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streaming.cabal
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streaming.cabal
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name: streaming
version: 0.1.4.5
cabal-version: >=1.10
build-type: Simple
synopsis: an elementary streaming prelude and general stream type.
description: This package contains two modules, <http://hackage.haskell.org/package/streaming/docs/Streaming.html Streaming>
and <http://hackage.haskell.org/package/streaming/docs/Streaming-Prelude.html Streaming.Prelude>.
The principal module, <http://hackage.haskell.org/package/streaming-0.1.4.3/docs/Streaming-Prelude.html Streaming.Prelude>, exports an elementary streaming prelude focused on
a simple \"source\" or \"producer\" type, namely @Stream (Of a) m r@.
This is a sort of effectful version of
@([a],r)@ in which successive elements of type @a@ arise from some sort of monadic
action before the succession ends with a value of type @r@.
Everything in the library is organized to make
programming with this type as simple as possible,
by the simple expedient of making it as close to @Prelude@
and @Data.List@ as possible. Thus for example
the trivial program
.
> >>> S.sum $ S.take 3 (S.readLn :: Stream (Of Int) IO ())
> 1<Enter>
> 2<Enter>
> 3<Enter>
> 6 :> ()
.
sums the first three valid integers from user input. Similarly,
.
> >>> S.stdoutLn $ S.map (map toUpper) $ S.take 2 S.stdinLn
> hello<Enter>
> HELLO
> world!<Enter>
> WORLD!
.
upper-cases the first two lines from stdin as they arise,
and sends them to stdout. And so on,
with filtering, mapping, breaking, chunking, zipping, unzipping, replicating
and so forth:
we program with streams of @Int@s or @String@s directly as
if they constituted something like a list. That's because streams really do constitute something
like a list, and the associated operations can mostly have the same names.
(A few, like @reverse@, don't stream and thus disappear;
others like @unzip@ are here given properly streaming formulation for the first time.)
And we everywhere
oppose \"extracting a pure list from IO\",
which is the origin of typical Haskell memory catastrophes.
Basically any case where you are
tempted to use @mapM@, @replicateM@, @traverse@ or @sequence@
with Haskell lists, you would do better to use something like
@Stream (Of a) m r@. The type signatures are a little fancier, but
the programs themselves are mostly the same. /In fact, they are mostly simpler./ Thus,
consider the trivial demo program mentioned in
<http://stackoverflow.com/questions/24068399/haskell-performance-of-iorefs this SO question>
.
> main = mapM newIORef [1..10^8::Int] >>= mapM readIORef >>= mapM_ print
.
The new user notices that this exhausts memory, and worries about the efficiency of Haskell @IORefs@.
But of course it exhausts memory! Look what it says!
The problem is immediately cured by writing
.
> main = S.print $ S.mapM readIORef $ S.mapM newIORef $ S.each [1..10^8::Int]
.
which really does what the other program was meant to do,
uses no more memory than @hello-world@, /and is simpler anyway/, since it
doesn't involve the detour of \"extracting a list from IO\". Almost
every use of list @mapM@, @replicateM@, @traverse@ and @sequence@ produces
this problem on a smaller scale. People get used to it, as if it were
characteristic of Haskell programs to use a lot of memory. But in truth
\"extracting a list or sequence from IO\" is mostly just bad practice pure and simple.
Of course, @mapM@, @replicateM@, @traverse@ and @sequence@ make sense for lists,
under certain conditions! But @unsafePerformIO@ also makes sense under
certain conditions.
.
The <http://hackage.haskell.org/package/streaming-0.1.4.3/docs/Streaming.html Streaming> module exports the general type,
@Stream f m r@, which can be used to stream successive distinct
steps characterized by /any/
functor @f@, though we are mostly interested in organizing computations
of the form @Stream (Of a) m r@. The streaming-IO libraries have
various devices for dealing
with effectful variants of @[a]@ or @([a],r)@ in which the emergence of
successive elements somehow depends on IO. But it is only with
the general type @Stream f m r@, or some equivalent,
that one can envisage (for example) the connected streaming of their
sorts of stream - as one makes lists of lists in the Haskell
@Prelude@ and @Data.List@. One needs some such type if we are
to express properly streaming equivalents of e.g.
.
> group :: Ord a => [a] -> [[a]]
> chunksOf :: Int -> [a] -> [[a]]
> lines :: [Char] -> [[Char]] -- but similarly with byte streams, etc.
.
to mention a few obviously desirable operations.
(This is explained more elaborately in the <https://hackage.haskell.org/package/streaming#readme readme> below.)
.
One could throw of course throw something
like the present @Stream@ type on top of a prior stream concept: this is how @pipes@ and
@pipes-group@ (which are very much our model here) use @FreeT@.
But once one grasps the iterable stream concept needed to express
those functions then one will also see that,
with it, one is /already/ in possession of a complete
elementary streaming library - since one possesses @Stream ((,) a) m r@
or equivalently @Stream (Of a) m r@. This
is the type of a \'generator\' or \'producer\' or \'source\' or whatever
you call an effectful stream of items.
