pipes-concurrency-1.2.0: Control/Proxy/Concurrent/Tutorial.hs
{-| This module provides a tutorial for the @pipes-concurrency@ library.
This tutorial assumes that you have read the @pipes@ tutorial in
@Control.Proxy.Tutorial@.
I've condensed all the code examples into self-contained code listings in
the Appendix section that you can use to follow along.
-}
module Control.Proxy.Concurrent.Tutorial (
-- * Introduction
-- $intro
-- * Work Stealing
-- $steal
-- * Termination
-- $termination
-- * Mailbox Sizes
-- $mailbox
-- * Broadcasts
-- $broadcast
-- * Updates
-- $updates
-- * Callbacks
-- $callback
-- * Safety
-- $safety
-- * Conclusion
-- $conclusion
-- * Appendix
-- $appendix
) where
import Control.Proxy
import Control.Proxy.Concurrent
import Data.Monoid
{- $intro
The @pipes-concurrency@ library provides a simple interface for
communicating between concurrent pipelines. Use this library if you want
to:
* merge multiple streams into a single stream,
* stream data from a callback \/ continuation,
* broadcast data,
* build a work-stealing setup, or
* implement basic functional reactive programming (FRP).
For example, let's say that we design a simple game with a single unit's
health as the global state. We'll define an event handler that modifies the
unit's health in response to events:
> import Control.Monad
> import Control.Proxy
> import Control.Proxy.Trans.Maybe
> import Control.Proxy.Trans.State
>
> -- The game events
> data Event = Harm Integer | Heal Integer | Quit
>
> -- The game state
> type Health = Integer
>
> handler :: (Proxy p) => () -> Consumer (StateP Health (MaybeP p)) Event IO r
> handler () = forever $ do
> event <- request ()
> case event of
> Harm n -> modify (subtract n)
> Heal n -> modify (+ n)
> Quit -> mzero
> health <- get
> lift $ putStrLn $ "Health = " ++ show health
However, we have two concurrent event sources that we wish to hook up to our
event handler. One translates user input to game events:
> user :: (Proxy p) => () -> Producer p Event IO r
> user () = runIdentityP $ forever $ do
> command <- lift getLine
> case command of
> "potion" -> respond (Heal 10)
> "quit" -> respond Quit
> _ -> lift $ putStrLn "Invalid command"
... while the other creates inclement weather:
> import Control.Concurrent
>
> acidRain :: (Proxy p) => () -> Producer p Event IO r
> acidRain () = runIdentityP $ forever $ do
> respond (Harm 1)
> lift $ threadDelay 2000000
To merge these sources, we 'spawn' a new FIFO mailbox which we will use to
merge the two streams of asynchronous events:
> spawn :: Buffer a -> IO (Input a, Output a)
'spawn' takes a mailbox 'Buffer' as an argument, and we will specify that we
want our mailbox to store an 'Unbounded' number of messages:
> import Control.Proxy.Concurrent
>
> main = do
> (input, output) <- spawn Unbounded
> ...
'spawn' creates this mailbox in the background and then returns two values:
* an @(Input a)@ that we use to add messages of type @a@ to the mailbox
* an @(Output a)@ that we use to consume messages of type @a@ from the
mailbox
We will be streaming @Event@s through our mailbox, so our @input@ has type
@(Input Event)@ and our @output@ has type @(Output Event)@.
To stream @Event@s into the mailbox , we use 'sendD', which writes values to
the mailbox's 'Input' end:
> sendD :: (Proxy p) => Input a -> () -> Pipe p a a IO ()
We can concurrently forward multiple streams to the same 'Input', which
asynchronously merges their messages into the same mailbox:
> ...
> forkIO $ do runProxy $ acidRain >-> sendD input
> performGC -- I'll explain 'performGC' below
> forkIO $ do runProxy $ user >-> sendD input
> performGC
> ...
To stream @Event@s out of the mailbox, we use 'recvS', which reads values
from the mailbox's 'Output' end:
> recvS :: (Proxy p) => Output a -> () -> Producer p a IO ()
We will forward our merged stream to our @handler@ so that it can listen to
both @Event@ sources:
> ...
> runProxy $ runMaybeK $ evalStateK 100 $ recvS output >-> handler
Our final @main@ becomes:
> main = do
> (input, output) <- spawn Unbounded
> forkIO $ do runProxy $ acidRain >-> sendD input
> performGC
> forkIO $ do runProxy $ user >-> sendD input
> performGC
> runProxy $ runMaybeK $ evalStateK 100 $ recvS output >-> handler
... and when we run it we get the desired concurrent behavior:
> $ ./game
> Health = 99
> Health = 98
> potion<Enter>
> Health = 108
> Health = 107
> Health = 106
> potion<Enter>
> Health = 116
> Health = 115
> quit<Enter>
> $
-}
{- $steal
You can also have multiple pipes reading from the same mailbox. Messages
get split between listening pipes on a first-come first-serve basis.
