{-# OPTIONS_GHC -fno-warn-unused-imports #-} {-| Conventional Haskell stream programming forces you to choose only two of the following three features: * Effects * Streaming * Composability If you sacrifice /Effects/ you get Haskell's pure and lazy lists, which you can transform using composable functions in constant space, but without interleaving effects. If you sacrifice /Streaming/ you get 'mapM', 'forM' and \"ListT done wrong\", which are composable and effectful, but do not return a single result until the whole list has first been processed and loaded into memory. If you sacrifice /Composability/ you write a tightly coupled read, transform, and write loop in 'IO', which is streaming and effectful, but is not modular or separable. @pipes@ gives you all three features: effectful, streaming, and composable programming. @pipes@ also provides a wide variety of stream programming abstractions which are all subsets of a single unified machinery: * effectful 'Producer's (like generators), * effectful 'Consumer's (like iteratees), * effectful 'Pipe's (like Unix pipes), and: * 'ListT' done right. All of these are connectable and you can combine them together in clever and unexpected ways because they all share the same underlying type. @pipes@ requires a basic understanding of monad transformers, which you can learn about by reading either: * the paper \"Monad Transformers - Step by Step\", * part III \"Monads in the Real World\" of the tutorial \"All About Monads\", * chapter 18 of \"Real World Haskell\" on monad transformers, or: * the documentation of the @transformers@ library. If you want a Quick Start guide to @pipes@, read the documentation in "Pipes.Prelude" from top to bottom. This tutorial is more extensive and explains the @pipes@ API in greater detail and illustrates several idioms. -} module Pipes.Tutorial ( -- * Introduction -- $introduction -- * Producers -- $producers -- * Composability -- $composability -- * Consumers -- $consumers -- * Pipes -- $pipes -- * ListT -- $listT -- * Tricks -- $tricks -- * Conclusion -- $conclusion -- * Appendix: Types -- $types -- * Appendix: Time Complexity -- $timecomplexity -- * Copyright -- $copyright ) where import Control.Category import Control.Monad import Pipes import qualified Pipes.Prelude as P import Prelude hiding ((.), id) {- $introduction The @pipes@ library decouples stream processing stages from each other so that you can mix and match diverse stages to produce useful streaming programs. If you are a library writer, @pipes@ lets you package up streaming components into a reusable interface. If you are an application writer, @pipes@ lets you connect pre-made streaming components with minimal effort to produce a highly-efficient program that streams data in constant memory. To enforce loose coupling, components can only communicate using two commands: * 'yield': Send output data * 'await': Receive input data @pipes@ has four types of components built around these two commands: * 'Producer's can only 'yield' values and they model streaming sources * 'Consumer's can only 'await' values and they model streaming sinks * 'Pipe's can both 'yield' and 'await' values and they model stream transformations * 'Effect's can neither 'yield' nor 'await' and they model non-streaming components You can connect these components together in four separate ways which parallel the four above types: * 'for' handles 'yield's * ('>~') handles 'await's * ('>->') handles both 'yield's and 'await's * ('>>=') handles return values As you connect components their types will change to reflect inputs and outputs that you've fused away. You know that you're done connecting things when you get an 'Effect', meaning that you have handled all inputs and outputs. You run this final 'Effect' to begin streaming. -} {- $producers 'Producer's are effectful streams of input. Specifically, a 'Producer' is a monad transformer that extends any base monad with a new 'yield' command. This 'yield' command lets you send output downstream to an anonymous handler, decoupling how you generate values from how you consume them. The following @stdinLn@ 'Producer' shows how to incrementally read in 'String's from standard input and 'yield' them downstream, terminating gracefully when reaching the end of the input: > -- echo.hs > > import Control.Monad (unless) > import Pipes > import System.IO (isEOF) > > -- +--------+-- A 'Producer' that yields 'String's > -- | | > -- | | +-- Every monad transformer has a base monad. > -- | | | This time the base monad is 'IO'. > -- | | | > -- | | | +-- Every monadic action has a return value. > -- | | | | This action returns '()' when finished > -- v v v v > stdinLn :: Producer String IO () > stdinLn = do > eof <- lift isEOF -- 'lift' an 'IO' action from the base monad > unless eof $ do > str <- lift getLine > yield str -- 'yield' the 'String' > stdinLn -- Loop 'yield' emits a value, suspending the current 'Producer' until the value is consumed. If nobody consumes the value (which is possible) then 'yield' never returns. You can think of 'yield' as having the following type: @ 'yield' :: 'Monad' m => a -> 'Producer' a m () @ The true type of 'yield' is actually more general and powerful. Throughout the tutorial I will present type signatures like this that are simplified at first and then later reveal more general versions. So read the above type signature as simply saying: \"You can use 'yield' within a 'Producer', but you may be able to use 'yield' in other contexts, too.