Haskell - Actor based mutability - multithreading

I'm working on a haskell network application and I use the actor pattern to manage multithreading. One thing I came across is how to store for example a set of client sockets/handles. Which of course must be accessible for all threads and can change when clients log on/off.
Since I'm coming from the imperative world I thought about some kind of lock-mechanism but when I noticed how ugly this is I thought about "pure" mutability, well actually it's kind of pure:
import Control.Concurrent
import Control.Monad
import Network
import System.IO
import Data.List
import Data.Maybe
import System.Environment
import Control.Exception
newStorage :: (Eq a, Show a) => IO (Chan (String, Maybe (Chan [a]), Maybe a))
newStorage = do
q <- newChan
forkIO $ storage [] q
return q
newHandleStorage :: IO (Chan (String, Maybe (Chan [Handle]), Maybe Handle))
newHandleStorage = newStorage
storage :: (Eq a, Show a) => [a] -> Chan (String, Maybe (Chan [a]), Maybe a) -> IO ()
storage s q = do
let loop = (`storage` q)
(req, reply, d) <- readChan q
print ("processing " ++ show(d))
case req of
"add" -> loop ((fromJust d) : s)
"remove" -> loop (delete (fromJust d) s)
"get" -> do
writeChan (fromJust reply) s
loop s
store s d = writeChan s ("add", Nothing, Just d)
unstore s d = writeChan s ("remove", Nothing, Just d)
request s = do
chan <- newChan
writeChan s ("get", Just chan, Nothing)
readChan chan
The point is that a thread (actor) is managing a list of items and modifies the list according to incoming requests. Since thread are really cheap I thought this could be a really nice functional alternative.
Of course this is just a prototype (a quick dirty proof of concept).
So my question is:
Is this a "good" way of managing shared mutable variables (in the actor world) ?
Is there already a library for this pattern ? (I already searched but I found nothing)
Regards,
Chris

