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 framework. 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 simplifies streaming and makes it as intuitive as plain lists. Unlike
other streaming libraries, no fancy types are required. Streamly is simply a
generalization of Haskell lists to monadic streaming optionally with concurrent
composition. The basic stream type in streamly SerialT m a
can be considered
as a list type [a]
parameterized by the monad m
. For example, SerialT IO a
is a moral equivalent of [a]
in the IO monad. SerialT Identity a
, is
equivalent to pure lists. Streams are constructed very much like lists, except
that they use nil
and cons
instead of []
and :
. Unlike lists, streams
can be constructed from monadic effects, not just pure elements. Streams are
processed just like lists, with list like combinators, except that they are
monadic and work in a streaming fashion. In other words streamly just completes
what lists lack, you do not need to learn anything new. Please see streamly vs
lists for a detailed comparison.
Not surprisingly, the monad instance of streamly is a list transformer, with
concurrency capability.
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?
High performance and simplicity are the two primary goals of streamly.
Streamly
employs two different stream representations (CPS and direct style)
and interconverts between the two to get the best of both worlds on different
operations. It uses both foldr/build (for CPS style) and stream fusion (for
direct style) techniques to fuse operations. In terms of performance,
Streamly's goal is to compete with equivalent C programs. Streamly redefines
"blazing fast" for streaming libraries, it competes with lists and vector
.
Other streaming libraries like "streaming", "pipes" and "conduit" are orders of
magnitude slower on most microbenchmarks. See streaming
benchmarks for detailed
comparison.
The following chart shows a comparison of those streamly and list operations
where performance of the two differs by more than 10%. Positive y-axis displays
how many times worse is a list operation compared to the same streamly
operation, negative y-axis shows where streamly is worse compared to lists.
Streamly uses lock-free synchronization for concurrent operations. It employs
auto-scaling of the degree of concurrency based on demand. For CPU bound tasks
it tries to keep the threads close to the number of CPUs available whereas for
IO bound tasks more threads can be utilized. Parallelism can be utilized with
little overhead even if the task size is very small. See concurrency
benchmarks for detailed
performance results and a comparison with the async
package.
Installing and using
Please see INSTALL.md for instructions on how to use streamly
with your Haskell build tool or package manager. You may want to go through it
before jumping to run the examples below.
The module Streamly
provides just the core stream types, type casting and
concurrency control combinators. Stream construction, transformation, folding,
merging, zipping combinators are found in Streamly.Prelude
.
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 = S.drain $
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.
Concurrent Stream Generation
consM
or its operator form |:
can be used to construct a stream from
monadic actions. A stream constructed with consM
can run the monadic actions
in the stream 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), note the
order of elements in the resulting output, the outputs are consumed as soon as
each action is finished (asyncly):
> let p n = threadDelay (n * 1000000) >> return n
> S.toList $ asyncly $ p 3 |: p 2 |: p 1 |: S.nil
[1,2,3]
Use aheadly
if you want speculative concurrency i.e. execute the actions in
the stream concurrently but consume the results in the specified order:
> S.toList $ aheadly $ p 3 |: p 2 |: p 1 |: S.nil
[3,2,1]
Monadic stream generation functions e.g. unfoldrM
, replicateM
, repeatM
,
iterateM
and fromFoldableM
etc. can work concurrently.
The following finishes in 10 seconds (100 seconds when serial):
S.drain $ asyncly $ S.replicateM 10 $ p 10
Concurrency Auto Scaling
Concurrency is auto-scaled i.e. more actions are executed concurrently if the
consumer is consuming the stream at a higher speed. How many tasks are executed
concurrently can be controlled by maxThreads
and how many results are
buffered ahead of consumption can be controlled by maxBuffer
. See the
documentation in the Streamly
module.
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 = S.drain $
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
> S.drain $ 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 = S.drain $ delay 3 <> delay 2 <> delay 1
ThreadId 36: Delay 3
ThreadId 36: Delay 2
ThreadId 36: Delay 1
Parallel
main = S.drain . parallely $ delay 3 <> delay 2 <> delay 1
ThreadId 42: Delay 1
ThreadId 41: Delay 2
ThreadId 40: Delay 3
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 = S.drain loops
(1,3)
(1,4)
(2,3)
(2,4)
Concurrent Nested Loops
To run the above code with speculative concurrency i.e. each iteration in the
loop can run concurrently but the results are presented to the consumer of the
output in the same order as serial execution:
main = S.drain $ aheadly $ loops
Different stream types execute the loop iterations in different ways. For
example, wSerially
interleaves the loop iterations. There are several
concurrent stream styles to execute the loop iterations concurrently in
different ways, see the Streamly.Tutorial
module for a detailed treatment.
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.