\" Click the link to 'yield' to navigate to its documentation. There you will see that 'yield' actually uses the 'Producer'' (with an apostrophe) type synonym which hides a lot of polymorphism behind a simple veneer. The documentation for 'yield' says that you can also use 'yield' within a 'Pipe', too, because of this polymorphism: @ 'yield' :: 'Monad' m => a -> 'Pipe' x a m () @ Use simpler types like these to guide you until you understand the fully general type. 'for' loops are the simplest way to consume a 'Producer' like @stdinLn@. 'for' has the following type: @ \-\- +-- Producer +-- The body of the +-- Result \-\- | to loop | loop | \-\- v over v v \-\- -------------- ------------------ ---------- 'for' :: 'Monad' m => 'Producer' a m r -> (a -> 'Effect' m ()) -> 'Effect' m r @ @(for producer body)@ loops over @(producer)@, substituting each 'yield' in @(producer)@ with @(body)@. You can also deduce that behavior purely from the type signature: * The body of the loop takes exactly one argument of type @(a)@, which is the same as the output type of the 'Producer'. Therefore, the body of the loop must get its input from that 'Producer' and nowhere else. * The return value of the input 'Producer' matches the return value of the result, therefore 'for' must loop over the entire 'Producer' and not skip anything. The above type signature is not the true type of 'for', which is actually more general. Think of the above type signature as saying: \"If the first argument of 'for' is a 'Producer' and the second argument returns an 'Effect', then the final result must be an 'Effect'.\" Click the link to 'for' to navigate to its documentation. There you will see the fully general type and underneath you will see equivalent simpler types. One of these says that if the body of the loop is a 'Producer', then the result is a 'Producer', too: @ 'for' :: 'Monad' m => 'Producer' a m r -> (a -> 'Producer' b m ()) -> 'Producer' b m r @ The first type signature I showed for 'for' was a special case of this slightly more general signature because a 'Producer' that never 'yield's is also an 'Effect': @ data 'X' -- The uninhabited type \ type 'Effect' m r = 'Producer' 'X' m r @ This is why 'for' permits two different type signatures. The first type signature is just a special case of the second one: @ 'for' :: 'Monad' m => 'Producer' a m r -> (a -> 'Producer' b m ()) -> 'Producer' b m r \ -- Specialize \'b\' to \'X\' 'for' :: 'Monad' m => 'Producer' a m r -> (a -> 'Producer' 'X' m ()) -> 'Producer' 'X' m r \ -- Producer X = Effect 'for' :: 'Monad' m => 'Producer' a m r -> (a -> 'Effect' m ()) -> 'Effect' m r @ This is the same trick that all @pipes@ functions use to work with various combinations of 'Producer's, 'Consumer's, 'Pipe's, and 'Effect's. Each function really has just one general type, which you can then simplify down to multiple useful alternative types. Here's an example use of a 'for' @loop@, where the second argument (the loop body) is an 'Effect': > -- echo.hs > > loop :: Effect IO () > loop = for stdinLn $ \str -> do -- Read this like: "for str in stdinLn" > lift $ putStrLn str -- The body of the 'for' loop > > -- more concise: loop = for stdinLn (lift . putStrLn) In this example, 'for' loops over @stdinLn@ and replaces every 'yield' in @stdinLn@ with the body of the loop, printing each line. This is exactly equivalent to the following code, which I've placed side-by-side with the original definition of @stdinLn@ for comparison: > loop = do | stdinLn = do > eof <- lift isEOF | eof <- lift isEOF > unless eof $ do | unless eof $ do > str <- lift getLine | str <- lift getLine > (lift . putStrLn) str | yield str > loop | stdinLn You can think of 'yield' as creating a hole and a 'for' loop is one way to fill that hole. Notice how the final @loop@ only 'lift's actions from the base monad and does nothing else. This property is true for all 'Effect's, which are just glorified wrappers around actions in the base monad. This means we can run these 'Effect's to remove their 'lift's and lower them back to the equivalent computation in the base monad: @ 'runEffect' :: 'Monad' m => 'Effect' m r -> m r @ This is the real type signature of 'runEffect', which refuses to accept anything other than an 'Effect'. This ensures that we handle all inputs and outputs before streaming data: > -- echo.hs > > main :: IO () > main = runEffect loop ... or you could inline the entire @loop@ into the following one-liner: > main = runEffect $ for stdinLn (lift . putStrLn) Our final program loops over standard input and echoes every line to standard output until we hit @Ctrl-D@ to end the input stream: > $ ghc -O2 echo.hs > $ ./echo > Test<Enter> > Test > ABC<Enter> > ABC > <Ctrl-D> > $ The final behavior is indistinguishable from just removing all the 'lift's from @loop@: > main = do | loop = do > eof <- isEof | eof <- lift isEof > unless eof $ do | unless eof $ do > str <- getLine | str <- lift getLine > putStrLn str | (lift . putStrLn) str > main | loop This @main@ is what we might have written by hand if we were not using @pipes@, but with @pipes@ we can decouple the input and output logic from each other. When we connect them back together, we still produce streaming code equivalent to what a sufficiently careful Haskell programmer would have written. You can also use 'for' to loop over lists, too. To do so, convert the list to a 'Producer' using 'each', which is exported by default from "Pipes": > each :: Monad m => [a] -> Producer a m () > each as = mapM_ yield as Combine 'for' and 'each' to iterate over lists using a \"foreach\" loop: >>> runEffect $ for (each [1..4]) (lift . print) 1 2 3 4 'each' is actually more general and works for any 'Foldable': @ 'each' :: ('Monad' m, 'Foldable' f) => f a -> 'Producer' a m () @ So