For example, we'll define a \"worker\" that takes a one-second break each
time it receives a new job:
> import Control.Concurrent
> import Control.Monad
> import Control.Proxy
>
> worker :: (Proxy p, Show a) => Int -> () -> Consumer p a IO r
> worker i () = runIdentityP $ forever $ do
> a <- request ()
> lift $ threadDelay 1000000 -- 1 second
> lift $ putStrLn $ "Worker #" ++ show i ++ ": Processed " ++ show a
Fortunately, these workers are cheap, so we can assign several of them to
the same job:
> import Control.Concurrent.Async
> import Control.Proxy.Concurrent
>
> main = do
> (input, output) <- spawn Unbounded
> as <- forM [1..3] $ \i ->
> async $ do runProxy $ recvS output >-> worker i
> performGC
> a <- async $ do runProxy $ fromListS [1..10] >-> sendD input
> performGC
> mapM_ wait (a:as)
The above example uses @Control.Concurrent.Async@ from the @async@ package
to fork each thread and wait for all of them to terminate:
> $ ./work
> Worker #2: Processed 3
> Worker #1: Processed 2
> Worker #3: Processed 1
> Worker #3: Processed 6
> Worker #1: Processed 5
> Worker #2: Processed 4
> Worker #2: Processed 9
> Worker #1: Processed 8
> Worker #3: Processed 7
> Worker #2: Processed 10
> $
What if we replace 'fromListS' with a different source that reads lines from
user input until the user types \"quit\":
> user :: (Proxy p) => () -> Producer p String IO ()
> user = stdinS >-> takeWhileD (/= "quit")
>
> main = do
> (input, output) <- spawn Unbounded
> as <- forM [1..3] $ \i ->
> async $ do runProxy $ recvS output >-> worker i
> performGC
> a <- async $ do runProxy $ user >-> sendD input
> performGC
> mapM_ wait (a:as)
This still produces the correct behavior:
> $ ./work
> Test<Enter>
> Worker #1: Processed "Test"
> Apple<Enter>
> Worker #2: Processed "Apple"
> 42<Enter>
> Worker #3: Processed "42"
> A<Enter>
> B<Enter>
> C<Enter>
> Worker #1: Processed "A"
> Worker #2: Processed "B"
> Worker #3: Processed "C"
> quit<Enter>
> $
-}
{- $termination
Wait... How do the workers know when to stop listening for data? After
all, anything that has a reference to 'Input' could potentially add more
data to the mailbox.
It turns out that 'recvS' is smart and only terminates when the upstream
'Input' is garbage collected. 'recvS' builds on top of the more primitive
'recv' command, which returns a 'Nothing' when the 'Input' is garbage
collected:
> recv :: Output a -> STM (Maybe a)
Otherwise, 'recv' blocks if the mailbox is empty since it assumes that if
the 'Input' has not been garbage collected then somebody might still produce
more data.
Does it work the other way around? What happens if the workers go on strike
before processing the entire data set?
> ...
> as <- forM [1..3] $ \i ->
> -- Each worker refuses to process more than two values
> async $ do runProxy $ recvS output >-> takeB_ 2 >-> worker i
> performGC
> ...
Let's find out:
> $ ./work
> How<Enter>
> Worker #1: Processed "How"
> many<Enter>
> roads<Enter>
> Worker #2: Processed "many"
> Worker #3: Processed "roads"
> must<Enter>
> a<Enter>
> man<Enter>
> Worker #1: Processed "must"
> Worker #2: Processed "a"
> Worker #3: Processed "man"
> walk<Enter>
> $
'sendD' similarly shuts down when the 'Output' is garbage collected,
preventing the user from submitting new values. 'sendD' builds on top of
the more primitive 'send' command, which returns a 'False' when the 'Output'
is garbage collected:
> send :: Input a -> a -> STM Bool
Otherwise, 'send' blocks if the mailbox is full, since it assumes that if
the 'Output' has not been garbage collected then somebody could still
consume a value from the mailbox, making room for a new value.
This is why we have to insert 'performGC' calls whenever we release a
reference to either the 'Input' or 'Output'. Without these calls we cannot
guarantee that the garbage collector will trigger and notify the opposing
end if the last reference was released. If you forget to insert a
'performGC' call then termination will delay until the next garbage
collection cycle.
