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benchmark | ||
charts-0 | ||
docs | ||
examples | ||
jsbits | ||
src | ||
test | ||
.hlint.yaml | ||
.travis.yml | ||
appveyor.yml | ||
bench.sh | ||
cabal.project.ghcjs | ||
Changelog.md | ||
configure | ||
configure.ac | ||
CONTRIBUTING.md | ||
LICENSE | ||
MAINTAINING.md | ||
README.md | ||
Setup.hs | ||
stack-7.10.yaml | ||
stack-8.0.yaml | ||
stack.yaml | ||
streamly.cabal |
Streamly
Streaming Concurrently
Haskell lists express pure computations using composable stream operations like
:
, unfold
, map
, filter
, zip
and fold
. Streamly is exactly like
lists except that it can express sequences of pure as well as monadic
computations aka streams. More importantly, it can express monadic sequences
with concurrent execution semantics without introducing any additional APIs.
Streamly expresses concurrency using standard, well known abstractions. Concurrency semantics are defined for list operations, semigroup, applicative and monadic compositions. Programmer does not need to know any low level notions of concurrency like threads, locking or synchronization. Concurrent and non-concurrent programs are fundamentally the same. A chosen segment of the program can be made concurrent by annotating it with an appropriate combinator. We can choose a combinator for lookahead style or asynchronous concurrency. Concurrency is automatically scaled up or down based on the demand from the consumer application, we can finally say goodbye to managing thread pools and associated sizing issues. The result is truly fearless and declarative monadic concurrency.
Where to use streamly?
Streamly is a general purpose programming framwework. It can be used equally
efficiently from a simple Hello World!
program to a massively concurrent
application. The answer to the question, "where to use streamly?" - would be
similar to the answer to - "Where to use Haskell lists or the IO monad?".
Streamly generalizes lists to monadic streams, and the IO
monad to
non-deterministic and concurrent stream composition. The IO
monad is a
special case of streamly; if we use single element streams the behavior of
streamly becomes identical to the IO monad. The IO monad code can be replaced
with streamly by just prefixing the IO actions with liftIO
, without any other
changes, and without any loss of performance. Pure lists too are a special
case of streamly; if we use Identity
as the underlying monad, streamly
streams turn into pure lists. Non-concurrent programs are just a special case
of concurrent ones, simply adding a combinator turns a non-concurrent program
into a concurrent one.
In other words, streamly combines the functionality of lists and IO, with
builtin concurrency. If you want to write a program that involves IO,
concurrent or not, then you can just use streamly as the base monad, in fact,
you could even use streamly for pure computations, as streamly performs at par
with pure lists or vector
.
Why data flow programming?
If you need some convincing for using streaming or data flow programming paradigm itself then try to answer this question - why do we use lists in Haskell? It boils down to why we use functional programming in the first place. Haskell is successful in enforcing the functional data flow paradigm for pure computations using lists, but not for monadic computations. In the absence of a standard and easy to use data flow programming paradigm for monadic computations, and the IO monad providing an escape hatch to an imperative model, we just love to fall into the imperative trap, and start asking the same fundamental question again - why do we have to use the streaming data model?
Show me an example
Here is an IO monad code to list a directory recursively:
import Control.Monad.IO.Class (liftIO)
import Path.IO (listDir, getCurrentDir) -- from path-io package
listDirRecursive = getCurrentDir >>= readdir
where
readdir dir = do
(dirs, files) <- listDir dir
liftIO $ mapM_ putStrLn
$ map show dirs ++ map show files
foldMap readdir dirs
This is your usual IO monad code, with no streamly specific code whatsoever. This is how you can run this:
main :: IO ()
main = listDirRecursive
And, this is how you can run exactly the same code using streamly with lookahead style concurrency, the only difference is that this time multiple directories are read concurrently:
import Streamly (runStream, aheadly)
main :: IO ()
main = runStream $ aheadly $ listDirRecursive
Isn't that magical? What's going on here? Streamly does not introduce any new
abstractions, it just uses standard abstractions like Semigroup
or
Monoid
to combine monadic streams concurrently, the way lists combine a
sequence of pure values non-concurrently. The foldMap
in the code
above turns into a concurrent monoidal composition of a stream of readdir
computations.
How does it perform?
Providing monadic streaming and high level declarative concurrency does not
mean that streamly
compromises with performance in any way. The
non-concurrent performance of streamly
competes with lists and the vector
library. The concurrent performance is as good as it gets, see concurrency
benchmarks for detailed
performance results and a comparison with the async
package.
The following chart shows a summary of the cost of key streaming operations
processing a million elements. The timings for streamly
and vector
are in
the 600-700 microseconds range and therefore can barely be seen in the graph.