you can loop over any 'Foldable' container or even a 'Maybe': >>> runEffect $ for (each (Just 1)) (lift . print) 1 -} {- $composability You might wonder why the body of a 'for' loop can be a 'Producer'. Let's test out this feature by defining a new loop body that creates three copies of every value: > -- nested.hs > > import Pipes > import qualified Pipes.Prelude as P -- Pipes.Prelude already has 'stdinLn' > > triple :: Monad m => a -> Producer a m () > triple x = do > yield x > yield x > yield x > > loop :: Producer String IO () > loop = for P.stdinLn triple > > -- This is the exact same as: > -- > -- loop = for P.stdinLn $ \x -> do > -- yield x > -- yield x > -- yield x This time our @loop@ is a 'Producer' that outputs 'String's, specifically three copies of each line that we read from standard input. Since @loop@ is a 'Producer' we cannot run it because there is still unhandled output. However, we can use yet another 'for' to handle this new repeated stream: > -- nested.hs > > main = runEffect $ for loop (lift . putStrLn) This creates a program which echoes every line from standard input to standard output three times: > $ ./nested > Test<Enter> > Test > Test > Test > ABC<Enter> > ABC > ABC > ABC > <Ctrl-D> > $ But is this really necessary? Couldn't we have instead written this using a nested for loop? > main = runEffect $ > for P.stdinLn $ \str1 -> > for (triple str1) $ \str2 -> > lift $ putStrLn str2 Yes, we could have! In fact, this is a special case of the following equality, which always holds no matter what: @ \-\- s :: Monad m => 'Producer' a m () -- i.e. \'P.stdinLn\' \-\- f :: Monad m => a -> 'Producer' b m () -- i.e. \'triple\' \-\- g :: Monad m => b -> 'Producer' c m () -- i.e. \'(lift . putStrLn)\' \ for (for s f) g = for s (\\x -> for (f x) g) @ We can understand the rationale behind this equality if we first define the following operator that is the point-free counterpart to 'for': @ (~>) :: Monad m => (a -> 'Producer' b m ()) -> (b -> 'Producer' c m ()) -> (a -> 'Producer' c m ()) (f ~> g) x = for (f x) g @ Using ('~>') (pronounced \"into\"), we can transform our original equality into the following more symmetric equation: @ f :: Monad m => a -> 'Producer' b m () g :: Monad m => b -> 'Producer' c m () h :: Monad m => c -> 'Producer' d m () \ \-\- Associativity (f ~> g) ~> h = f ~> (g ~> h) @ This looks just like an associativity law. In fact, ('~>') has another nice property, which is that 'yield' is its left and right identity: > -- Left Identity > yield ~> f = f > -- Right Identity > f ~> yield = f In other words, 'yield' and ('~>') form a 'Category', specifically the generator category, where ('~>') plays the role of the composition operator and 'yield' is the identity. If you don't know what a 'Category' is, that's okay, and category theory is not a prerequisite for using @pipes@. All you really need to know is that @pipes@ uses some simple category theory to keep the API intuitive and easy to use. Notice that if we translate the left identity law to use 'for' instead of ('~>') we get: > for (yield x) f = f x This just says that if you iterate over a pure single-element 'Producer', then you could instead cut out the middle man and directly apply the body of the loop to that single element. If we translate the right identity law to use 'for' instead of ('~>') we get: > for s yield = s This just says that if the only thing you do is re-'yield' every element of a stream, you get back your original stream. These three \"for loop\" laws summarize our intuition for how 'for' loops should behave and because these are 'Category' laws in disguise that means that 'Producer's are composable in a rigorous sense of the word. In fact, we get more out of this than just a bunch of equations. We also get a useful operator: ('~>'). We can use this operator to condense our original code into the following more succinct form that composes two transformations: > main = runEffect $ for P.stdinLn (triple ~> lift . putStrLn) This means that we can also choose to program in a more functional style and think of stream processing in terms of composing transformations using ('~>') instead of nesting a bunch of 'for' loops. The above example is a microcosm of the design philosophy behind the @pipes@ library: * Define the API in terms of categories * Specify expected behavior in terms of category laws * Think compositionally instead of sequentially -} {- $consumers Sometimes you don't want to use a 'for' loop because you don't want to consume every element of a 'Producer' or because you don't want to process every value of a 'Producer' the exact same way. The most general solution is to externally iterate over the 'Producer' using the 'next' command: @ 'next' :: 'Monad' m => 'Producer' a m r -> m ('Either' r (a, 'Producer' a m r)) @ Think of 'next' as pattern matching on the head of the 'Producer'. This 'Either' returns a 'Left' if the 'Producer' is done or it returns a 'Right' containing the next value, @a@, along with the remainder of the 'Producer'. However, sometimes we can get away with something a little more simple and elegant, like a 'Consumer', which represents an effectful sink of values. A 'Consumer' is a monad transformer that extends the base monad with a new 'await' command. This 'await' command lets you receive input from an anonymous upstream source. The following @stdoutLn@ 'Consumer' shows how to incrementally 'await' 'String's and print them to standard output, terminating gracefully when receiving a broken pipe error: > import Control.Monad (unless) > import Control.Exception (try, throwIO) > import qualified GHC.IO.Exception as G > import