-}
{- $mailbox
So far we haven't observed 'send' blocking because we only 'spawn'ed
'Unbounded' mailboxes. However, we can control the size of the mailbox to
tune the coupling between the 'Input' and the 'Output' ends.
If we set the mailbox 'Buffer' to 'Single', then the mailbox holds exactly
one message, forcing synchronization between 'send's and 'recv's. Let's
observe this by sending an infinite stream of values, logging all values to
'stdout':
> main = do
> (input, output) <- spawn Single
> as <- forM [1..3] $ \i ->
> async $ do runProxy $ recvS output >-> takeB_ 2 >-> worker i
> performGC
> a <- async $ do runProxy $ enumFromS 1 >-> printD >-> sendD input
> performGC
> mapM_ wait (a:as)
The 7th value gets stuck in the mailbox, and the 8th value blocks because
the mailbox never clears the 7th value:
> $ ./work
> 1
> 2
> 3
> 4
> 5
> Worker #3: Processed 3
> Worker #2: Processed 2
> Worker #1: Processed 1
> 6
> 7
> 8
> Worker #1: Processed 6
> Worker #2: Processed 5
> Worker #3: Processed 4
> $
Contrast this with an 'Unbounded' mailbox for the same program, which keeps
accepting values until downstream finishes processing the first six values:
> $ ./work
> 1
> 2
> 3
> 4
> 5
> 6
> 7
> 8
> 9
> ...
> 487887
> 487888
> Worker #3: Processed 3
> Worker #2: Processed 2
> Worker #1: Processed 1
> 487889
> 487890
> ...
> 969188
> 969189
> Worker #1: Processed 6
> Worker #2: Processed 5
> Worker #3: Processed 4
> 969190
> 969191
> $
You can also choose something in between by using a 'Bounded' mailbox which
caps the mailbox size to a fixed value. Use 'Bounded' when you want mostly
loose coupling but still want to guarantee bounded memory usage:
> main = do
> (input, output) <- spawn (Bounded 100)
> ...
> $ ./work
> ...
> 103
> 104
> Worker #3: Processed 3
> Worker #2: Processed 2
> Worker #1: Processed 1
> 105
> 106
> 107
> Worker #1: Processed 6
> Worker #2: Processed 5
> Worker #3: Processed 4
> $
-}
{- $broadcast
You can also broadcast data to multiple listeners instead of dividing up the
data. Just use the 'Monoid' instance for 'Input' to combine multiple
'Input' ends together into a single broadcast 'Input':
> import Control.Monad
> import Control.Concurrent.Async
> import Control.Proxy
> import Control.Proxy.Concurrent
> import Data.Monoid
>
> main = do
> (input1, output1) <- spawn Unbounded
> (input2, output2) <- spawn Unbounded
> a1 <- async $ do
> runProxy $ stdinS >-> sendD (input1 <> input2)
> performGC
> as <- forM [output1, output2] $ \output -> async $ do
> runProxy $ recvS output >-> takeB_ 2 >-> stdoutD
> performGC
> mapM_ wait (a1:as)
In the above example, 'stdinS' will broadcast user input to both mailboxes,
and each mailbox forwards its values to 'stdoutD', echoing the message to
standard output:
> $ ./broadcast
> ABC<Enter>
> ABC
> ABC
> DEF<Enter>
> DEF
> DEF
> GHI<Enter>
> $
The combined 'Input' stays alive as long as any of the original 'Input's
remains alive. In the above example, 'sendD' terminates on the third 'send'
attempt because it detects that both listeners died after receiving two
messages.
Use 'mconcat' to broadcast to a list of 'Input's, but keep in mind that you
will incur a performance price if you combine thousands of 'Input's or more
because they will create a very large 'STM' transaction. You can improve
performance for very large broadcasts if you sacrifice atomicity and
manually combine multiple 'send' actions in 'IO' instead of 'STM'.
