Safe Haskell | Safe-Inferred |
---|---|

Language | Haskell2010 |

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",
- 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.

# 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:

`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:

`>>>`

1 2 3 4`runEffect $ for (each [1..4]) (lift . print)`

`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`

:

`>>>`

1`runEffect $ for (each (Just 1)) (lift . print)`

# 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 `duplicate`

s every
value:

-- nested.hs import Pipes import qualified Pipes.Prelude as P -- Pipes.Prelude already has 'stdinLn' duplicate :: Monad m => a -> Producer a m () duplicate x = do yield x yield x loop :: Producer String IO () loop = for P.stdinLn duplicate -- This is the exact same as: -- -- loop = for P.stdinLn $ \x -> do -- yield x -- yield x

This time our `loop`

is a `Producer`

that outputs `String`

s, specifically
two 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 duplicated stream:

-- nested.hs main = runEffect $ for loop (lift . putStrLn)

This creates a program which echoes every line from standard input to standard output twice:

$ ./nested Test<Enter> Test Test ABC<Enter> 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 (duplicate 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. 'duplicate' -- 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 (duplicate ~> 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 `stdoutLn`

with
`(lift getLine)`

and then removes all the `lift`

s:

`>>>`

Test<Enter> Test ABC<Enter> ABC 42<Enter> 42 ...`runEffect $ lift getLine >~ stdoutLn`

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:

`>>>`

Test<Enter> ing<Enter> Testing ABC<Enter> DEF<Enter> ABCDEF 42<Enter> 000<Enter> 42000 ...`runEffect $ lift getLine >~ doubleUp >~ stdoutLn`

`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 `stdinLn`

) or
gracefully handling end of output (using `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 `stdinLn`

to `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. `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 `stdoutLn`

with a `yield`

from `stdinLn`

.

Streaming stops when either `stdinLn`

terminates (i.e. end of input) or
`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 `take`

after `stdinLn`

to limit the number
of lines drawn from standard input:

maxInput :: Int -> Producer String IO () maxInput n = P.stdinLn >-> take n

`>>>`

Test<Enter> Test ABC<Enter> ABC 42<Enter> 42 You shall not pass!`runEffect $ maxInput 3 >-> P.stdoutLn`

`>>>`

... or you can pre-compose `take`

before `stdoutLn`

to limit the number
of lines written to standard output:

maxOutput :: Int -> Consumer String IO () maxOutput n = take n >-> P.stdoutLn

`>>>`

<Exact same behavior>`runEffect $ P.stdinLn >-> maxOutput 3`

Those both gave the same behavior because (`>->`

) is associative:

(p1 >-> p2) >-> p3 = p1 >-> (p2 >-> p3)

Therefore we can just leave out the parentheses:

`>>>`

<Exact same behavior>`runEffect $ P.stdinLn >-> take 3 >-> P.stdoutLn`

(`>->`

) 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:

`>>>`

x = 1 y = 3 (1,3) y = 4 (1,4) x = 2 y = 3 (2,3) y = 4 (2,4)`runEffect $ for (every pair) (lift . print)`

... or a pipeline:

`>>>`

`import qualified Pipes.Prelude as P`

`>>>`

<Exact same behavior>`runEffect $ every pair >-> P.print`

Note that `ListT`

is lazy and only produces as many elements as we request:

`>>>`

x = 1 y = 3 (1,3) y = 4 (1,4)`runEffect $ for (every pair >-> P.take 2) (lift . print)`

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:

`>>>`

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>`runEffect $ every name >-> P.stdoutLn`

`>>>`

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.

# 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 `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 `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:

`>>>`

1 2 3`runEffect $ (each ~> each ~> each ~> lift . print) [[Just 1, Nothing], [Just 2, Just 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 `MaybeT`

:

input :: MaybeT IO String input = do str <- lift getLine guard (str /= "Fail") return str

`>>>`

Test<Enter> Test`runEffect $ every input >-> P.stdoutLn`

`>>>`

Fail<Enter>`runEffect $ every input >-> P.stdoutLn`

`>>>`

# 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.

# Appendix: 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:

type Effect = Proxy X () () X Upstream | Downstream +---------+ | | X <== <== () | | () ==> ==> X | | | +----|----+ v r

type Producer b = Proxy X () () b Upstream | Downstream +---------+ | | X <== <== () | | () ==> ==> b | | | +----|----+ v r

type Consumer a = Proxy () a () X Upstream | Downstream +---------+ | | () <== <== () | | a ==> ==> X | | | +----|----+ v r

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`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 `stdinLn`

`Producer`

. This
produces the following type error:

`>>>`

<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 P.stdinLn`

`runEffect`

expects an `Effect`

, which is equivalent to the following type:

Effect IO () = Proxy X () () X IO ()

... but `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 `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

# Appendix: Time Complexity

There are three functions that give quadratic time complexity when used in
within `pipes`

:

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.