Pipes > > -- +--------+-- A 'Consumer' that awaits 'String's > -- | | > -- v v > stdoutLn :: Consumer String IO () > stdoutLn = do > str <- await -- 'await' a 'String' > x <- lift $ try $ putStrLn str > case x of > -- Gracefully terminate if we got a broken pipe error > Left e@(G.IOError { G.ioe_type = t}) -> > lift $ unless (t == G.ResourceVanished) $ throwIO e > -- Otherwise loop > Right () -> stdoutLn 'await' is the dual of 'yield': we suspend our 'Consumer' until we receive a new value. If nobody provides a value (which is possible) then 'await' never returns. You can think of 'await' as having the following type: @ 'await' :: 'Monad' m => 'Consumer' a m a @ One way to feed a 'Consumer' is to repeatedly feed the same input using ('>~') (pronounced \"feed\"): @ \-\- +- Feed +- Consumer to +- Returns new \-\- | action | feed | Effect \-\- v v v \-\- ---------- -------------- ---------- ('>~') :: 'Monad' m => 'Effect' m b -> 'Consumer' b m c -> 'Effect' m c @ @(draw >~ consumer)@ loops over @(consumer)@, substituting each 'await' in @(consumer)@ with @(draw)@. So the following code replaces every 'await' in 'P.stdoutLn' with @(lift getLine)@ and then removes all the 'lift's: >>> runEffect $ lift getLine >~ stdoutLn Test<Enter> Test ABC<Enter> ABC 42<Enter> 42 ... You might wonder why ('>~') uses an 'Effect' instead of a raw action in the base monad. The reason why is that ('>~') actually permits the following more general type: @ ('>~') :: 'Monad' m => 'Consumer' a m b -> 'Consumer' b m c -> 'Consumer' a m c @ ('>~') is the dual of ('~>'), composing 'Consumer's instead of 'Producer's. This means that you can feed a 'Consumer' with yet another 'Consumer' so that you can 'await' while you 'await'. For example, we could define the following intermediate 'Consumer' that requests two 'String's and returns them concatenated: > doubleUp :: Monad m => Consumer String m String > doubleUp = do > str1 <- await > str2 <- await > return (str1 ++ str2) > > -- more concise: doubleUp = (++) <$> await <*> await We can now insert this in between @(lift getLine)@ and @stdoutLn@ and see what happens: >>> runEffect $ lift getLine >~ doubleUp >~ stdoutLn Test<Enter> ing<Enter> Testing ABC<Enter> DEF<Enter> ABCDEF 42<Enter> 000<Enter> 42000 ... 'doubleUp' splits every request from 'stdoutLn' into two separate requests and returns back the concatenated result. We didn't need to parenthesize the above chain of ('>~') operators, because ('>~') is associative: > -- Associativity > (f >~ g) >~ h = f >~ (g >~ h) ... so we can always omit the parentheses since the meaning is unambiguous: > f >~ g >~ h Also, ('>~') has an identity, which is 'await'! > -- Left identity > await >~ f = f > > -- Right Identity > f >~ await = f In other words, ('>~') and 'await' form a 'Category', too, specifically the iteratee category, and 'Consumer's are also composable. -} {- $pipes Our previous programs were unsatisfactory because they were biased either towards the 'Producer' end or the 'Consumer' end. As a result, we had to choose between gracefully handling end of input (using 'P.stdinLn') or gracefully handling end of output (using 'P.stdoutLn'), but not both at the same time. However, we don't need to restrict ourselves to using 'Producer's exclusively or 'Consumer's exclusively. We can connect 'Producer's and 'Consumer's directly together using ('>->') (pronounced \"pipe\"): @ ('>->') :: 'Monad' m => 'Producer' a m r -> 'Consumer' a m r -> 'Effect' m r @ This returns an 'Effect' which we can run: > -- echo2.hs > > import Pipes > import qualified Pipes.Prelude as P -- Pipes.Prelude also provides 'stdoutLn' > > main = runEffect $ P.stdinLn >-> P.stdoutLn This program is more declarative of our intent: we want to stream values from 'P.stdinLn' to 'P.stdoutLn'. The above \"pipeline\" not only echoes standard input to standard output, but also handles both end of input and broken pipe errors: > $ ./echo2 > Test<Enter> > Test > ABC<Enter> > ABC > 42<Enter> > 42 > <Ctrl-D> > $ ('>->') is \"pull-based\" meaning that control flow begins at the most downstream component (i.e. 'P.stdoutLn' in the above example). Any time a component 'await's a value it blocks and transfers control upstream and every time a component 'yield's a value it blocks and restores control back downstream, satisfying the 'await'. So in the above example, ('>->') matches every 'await' from 'P.stdoutLn' with a 'yield' from 'P.stdinLn'. Streaming stops when either 'P.stdinLn' terminates (i.e. end of input) or 'P.stdoutLn' terminates (i.e. broken pipe). This is why ('>->') requires that both the 'Producer' and 'Consumer' share the same type of return value: whichever one terminates first provides the return value for the entire 'Effect'. Let's test this by modifying our 'Producer' and 'Consumer' to each return a diagnostic 'String': > -- echo3.hs > > import Control.Applicative ((<$)) -- (<$) modifies return values > import Pipes > import qualified Pipes.Prelude as P > import System.IO > > main = do > hSetBuffering stdout NoBuffering > str <- runEffect $ > ("End of input!" <$ P.stdinLn) >-> ("Broken pipe!" <$ P.stdoutLn) > hPutStrLn stderr str This lets us diagnose whether the 'Producer' or 'Consumer' terminated first: > $ ./echo3 > Test<Enter> > Test > <Ctrl-D> > End of input! > $ ./echo3 | perl -e 'close STDIN' > Test<Enter> > Broken pipe! > $ You might wonder why ('>->') returns an 'Effect' that we have to run instead of directly returning an action in the base monad. This is because you can connect things other than 'Producer's and 'Consumer's, like 'Pipe's, which are effectful stream transformations. A 'Pipe' is a monad