-}
{- $updates
Sometimes you don't want to handle every single event. For example, you
might have an input and output device (like a mouse and a monitor) where the
input device updates at a different pace than the output device
> import Control.Concurrent
> import Control.Proxy
>
> -- Fast input updates
> inputDevice :: (Monad m, Proxy p) => () -> Producer p Integer m r
> inputDevice = enumFromS 1
>
> -- Slow output updates
> outputDevice :: (Proxy p) => () -> Consumer p Integer IO r
> outputDevice () = runIdentityP $ forever $ do
> n <- request ()
> lift $ do
> print n
> threadDelay 1000000
In this scenario you don't want to enforce a one-to-one correspondence
between input device updates and output device updates because you don't
want either end to block waiting for the other end. Instead, you just need
the output device to consult the 'Latest' value received from the 'Input':
> import Control.Concurrent.Async
> import Control.Proxy.Concurrent
>
> main = do
> (input, output) <- spawn (Latest 0)
> a1 <- async $ do
> runProxy $ inputDevice >-> sendD input
> performGC
> a2 <- async $ do
> runProxy $ recvS output >-> takeB_ 5 >-> outputDevice
> performGC
> mapM_ wait [a1, a2]
'Latest' selects a mailbox that always stores exactly one value. The
'Latest' constructor takes a single argument (@0@, in the above example)
specifying the starting value to store in the mailbox. 'send' overrides the
currently stored value and 'recv' peeks at the latest stored value without
consuming it. In the above example the @outputDevice@ periodically peeks at the latest value stashed inside the mailbox:
> $ ./peek
> 5
> 752452
> 1502636
> 2248278
> 2997705
> $
A 'Latest' mailbox is never empty because it begins with a default value and
'recv' never removes the value from the mailbox. A 'Latest' mailbox is also
never full because 'send' always succeeds, overwriting the previously stored
value.
-}
{- $callback
@pipes-concurrency@ also solves the common problem of getting data out of a
callback-based framework into @pipes@.
For example, suppose that we have the following callback-based function:
> import Control.Monad
>
> onLines :: (String -> IO a) -> IO b
> onLines callback = forever $ do
> str <- getLine
> callback str
We can use 'send' to free the data from the callback and then we can
retrieve the data on the outside using 'recvS':
> import Control.Proxy
> import Control.Proxy.Concurrent
>
> onLines' :: (Proxy p) => () -> Producer p String IO ()
> onLines' () = runIdentityP $ do
> (input, output) <- lift $ spawn Single
> lift $ forkIO $ onLines (\str -> atomically $ send input str)
> recvS output ()
>
> main = runProxy $ onLines' >-> takeWhileD (/= "quit") >-> stdoutD
Now we can stream from the callback as if it were an ordinary 'Producer':
> $ ./callback
> Test<Enter>
> Test
> Apple<Enter>
> Apple
> quit<Enter>
> $
-}
{- $safety
@pipes-concurrency@ avoids deadlocks because 'send' and 'recv' always
cleanly return before triggering a deadlock. This behavior works even in
complicated scenarios like:
* cyclic graphs of connected mailboxes,
* multiple readers and multiple writers to the same mailbox, and
* dynamically adding or garbage collecting mailboxes.
The following example shows how @pipes-concurrency@ will do the right thing
even in the case of cycles:
> import Control.Concurrent.Async
> import Control.Proxy
> import Control.Proxy.Concurrent
>
> main = do
> (in1, out1) <- spawn Unbounded
> (in2, out2) <- spawn Unbounded
> a1 <- async $ do runProxy $ (fromListS [1,2] >=> recvS out1) >-> sendD in2
> performGC
> a2 <- async $ do runProxy $ recvS out2 >-> printD >-> takeB_ 6 >-> sendD in1
> performGC
> mapM_ wait [a1, a2]
The above program jump-starts a cyclic chain with two input values and
terminates one branch of the cycle after six values flow through. Both
branches correctly terminate and get garbage collected without triggering
deadlocks when 'takeB_' finishes:
> $ ./cycle
> 1
> 2
> 1
> 2
> 1
> 2
> $
-}
{- $conclusion
@pipes-concurrency@ adds an asynchronous dimension to @pipes@. This
promotes a natural division of labor for concurrent programs:
* Fork one pipeline per deterministic behavior
* Communicate between concurrent pipelines using @pipes-concurrency@
This promotes an actor-style approach to concurrent programming where
pipelines behave like processes and mailboxes behave like ... mailboxes.
You can ask questions about @pipes-concurrency@ and other @pipes@ libraries
on the official @pipes@ mailing list at
<mailto:haskell-pipes@googlegroups.com>.