For more details, see streaming
benchmarks.
Streaming Pipelines
The following snippet provides a simple stream composition example that reads numbers from stdin, prints the squares of even numbers and exits if an even number more than 9 is entered.
import Streamly
import qualified Streamly.Prelude as S
import Data.Function ((&))
main = runStream $
S.repeatM getLine
& fmap read
& S.filter even
& S.takeWhile (<= 9)
& fmap (\x -> x * x)
& S.mapM print
Unlike pipes
or conduit
and like vector
and streaming
, streamly
composes stream data instead of stream processors (functions). A stream is
just like a list and is explicitly passed around to functions that process the
stream. Therefore, no special operator is needed to join stages in a streaming
pipeline, just the standard function application ($
) or reverse function
application (&
) operator is enough. Combinators are provided in
Streamly.Prelude
to transform or fold streams.
Concurrent Stream Generation
Monadic construction and generation functions e.g. consM
, unfoldrM
,
replicateM
, repeatM
, iterateM
and fromFoldableM
etc. work concurrently
when used with appropriate stream type combinator (e.g. asyncly
, aheadly
or
parallely
).
The following code finishes in 3 seconds (6 seconds when serial):
> let p n = threadDelay (n * 1000000) >> return n
> S.toList $ aheadly $ p 3 |: p 2 |: p 1 |: S.nil
[3,2,1]
> S.toList $ parallely $ p 3 |: p 2 |: p 1 |: S.nil
[1,2,3]
The following finishes in 10 seconds (100 seconds when serial):
runStream $ asyncly $ S.replicateM 10 $ p 10
Concurrent Streaming Pipelines
Use |&
or |$
to apply stream processing functions concurrently. The
following example prints a "hello" every second; if you use &
instead of
|&
you will see that the delay doubles to 2 seconds instead because of serial
application.
main = runStream $
S.repeatM (threadDelay 1000000 >> return "hello")
|& S.mapM (\x -> threadDelay 1000000 >> putStrLn x)
Mapping Concurrently
We can use mapM
or sequence
functions concurrently on a stream.
> let p n = threadDelay (n * 1000000) >> return n
> runStream $ aheadly $ S.mapM (\x -> p 1 >> print x) (serially $ repeatM (p 1))
Serial and Concurrent Merging
Semigroup and Monoid instances can be used to fold streams serially or concurrently. In the following example we compose ten actions in the stream, each with a delay of 1 to 10 seconds, respectively. Since all the actions are concurrent we see one output printed every second:
import Streamly
import qualified Streamly.Prelude as S
import Control.Concurrent (threadDelay)
main = S.toList $ parallely $ foldMap delay [1..10]
where delay n = S.yieldM $ threadDelay (n * 1000000) >> print n
Streams can be combined together in many ways. We provide some examples
below, see the tutorial for more ways. We use the following delay
function in the examples to demonstrate the concurrency aspects:
import Streamly
import qualified Streamly.Prelude as S
import Control.Concurrent
delay n = S.yieldM $ do
threadDelay (n * 1000000)
tid <- myThreadId
putStrLn (show tid ++ ": Delay " ++ show n)
Serial
main = runStream $ delay 3 <> delay 2 <> delay 1
ThreadId 36: Delay 3
ThreadId 36: Delay 2
ThreadId 36: Delay 1
Parallel
main = runStream . parallely $ delay 3 <> delay 2 <> delay 1
ThreadId 42: Delay 1
ThreadId 41: Delay 2
ThreadId 40: Delay 3
Nested Loops (aka List Transformer)
The monad instance composes like a list monad.
import Streamly
import qualified Streamly.Prelude as S
loops = do
x <- S.fromFoldable [1,2]
y <- S.fromFoldable [3,4]
S.yieldM $ putStrLn $ show (x, y)
main = runStream loops
(1,3)
(1,4)
(2,3)
(2,4)
Concurrent Nested Loops
To run the above code with, lookahead style concurrency i.e. each iteration in the loop can run run concurrently by but the results are presented in the same order as serial execution:
main = runStream $ aheadly $ loops
To run it with depth first concurrency yielding results asynchronously in the same order as they become available (deep async composition):
main = runStream $ asyncly $ loops
To run it with breadth first concurrency and yeilding results asynchronously (wide async composition):
main = runStream $ wAsyncly $ loops
The above streams provide lazy/demand-driven concurrency which is automatically scaled as per demand and is controlled/bounded so that it can be used on infinite streams. The following combinator provides strict, unbounded concurrency irrespective of demand:
main = runStream $ parallely $ loops
To run it serially but interleaving the outer and inner loop iterations (breadth first serial):
main = runStream $ wSerially $ loops
Magical Concurrency
Streams can perform semigroup (<>) and monadic bind (>>=) operations
concurrently using combinators like asyncly
, parallelly
. For example,
to concurrently generate squares of a stream of numbers and then concurrently
sum the square roots of all combinations of two streams:
import Streamly
import qualified Streamly.Prelude as S
main = do
s <- S.sum $ asyncly $ do
-- Each square is performed concurrently, (<>) is concurrent
x2 <- foldMap (\x -> return $ x * x) [1..100]
y2 <- foldMap (\y -> return $ y * y) [1..100]
-- Each addition is performed concurrently, monadic bind is concurrent
return $ sqrt (x2 + y2)
print s
The concurrency facilities provided by streamly can be compared with OpenMP and Cilk but with a more declarative expression.