transformer that is a mix between a 'Producer' and 'Consumer', because a 'Pipe' can both 'await' and 'yield'. The following example 'Pipe' is analagous to the Prelude's 'take', only allowing a fixed number of values to flow through: > -- take.hs > > import Control.Monad (replicateM_) > import Pipes > import Prelude hiding (take) > > -- +--------- A 'Pipe' that > -- | +---- 'await's 'a's and > -- | | +-- 'yield's 'a's > -- | | | > -- v v v > take :: Int -> Pipe a a IO () > take n = do > replicateM_ n $ do -- Repeat this block 'n' times > x <- await -- 'await' a value of type 'a' > yield x -- 'yield' a value of type 'a' > lift $ putStrLn "You shall not pass!" -- Fly, you fools! You can use 'Pipe's to transform 'Producer's, 'Consumer's, or even other 'Pipe's using the same ('>->') operator: @ ('>->') :: 'Monad' m => 'Producer' a m r -> 'Pipe' a b m r -> 'Producer' b m r ('>->') :: 'Monad' m => 'Pipe' a b m r -> 'Consumer' b m r -> 'Consumer' a m r ('>->') :: 'Monad' m => 'Pipe' a b m r -> 'Pipe' b c m r -> 'Pipe' a c m r @ For example, you can compose 'P.take' after 'P.stdinLn' to limit the number of lines drawn from standard input: > maxInput :: Int -> Producer String IO () > maxInput n = P.stdinLn >-> take n >>> runEffect $ maxInput 3 >-> P.stdoutLn Test<Enter> Test ABC<Enter> ABC 42<Enter> 42 You shall not pass! >>> ... or you can pre-compose 'P.take' before 'P.stdoutLn' to limit the number of lines written to standard output: > maxOutput :: Int -> Consumer String IO () > maxOutput n = take n >-> P.stdoutLn >>> runEffect $ P.stdinLn >-> maxOutput 3 <Exact same behavior> Those both gave the same behavior because ('>->') is associative: > (p1 >-> p2) >-> p3 = p1 >-> (p2 >-> p3) Therefore we can just leave out the parentheses: >>> runEffect $ P.stdinLn >-> take 3 >-> P.stdoutLn <Exact same behavior> ('>->') is designed to behave like the Unix pipe operator, except with less quirks. In fact, we can continue the analogy to Unix by defining 'cat' (named after the Unix @cat@ utility), which reforwards elements endlessly: > cat :: Monad m => Pipe a a m r > cat = forever $ do > x <- await > yield x 'cat' is the identity of ('>->'), meaning that 'cat' satisfies the following two laws: > -- Useless use of 'cat' > cat >-> p = p > > -- Forwarding output to 'cat' does nothing > p >-> cat = p Therefore, ('>->') and 'cat' form a 'Category', specifically the category of Unix pipes, and 'Pipe's are also composable. A lot of Unix tools have very simple definitions when written using @pipes@: > -- unix.hs > > import Control.Monad (forever) > import Pipes > import qualified Pipes.Prelude as P -- Pipes.Prelude provides 'take', too > import Prelude hiding (head) > > head :: Monad m => Int -> Pipe a a m () > head = P.take > > yes :: Monad m => Producer String m r > yes = forever $ yield "y" > > main = runEffect $ yes >-> head 3 >-> P.stdoutLn This prints out 3 \'@y@\'s, just like the equivalent Unix pipeline: > $ ./unix > y > y > y > $ yes | head -3 > y > y > y > $ This lets us write \"Haskell pipes\" instead of Unix pipes. These are much easier to build than Unix pipes and we can connect them directly within Haskell for interoperability with the Haskell language and ecosystem. -} {- $listT @pipes@ also provides a \"ListT done right\" implementation. This differs from the implementation in @transformers@ because this 'ListT': * obeys the monad laws, and * streams data immediately instead of collecting all results into memory. The latter property is actually an elegant consequence of obeying the monad laws. To bind a list within a 'ListT' computation, combine 'Select' and 'each': > import Pipes > > pair :: ListT IO (Int, Int) > pair = do > x <- Select $ each [1, 2] > lift $ putStrLn $ "x = " ++ show x > y <- Select $ each [3, 4] > lift $ putStrLn $ "y = " ++ show y > return (x, y) You can then loop over a 'ListT' by using 'every': @ 'every' :: 'Monad' m => 'ListT' m a -> 'Producer' a m () @ So you can use your 'ListT' within a 'for' loop: >>> runEffect $ for (every pair) (lift . print) x = 1 y = 3 (1,3) y = 4 (1,4) x = 2 y = 3 (2,3) y = 4 (2,4) ... or a pipeline: >>> import qualified Pipes.Prelude as P >>> runEffect $ every pair >-> P.print <Exact same behavior> Note that 'ListT' is lazy and only produces as many elements as we request: >>> runEffect $ for (every pair >-> P.take 2) (lift . print) x = 1 y = 3 (1,3) y = 4 (1,4) You can also go the other way, binding 'Producer's directly within a 'ListT'. In fact, this is actually what 'Select' was already doing: @ 'Select' :: 'Producer' a m () -> 'ListT' m a @ This lets you write crazy code like: > import Pipes > import qualified Pipes.Prelude as P > > input :: Producer String IO () > input = P.stdinLn >-> P.takeWhile (/= "quit") > > name :: ListT IO String > name = do > firstName <- Select input > lastName <- Select input > return (firstName ++ " " ++ lastName) Here we're binding standard input non-deterministically (twice) as if it were an effectful list: >>> runEffect $ every name >-> P.stdoutLn Daniel<Enter> Fischer<Enter> Daniel Fischer Wagner<Enter> Daniel Wagner quit<Enter> Donald<Enter> Stewart<Enter> Donald Stewart Duck<Enter> Donald Duck quit<Enter> quit<Enter> >>> Notice how this streams out values immediately as they are generated, rather than building up a large intermediate result and then printing all the values in one batch at the end. `ListT` computations can be combined in more ways than `Pipe`s, so try to program in `ListT` as much as possible and defer converting it to a `Pipe` as late as possible using `P.loop`. You can combine `ListT` computations even if their inputs and outputs are completely different: > data