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pipes concurrency tutorial
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tutorial.hs
{-| This module provides a tutorial for the @pipes-concurrency@ library.
This tutorial assumes that you have read the @pipes@ tutorial in
@Pipes.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 Pipes.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.Concurrent hiding (yield)
import Control.Monad
import Pipes
import Pipes.Concurrent
import qualified Pipes.Prelude as P
import Data.Monoid
import System.Mem
import Control.Concurrent.Async
import Control.Monad.Managed
{- $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 want to design a simple game with two
concurrent sources of game @Event@s.
One source translates user input to game events:
-}
-- The game events
data Event = Harm Integer | Heal Integer | Quit deriving (Show)
user :: IO Event
user = do
command <- getLine
case command of
"potion" -> return (Heal 10)
"quit" -> return Quit
_ -> do
putStrLn "Invalid command"
user -- Try again
-- ... while the other creates inclement weather:
acidRain :: Producer Event IO r
acidRain = forever $ do
lift $ threadDelay 2000000 -- Wait 2 seconds
yield (Harm 1)
{-
We can asynchronously merge these two separate sources of @Event@s into a
single stream by 'spawn'ing a first-in-first-out (FIFO) mailbox:
@
'spawn' :: 'Buffer' a -> 'IO' ('Output' a, 'Input' a)
@
'spawn' takes a 'Buffer' as an argument which specifies how many messages to
store. In this case we want our mailbox to store an 'Unbounded' number of
messages:
> import Pipes.Concurrent
>
> main = do
> (output, input) <- spawn Unbounded
> ...
'spawn' creates this mailbox in the background and then returns two values:
* an @(Output a)@ that we use to add messages of type @a@ to the mailbox
* an @(Input a)@ that we use to consume messages of type @a@ from the
mailbox
We will be streaming @Event@s through our mailbox, so our @output@ has type
@(Output Event)@ and our @input@ has type @(Input Event)@.
To stream @Event@s into the mailbox , we use 'toOutput', which writes values
to the mailbox's 'Output' end:
@
'toOutput' :: ('MonadIO' m) => 'Output' a -> 'Consumer' a m ()
@
We can concurrently forward multiple streams to the same 'Output', which
asynchronously merges their messages into the same mailbox:
> ...
> forkIO $ do runEffect $ lift user >~ toOutput output
> performGC -- I'll explain 'performGC' below
>
> forkIO $ do runEffect $ acidRain >-> toOutput output
> performGC
> ...
To stream @Event@s out of the mailbox, we use 'fromInput', which streams
values from the mailbox's 'Input' end using a 'Producer':
@
'fromInput' :: ('MonadIO' m) => 'Input' a -> 'Producer' a m ()
@
For this example we'll build a 'Consumer' to handle this stream of @Event@s,
that either harms or heals our intrepid adventurer depending on which
@Event@ we receive:
-}
handler :: Consumer Event IO ()
handler = loop 100
where
loop health = do
lift $ putStrLn $ "Health = " ++ show health
event <- await
case event of
Harm n -> loop (health - n)
Heal n -> loop (health + n)
Quit -> return ()
{-
Now we can just connect our @Event@ 'Producer' to our @Event@ 'Consumer'
using ('>->'):
> ...
> runEffect $ fromInput input >-> handler
Our final @main@ looks like this:
-}
-- these both work as in the tutorial
main0 = do
(output, input, seal) <- spawn unbounded
do forkIO $ do runEffect $ lift user >~ toOutput output
atomically seal
forkIO $ do runEffect $ acidRain >-> toOutput output
atomically seal
runEffect $ fromInput input >-> handler
main0' = withSpawn unbounded $ \(output, input) ->
do forkIO $ do runEffect $ lift user >~ toOutput output
forkIO $ do runEffect $ acidRain >-> toOutput output
runEffect $ fromInput input >-> handler
{-
... and when we run it we get the desired concurrent behavior:
> $ ./game
> Health = 100
> 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 (threadDelay)
> import Control.Monad
> import Pipes
>
-}
worker :: (Show a) => Int -> Consumer a IO r
worker i = forever $ do
a <- await
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 qualified Pipes.Prelude as P
> import Pipes.Concurrent
>
-}
main1 = do
(output, input, seal) <- spawn unbounded
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> worker i
atomically seal
a <- async $ do runEffect $ each [1..10] >-> toOutput output
atomically seal
mapM_ wait (a:as)
-- ^^^ works fine
main1' = withSpawn unbounded $ \(output, input) -> do
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> worker i
a <- async $ do runEffect $ each [1..10] >-> toOutput output
mapM_ wait (a:as)
-- ^^^ tutorial: thread blocked indefinitely in an STM transaction
{-
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 'each' with a different source that reads lines from user
input until the user types \"quit\":
-}
user' :: Producer String IO ()
user' = P.stdinLn >-> P.takeWhile (/= "quit")
main2 = do
(output, input, seal) <- spawn unbounded
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> worker i
atomically seal
a <- async $ do runEffect $ user' >-> toOutput output
atomically seal
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 'Output' could potentially add more
data to the mailbox.
