streaming alternatives and similar packages
Based on the "streaming" category.
Alternatively, view streaming alternatives based on common mentions on social networks and blogs.

streamingcommons
Common lowerlevel functions needed by various streaming data libraries 
streamingbytestring
effectful sequences of bytes; an alternative nolazyio implementation of Data.ByteString.Lazy 
streamingconcurrency
Concurrency for the streaming ecosystem 
streamingfft
sliding fast fourier transform using haskell streaming 
streamingconduit
Bidirectional support between the streaming and conduit libraries 
streamingutils
experimental http, attoparsec and pipes material for `streaming` and `streamingbytestring`
Build timeseriesbased applications quickly and at scale.
Do you think we are missing an alternative of streaming or a related project?
README
streaming
Contents
§ 1. The freely generated stream on a streamable functor
§ 2. A freely generated stream of individual Haskell values is a Producer, Generator or Source
§ 3. Streaming.Prelude
§ 4. Mother's Prelude
v. Streaming.Prelude
§ 5. How come there's not one of those fancy "ListT done right" implementations in here?
§ 6. Didn't I hear that free monads are a dog from the point of view of efficiency?
§ 7. Interoperation with the streamingio libraries
§ 8. Where can I find examples of use?
§ 9. Problems
§ 10. Implementation and benchmarking notes
§ 1. The freely generated stream on a streamable functor
Stream
can be used wherever FreeT
or Coroutine
are used. The compiler's standard range of optimizations work better for operations written in terms of Stream
. Stream f m r
, like FreeT f m r
or Couroutine f m r
 is of course extremely general, and many functorgeneral combinators are exported by the general module Streaming
.
In the applications we are thinking of, the general type Stream f m r
expresses a succession of steps arising in a monad m
, with a shape determined by the 'functor' parameter f
, and resulting in a final value r
. In the first instance you might read Stream
as Repeatedly
, with the understanding that one way of doing something some number of times, is to do it no times at all.
Readings of f
can be wildly various. Thus, for example,
Stream Identity IO r
is the type of an indefinitely delayed IO r
, or an extended IO
process broken into stages marked by the Identity
constructor. This is the Trampoline
type of the "Coroutine Pipelines" tutorial, and the IterT
of the free
library (which is mysteriously not identified with FreeT Identity
 all of the associated combinators are found within the general Streaming
module.)
In particular, though, given readings of f
and m
we can, for example, always consider the type Stream (Stream f m) m r
, in which steps of the form Stream f m
are joined end to end. Such a streamofstreams might arise in any number of ways; a crude (because hypergeneral) way would be with
chunksOf :: Monad m, Functor f => Int > Stream f m r > Stream (Stream f m) m r
and we can always rejoin such a stream with
concats :: Monad m, Functor f => Stream (Stream f m) m r > Stream f m r
But other things can be chunked and concatenated in that sense; they need not themselves be explicitly represented in terms of Stream
; indeed chunksOf
and concats
are modeled on those in pipesgroup
. In our variant of pipesgroup
, these have the types
chunksOf :: Monad m => Int > Producer a m r > Stream (Producer a m) m r
concats :: Monad m => Stream (Producer a m) m r > Producer a m r
§ 2. A freely generated stream of individual Haskell values is a Producer, Generator or Source
Of course, as soon as you grasp the general form of succession you are already in possession of the most basic concrete form: a simple succession of individual Haskell values one after another, the effectful list or sequence. This is just Stream ((,) a) m r
. Here we prefer to write Stream (Of a) m r
, strictifying the left element of the pair with
data Of a r = !a :> r deriving Functor
Either way, the pairing just links the present element with the rest of the stream. The primitive yield
statement just expresses the pairing of the yielded item with the rest of the stream; or rather it is itself the trivial singleton stream.
yield 17 :: Stream (Of Int) IO ()
Streaming.Prelude
is focused on the manipulation of this allimportant streamform, which appears in the streaming IO libraries under titles like:
iostreams: Generator a r
pipes: Producer a m r
conduit: ConduitM () o m r
streaming: Stream (Of a) m r
The only difference is that in streaming
the simple generator or producer concept is formulated explicitly in terms of the general concept of successive connection. But this is a concept you need and already possess anyway, as your comprehension of the streaming ABCs showed.