In > = InA A > | InB B > | InC C > > data Out > = OutD D > | OutE E > | OutF F > > -- Independent computations > > example1 :: A -> ListT IO D > example2 :: B -> ListT IO E > example3 :: C -> ListT IO F > > -- Combined computation > > total :: In -> ListT IO Out > total input = case input of > InA a -> fmap OutD (example1 a) > InB b -> fmap OutE (example2 b) > InC c -> fmap OutF (example3 c) Sometimes you have multiple computations that handle different inputs but the same output, in which case you don't need to unify their outputs: > -- Overlapping outputs > > example1 :: A -> ListT IO Out > example2 :: B -> ListT IO Out > example3 :: C -> ListT IO Out > > -- Combined computation > > total :: In -> ListT IO Out > total input = case input of > InA a -> example1 a > InB b -> example2 b > InC c -> example3 c Other times you have multiple computations that handle the same input but produce different outputs. You can unify their outputs using the `Monoid` and `Functor` instances for `ListT`: > -- Overlapping inputs > > example1 :: In -> ListT IO D > example2 :: In -> ListT IO E > example3 :: In -> ListT IO F > > -- Combined computation > > total :: In -> ListT IO Out > total input = > fmap OutD (example1 input) > <> fmap OutE (example2 input) > <> fmap OutF (example3 input) You can also chain `ListT` computations, feeding the output of the first computation as the input to the next computation: > -- End-to-end > > aToB :: A -> ListT IO B > bToC :: B -> ListT IO C > > -- Combined computation > > aToC :: A -> LIstT IO C > aToC = aToB >=> bToC ... or you can just use @do@ notation if you prefer. However, the `Pipe` type is more general than `ListT` and can represent things like termination. Therefore you should consider mixing `Pipe`s with `ListT` when you need to take advantage of these extra features: > -- Mix ListT with Pipes > > example :: In -> ListT IO Out > > pipe :: Pipe In Out IO () > pipe = Pipes.takeWhile (not . isC) >-> loop example > where > isC (InC _) = True > isC _ = False So promote your `ListT` logic to a `Pipe` when you need to take advantage of these `Pipe`-specific features. -} {- $tricks @pipes@ is more powerful than meets the eye so this section presents some non-obvious tricks you may find useful. Many pipe combinators will work on unusual pipe types and the next few examples will use the 'cat' pipe to demonstrate this. For example, you can loop over the output of a 'Pipe' using 'for', which is how 'P.map' is defined: > map :: Monad m => (a -> b) -> Pipe a b m r > map f = for cat $ \x -> yield (f x) > > -- Read this as: For all values flowing downstream, apply 'f' This is equivalent to: > map f = forever $ do > x <- await > yield (f x) You can also feed a 'Pipe' input using ('>~'). This means we could have instead defined the @yes@ pipe like this: > yes :: Monad m => Producer String m r > yes = return "y" >~ cat > > -- Read this as: Keep feeding "y" downstream This is equivalent to: > yes = forever $ yield "y" You can also sequence two 'Pipe's together. This is how 'P.drop' is defined: > drop :: Monad m => Int -> Pipe a a m r > drop n = do > replicateM_ n await > cat This is equivalent to: > drop n = do > replicateM_ n await > forever $ do > x <- await > yield x You can even compose pipes inside of another pipe: > customerService :: Producer String IO () > customerService = do > each [ "Hello, how can I help you?" -- Begin with a script > , "Hold for one second." > ] > P.stdinLn >-> P.takeWhile (/= "Goodbye!") -- Now continue with a human Also, you can often use 'each' in conjunction with ('~>') to traverse nested data structures. For example, you can print all non-'Nothing' elements from a doubly-nested list: >>> runEffect $ (each ~> each ~> each ~> lift . print) [[Just 1, Nothing], [Just 2, Just 3]] 1 2 3 Another neat thing to know is that 'every' has a more general type: @ 'every' :: ('Monad' m, 'Enumerable' t) => t m a -> 'Producer' a m () @ 'Enumerable' generalizes 'Foldable' and if you have an effectful container of your own that you want others to traverse using @pipes@, just have your container implement the 'toListT' method of the 'Enumerable' class: > class Enumerable t where > toListT :: Monad m => t m a -> ListT m a You can even use 'Enumerable' to traverse effectful types that are not even proper containers, like 'Control.Monad.Trans.Maybe.MaybeT': > input :: MaybeT IO String > input = do > str <- lift getLine > guard (str /= "Fail") > return str >>> runEffect $ every input >-> P.stdoutLn Test<Enter> Test >>> runEffect $ every input >-> P.stdoutLn Fail<Enter> >>> -} {- $conclusion This tutorial covers the concepts of connecting, building, and reading @pipes@ code. However, this library is only the core component in an ecosystem of streaming components. Derived libraries that build immediately upon @pipes@ include: * @pipes-concurrency@: Concurrent reactive programming and message passing * @pipes-parse@: Minimal utilities for stream parsing * @pipes-safe@: Resource management and exception safety for @pipes@ * @pipes-group@: Grouping streams in constant space These libraries provide functionality specialized to common streaming domains. Additionally, there are several libraries on Hackage that provide even higher-level functionality, which you can find by searching under the \"Pipes\" category or by looking for packages with a @pipes-@ prefix in their name. Current examples include: * @pipes-extras@: Miscellaneous utilities * @pipes-network@/@pipes-network-tls@: Networking * @pipes-zlib@: Compression and decompression * @pipes-binary@: Binary serialization * @pipes-attoparsec@: High-performance parsing * @pipes-aeson@: