Haskell lists express pure computations using composable stream operations like
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
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
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
How does it perform?
Providing monadic streaming and high level declarative concurrency does not
streamly compromises with performance in any way. The
non-concurrent performance of
streamly competes with lists and the
library. The concurrent performance is as good as it gets, see concurrency
benchmarks for detailed
performance results and a comparison with the
The following chart shows a summary of the cost of key streaming operations
processing a million elements. The timings for
vector are in
the 600-700 microseconds range and therefore can barely be seen in the graph.
For more details, see streaming
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
conduit and like
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
&) operator is enough. Combinators are provided in
Streamly.Prelude to transform or fold streams.
Concurrent Stream Generation
Monadic construction and generation functions e.g.
fromFoldableM etc. work concurrently
when used with appropriate stream type combinator (e.g.
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
|$ 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
main = runStream $ S.repeatM (threadDelay 1000000 >> return "hello") |& S.mapM (\x -> threadDelay 1000000 >> putStrLn x)
We can use
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
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)
main = runStream $ delay 3 <> delay 2 <> delay 1
ThreadId 36: Delay 3 ThreadId 36: Delay 2 ThreadId 36: Delay 1
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
Streams can perform semigroup (<>) and monadic bind (>>=) operations
concurrently using combinators like
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
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.
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
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
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.
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
vectorlibrary 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
In addition to providing streaming functionality, streamly subsumes
the functionality of list transformer libraries like
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
Comparison with existing packages section at the end of the
For more information, see:
- Detailed tutorial
- Reference documentation
- Streaming benchmarks
- Concurrency benchmarks
If you require professional support, consulting, training or timely enhancements to the library please contact email@example.com.
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.
- Fix a bug that caused
maxThreadsdirective to be ignored when rate control was not used.
- Add GHCJS support
- Remove dependency on “clock” package
Monadconstraint may be needed on some of the existing APIs (
- Add the following functions to Streamly.Prelude:
- Following instances were added for
Identity: IsList, Eq, Ord, Show, Read, IsString, NFData, NFData1, Traversable
- Performance improvements
- Add benchmarks to measure composed and iterated operations
- Cleanup any pending threads when an exception occurs.
- Fixed a livelock in ahead style streams. The problem manifests sometimes when multiple streams are merged together in ahead style and one of them is a nil stream.
- As per expected concurrency semantics each forked concurrent task must run
with the monadic state captured at the fork point. This release fixes a bug,
which, in some cases caused an incorrect monadic state to be used for a
concurrent action, leading to unexpected behavior when concurrent streams are
used in a stateful monad e.g.
StateT. Particularly, this bug cannot affect
- Performance improvements, especially space consumption, for concurrent streams
- Leftover threads are now cleaned up as soon as the consumer is garbage collected.
- Fix a bug in concurrent function application that in certain cases would unnecessarily share the concurrency state resulting in incorrect output stream.
- Fix passing of state across
wSerialcombinators. Without this fix combinators that rely on state passing e.g.
maxBufferwon’t work across these combinators.
- Added rate limiting combinators
constRateto control the yield rate of a stream.
Streamly.Timemodule is now deprecated, its functionality is subsumed by the new rate limiting combinators.
- foldxM was not fully strict, fixed.
- Signatures of
- Some functions in prelude now require an additional
Monadconstraint on the underlying type of the stream.
oncehas been deprecated and renamed to
- Add concurrency control primitives
- Concurrency of a stream with bounded concurrency when used with
takeis now limited by the number of elements demanded by
- Significant performance improvements utilizing stream fusion optimizations.
yieldto construct a singleton stream from a pure value
repeatto generate an infinite stream by repeating a pure value
fromListMto generate streams from lists, faster than
mapas a synonym of fmap
scanlM', the monadic version of scanl’
- Some prelude functions, to whom concurrency capability has been added, will
now require a
- Fixed a race due to which, in a rare case, we might block indefinitely on an MVar due to a lost wakeup.
- Fixed an issue in adaptive concurrency. The issue caused us to stop creating more worker threads in some cases due to a race. This bug would not cause any functional issue but may reduce concurrency in some cases.
- Added a concurrent lookahead stream type
fromFoldableMAPI that creates a stream from a container of monadic actions
- Monadic stream generation functions
fromFoldableMcan now generate streams concurrently when used with concurrent stream types.
- Monad transformation functions
sequencecan now map actions concurrently when used at appropriate stream types.
- Added concurrent function application operators to run stages of a stream processing function application pipeline concurrently.
- Fixed a bug that caused some transformation ops to return incorrect results
when used with concurrent streams. The affected ops are
Changed the semantics of the Semigroup instance for
ParallelT. The new semantics are as follows:
<>operation interleaves two streams
<>now concurrently merges two streams in a left biased manner using demand based concurrency.
<>operation now concurrently meges the two streams in a fairly parallel manner.
To adapt to the new changes, replace
serialwherever it is used for stream types other than
Alternativeinstance. To adapt to this change replace any usage of
Stream type now defaults to the
SerialTtype unless explicitly specified using a type combinator or a monomorphic type. This change reduces puzzling type errors for beginners. It includes the following two changes:
- Change the type of all stream elimination functions to use
SerialTinstead of a polymorphic type. This makes sure that the stream type is always fixed at all exits.
- Change the type combinators (e.g.
parallely) to only fix the argument stream type and the output stream type remains polymorphic.
Stream types may have to be changed or type combinators may have to be added or removed to adapt to this change.
- Change the type of all stream elimination functions to use
Change the type of
foldrMto make it consistent with
asyncis renamed to
asyncis now a new API with a different meaning.
ZipAsyncis renamed to
ZipAsyncis now ZipAsyncM specialized to the IO Monad.
MonadErrorinstance as it was not working correctly for parallel compositions. Use
MonadThrowinstead for error propagation.
Remove Num/Fractional/Floating instances as they are not very useful. Use
- Deprecate and rename the following symbols:
- Deprecate the following symbols for future removal:
- Add the following functions:
|:operator to construct streams from monadic actions
onceto create a singleton stream from a monadic action
repeatMto construct a stream by repeating a monadic action
scanl'strict left scan
foldl'strict left fold
foldlM'strict left fold with a monadic fold function
serialrun two streams serially one after the other
asyncrun two streams asynchronously
parallelrun two streams in parallel (replaces
WAsyncTstream type for BFS version of
- Add simpler stream types that are specialized to the IO monad
- Put a bound (1500) on the output buffer used for asynchronous tasks
- Put a limit (1500) on the number of threads used for Async and WAsync types
- Fixed a bug that casued unexpected behavior when
purewas used to inject values in Applicative composition of
consright associative and provide an operator form
- Improve performance of some stream operations (
- Fix the
productoperation. Earlier, it always returned 0 due to a bug
- Fix the
lastoperation, which returned
Nothingfor singleton streams
- Initial release