Here is a quick and dirty example using stm and pipes-network. This will set up a simple server that allows clients to connect and increment or decrement a counter. It will display a very simple status bar showing the current tallies of all connected clients and will remove client tallies from the bar when they disconnect.
First I will begin with the server, and I've generously commented the code to explain how it works:
import Control.Concurrent.STM (STM, atomically)
import Control.Concurrent.STM.TVar
import qualified Data.HashMap.Strict as H
import Data.Foldable (forM_)
import Control.Concurrent (forkIO, threadDelay)
import Control.Monad (unless)
import Control.Monad.Trans.State.Strict
import qualified Data.ByteString.Char8 as B
import Control.Proxy
import Control.Proxy.TCP
import System.IO
main = do
hSetBuffering stdout NoBuffering
{- These are the internal data structures. They should be an implementation
detail and you should never expose these references to the
"business logic" part of the application. -}
-- I use nRef to keep track of creating fresh Ints (which identify users)
nRef <- newTVarIO 0 :: IO (TVar Int)
{- hMap associates every user (i.e. Int) with a counter
Notice how I've "striped" the hash map by storing STM references to the
values instead of storing the values directly. This means that I only
actually write the hashmap when adding or removing users, which reduces
contention for the hash map.
Since each user gets their own unique STM reference for their counter,
modifying counters does not cause contention with other counters or
contention with the hash map. -}
hMap <- newTVarIO H.empty :: IO (TVar (H.HashMap Int (TVar Int)))
{- The following code makes heavy use of Haskell's pure closures. Each
'let' binding closes over its current environment, which is safe since
Haskell is pure. -}
let {- 'getCounters' is the only server-facing command in our STM API. The
only permitted operation is retrieving the current set of user
counters.
'getCounters' closes over the 'hMap' reference currently in scope so
that the server never needs to be aware about our internal
implementation. -}
getCounters :: STM [Int]
getCounters = do
refs <- fmap H.elems (readTVar hMap)
mapM readTVar refs
{- 'init' is the only client-facing command in our STM API. It
initializes the client's entry in the hash map and returns two
commands: the first command is what the client calls to 'increment'
their counter and the second command is what the client calls to log
off and delete
'delete' command.
Notice that those two returned commands each close over the client's
unique STM reference so the client never needs to be aware of how
exactly 'init' is implemented under the hood. -}
init :: STM (STM (), STM ())
init = do
n <- readTVar nRef
writeTVar nRef $! n + 1
ref <- newTVar 0
modifyTVar' hMap (H.insert n ref)
let incrementRef :: STM ()
incrementRef = do
mRef <- fmap (H.lookup n) (readTVar hMap)
forM_ mRef $ \ref -> modifyTVar' ref (+ 1)
deleteRef :: STM ()
deleteRef = modifyTVar' hMap (H.delete n)
return (incrementRef, deleteRef)
{- Now for the actual program logic. Everything past this point only uses
the approved STM API (i.e. 'getCounters' and 'init'). If I wanted I
could factor the above approved STM API into a separate module to enforce