-}
{- $appendix
I've provided the full code for the above examples here so you can easily
try them out:
> -- game.hs
>
> import Control.Concurrent
> import Control.Monad
> import Control.Proxy
> import Control.Proxy.Concurrent
> import Control.Proxy.Trans.Maybe
> import Control.Proxy.Trans.State
>
> -- The game events
> data Event = Harm Integer | Heal Integer | Quit
>
> -- The game state
> type Health = Integer
>
> handler :: (Proxy p) => () -> Consumer (StateP Health (MaybeP p)) Event IO r
> handler () = forever $ do
> event <- request ()
> case event of
> Harm n -> modify (subtract n)
> Heal n -> modify (+ n)
> Quit -> mzero
> health <- get
> lift $ putStrLn $ "Health = " ++ show health
>
> user :: (Proxy p) => () -> Producer p Event IO r
> user () = runIdentityP $ forever $ do
> command <- lift getLine
> case command of
> "potion" -> respond (Heal 10)
> "quit" -> respond Quit
> _ -> lift $ putStrLn "Invalid command"
>
> acidRain :: (Proxy p) => () -> Producer p Event IO r
> acidRain () = runIdentityP $ forever $ do
> respond (Harm 1)
> lift $ threadDelay 2000000
>
> main = do
> (input, output) <- spawn Unbounded
> forkIO $ do runProxy $ acidRain >-> sendD input
> performGC -- I'll explain 'performGC' below
> forkIO $ do runProxy $ user >-> sendD input
> performGC
> runProxy $ runMaybeK $ evalStateK 100 $ recvS output >-> handler
> -- work.hs
>
> import Control.Concurrent
> import Control.Monad
> import Control.Proxy
> import Control.Concurrent.Async
> import Control.Proxy.Concurrent
>
> worker :: (Proxy p, Show a) => Int -> () -> Consumer p a IO r
> worker i () = runIdentityP $ forever $ do
> a <- request ()
> lift $ threadDelay 1000000 -- 1 second
> lift $ putStrLn $ "Worker #" ++ show i ++ ": Processed " ++ show a
> {-
> worker :: (Proxy p, Show a) => Int -> () -> Consumer p a IO ()
> worker i () = runIdentityP $ replicateM_ 2 $ do
> a <- request ()
> lift $ threadDelay 1000000
> lift $ putStrLn $ "Worker #" ++ show i ++ ": Processed " ++ show a
> -}
>
> user :: (Proxy p) => () -> Producer p String IO ()
> user = stdinS >-> takeWhileD (/= "quit")
>
> main = do
> (input, output) <- spawn Unbounded
> -- (input, output) <- spawn Single
> -- (input, output) <- spawn (Bounded 100)
> as <- forM [1..3] $ \i ->
> async $ do runProxy $ recvS output >-> worker i
> -- async $ do runProxy $ recvS output >-> takeB_ 2 >-> worker i
> performGC
> a <- async $ do runProxy $ fromListS [1..10] >-> sendD input
> -- a <- async $ do runProxy $ user >-> sendD input
> -- a <- async $ do runProxy $ enumFromS 1 >-> printD >-> sendD input
> performGC
> mapM_ wait (a:as)
> -- broadcast.hs
>
> import Control.Monad
> import Control.Concurrent.Async
> import Control.Proxy
> import Control.Proxy.Concurrent
> import Data.Monoid
>
> main = do
> (input1, output1) <- spawn Unbounded
> (input2, output2) <- spawn Unbounded
> a1 <- async $ do
> runProxy $ stdinS >-> sendD (input1 <> input2)
> performGC
> as <- forM [output1, output2] $ \output -> async $ do
> runProxy $ recvS output >-> takeB_ 2 >-> stdoutD
> performGC
> mapM_ wait (a1:as)
> -- peek.hs
>
> import Control.Concurrent
> import Control.Concurrent.Async
> import Control.Proxy
> import Control.Proxy.Concurrent
>
> inputDevice :: (Monad m, Proxy p) => () -> Producer p Integer m r
> inputDevice = enumFromS 1
>
> outputDevice :: (Proxy p) => () -> Consumer p Integer IO r
> outputDevice () = runIdentityP $ forever $ do
> n <- request ()
> lift $ do
> print n
> threadDelay 1000000
>
> main = do
> (input, output) <- spawn (Latest 0)
> a1 <- async $ do
> runProxy $ inputDevice >-> sendD input
> performGC
> a2 <- async $ do
> runProxy $ recvS output >-> takeB_ 5 >-> outputDevice
> performGC
> mapM_ wait [a1, a2]
> -- callback.hs
>
> import Control.Proxy
> import Control.Proxy.Concurrent
>
> onLines' :: (Proxy p) => () -> Producer p String IO ()
> onLines' () = runIdentityP $ do
> (input, output) <- lift $ spawn Single
> lift $ forkIO $ onLines (\str -> atomically $ send input str)
> recvS output ()
>
> main = runProxy $ onLines' >-> takeWhileD (/= "quit) >-> stdoutD
-}