It turns out that 'spawn' is smart and instruments the 'Input' to
terminate when the 'Output' is garbage collected. 'fromInput' builds on top
of the more primitive 'recv' command, which returns a 'Nothing' when the
'Input' terminates:
@
'recv' :: 'Input' a -> 'STM' ('Maybe' a)
@
Otherwise, 'recv' will block if the mailbox is empty since if the 'Output'
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 runEffect $ fromInput input >-> P.take 2 >-> worker i
> performGC
> ...
Let's find out:
-}
main3 = do
(output, input, seal) <- spawn unbounded
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
atomically seal
a <- async $ do runEffect $ user' >-> toOutput output
atomically seal
mapM_ wait (a:as)
-- ^^^ closes when the first worker takes a second task (?)
-- since any worker can 'seal'
main3' = do
withSpawn unbounded $ \(output, input) -> do
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
a <- async $ do runEffect $ user' >-> toOutput output
mapM_ wait (a:as)
-- ^^^ every worker does 2; but input never stops accepting.
{-
> $ ./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>
> $
'spawn' tells the 'Output' to similarly terminate when the 'Input' is
garbage collected, preventing the user from submitting new values.
'toOutput' builds on top of the more primitive 'send' command, which returns
a 'False' when the 'Output' terminates:
@
'send' :: 'Output' a -> a -> 'STM' 'Bool'
@
Otherwise, 'send' will blocks if the mailbox is full, since if the 'Input'
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 'Output' or 'Input'. Without these calls we cannot
guarantee that the garbage collector will trigger and notify the opposing
end if the last reference was released.
There are two ways to avoid using 'performGC'. First, you can omit the
'performGC' call, which is safe and preferable for long-running programs.
This simply delays garbage collecting mailboxes until the next garbage
collection cycle.
Second, you can use the 'spawn'' command, which returns a third @seal@
action:
> (output, input, seal) <- spawn' buffer
> ...
Use this to @seal@ the mailbox so that it cannot receive new messages. This
allows both readers and writers to shut down early without relying on
garbage collection:
* writers will shut down immediately because they can no longer write to the
mailbox
* readers will shut down when the mailbox goes empty because they know that
no new data will arrive
For simplicity, this tutorial will continue to use `performGC` since all
the examples are short-lived programs that do not build up a large heap.
However, when the heap grows large you want to avoid `performGC` and
consider using one of the above two alternatives instead.
Note only 'Input's and 'Output's specifically built using 'spawn' or
'spawn'' make use of the garbage collector. If you build your own custom
'Input's and 'Output's then you do not need to use 'performGC' at all.
-}
{- $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 'Output' and the 'Input' 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
the console:
-}
main4 = do
(output, input, seal) <- spawn $ bounded 1
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
atomically seal
a <- async $ do runEffect $ each [1..] >-> P.chain print >-> toOutput output
atomically seal
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
> $
-}
main5 = do
(output, input, seal) <- spawn unbounded
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
atomically seal
a <- async $ do runEffect $ each [1..] >-> P.chain print >-> toOutput output
atomically seal
mapM_ wait (a:as)
{-
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
> (output, input) <- spawn (Bounded 100)
> ...