The special case of a stream of individual Haskell values that simply comes to an end without a special result is variously expressed thus:
iostreams: InputStream a
pipes: Producer a m ()
conduit: Source m a
machines: SourceT m a (= forall k. MachineT m k a)
streaming: Stream (Of a) m ()
Note that the above libraries generally employ elaborate systems of type synonyms in order to intimate to the reader the meaning of specialized forms. iostreams
is an exception. This libary is completely opposed to this tendency, and exports no synonyms.
§ 3. Streaming.Prelude
Streaming.Prelude
closely follows Pipes.Prelude
. But since it restricts itself to use only of the general idea of streaming, it cleverly omits the pipes:
ghci> S.stdoutLn $ S.take 2 S.stdinLn
let's<Enter>
let's
stream<Enter>
stream
Here's a little connect and resume, as the streamingio experts call it:
ghci> rest < S.print $ S.splitAt 3 $ S.each [1..10]
1
2
3
ghci> S.sum rest
49
Somehow, we didn't even need a fourcharacter operator for that, nor advice about best practices!  just ordinary Haskell common sense.
§ 4. Mother's Prelude
v. Streaming.Prelude
The effort of Streaming.Prelude
is to leverage the intuition the user has acquired in mastering Prelude
and Data.List
and to elevate her understanding into a general comprehension of effectful streaming transformations. Unsurprisingly, it takes longer to type out the signatures. It cannot be emphasized enough, though, that the transpositions are totally mechanical:
Data.List.Split.chunksOf :: Int > [a] > [[a]]
Streaming.chunksOf :: Int > Stream f m r > Stream (Stream f m) m r
Prelude.splitAt :: Int > [a] > ([a],[a])
Streaming.splitAt :: Int > Stream f m r > Stream f m (Stream f m r)
These concepts are "functor general", in the jargon used in the documentation, and are thus exported by the main Streaming
module. Something like break
requires us to inspect individual values for their properties, so it is found in the Streaming.Prelude
Prelude.break :: (a > Bool) > [a] > ([a],[a])
Streaming.Prelude.break :: (a > Bool) > Stream (Of a) m r > Stream (Of a) m (Stream (Of a) m r)
It is easy to prove that resistance to these types is resistance to effectful streaming itself. I will labor this point a bit more below, but you can also find it developed, with greater skill, in the documentation for the pipes libraries.
§ 5. How come there's not one of those fancy "ListT done right" implementations in here?
The use of the final return value appears to be a complication, but in fact it is essentially contained in the idea of effectful streaming. This is why this library does not export a _ListT done right/, which would be simple enough  following pipes
, as usual:
newtype ListT m a = ListT (Stream (Of a) m ())
The associated monad instance would wrap
yield :: (Monad m) => a > Stream (Of a) m ()
for :: (Monad m, Functor f) => Stream (Of a) m r > (a > Stream f m ()) > Stream f m r
To see the trouble, consider this signature for splitting a ListT very much done right. Here's what becomes of chunksOf. As long as we are trapped in some sort of ListT, however much rightly implemented, these operations can't be made to stream; something like a list must be accumulated. Similarly, try to imagine adding a splitAt
or lines
function to this API. It would accumulate strict text forever, just as this does and this doesn't and this doesn't The difference is simply that the latter libraries operate with the general concept of streaming, and the whole implementation is governed by it. The attractions of the various "ListT
done right" implementations are superficial; the concept belongs to logic programming, not stream programming.
Note similarly that you can write a certain kind of take and drop with the machines
library  as you can even with a "ListT
done right". But I wish you luck writing splitAt
! Similarly you can write a getContents; but I wish you luck dividing the resulting bytestream on its lines. This is  as usual!  because the library was not written with the general concept of effectful succession or streaming in view. Materials for sinking some elements of a stream in one way, and others in other ways  copying each line to a different file, as it might be, but without accumulation  are documented within. So are are myriad other elementary operations of streaming io.
§ 6. Didn't I hear that free monads are a dog from the point of view of efficiency?
We noted above that if we instantiate Stream f m r
to Stream ((,) a) m r
or the like, we get the standard idea of a producer or generator. If it is instantiated to Stream f Identity m r
then we have the standard _free monad construction/. This construction is subject to certain familiar objections from an efficiency perspective; efforts have been made to substitute exotic cpsed implementations and so forth. It is an interesting topic.
But in fact, the standard alarmist talk about retraversing binds and quadratic explosions and costly appends, and so on become transparent nonsense with Stream f m r
\
in its streaming use. The conceptual power needed to see this is basically nil: Where m
is read as IO
, or some transformed IO
, then the dreaded retraversing of the binds in a stream expression would involve repeating all the past actions. Don't worry, to get e.g. the second chunk of bytes from a handle, you won't need to start over and get the first one again! The first chunk has vanished into an unrepeatable past.
