{-| This module provides a tutorial for the @pipes-concurrency@ library.

    This tutorial assumes that you have read the @pipes@ tutorial in
    @Control.Proxy.Tutorial@.
-}

module Control.Proxy.Concurrent.Tutorial (
    -- * Introduction
    -- $intro

    -- * Work Stealing
    -- $steal

    -- * Termination
    -- $termination

    -- * Mailbox Sizes
    -- $mailbox

    -- * Callbacks
    -- $callback

    -- * Safety
    -- $safety

    -- * Conclusion
    -- $conclusion

    -- * Appendix
    -- $appendix
    ) where

import Control.Proxy
import Control.Proxy.Concurrent

{- $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,

    * implement 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 ()
> 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 :: Size -> IO (Input a, Output a)

    'spawn' takes a mailbox 'Size' as an argument, and we specify that we want
    our mailbox to store an 'Unbounded' number of message.  '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

> import Control.Proxy.Concurrent
>
> main = do
>     (input, output) <- spawn Unbounded
>     ...

    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 -> x -> p x a x 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  -- I'll explain 'performGC' below
>     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@ 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?

> -- Each worker refuses to process more than two values
> 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

> $ ./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.
-}

{- $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 'Size' 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 >-> 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
> $
-}

{- $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.
-}

{- $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-stm@

    This promotes an actor-style approach to concurrent programming where
    pipelines behave like processes and mailboxes behave like ... mailboxes.
-}

{- $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 ()
> 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
>                      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)

> -- 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

-}