.
Anyway, in the case your MPredicate is only there to delay the function evaluation, MPredicat reduces to m Bool and doUnless to unless.
Your first example is strictly equivalent :
main = do
b <- newMVar False
unless (readMVar b) (print "foo")
Now, if you want to loop a monad until a condition is reach (or equivalent) you should have a look at the monad-loop package. What you are looking it at is probably untilM_ or equivalent.
I am working on designing a Haskell module that is designed solve a math problem, which may have various parameterizations. The module exports a function:
run_and_output_parameterization :: ProblemParams -> String -> IO ()
where the idea is that ProblemParams objects will be generated in some "controller" and called as follows:
map (\(pp, name) -> run_and_output_parameterization pp name) (zip pp_list names_list)
My question is, within the module, there are some functions such as indexing functions which I would like to partially apply for a particular parameterization. For example,
evenly_spaced_point_approx :: Int -> Int -> Int -> Double -> Double -> Int
evenly_spaced_point_approx xmin xmax xstep_i xstep_d target = pt
where
pt = max (min (round (target/xstep_d) * xstep_i) xmax) xmin
evenly_spaced_si_approx target = evenly_spaced_point_approx (_pp_si_min pp) (_pp_si_max pp) (_pp_nstep_s pp) (_pp_nstep_sd pp) target
evenly_spaced_wi_approx target = evenly_spaced_point_approx (_pp_wi_min pp) (_pp_wi_max pp) (_pp_nstep_w pp) (_pp_nstep_wd pp) target
I would like to use the functions evenly_spaced_si_approx and evenly_spaced_wi_approx within the module for a particular ProblemParameter data structure (called pp).
Is there a way I can tell Haskell to partially apply all dependent functions, or is this something I have to do by hand? Also, my apologies for being imprecise with the functional programming terminology.
If you have a lot of functions that need the same parameter, and that's the only (or last) parameter they take then you can take advantage of the Monad instance for (->) r. Alternatively, you can wrap everything in the Reader monad, whose definition is basically
newtype Reader r a = Reader { runReader :: r -> a }
instance Monad (Reader r) where
return a = Reader $ \_ -> a
m >>= f = Reader $ \r -> runReader (f (runReader m r)) r
Which, compared to the Monad instance for (->) r:
instance Monad ((->) r) where
return a = const a
m >>= f = \r -> f (m r) r
How can you use this? For example, if you had a single parameter pp :: ProblemParams, then you could write functions as
-- Some declarations
smallFunc1 :: ProblemParams -> Double
smallFunc2 :: ProblemParams -> Double
smallFunc3 :: Int -> ProblemParams -> Double
doStuff :: ProblemParams -> Double -- Just a random return type
doStuff = do -- Keep the parameter implicit
result1 <- smallFunc1 -- The ProblemParams are automatically passed
result2 <- smallFunc2
result3 <- smallFunc3 10
return $ result1 + result2 + result3
And this works quite well, you just have to make sure that all of smallFunc1, smallFunc2, and smallFunc3 10 take a ProblemParams as their last parameter (notice the inclusion of 10 with smallFunc3). The Monad instance for functions will pass that parameter implicitly in all the binds. Think of it as returning a value before that value has been computed. You get to bind the "future" return value of smallFunc1 to result1.
Alternatively, you could use the Reader monad:
type Problem a = Reader ProblemParams a
reader :: (r -> a) -> Reader r a
reader f = do
r <- ask
return $ f r
-- reader f = ask >>= return . f
smallFunc1' :: Problem Double
smallFunc1' = reader smallFunc1
smallFunc2' :: Problem Double
smallFunc2' = reader smallFunc2
smallFunc3' :: Int -> Problem Double
smallFunc3' i = reader (smallFunc3 i)
doStuff :: ProblemParams -> Double
doStuff pp = flip runReader pp $ do
result1 <- smallFunc1'
result2 <- smallFunc2'
result3 <- smallFunc3' 10
return $ result1 + result2 + result3
The reason why we have to create a reader function that lifts our primitives to the Reader monad is that Reader is actually defined in terms of the transformer ReaderT as
type Reader r a = ReaderT r Identity a
around the Identity monad.