the encapsulation boundary, but I am lazy. -}
{- Fork a thread which polls the current state of the counters and displays
it to the console. There is a way to implement this without polling but
this gets the job done for now.
Most of what it is doing is just some simple tricks to reuse the same
console line instead of outputting a stream of lines. Otherwise it
would be just:
forkIO $ forever $ do
ns <- atomically getCounters
print ns
-}
forkIO $ (`evalStateT` 0) $ forever $ do
del <- get
lift $ do
putStr (replicate del '\b')
putStr (replicate del ' ' )
putStr (replicate del '\b')
ns <- lift $ atomically getCounters
let str = show ns
lift $ putStr str
put $! length str
lift $ threadDelay 10000
{- Fork a thread for each incoming connection, which listens to the client's
commands and translates them into 'STM' actions -}
serve HostAny "8080" $ \(socket, _) -> do
(increment, delete) <- atomically init
{- Right now, just do the dumb thing and convert all keypresses into
increment commands, with the exception of the 'q' key, which will
quit -}
let handler :: (Proxy p) => () -> Consumer p Char IO ()
handler () = runIdentityP loop
where
loop = do
c <- request ()
unless (c == 'q') $ do
lift $ atomically increment
loop
{- This uses my 'pipes' library. It basically is a high-level way to
say:
* Read binary packets from the socket no bigger than 4096 bytes
* Get the first character from each packet and discard the rest
* Handle the character using the above 'handler' function -}
runProxy $ socketReadS 4096 socket >-> mapD B.head >-> handler
{- The above pipeline finishes either when the socket closes or
'handler' stops looping because it received a 'q'. Either case means
that the client is done so we log them out using 'delete'. -}
atomically delete
Next up is the client, which simply opens a connections and forwards all key presses as single packets:
import Control.Monad
import Control.Proxy
import Control.Proxy.Safe
import Control.Proxy.TCP.Safe
import Data.ByteString.Char8 (pack)
import System.IO
main = do
hSetBuffering stdin NoBuffering
hSetEcho stdin False
{- Again, this uses my 'pipes' library. It basically says:
* Read characters from the console using 'commands'
* Pack them into a binary format
* send them to a server running at 127.0.0.1:8080
This finishes looping when the user types a 'q' or the connection is
closed for whatever reason.
-}
runSafeIO $ runProxy $ runEitherK $
try . commands
>-> mapD (\c -> pack [c])
>-> connectWriteD Nothing "127.0.0.1" "8080"
commands :: (Proxy p) => () -> Producer p Char IO ()
commands () = runIdentityP loop
where
loop = do
c <- lift getChar
respond c
unless (c == 'q') loop
It's pretty simple: commands generates a stream of Chars, which then get converted to ByteStrings and then sent as packets to the server.
If you run the server and a few clients and have them each type in a few keys, your server display will output a list showing how many keys each client typed:
[1,6,4]
... and if some of the clients disconnect they will be removed from the list:
[1,4]
Note that the pipes component of these examples will simplify greatly in the upcoming pipes-4.0.0 release, but the current pipes ecosystem still gets the job done as is.