-}
main6 = do
(output, input, seal) <- spawn $ bounded 100
as <- forM [1..3] $ \i ->
async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
atomically seal
a <- async $ do runEffect $ each [1..] >-> P.chain print >-> toOutput output
atomically seal
mapM_ wait (a:as)
-- as in tutorial
main6' =
withSpawn (bounded 100) $ \(output, input) -> do
as <- forM [1..3] $ \i ->
async $ do
runEffect $ fromInput input >-> P.take 2 >-> worker i
a <- async $ do
runEffect $ each [1..] >-> P.chain print >-> toOutput output
mapM_ wait (a:as)
-- tutorial: thread blocked indefinitely in an STM transaction
{-
> $ ./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 'Output' to combine multiple
'Output' ends together into a single broadcast 'Output':
> -- broadcast.hs
>
> import Control.Monad
> import Control.Concurrent.Async
> import Pipes
> import Pipes.Concurrent
> import qualified Pipes.Prelude as P
> import Data.Monoid
>
-}
main7 = do
(output1, input1, seal1) <- spawn unbounded
(output2, input2, seal2) <- spawn unbounded
a1 <- async $ do
runEffect $ P.stdinLn >-> toOutput (output1 <> output2)
atomically seal1
atomically seal2
as <- forM [input1, input2] $ \input -> async $ do
runEffect $ fromInput input >-> P.take 2 >-> P.stdoutLn
atomically seal1
atomically seal2
mapM_ wait (a1:as)
-- ^^^ as in tutorial
main7' =
withSpawn unbounded $ \(output1, input1) ->
withSpawn unbounded $ \(output2, input2) ->
do a1 <- async $ runEffect $ P.stdinLn >-> toOutput (output1 <> output2)
as <- forM [input1, input2] $ \input -> async $ do
runEffect $ fromInput input >-> P.take 2 >-> P.stdoutLn
mapM_ wait (a1:as)
-- ^^^ continues to take input after worker finishes
main7'' =
withSpawn unbounded $ \(output1, input1) -> do
(output2, input2, seal) <- spawn unbounded
a1 <- async $ do
runEffect $ P.stdinLn >-> toOutput (output1 <> output2)
atomically seal
as <- forM [input1, input2] $ \input -> async $ do
runEffect $ fromInput input >-> P.take 2 >-> P.stdoutLn
atomically seal
mapM_ wait (a1:as)
-- ^^^ continues to take input after worker finishes
{-
In the above example, 'P.stdinLn' will broadcast user input to both
mailboxes, and each mailbox forwards its values to 'P.stdoutLn', echoing the
message to standard output:
> $ ./broadcast
> ABC<Enter>
> ABC
> ABC
> DEF<Enter>
> DEF
> DEF
> GHI<Enter>
> $
The combined 'Output' stays alive as long as any of the original 'Output's
remains alive. In the above example, 'toOutput' 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 'Output's, but keep in mind that you
will incur a performance price if you combine thousands of 'Output'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 (threadDelay)
> import Control.Monad
> import Pipes
> import qualified Pipes.Prelude as P
>
-}
-- Fast input updates
inputDevice :: (Monad m) => Producer Integer m ()
inputDevice = each [1..]
-- Slow output updates
outputDevice :: Consumer Integer IO r
outputDevice = forever $ do
n <- await
lift $ do
print n
threadDelay 1000000
main8 = do
(output, input, seal) <- spawn (latest 0)
a1 <- async $ do runEffect $ inputDevice >-> toOutput output
atomically seal
a2 <- async $ do runEffect $ fromInput input >-> P.take 5 >-> outputDevice
atomically seal
mapM_ wait [a1, a2]
-- ^^^ as in tutorial
main8' = do
withSpawn (latest 0) $ \ (output, input) -> do
a1 <- async $ do runEffect $ inputDevice >-> toOutput output
a2 <- async $ do runEffect $ fromInput input >-> P.take 5 >-> outputDevice
mapM_ wait [a1, a2]
-- ^^^ ends indefinitely blocking without error
{-
'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
> 7
> 2626943
> 5303844
> 7983519
> 10604940
> $
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.
Another alternative is to use the 'Newest' mailbox, which is like a
'Bounded' mailbox, except 'send' never blocks (the mailbox is never full).
Instead, if there is no room 'send' will remove the oldest message from the
mailbox to make room for a new message.
The 'New' mailbox is like the 'Newest' mailbox, except optimized for the
special case where you want to store a single message. You can use 'New' to
read from a source that might potentially update rapidly, but still sleep if
the source has no new values:
-}
inputDevice' :: Producer Integer IO ()
inputDevice' = do
each [1..100] -- Rapid updates
lift $ threadDelay 4000000 -- Source goes quiet for 4 seconds
each [101..] -- More rapid updates
main9 = do
(output, input, seal) <- spawn (latest 0)
a1 <- async $ do runEffect $ inputDevice' >-> toOutput output
atomically seal
a2 <- async $ do runEffect $ fromInput input >-> P.take 5 >-> outputDevice
atomically seal
mapM_ wait [a1, a2]
-- ^^^ as in tutorial
main9' = do