Whichever you decide to use is up to you. I think most people would be more familiar with the Reader version, and if you decided to stack on some more transformers later it'd be really simple. The Reader monad basically helps to make the function signatures look monadic, since ProblemParams -> Double doesn't look like a normal monad signature. It will use a bit more code, but it may be that it helps you reason about your program.
Note: I haven't run any of this code, so be warned that small errors may exist. If anyone spots a problem, just let me know and I'll fix it.
An example with the Par monad and ReaderT:
type App a = ReaderT ProblemParams Par a
runApp :: ProblemParams -> App a -> a
runApp pp app = runPar $ runReaderT app pp
Then you can simply use lift to raise Par actions to App actions:
parReader :: (ProblemParams -> Par a) -> App a
parReader f = do
r <- ask
lift $ f r
-- parReader f = ask >>= lift . f
doStuff :: ProblemParams -> Double
doStuff pp = runApp pp $ do
result1 <- parReader parAction1
result2 <- parReader parAction2
result3 <- parReader (parAction3 10)
return $ result1 + result2 + result3
I'm about 99% sure that the monad stack will not affect your parallelism at all, since the Reader monad executes first, essentially applying your ProblemParams to all the functions, then it runs the Par action.
Scenario: I have an interpreter that builds up values bottom-up from an AST. Certain nodes come with permissions -- additional boolean expressions. Permission failures should propagate, but if a node above in the AST comes with a permission, a success can recover the computation and stop the propagation of the error.
At first I thought the Error MyError MyValue monad would be enough: one of the members of MyError could be PermError, and I could use catchError to recover from PermError if the second check succeeds. However, MyValue is gone by the time I get to the handler. I guess there could ultimately be a way of having PermError carry a MyValue field so that the handler could restore it, but it would probably be ugly and checking for an exception at each step would defeat the concept of an exceptional occurrence.
I'm trying to think of an alternative abstraction. Basically I have to return a datatype Either AllErrorsExceptPermError (Maybe PermError, MyValue) or more simply (Maybe AllErrors, MyValue) (the other errors are unrecoverable and fit the error monad pretty well) and I'm looking for something that would save me from juggling the tuple around, since there seems to be a common pattern in how the operations are chained. My haskell knowledge only goes so far. How would you use haskell to your advantage in this situation?
While I write this I came up with an idea (SO is a fancy rubber duck): a Monad that that handles internally a type (a, b) (and ultimately returns it when the monadic computation terminates, there has to be some kind of runMyMonad), but lets me work with the type b directly as much as possible. Something like
data T = Pass | Fail | Nothing
instance Monad (T , b) where
return v = (Nothing, v)
(Pass, v) >>= g = let (r', v') = g v in (if r' == Fail then Fail else Pass, v')
(Fail, v) >>= g = let (r', v') = g v in (if r' == Pass then Pass else Fail, v')
(Nothing, _) >>= g = error "This should not have been propagated, all chains should start with Pass or Fail"
errors have been simplified into T, and the instance line probably has a syntax error, but you should get the idea. Does this make sense?
I think you can use State monad for permissions and value calculation and wrap that inside ErrorT monad transformer to handle the errors. Below is such an example which shows the idea , here the calculation is summing up a list, permissions are number of even numbers in the list and error condition is when we see 0 in the list.
import Control.Monad.Error
import Control.Monad.State
data ZeroError = ZeroError String
deriving (Show)
instance Error ZeroError where
fun :: [Int] -> ErrorT ZeroError (State Int) Int
fun [] = return 0
fun (0:xs) = throwError $ ZeroError "Zero found"
fun (x:xs) = do
i <- get
put $ (if even(x) then i+1 else i)
z <- fun xs
return $ x+z
main = f $ runState (runErrorT $ fun [1,2,4,5,10]) 0
where
f (Left e,evens) = putStr $ show e
f (Right r,evens) = putStr $ show (r,evens)