First, I'd definitely recommend using your own specific data type for representing commands. When using (String, Maybe (Chan [a]), Maybe a) a buggy client can crash your actor simply by sending an unknown command or by sending ("add", Nothing, Nothing), etc. I'd suggest something like
data Command a = Add a | Remove a | Get (Chan [a])
Then you can pattern match on commands in storage in a save way.
Actors have their advantages, but also I feel that they have some drawbacks. For example, getting an answer from an actor requires sending it a command and then waiting for a reply. And the client can't be completely sure that it gets a reply and that the reply will be of some specific type - you can't say I want only answers of this type (and how many of them) for this particular command.
So as an example I'll give a simple, STM solution. It'd be better to use a hash table or a (balanced tree) set, but since Handle implements neither Ord nor Hashable, we can't use these data structures, so I'll keep using lists.
module ThreadSet (
TSet, add, remove, get
) where
import Control.Monad
import Control.Monad.STM
import Control.Concurrent.STM.TVar
import Data.List (delete)
newtype TSet a = TSet (TVar [a])
add :: (Eq a) => a -> TSet a -> STM ()
add x (TSet v) = readTVar v >>= writeTVar v . (x :)
remove :: (Eq a) => a -> TSet a -> STM ()
remove x (TSet v) = readTVar v >>= writeTVar v . delete x
get :: (Eq a) => TSet a -> STM [a]
get (TSet v) = readTVar v
This module implements a STM based set of arbitrary elements. You can have multiple such sets and use them together in a single STM transaction that succeeds or fails at once. For example
-- | Ensures that there is exactly one element `x` in the set.
add1 :: (Eq a) => a -> TSet a -> STM ()
add1 x v = remove x v >> add x v
This would be difficult with actors, you'd have to add it as another command for the actor, you can't compose it of existing actions and still have atomicity.
Update: There is an interesting article explaining why Clojure designers chose not to use actors. For example, using actors, even if you have many reads and only very little writes to a mutable structure, they're all serialized, which can greatly impact performance.

Related

Convert IO callback to infinite list

I am using a library that I can provide with a function a -> IO (), which it will call occasionally.
Because the output of my function depends not only on the a it receives as input, but also on the previous a's, it would be much easier for me to write a function [a] -> IO (), where [a] is infinite.
Can I write a function:
magical :: ([a] -> IO ()) -> (a -> IO ())
That collects the a's it receives from the callback and passes them to my function as a lazy infinite list?
The IORef solution is indeed the simplest one. If you'd like to explore a pure (but more complex) variant, have a look at conduit. There are other implementations of the same concept, see Iteratee I/O, but I found myself conduit to be very easy to use.
A conduit (AKA pipe) is an abstraction of of program that can accept input and/or produce output. As such, it can keep internal state, if needed. In your case, magical would be a sink, that is, a conduit that accepts input of some type, but produces no output. By wiring it into a source, a program that produces output, you complete the pipeline and then ever time the sink asks for an input, the source is run until it produces its output.
In your case you'd have roughly something like
magical :: Sink a IO () -- consumes a stream of `a`s, no result
magical = go (some initial state)
where
go state = do
m'input <- await
case m'input of
Nothing -> return () -- finish
Just input -> do
-- do something with the input
go (some updated state)
This is not exactly what you asked for, but it might be enough for your purposes, I think.
magical :: ([a] -> IO ()) -> IO (a -> IO ())
magical f = do
list <- newIORef []
let g x = do
modifyIORef list (x:)
xs <- readIORef list
f xs -- or (reverse xs), if you need FIFO ordering
return g
So if you have a function fooHistory :: [a] -> IO (), you can use
main = do
...
foo <- magical fooHistory
setHandler foo -- here we have foo :: a -> IO ()
...
As #danidaz wrote above, you probably do not need magical, but can play the same trick directly in your fooHistory, modifying a list reference (IORef [a]).
main = do
...
list <- newIORef []
let fooHistory x = do
modifyIORef list (x:)
xs <- readIORef list
use xs -- or (reverse xs), if you need FIFO ordering
setHandler fooHistory -- here we have fooHistory :: a -> IO ()
...
Control.Concurrent.Chan does almost exactly what I wanted!
import Control.Monad (forever)
import Control.Concurrent (forkIO)
import Control.Concurrent.Chan
setHandler :: (Char -> IO ()) -> IO ()
setHandler f = void . forkIO . forever $ getChar >>= f
process :: String -> IO ()
process ('h':'i':xs) = putStrLn "hi" >> process xs
process ('a':xs) = putStrLn "a" >> process xs
process (x:xs) = process xs
process _ = error "Guaranteed to be infinite"
main :: IO ()
main = do
c <- newChan
setHandler $ writeChan c
list <- getChanContents c
process list
This seems like a flaw in the library design to me. You might consider an upstream patch so that you could provide something more versatile as input.