withSpawn (latest 0) $ \ (output, input) -> do
a1 <- async $ do runEffect $ inputDevice' >-> toOutput output
a2 <- async $ do runEffect $ fromInput input >-> P.take 5 >-> outputDevice
mapM_ wait [a1, a2]
{-
When the source goes quiet, the 'Input' will now block and wait, and will
never read the same value twice:
> $ ./peek
> 7
> 100
> <Longer pause>
> 16793
> 5239440
> 10474439
> $
-}
{- $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
>
We can use 'send' to free the data from the callback and then we can
retrieve the data on the outside using 'fromInput':
> import Pipes
> import Pipes.Concurrent
> import qualified Pipes.Prelude as P
-}
onLines :: (String -> IO a) -> IO b
onLines callback = forever $ do
str <- getLine
callback str
onLines' :: Producer String IO ()
onLines' = do
(output, input,seal) <- lift $ spawn (bounded 1)
lift $ forkIO $ do
onLines (\str -> atomically $ send output str)
fromInput input
main10 = runEffect $ onLines' >-> P.takeWhile (/= "quit") >-> P.stdoutLn
{-
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:
> -- cycle.hs
>
> import Control.Concurrent.Async
> import Pipes
> import Pipes.Concurrent
> import qualified Pipes.Prelude as P
>
-}
main11 = do
(out1, in1, seal1) <- spawn unbounded
(out2, in2, seal2) <- spawn unbounded
a1 <- async $ do
runEffect $ (each [1,2] >> fromInput in1) >-> toOutput out2
atomically seal1
atomically seal2
a2 <- async $ do
runEffect $ fromInput in2 >-> P.chain print >-> P.take 6 >-> toOutput out1
atomically seal1
atomically seal2
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 (threadDelay)
>import Control.Monad (forever)
>import Pipes
>import Pipes.Concurrent
>
>data Event = Harm Integer | Heal Integer | Quit deriving (Show)
>
>user :: IO Event
>user = do
> command <- getLine
> case command of
> "potion" -> return (Heal 10)
> "quit" -> return Quit
> _ -> do
> putStrLn "Invalid command"
> user
>
>acidRain :: Producer Event IO r
>acidRain = forever $ do
> lift $ threadDelay 2000000 -- Wait 2 seconds
> yield (Harm 1)
>
>handler :: Consumer Event IO ()
>handler = loop 100
> where
> loop health = do
> lift $ putStrLn $ "Health = " ++ show health
> event <- await
> case event of
> Harm n -> loop (health - n)
> Heal n -> loop (health + n)
> Quit -> return ()
>
>main = do
> (output, input) <- spawn Unbounded
>
> forkIO $ do runEffect $ lift user >~ toOutput output
> performGC
>
> forkIO $ do runEffect $ acidRain >-> toOutput output
> performGC
>
> runEffect $ fromInput input >-> handler
>-- work.hs
>
>import Control.Concurrent (threadDelay)
>import Control.Concurrent.Async
>import Control.Monad
>import Pipes
>import Pipes.Concurrent
>import qualified Pipes.Prelude as P
>
>worker :: (Show a) => Int -> Consumer a IO r
>worker i = forever $ do
> a <- await
> lift $ threadDelay 1000000 -- 1 second
> lift $ putStrLn $ "Worker #" ++ show i ++ ": Processed " ++ show a
>
>user :: Producer String IO ()
>user = P.stdinLn >-> P.takeWhile (/= "quit")
>
>main = do
>-- (output, input) <- spawn Unbounded
>-- (output, input) <- spawn Single
> (output, input) <- spawn (Bounded 100)
>
> as <- forM [1..3] $ \i ->
>-- async $ do runEffect $ fromInput input >-> worker i
> async $ do runEffect $ fromInput input >-> P.take 2 >-> worker i
> performGC
>
>-- a <- async $ do runEffect $ each [1..10] >-> toOutput output
>-- a <- async $ do runEffect $ user >-> toOutput output
> a <- async $ do runEffect $ each [1..] >-> P.chain print >-> toOutput output
> performGC
>
> mapM_ wait (a:as)
>-- peek.hs
>
>import Control.Concurrent (threadDelay)
>import Control.Concurrent.Async
>import Control.Monad
>import Pipes
>import Pipes.Concurrent
>import qualified Pipes.Prelude as P
>
>inputDevice :: (Monad m) => Producer Integer m ()
>inputDevice = each [1..]
>
>outputDevice :: Consumer Integer IO r
>outputDevice = forever $ do
> n <- await
> lift $ do
> print n
> threadDelay 1000000
>
>main = do
> (output, input) <- spawn (Latest 0)
> a1 <- async $ do runEffect $ inputDevice >-> toOutput output
> performGC
> a2 <- async $ do runEffect $ fromInput input >-> P.take 5 >-> outputDevice
> performGC
> mapM_ wait [a1, a2]
>-- callback.hs
>
>import Control.Monad
>import Pipes
>import Pipes.Concurrent
>import qualified Pipes.Prelude as P
>
>onLines :: (String -> IO a) -> IO b
>onLines callback = forever $ do
> str <- getLine
> callback str
>
>onLines' :: Producer String IO ()
>onLines' = do
> (output, input) <- lift $ spawn Single
> lift $ forkIO $ onLines (\str -> atomically $ send output str)
> fromInput input
>
>main = runEffect $ onLines' >-> P.takeWhile (/= "quit") >-> P.stdoutLn
-}
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