/The present Streaming.Prelude is thus the simplest streaming library that can replicate anything like the API of the Prelude and Data.List/.
.
The emphasis of the library is on interoperation; for
the rest its advantages are: extreme simplicity, re-use of
intuitions the user has gathered from mastery of @Prelude@ and
@Data.List@, and a total and systematic rejection of type synonyms.
The two conceptual pre-requisites are some
comprehension of monad transformers and some familiarity
with \'rank 2 types\'. It is hoped that experimentation with this
simple material, starting with the ghci examples in @Streaming.Prelude@,
will give people who are new to these concepts some
intuition about their importance. The most fundamental purpose of the
library is to express elementary streaming ideas without reliance on
a complex framework, but in a way that integrates transparently with
the rest of Haskell, using ideas - e.g. rank 2 types, which are here
implicit or explicit in most mapping - that the user can carry elsewhere,
rather than chaining her understanding to the curiosities of
a so-called streaming IO framework (as necessary as that is for certain purposes.)
.
See the
<https://hackage.haskell.org/package/streaming#readme readme>
below for further explanation, including the examples linked there.
Elementary usage can be divined from the ghci examples in
@Streaming.Prelude@ and perhaps from this rough beginning of a
<https://github.com/michaelt/streaming-tutorial/blob/master/tutorial.md tutorial>.
Note also the
<https://hackage.haskell.org/package/streaming-bytestring streaming bytestring>
and
<https://hackage.haskell.org/package/streaming-utils streaming utils>
packages. Questions about usage can be put
raised on StackOverflow with the tag @[haskell-streaming]@,
or as an issue on Github, or on the
<https://groups.google.com/forum/#!forum/haskell-pipes pipes list>
(the package understands itself as part of the pipes \'ecosystem\'.)
.
The simplest form of interoperation with
<http://hackage.haskell.org/package/pipes pipes>
is accomplished with this isomorphism:
.
> Pipes.unfoldr Streaming.next :: Stream (Of a) m r -> Producer a m r
> Streaming.unfoldr Pipes.next :: Producer a m r -> Stream (Of a) m r
.
Interoperation with
<http://hackage.haskell.org/package/io-streams io-streams>
is thus:
.
> Streaming.reread IOStreams.read :: InputStream a -> Stream (Of a) IO ()
> IOStreams.unfoldM Streaming.uncons :: Stream (Of a) IO () -> IO (InputStream a)
.
With
<http://hackage.haskell.org/package/conduit conduit>
one might use, e.g.:
.
> Conduit.unfoldM Streaming.uncons :: Stream (Of a) m () -> Source m a
> \str -> Streaming.mapM_ Conduit.yield (hoist lift str) :: Stream (Of o) m r -> ConduitM i o m r
> \src -> hoist lift str $$ Conduit.mapM_ Streaming.yield :: Source m a -> Stream (Of a) m ()
.
These conversions should never be more expensive than a single @>->@ or @=$=@.
The simplest interoperation with regular Haskell lists is provided by, say
.
> Streaming.each :: [a] -> Stream (Of a) m ()
> Streaming.toList_ :: Stream (Of a) m r -> m [a]
.
The latter of course accumulates the whole list in memory, and is mostly what we are trying
to avoid. Every use of @Prelude.mapM f@ should be reconceived as using the
composition @Streaming.toList_ . Streaming.mapM f . Streaming.each@ with a view to
considering whether the accumulation required by @Streaming.toList_@ is really necessary.
.
Here are the results of some
<https://gist.github.com/michaelt/96606bbf05b29bf43a05aba081dc9bd4#file-benchmachines-hs microbenchmarks>
based on the
<https://github.com/ekmett/machines/blob/master/benchmarks/Benchmarks.hs benchmarks>
included in the machines package:
.
<<http://i.imgur.com/YbQtlXm.png>>
.
Because these are microbenchmarks for individual functions,
they represent a sort of \"worst case\"; many other factors can influence
the speed of a complex program.
.
license: BSD3
license-file: LICENSE
author: michaelt
maintainer: [email protected]
stability: Experimental
homepage: https://github.com/michaelt/streaming
bug-reports: https://github.com/michaelt/streaming/issues
category: Data, Pipes, Streaming
extra-source-files: README.md
source-repository head
type: git
location: https://github.com/michaelt/streaming
library
exposed-modules: Streaming,
Streaming.Prelude,
Streaming.Internal,
Data.Functor.Of
-- other-modules:
other-extensions: RankNTypes, CPP,
StandaloneDeriving, FlexibleContexts,
DeriveDataTypeable, DeriveFoldable,
DeriveFunctor, DeriveTraversable,
UndecidableInstances
build-depends: base >=4.6 && <5
, mtl >=2.1 && <2.3
, mmorph >=1.0 && <1.1
, transformers >=0.4 && <0.6
, transformers-base < 0.5
, resourcet > 1.1.0 && < 1.2
, exceptions > 0.5 && < 0.9
, monad-control >=0.3.1 && <1.1
, time
, ghc-prim
, containers
hs-source-dirs: src
default-language: Haskell2010