All of the difficulties a streaming library is attempting to avoid are concentrated in the deep irrationality of
sequence :: (Monad m, Traversable t) => t (m a) > m (t a)
In the streaming context, this becomes
sequence :: Monad m, Functor f => Stream f m r > Stream f m r
sequence = id
It is of course easy enough to define
accumulate :: Monad m, Functor f => Stream f m r > m (Stream f Identity r)
or reifyBindsRetraversingWherePossible
or _ICan'tTakeThisStreamingAnymore
, as you might call it. The types themselves teach the user how to avoid or control the sort of accumulation characteristic of sequence
in its various guises e.g. mapM f = sequence . map f
and traverse f = sequence . fmap f
and replicateM n = sequence . replicate n
. See for example the types of
Control.Monad.replicateM :: Int > m a > m [a]
Streaming.Prelude.replicateM :: Int > m a > Stream (Of a) m ()
If you want to tempt fate and replicate the irrationality of Control.Monad.replicateM
, then sure, you can define the hermaphroditic chimera
accumulate . Streaming.Prelude.replicateM :: Int > m a > m (Stream (Of a) Identity ())
which is what we find in our diseased base libraries. But once you know how to operate with a stream directly you will see less and less point in what is called extracting the (structured) value from IO. Consider the apparently innocent distinction between
"getContents" :: String
and
getContents :: IO String
Omitting consideration of eof, we might define getContents
thus
getContents = sequence $ repeat getChar
There it is again! The very devil! By contrast there is no distinction between
"getContents" :: Stream (Of Char) m ()  the IsString instance is monadgeneral
and
getContents :: MonadIO m => Stream (Of Char) m ()
They unify just fine. That is, if I make the type synonym
type String m r = Stream (Of Char) m r
I get, for example:
"getLine" :: String m ()
getLine :: String IO ()
"getLine" >> getLine :: String IO ()
splitAt 20 $ "getLine" >> getLine :: String IO (String IO ())
length $ "getLine" >> getLine :: IO Int
and can dispense with half the advice they will give you on #haskell
. It is only a slight exaggeration to say that a stream should never be "extracted from IO".
With sequence
and traverse
, we accumulate a pure succession of pure values from a pure succession of monadic values. Why bother if you have intrinsically monadic conception of succession or traversal? Stream f m r
gives you an immense body of such structures and a simple discipline for working with them. Spinkle id
freely though your program, under various names, if you get homesick for sequence
and company.
§ 7. Interoperation with the streamingio libraries
The simplest form of interoperation with 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
Of course, streaming
can be mixed with pipes
wherever pipes
itself employs Control.Monad.Trans.Free
; speedups are frequently appreciable. (This was the original purpose of the main Streaming
module, which just mechanically transposes a simple optimization employed in Pipes.Internal
.) Interoperation with iostreams is thus:
Streaming.reread IOStreams.read :: InputStream a > Stream (Of a) IO ()
IOStreams.unfoldM Streaming.uncons :: Stream (Of a) IO () > IO (InputStream a)
A simple exit to conduit would be, e.g.:
Conduit.unfoldM Streaming.uncons :: Stream (Of a) m () > Source m a
These conversions should never be more expensive than a single >>
or =$=
.
At a much more general level, we also of course have interoperation with free:
Free.iterTM Stream.wrap :: FreeT f m a > Stream f m a
Stream.iterTM Free.wrap :: Stream f m a > FreeT f m a
§ 8. Where can I find examples of use?
For some simple ghci examples, see the commentary throughout the Prelude module. For slightly more advanced usage see the commentary in the haddocks of streamingbytestring and e.g. these replicas of shelllike programs from the iostreams tutorial. Here's a simple streaming GET request with intrinsically streaming byte streams. Here is a comically simple 'high  low' game
§ 9. Problems
Questions about this library can be put as issues through the github site or on the pipes mailing list. (This library understands itself as part of the pipes "ecosystem.")