Storing arbitrary function calls across threads

I'm trying to write a library aiming to reproduce Qt's threading semantics: signals can be connected to slots, and all slots execute in a known thread, so that slots tied to the same thread are threadsafe with regards to each other.
I have the following API:
data Signal a = Signal Unique a
data Slot a = Slot Unique ThreadId (a -> IO ())
mkSignal :: IO (Signal a)
mkSlot :: ThreadId -> (Slot a -> a -> IO ()) -> IO (Slot a)
connect :: Signal a -> Slot a -> IO ()
-- callable from any thread
emit :: Signal a -> a -> IO ()
-- runs in Slot's thread as a result of `emit`
execute :: Slot a -> a -> IO ()
execute (Slot _ _ f) arg = f arg
The problem is getting from emit to execute. The argument needs to be stored at runtime somehow, and then an IO action performed, but I can't seem to get past the type checker.
The things I need:
Type safety: signals shouldn't be connected to slots expecting a different type.
Type-independence: there can be more than one slots for any given type (Perhaps this can be relaxed with newtype and/or TH).
Ease of use: since this is a library, signals and slots should be easy to create.
The things I've tried:
Data.Dynamic: makes the whole thing really fragile, and I haven't found a way to perform a correctly-typed IO action on a Dynamic. There's dynApply, but it's pure.
Existential types: I need to execute the function passed to mkSlot, as opposed to an arbitrary function based on the type.
Data.HList: I'm not smart enough to figure it out.
What am I missing?
Firstly, are you sure Slots really want to execute in a specific thread? It's easy to write thread-safe code in Haskell, and threads are very lightweight in GHC, so you're not gaining much by tying all event-handler execution to a specific Haskell thread.
Also, mkSlot's callback doesn't need to be given the Slot itself: you can use recursive do-notation to bind the slot in its callback without adding the concern of tying the knot to mkSlot.
Anyway, you don't need anything as complicated as those solutions. I expect when you talk about existential types, you're thinking about sending something like (a -> IO (), a) through a TChan (which you mentioned using in the comments) and applying it on the other end, but you want the TChan to accept values of this type for any a, rather than just one specific a. The key insight here is that if you have (a -> IO (), a) and don't know what a is, the only thing you can do is apply the function to the value, giving you an IO () — so we can just send those through the channel instead!
Here's an example:
import Data.Unique
import Control.Applicative
import Control.Monad
import Control.Concurrent
import Control.Concurrent.STM
newtype SlotGroup = SlotGroup (IO () -> IO ())
data Signal a = Signal Unique (TVar [Slot a])
data Slot a = Slot Unique SlotGroup (a -> IO ())
-- When executed, this produces a function taking an IO action and returning
-- an IO action that writes that action to the internal TChan. The advantage
-- of this approach is that it's impossible for clients of newSlotGroup to
-- misuse the internals by reading the TChan or similar, and the interface is
-- kept abstract.
newSlotGroup :: IO SlotGroup
newSlotGroup = do
chan <- newTChanIO
_ <- forkIO . forever . join . atomically . readTChan $ chan
return $ SlotGroup (atomically . writeTChan chan)
mkSignal :: IO (Signal a)
mkSignal = Signal <$> newUnique <*> newTVarIO []
mkSlot :: SlotGroup -> (a -> IO ()) -> IO (Slot a)
mkSlot group f = Slot <$> newUnique <*> pure group <*> pure f
connect :: Signal a -> Slot a -> IO ()
connect (Signal _ v) slot = atomically $ do
slots <- readTVar v
writeTVar v (slot:slots)
emit :: Signal a -> a -> IO ()
emit (Signal _ v) a = atomically (readTVar v) >>= mapM_ (`execute` a)
execute :: Slot a -> a -> IO ()
execute (Slot _ (SlotGroup send) f) a = send (f a)
This uses a TChan to send actions to the worker thread each slot is tied to.
Note that I'm not very familiar with Qt, so I may have missed some subtlety of the model. You can also disconnect Slots with this:
disconnect :: Signal a -> Slot a -> IO ()
disconnect (Signal _ v) (Slot u _ _) = atomically $ do
slots <- readTVar v
writeTVar v $ filter keep slots
where keep (Slot u' _) = u' /= u
You might want something like Map Unique (Slot a) instead of [Slot a] if this is likely to be a bottleneck.
So, the solution here is to (a) recognise that you have something that's fundamentally based upon mutable state, and use a mutable variable to structure it; (b) realise that functions and IO actions are first-class just like everything else, so you don't have to do anything special to construct them at runtime :)
By the way, I suggest keeping the implementations of Signal and Slot abstract by not exporting their constructors from the module defining them; there are many ways to tackle this approach without changing the API, after all.