JSON serialization and deserialization Even these derived packages still do not explore the full potential of @pipes@ functionality, which actually permits bidirectional communication. Advanced @pipes@ users can explore this library in greater detail by studying the documentation in the "Pipes.Core" module to learn about the symmetry of the underlying 'Proxy' type and operators. To learn more about @pipes@, ask questions, or follow @pipes@ development, you can subscribe to the @haskell-pipes@ mailing list at: <https://groups.google.com/forum/#!forum/haskell-pipes> ... or you can mail the list directly at: <mailto:haskell-pipes@googlegroups.com> Additionally, for questions regarding types or type errors, you might find the following appendix on types very useful. -} {- $types @pipes@ uses parametric polymorphism (i.e. generics) to overload all operations. You've probably noticed this overloading already: * 'yield' works within both 'Producer's and 'Pipe's * 'await' works within both 'Consumer's and 'Pipe's * ('>->') connects 'Producer's, 'Consumer's, and 'Pipe's in varying ways This overloading is great when it works, but when connections fail they produce type errors that appear intimidating at first. This section explains the underlying types so that you can work through type errors intelligently. 'Producer's, 'Consumer's, 'Pipe's, and 'Effect's are all special cases of a single underlying type: a 'Proxy'. This overarching type permits fully bidirectional communication on both an upstream and downstream interface. You can think of it as having the following shape: > Proxy a' a b' b m r > > Upstream | Downstream > +---------+ > | | > a' <== <== b' -- Information flowing upstream > | | > a ==> ==> b -- Information flowing downstream > | | | > +----|----+ > v > r The four core types do not use the upstream flow of information. This means that the @a'@ and @b'@ in the above diagram go unused unless you use the more advanced features provided in "Pipes.Core". @pipes@ uses type synonyms to hide unused inputs or outputs and clean up type signatures. These type synonyms come in two flavors: * Concrete type synonyms that explicitly close unused inputs and outputs of the 'Proxy' type * Polymorphic type synonyms that don't explicitly close unused inputs or outputs The concrete type synonyms use @()@ to close unused inputs and 'X' (the uninhabited type) to close unused outputs: * 'Effect': explicitly closes both ends, forbidding 'await's and 'yield's > type Effect = Proxy X () () X > > Upstream | Downstream > +---------+ > | | > X <== <== () > | | > () ==> ==> X > | | | > +----|----+ > v > r * 'Producer': explicitly closes the upstream end, forbidding 'await's > type Producer b = Proxy X () () b > > Upstream | Downstream > +---------+ > | | > X <== <== () > | | > () ==> ==> b > | | | > +----|----+ > v > r * 'Consumer': explicitly closes the downstream end, forbidding 'yield's > type Consumer a = Proxy () a () X > > Upstream | Downstream > +---------+ > | | > () <== <== () > | | > a ==> ==> X > | | | > +----|----+ > v > r * 'Pipe': marks both ends open, allowing both 'await's and 'yield's > type Pipe a b = Proxy () a () b > > Upstream | Downstream > +---------+ > | | > () <== <== () > | | > a ==> ==> b > | | | > +----|----+ > v > r When you compose 'Proxy's using ('>->') all you are doing is placing them side by side and fusing them laterally. For example, when you compose a 'Producer', 'Pipe', and a 'Consumer', you can think of information flowing like this: > Producer Pipe Consumer > +-----------+ +----------+ +------------+ > | | | | | | > X <== <== () <== <== () <== <== () > | stdinLn | | take 3 | | stdoutLn | > () ==> ==> String ==> ==> String ==> ==> X > | | | | | | | | | > +-----|-----+ +----|-----+ +------|-----+ > v v v > () () () Composition fuses away the intermediate interfaces, leaving behind an 'Effect': > Effect > +-----------------------------------+ > | | > X <== <== () > | stdinLn >-> take 3 >-> stdoutLn | > () ==> ==> X > | | > +----------------|------------------+ > v > () @pipes@ also provides polymorphic type synonyms with apostrophes at the end of their names. These use universal quantification to leave open any unused input or output ends (which I mark using @*@): * 'Producer'': marks the upstream end unused but still open > type Producer' b m r = forall x' x . Proxy x' x () b m r > > Upstream | Downstream > +---------+ > | | > * <== <== () > | | > * ==> ==> b > | | | > +----|----+ > v > r * 'Consumer'': marks the downstream end unused but still open > type Consumer' a m r = forall y' y . Proxy () a y' y m r > > Upstream | Downstream > +---------+ > | | > () <== <== * > | | > a ==> ==> * > | | | > +----|----+ > v > r * 'Effect'': marks both ends unused but still open > type Effect' m r = forall x' x y' y . Proxy x' x y' y m r > > Upstream | Downstream > +---------+ > | | > * <== <== * > | | > * ==> ==> * > | | | > +----|----+ > v > r Note that there is no polymorphic generalization of a 'Pipe'. Like before, if you compose a 'Producer'', a 'Pipe', and a 'Consumer'': > Producer' Pipe Consumer' > +-----------+ +----------+ +------------+ > | | | | | | > * <== <== () <== <== () <== <== * > | stdinLn | | take 3 | | stdoutLn | > * ==> ==> String ==> ==> String ==> ==> * > | | | | | | | | | > +-----|-----+ +-----|----+ +------|-----+ > v v v > () () () ... they fuse into an 'Effect'': > Effect' > +-----------------------------------+ > | | > * <== <== * > | stdinLn >-> take 3 >-> stdoutLn | > * ==> ==> * > | | > +----------------|------------------+ > v > () Polymorphic type synonyms come in handy when you want to keep the type as general as possible. For example, the type signature for 'yield' uses 'Producer'' to keep the type signature simple while still leaving the upstream input end open: @ 'yield' :: 'Monad' m => a -> 'Producer'' a m () @ This type signature lets us use 'yield' within a 'Pipe', too, because the 'Pipe' type synonym is a special case of the polymorphic 'Producer'' type synonym: @ type 'Producer'' b m r = forall x' x . 