§ 10. Implementation and benchmarking notes
This library defines an optimized FreeT
with an eye to use with streaming libraries, namely:
data Stream f m r
= Return r
 Step !(f (Stream f m r))
 Effect (m (Stream f m r))
in place of the standard FreeT
that we find in the free
library, which is approximately:
newtype FreeT f m r = FreeT {runFreeT :: m (Either r (f (FreeT f m r)))}
Rather than wrapping each step in a monadic 'layer', such a layer is put alongside separate 'pure' constructors for a functor 'layer' and a final return value. The maneuver is very friendly to the compiler, but requires a bit of subtlety to protect a sound monad instance. Just such an optimization is adopted internally by the pipes
library. As in pipes
, the constructors are here left in an Internal
module; the main Streaming
module exporting the type itself and various operations and instances.
I ran a simple benchmark (adjusting a script of John Weigly) using a very simple composition of functions:
toList
. filter (\x > x `mod` 2 == 0)
. map (+1)
. drop 1000
. map (+1)
. filter even
. each
as it interpreted by various libraries  streaming
, conduit
, iostreams
and machines
.
The results were fairly pleasing:
benchmarking sum/streaming
time 8.996 ms (8.910 ms .. 9.068 ms)
0.999 R² (0.998 R² .. 1.000 R²)
mean 9.060 ms (9.004 ms .. 9.122 ms)
std dev 164.6 μs (123.9 μs .. 251.9 μs)
benchmarking sum/conduit
time 15.77 ms (15.66 ms .. 15.89 ms)
0.999 R² (0.998 R² .. 1.000 R²)
mean 15.78 ms (15.70 ms .. 15.89 ms)
std dev 245.3 μs (176.5 μs .. 379.7 μs)
benchmarking sum/pipes
time 57.94 ms (57.68 ms .. 58.27 ms)
1.000 R² (1.000 R² .. 1.000 R²)
mean 58.10 ms (57.92 ms .. 58.27 ms)
std dev 324.2 μs (214.1 μs .. 468.8 μs)
benchmarking sum/iostreams
time 61.96 ms (61.36 ms .. 62.53 ms)
1.000 R² (0.999 R² .. 1.000 R²)
mean 61.80 ms (61.54 ms .. 62.08 ms)
std dev 543.7 μs (375.1 μs .. 715.7 μs)
benchmarking sum/machine
time 260.4 ms (257.2 ms .. 263.6 ms)
1.000 R² (0.999 R² .. 1.000 R²)
mean 259.7 ms (258.4 ms .. 260.6 ms)
std dev 1.284 ms (565.9 μs .. 1.690 ms)
variance introduced by outliers: 16% (moderately inflated)
benchmarking basic/streaming
time 74.86 ms (70.07 ms .. 78.78 ms)
0.994 R² (0.987 R² .. 0.999 R²)
mean 78.25 ms (75.55 ms .. 84.10 ms)
std dev 6.301 ms (1.995 ms .. 10.17 ms)
variance introduced by outliers: 19% (moderately inflated)
benchmarking basic/conduit
time 90.06 ms (66.61 ms .. 114.4 ms)
0.876 R² (0.658 R² .. 0.977 R²)
mean 98.63 ms (85.28 ms .. 116.5 ms)
std dev 23.06 ms (10.61 ms .. 30.72 ms)
variance introduced by outliers: 65% (severely inflated)
benchmarking basic/pipes
time 180.9 ms (158.7 ms .. 201.3 ms)
0.989 R² (0.971 R² .. 1.000 R²)
mean 190.5 ms (183.0 ms .. 197.8 ms)
std dev 10.16 ms (4.910 ms .. 14.86 ms)
variance introduced by outliers: 14% (moderately inflated)
benchmarking basic/iostreams
time 269.7 ms (243.8 ms .. 303.9 ms)
0.995 R² (0.985 R² .. 1.000 R²)
mean 264.2 ms (254.0 ms .. 272.0 ms)
std dev 10.87 ms (5.762 ms .. 15.06 ms)
variance introduced by outliers: 16% (moderately inflated)
benchmarking basic/machine
time 397.7 ms (324.4 ms .. 504.8 ms)
0.992 R² (0.977 R² .. 1.000 R²)
mean 407.7 ms (391.1 ms .. 420.3 ms)
std dev 19.40 ms (0.0 s .. 21.88 ms)
variance introduced by outliers: 19% (moderately inflated)
This sequence of prepackaged combinators is, I think, as friendly as it could possibly be to the more recent conduit fusion framework. That framework of course doesn't apply to userdefined operations; there we should expect times like those shown for pipes. Since the combinators from streaming
are defined with naive recursion, more or less as the user might, we have reason to think this result is characteristic, but much more benchmarking is needed before anything can be said with certainty.