How can I refactor this without IORefs?

How could I refactor this so that eventually IORefs would not be necessary?
inc :: IORef Int -> IO ()
inc ref = modifyIORef ref (+1)
main = withSocketsDo $ do
s <- socket AF_INET Datagram defaultProtocol
c <- newIORef 0
f <- newIORef 0
hostAddr <- inet_addr host
time $ forM [0 .. 10000] $ \i -> do
sendAllTo s (B.pack "ping") (SockAddrInet port hostAddr)
(r, _) <- recvFrom s 1024
if (B.unpack r) == "PING" then (inc c) else (inc f)
c' <- readIORef c
print (c')
sClose s
return()
What's wrong with using IORefs here? You're in IO anyways with the networking operations. IORefs aren't always the cleanest solution, but they seem to do the job well in this case.
Regardless, for the sake of answering the question, let's remove the IORefs. These references serve as a way of keeping state, so we'll have to come up with an alternate way to keep the stateful information.
The pseudocode for what we want to do is this:
open the connection
10000 times:
send a message
receive the response
(keep track of how many responses are the message "PING")
print how many responses were the message "PING"
The chunk that is indented under 1000 times can be abstracted into its own function. If we are to avoid IORefs, then this function will have to take in a previous state and produce a next state.
main = withSocketsDo $ do
s <- socket AF_INET Datagram defaultProtocol
hostAddr <- inet_addr host
let sendMsg = sendAllTo s (B.pack "ping") (SockAddrInet port hostAddr)
recvMsg = fst `fmap` recvFrom s 1024
(c,f) <- ???
print c
sClose s
So the question is this: what do we put at the ??? place? We need to define some way to "perform" an IO action, take its result, and modify state with that result somehow. We also need to know how many times to do it.
performRepeatedlyWithState :: a -- some state
-> IO b -- some IO action that yields a value
-> (a -> b -> a) -- some way to produce a new state
-> Int -- how many times to do it
-> IO a -- the resultant state, as an IO action
performRepeatedlyWithState s _ _ 0 = return s
performRepeatedlyWithState someState someAction produceNewState timesToDoIt = do
actionresult <- someAction
let newState = produceNewState someState actionResult
doWithState newState someAction produceNewState (pred timesToDoIt)
All I did here was write down the type signature that matched what I said above, and produced the relatively obvious implementation. I gave everything a very verbose name to hopefully make it apparent exactly what this function means. Equipped with this simple function, we just need to use it.
let origState = (0,0)
action = ???
mkNewState = ???
times = 10000
(c,f) <- performRepeatedlyWithState origState action mkNewState times
I've filled in the easy parameters here. The original state is (c,f) = (0,0), and we want to perform this 10000 times. (Or is it 10001?) But what should action and mkNewState look like? The action should have type IO b; it's some IO action that produces something.
action = sendMsg >> recvMsg
I bound sendMsg and recvMsg to expressions from your code earlier. The action we want to perform is to send a message, and then receive a message. The value this action produces is the message received.
Now, what should mkNewState look like? It should have the type a -> b -> a, where a is the type of the State, and b is the type of the action result.
mkNewState (c,f) val = if (B.unpack val) == "PING"
then (succ c, f)
else (c, succ f)
This isn't the cleanest solution, but do you get the general idea? You can replace IORefs by writing a function that recursively calls itself, passing extra parameters along in order to keep track of state. The exact same idea is embodied in the foldM solution suggested on the similar question.
Bang patterns, as Nathan Howell suggests, would be wise, to avoid building up a large thunk of succ (succ (succ ...))) in your state:
mkNewState (!c, !f) val = ...
Building on the earlier comment regarding a stack overflow.
The accumulators 'f' and 'c' in either the IORef or foldM case need to be evaluated to prevent a long chain of thunks from being allocated while you're iterating. One way of forcing evaluation of the thunks is to use a bang pattern. This tells the compiler to evaluate the value, removing the thunk, even though it's value is not demanded in the function.
{-# LANGUAGE BangPatterns #-}
{-# LANGUAGE OverloadedStrings #-}
import Control.Concurrent
import Control.Monad
import Data.ByteString.Char8
import Data.Foldable (foldlM)
import Data.IORef
import Network.Socket hiding (recvFrom)
import Network.Socket.ByteString (recvFrom, sendAllTo)
main = withSocketsDo $ do
let host = "127.0.0.1"
port= 9898
s <- socket AF_INET Datagram defaultProtocol
hostAddr <- inet_addr host
-- explicitly mark both accumulators as strict using bang patterns
let step (!c, !f) i = do
sendAllTo s "PING" (SockAddrInet port hostAddr)
(r, _) <- recvFrom s 1024
return $ case r of
-- because c and f are never used, the addition operator below
-- builds a thunk chain. these can lead to a stack overflow
-- when the chain is being evalulated by the 'print c' call below.
"PING" -> (c+1, f)
_ -> (c, f+1)
(c, f) <- foldlM step (0, 0) [0..10000]
print c
sClose s
return ()