'Proxy' x' x () b m r type 'Pipe' a b m r = 'Proxy' () a () b m r @ The same is true for 'await', which uses the polymorphic 'Consumer'' type synonym: @ 'await' :: 'Monad' m => 'Consumer'' a m a @ We can use 'await' within a 'Pipe' because a 'Pipe' is a special case of the polymorphic 'Consumer'' type synonym: @ type 'Consumer'' a m r = forall y' y . 'Proxy' () a y' y m r type 'Pipe' a b m r = 'Proxy' () a () b m r @ However, polymorphic type synonyms cause problems in many other cases: * They usually give the wrong behavior when used as the argument of a function (known as the \"negative\" or \"contravariant\" position) like this: > f :: Producer' a m r -> ... -- Wrong > > f :: Producer a m r -> ... -- Right The former function only accepts polymorphic 'Producer's as arguments. The latter function accepts both polymorphic and concrete 'Producer's, which is probably what you want. * Even when you desire a polymorphic argument, this induces a higher-ranked type, because it translates to a @forall@ which you cannot factor out to the top-level to simplify the type signature: > f :: (forall x' x y' . Proxy x' x y' m r) -> ... These kinds of type signatures require the @RankNTypes@ extension. * Even when you have polymorphic type synonyms as the result of a function (i.e. the \"positive\" or \"covariant\" position), recent versions of @ghc@ such still require the @RankNTypes@ extension. For example, the 'Pipes.Prelude.fromHandle' function from "Pipes.Prelude" requires @RankNTypes@ to compile correctly on @ghc-7.6.3@: > fromHandle :: MonadIO m => Handle -> Producer' String m () * You can't use polymorphic type synonyms inside other type constructors without the @ImpredicativeTypes@ extension: > io :: IO (Producer' a m r) -- Type error without ImpredicativeTypes * You can't partially apply polymorphic type synonyms: > stack :: MaybeT (Producer' a m) r -- Type error In these scenarios you should fall back on the concrete type synonyms, which are better behaved. If concrete type synonyms are unsatisfactory, then ask @ghc@ to infer the most general type signature and use that. For the purposes of debugging type errors you can just remember that: > Input --+ +-- Output > | | > v v > Proxy a' a b' b m r > ^ ^ > | | > +----+-- Ignore these For example, let's say that you try to run the 'P.stdinLn' 'Producer'. This produces the following type error: >>> runEffect P.stdinLn <interactive>:4:5: Couldn't match expected type `X' with actual type `String' Expected type: Effect m0 r0 Actual type: Proxy X () () String IO () In the first argument of `runEffect', namely `P.stdinLn' In the expression: runEffect P.stdinLn 'runEffect' expects an 'Effect', which is equivalent to the following type: > Effect IO () = Proxy X () () X IO () ... but 'P.stdinLn' type-checks as a 'Producer', which has the following type: > Producer String IO () = Proxy X () () String IO () The fourth type variable (the output) does not match. For an 'Effect' this type variable should be closed (i.e. 'X'), but 'P.stdinLn' has a 'String' output, thus the type error: > Couldn't match expected type `X' with actual type `String' Any time you get type errors like these you can work through them by expanding out the type synonyms and seeing which type variables do not match. You may also consult this table of type synonyms to more easily compare them: > type Effect = Proxy X () () X > type Producer b = Proxy X () () b > type Consumer a = Proxy () a () X > type Pipe a b = Proxy () a () b > > type Server b' b = Proxy X () b' b > type Client a' a = Proxy a' a () X > > type Effect' m r = forall x' x y' y . Proxy x' x y' y m r > type Producer' b m r = forall x' x . Proxy x' x () b m r > type Consumer' a m r = forall y' y . Proxy () a y' y m r > > type Server' b' b m r = forall x' x . Proxy x' x b' b m r > type Client' a' a m r = forall y' y . Proxy a' a y' y m r -} {- $timecomplexity There are three functions that give quadratic time complexity when used in within @pipes@: * 'sequence' * 'replicateM' * 'mapM' For example, the time complexity of this code segment scales quadratically with `n`: > import Control.Monad (replicateM) > import Pipes > > quadratic :: Int -> Consumer a m [a] > quadratic n = replicateM n await These three functions are generally bad practice to use, because all three of them correspond to \"ListT done wrong\", building a list in memory instead of streaming results. However, sometimes situations arise where one deliberately intends to build a list in memory. The solution is to use the \"codensity transformation\" to transform the code to run with linear time complexity. This involves: * wrapping the code in the @Codensity@ monad transformer (from @Control.Monad.Codensity@ module of the @kan-extensions@ package) using 'lift' * applying 'sequence' \/ 'replicateM' \/ 'mapM' * unwrapping the code using @lowerCodensity@ To illustrate this, we'd transform the above example to: > import Control.Monad.Codensity (lowerCodensity) > > linear :: Monad m => Int -> Consumer a m [a] > linear n = lowerCodensity $ replicateM n $ lift await This will produce the exact same result, but in linear time. -} {- $copyright This tutorial is licensed under a <http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License> -}