Rate Limiting
For bounded concurrent streams, stream yield rate can be specified. For example, to print hello once every second you can simply write this:
import Streamly
import Streamly.Prelude as S
main = runStream $ asyncly $ avgRate 1 $ S.repeatM $ putStrLn "hello"
For some practical uses of rate control, see AcidRain.hs and CirclingSquare.hs . Concurrency of the stream is automatically controlled to match the specified rate. Rate control works precisely even at throughputs as high as millions of yields per second. For more sophisticated rate control see the haddock documentation.
Exceptions
From a library user point of view, there is nothing much to learn or talk about
exceptions. Synchronous exceptions work just the way they are supposed to work
in any standard non-concurrent code. When concurrent streams are combined
together, exceptions from the constituent streams are propagated to the
consumer stream. When an exception occurs in any of the constituent streams
other concurrent streams are promptly terminated. Exceptions can be thrown
using the MonadThrow
instance.
There is no notion of explicit threads in streamly, therefore, no
asynchronous exceptions to deal with. You can just ignore the zillions of
blogs, talks, caveats about async exceptions. Async exceptions just don't
exist. Please don't use things like myThreadId
and throwTo
just for fun!
Reactive Programming (FRP)
Streamly is a foundation for first class reactive programming as well by virtue of integrating concurrency and streaming. See AcidRain.hs for a console based FRP game example and CirclingSquare.hs for an SDL based animation example.
Conclusion
Streamly, short for streaming concurrently, provides monadic streams, with a simple API, almost identical to standard lists, and an in-built support for concurrency. By using stream-style combinators on stream composition, streams can be generated, merged, chained, mapped, zipped, and consumed concurrently – providing a generalized high level programming framework unifying streaming and concurrency. Controlled concurrency allows even infinite streams to be evaluated concurrently. Concurrency is auto scaled based on feedback from the stream consumer. The programmer does not have to be aware of threads, locking or synchronization to write scalable concurrent programs.
Streamly is a programmer first library, designed to be useful and friendly to programmers for solving practical problems in a simple and concise manner. Some key points in favor of streamly are:
- Simplicity: Simple list like streaming API, if you know how to use lists then you know how to use streamly. This library is built with simplicity and ease of use as a design goal.
- Concurrency: Simple, powerful, and scalable concurrency. Concurrency is built-in, and not intrusive, concurrent programs are written exactly the same way as non-concurrent ones.
- Generality: Unifies functionality provided by several disparate packages (streaming, concurrency, list transformer, logic programming, reactive programming) in a concise API.
- Performance: Streamly is designed for high performance. It employs stream
fusion optimizations for best possible performance. Serial peformance is
equivalent to the venerable
vector
library in most cases and even better in some cases. Concurrent performance is unbeatable. See streaming-benchmarks for a comparison of popular streaming libraries on micro-benchmarks.
The basic streaming functionality of streamly is equivalent to that provided by
streaming libraries like
vector,
streaming,
pipes, and
conduit.
In addition to providing streaming functionality, streamly subsumes
the functionality of list transformer libraries like pipes
or
list-t, and also the logic
programming library logict. On
the concurrency side, it subsumes the functionality of the
async package, and provides even
higher level concurrent composition. Because it supports
streaming with concurrency we can write FRP applications similar in concept to
Yampa or
reflex.
See the Comparison with existing packages
section at the end of the
tutorial.
Further Reading
For more information, see:
- Detailed tutorial
- Reference documentation
- Examples
- Guides
- Streaming benchmarks
- Concurrency benchmarks
Support
If you require professional support, consulting, training or timely enhancements to the library please contact support@composewell.com.
Contributing
The code is available under BSD-3 license on github. Join the gitter chat channel for discussions. You can find some of the todo items on the github wiki. Please ask on the gitter channel or contact the maintainer directly for more details on each item. All contributions are welcome!
This library was originally inspired by the transient
package authored by
Alberto G. Corona.