Reentrant caching of "referentially transparent" IO calls

Assume we have an IO action such as
lookupStuff :: InputType -> IO OutputType
which could be something simple such as DNS lookup, or some web-service call against a time-invariant data.
Let's assume that:
The operation never throws any exception and/or never diverges
If it wasn't for the IO monad, the function would be pure, i.e. the result is always the same for equal input parameters
The action is reentrant, i.e. it can be called from multiple threads at the same time safely.
The lookupStuff operation is quite (time-)expensive.
The problem I'm facing is how to properly (and w/o using any unsafe*IO* cheat) implement a reentrant cache, that can be called from multiple threads, and coalesces multiple queries for the same input-parameters into a single request.
I guess I'm after something similiar as GHC's blackhole-concept for pure computations but in the IO "calculation" context.
What is the idiomatic Haskell/GHC solution for the stated problem?
Yeah, basically reimplement the logic. Although it seems similar to what GHC is already doing, that's GHC's choice. Haskell can be implemented on VMs that work very differently, so in that sense it isn't already done for you.
But yeah, just use an MVar (Map InputType OutputType) or even an IORef (Map InputType OutputType) (make sure to modify with atomicModifyIORef), and just store the cache in there. If this imperative solution seems wrong, it's the "if not for the IO, this function would be pure" constraint. If it were just an arbitrary IO action, then the idea that you would have to keep state in order to know what to execute or not seems perfectly natural. The problem is that Haskell does not have a type for "pure IO" (which, if it depends on a database, it is just behaving pure under certain conditions, which is not the same as being a hereditarily pure).
import qualified Data.Map as Map
import Control.Concurrent.MVar
-- takes an IO function and returns a cached version
cache :: (Ord a) => (a -> IO b) -> IO (a -> IO b)
cache f = do
r <- newMVar Map.empty
return $ \x -> do
cacheMap <- takeMVar r
case Map.lookup x cacheMap of
Just y -> do
putMVar r cacheMap
return y
Nothing -> do
y <- f x
putMVar (Map.insert x y cacheMap)
return y
Yeah it's ugly on the inside. But on the outside, look at that! It's just like the type of a pure memoization function, except for it has IO stained all over it.
Here's some code implementing more or less what I was after in my original question:
import Control.Concurrent
import Control.Exception
import Data.Either
import Data.Map (Map)
import qualified Data.Map as Map
import Prelude hiding (catch)
-- |Memoizing wrapper for 'IO' actions
memoizeIO :: Ord a => (a -> IO b) -> IO (a -> IO b)
memoizeIO action = do
cache <- newMVar Map.empty
return $ memolup cache action
where
-- Lookup helper
memolup :: Ord a => MVar (Map a (Async b)) -> (a -> IO b) -> a -> IO b
memolup cache action' args = wait' =<< modifyMVar cache lup
where
lup tab = case Map.lookup args tab of
Just ares' ->
return (tab, ares')
Nothing -> do
ares' <- async $ action' args
return (Map.insert args ares' tab, ares')
The code above builds upon Simon Marlow's Async abstraction as described in Tutorial: Parallel and Concurrent Programming in Haskell:
-- |Opaque type representing asynchronous results.
data Async a = Async ThreadId (MVar (Either SomeException a))
-- |Construct 'Async' result. Can be waited on with 'wait'.
async :: IO a -> IO (Async a)
async io = do
var <- newEmptyMVar
tid <- forkIO ((do r <- io; putMVar var (Right r))
`catch` \e -> putMVar var (Left e))
return $ Async tid var
-- |Extract value from asynchronous result. May block if result is not
-- available yet. Exceptions are returned as 'Left' values.
wait :: Async a -> IO (Either SomeException a)
wait (Async _ m) = readMVar m
-- |Version of 'wait' that raises exception.
wait' :: Async a -> IO a
wait' a = either throw return =<< wait a
-- |Cancels asynchronous computation if not yet completed (non-blocking).
cancel :: Async a -> IO ()
cancel (Async t _) = throwTo t ThreadKilled

