name: streaming version: 0.1.4.2 x-revision: 1 cabal-version: >=1.10 build-type: Simple synopsis: an elementary streaming prelude and general stream type. description: @Streaming.Prelude@ exports an elementary streaming prelude focused on a simple \"source\" or \"producer\" type, namely @Stream (Of a) m r@. @Stream (Of a) m r@ is a sort of effectful version of @([a],r)@ in which successive elements arise from some sort of monadic action. Everything is the library is organized to make programming with this type as simple as possible by making it as close to @Prelude@ and @Data.List@. Thus for example the trivial program . > S.sum (S.take 3 (S.readLn :: Stream (Of Int) IO ())) . sums the first three valid integers from user input. Similarly, . > S.stdoutLn (S.map reverse (S.take 3 S.stdinLn)) . reverses the first three lines from stdin as they arise, and sends them to stdout. And so on, with filtering, mapping, breaking, chunking and so forth. We program with streams of @Int@s or @String@s directly as if they constituted something like a list rather than \"extracting a 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 or simpler. Thus, the little demo program from . > main = mapM newIORef [1..10^8::Int] >>= mapM readIORef >>= mapM_ print . quickly exhausts memory; this of course has nothing to do with @IORefs@ and is cured by . > import qualified Streaming.Prelude as S > main = S.print (S.mapM readIORef (S.mapM newIORef (S.each [1..10^8::Int]))) . which uses no more memory than @hello-world@, and is simpler anyway, since it doesn't involve \"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, when \"extracting a list or sequence from IO\" is just bad practice pure and simple. List @mapM@, @replicateM@, @traverse@ and @sequence@ make sense under certain conditions. Similarly, @unsafePerformIO@ makes sense under certain conditions. . The @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)@. 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 bytestring, etc. . to mention a few obviously desirable operations. (This is explained more elaborately in the below.) One could throw something like @Stream@ 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 - here given a somewhat optimized implementation as @Stream f m r@ (following, as usual, models derived from the @pipes@ library) - 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 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 binding her intelligence to a so-called streaming IO framework (as necessary as that is for certain purposes.) . See the 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 . Note also the and packages. Questions about usage can be put raised on StackOverflow with the tag @[haskell-streaming]@, or as an issue on Github, or on the (the package understands itself as part of the pipes \'ecosystem\'.) . The simplest form of interoperation with 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 is thus: . > Streaming.reread IOStreams.read :: InputStream a -> Stream (Of a) IO () > IOStreams.unfoldM Streaming.uncons :: Stream (Of a) IO () -> IO (InputStream a) . With one might use, e.g.: . > Conduit.unfoldM Streaming.uncons :: Stream (Of a) m () -> Source m a > Streaming.mapM_ Conduit.yield . hoist lift :: Stream (Of o) m r -> ConduitM i o m r > ($$ Conduit.mapM_ Streaming.yield) . hoist lift :: 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 based on the included in the machines package: . <> . 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: what_is_it_to_do_anything@yahoo.com 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 -- other-modules: other-extensions: RankNTypes, CPP, StandaloneDeriving, FlexibleContexts, DeriveDataTypeable, DeriveFoldable, DeriveFunctor, DeriveTraversable, UndecidableInstances build-depends: base >=4.6 && <4.11 , 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 default-language: Haskell2010