Strict evaluation techniques for concurrent channels in Haskell

I'm toying with Haskell threads, and I'm running into the problem of communicating lazily-evaluated values across a channel. For example, with N worker threads and 1 output thread, the workers communicate unevaluated work and the output thread ends up doing the work for them.
I've read about this problem in various documentation and seen various solutions, but I only found one solution that works and the rest do not. Below is some code in which worker threads start some computation that can take a long time. I start the threads in descending order, so that the first thread should take the longest, and the later threads should finish earlier.
import Control.Concurrent (forkIO)
import Control.Concurrent.Chan -- .Strict
import Control.Concurrent.MVar
import Control.Exception (finally, evaluate)
import Control.Monad (forM_)
import Control.Parallel.Strategies (using, rdeepseq)
main = (>>=) newChan $ (>>=) (newMVar []) . run
run :: Chan (Maybe String) -> MVar [MVar ()] -> IO ()
run logCh statVars = do
logV <- spawn1 readWriteLoop
say "START"
forM_ [18,17..10] $ spawn . busyWork
await
writeChan logCh Nothing -- poison the logger
takeMVar logV
putStrLn "DONE"
where
say mesg = force mesg >>= writeChan logCh . Just
force s = mapM evaluate s -- works
-- force s = return $ s `using` rdeepseq -- no difference
-- force s = return s -- no-op; try this with strict channel
busyWork = say . show . sum . filter odd . enumFromTo 2 . embiggen
embiggen i = i*i*i*i*i
readWriteLoop = readChan logCh >>= writeReadLoop
writeReadLoop Nothing = return ()
writeReadLoop (Just mesg) = putStrLn mesg >> readWriteLoop
spawn1 action = do
v <- newEmptyMVar
forkIO $ action `finally` putMVar v ()
return v
spawn action = do
v <- spawn1 action
modifyMVar statVars $ \vs -> return (v:vs, ())
await = do
vs <- modifyMVar statVars $ \vs -> return ([], vs)
mapM_ takeMVar vs
Using most techniques, the results are reported in the order spawned; that is, the longest-running computation first. I interpret this to mean that the output thread is doing all the work:
-- results in order spawned (longest-running first = broken)
START
892616806655
503999185040
274877906943
144162977343
72313663743
34464808608
15479341055
6484436675
2499999999
DONE
I thought the answer to this would be strict channels, but they didn't work. I understand that WHNF for strings is insufficient because that would just force the outermost constructor (nil or cons for the first character of the string). The rdeepseq is supposed to fully evaluate, but it makes no difference. The only thing I've found that works is to map Control.Exception.evaluate :: a -> IO a over all the characters in the string. (See the force function comments in the code for several different alternatives.) Here's the result with Control.Exception.evaluate:
-- results in order finished (shortest-running first = correct)
START
2499999999
6484436675
15479341055
34464808608
72313663743
144162977343
274877906943
503999185040
892616806655
DONE
So why don't strict channels or rdeepseq produce this result? Are there other techniques? Am I misinterpreting why the first result is broken?
There are two issues going on here.
The reason the first attempt (using an explicit rnf) doesn't work is that, by using return, you've created a thunk that fully evaluates itself when it is evaluated, but the thunk itself has not being evaluated. Notice that the type of evaluate is a -> IO a: the fact that it returns a value in IO means that evaluate can impose ordering:
return (error "foo") >> return 1 == return 1
evaluate (error "foo") >> return 1 == error "foo"
The upshot is that this code:
force s = evaluate $ s `using` rdeepseq
will work (as in, have the same behavior as mapM_ evaluate s).
The case of using strict channels is a little trickier, but I believe this is due to a bug in strict-concurrency. The expensive computation is actually being run on the worker threads, but it's not doing you much good (you can check for this explicitly by hiding some asynchronous exceptions in your strings and seeing which thread the exception surfaces on).
What's the bug? Let's take a look at the code for strict writeChan:
writeChan :: NFData a => Chan a -> a -> IO ()
writeChan (Chan _read write) val = do
new_hole <- newEmptyMVar
modifyMVar_ write $ \old_hole -> do
putMVar old_hole $! ChItem val new_hole
return new_hole
We see that modifyMVar_ is called on write before we evaluate the thunk. The sequence of operations then is:
writeChan is entered
We takeMVar write (blocking anyone else who wants to write to the channel)
We evaluate the expensive thunk
We put the expensive thunk onto the channel
We putMVar write, unblocking all of the other threads
You don't see this behavior with the evaluate variants, because they perform the evaluation before the lock is acquired.
I’ll send Don mail about this and see if he agrees that this behavior is kind of suboptimal.
Don agrees that this behavior is suboptimal. We're working on a patch.

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