Example: Listing Directories Recursively/Concurrently
The following code snippet lists a directory tree recursively, reading multiple
directories concurrently:
import Control.Monad.IO.Class (liftIO)
import Path.IO (listDir, getCurrentDir) -- from path-io package
import Streamly (AsyncT, adapt)
import qualified Streamly.Prelude as S
listDirRecursive :: AsyncT IO ()
listDirRecursive = getCurrentDir >>= readdir >>= liftIO . mapM_ putStrLn
where
readdir dir = do
(dirs, files) <- listDir dir
S.yield (map show dirs ++ map show files) <> foldMap readdir dirs
main :: IO ()
main = S.drain $ adapt $ listDirRecursive
AsyncT
is a stream monad transformer. If you are familiar with a list
transformer, it is nothing but ListT
with concurrency semantics. For example,
the semigroup operation <>
is concurrent. This makes foldMap
concurrent
too. You can replace AsyncT
with SerialT
and the above code will become
serial, exactly equivalent to a ListT
.
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 = S.drain $ 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.
Arrays
The Streamly.Memory.Array
module provides immutable arrays. Arrays are the
computing duals of streams. Streams are good at sequential access and immutable
transformations of in-transit data whereas arrays are good at random access and
in-place transformations of buffered data. Unlike streams which are potentially
infinite, arrays are necessarily finite. Arrays can be used as an efficient
interface between streams and external storage systems like memory, files and
network. Streams and arrays complete each other to provide a general purpose
computing system. The design of streamly as a general purpose computing
framework is centered around these two fundamental aspects of computing and
storage.
Streamly.Memory.Array
uses pinned memory outside GC and therefore avoid any
GC overhead for the storage in arrays. Streamly allows efficient
transformations over arrays using streams. It uses arrays to transfer data to
and from the operating system and to store data in memory.
Folds
Folds are consumers of streams. Streamly.Data.Fold
module provides a Fold
type that represents a foldl'
. Such folds can be efficiently composed
allowing the compiler to perform stream fusion and therefore implement high
performance combinators for consuming streams. A stream can be distributed to
multiple folds, or it can be partitioned across multiple folds, or
demultiplexed over multiple folds, or unzipped to two folds. We can also use
folds to fold segments of stream generating a stream of the folded results.
If you are familiar with the foldl
library, these are the same composable
left folds but simpler and better integrated with streamly, and with many more
powerful ways of composing and applying them.
Unfolds
Unfolds are duals of folds. Folds help us compose consumers of streams
efficiently and unfolds help us compose producers of streams efficiently.
Streamly.Data.Unfold
provides an Unfold
type that represents an unfoldr
or a stream generator. Such generators can be combined together efficiently
allowing the compiler to perform stream fusion and implement high performance
stream merging combinators.
File IO
The following code snippets implement some common Unix command line utilities
using streamly. You can compile these with ghc -O2 -fspec-constr-recursive=16 -fmax-worker-args=16
and compare the performance with regular GNU coreutils
available on your system. Though many of these are not most optimal solutions
to keep them short and elegant. Source file
HandleIO.hs
in the examples directory includes these examples.
module Main where
import qualified Streamly.Prelude as S
import qualified Streamly.Data.Fold as FL
import qualified Streamly.Memory.Array as A
import qualified Streamly.FileSystem.Handle as FH
import qualified System.IO as FH
import Data.Char (ord)
import System.Environment (getArgs)
import System.IO (openFile, IOMode(..), stdout)
withArg f = do
(name : _) <- getArgs
src <- openFile name ReadMode
f src
withArg2 f = do
(sname : dname : _) <- getArgs
src <- openFile sname ReadMode
dst <- openFile dname WriteMode
f src dst
cat
cat = S.fold (FH.writeChunks stdout) . S.unfold FH.readChunks
main = withArg cat
cp
cp src dst = S.fold (FH.writeChunks dst) $ S.unfold FH.readChunks src
main = withArg2 cp
wc -l
wcl = S.length . S.splitOn (== 10) FL.drain . S.unfold FH.read
main = withArg wcl >>= print
Average Line Length
avgll =
S.fold avg
. S.splitOn (== 10) FL.length
. S.unfold FH.read
where avg = (/) <$> toDouble FL.sum <*> toDouble FL.length
toDouble = fmap (fromIntegral :: Int -> Double)
main = withArg avgll >>= print
Line Length Histogram
classify
is not released yet, and is available in
Streamly.Internal.Data.Fold
llhisto =
S.fold (FL.classify FL.length)
. S.map bucket
. S.splitOn (== 10) FL.length
. S.unfold FH.read
where
bucket n = let i = n `mod` 10 in if i > 9 then (9,n) else (i,n)
main = withArg llhisto >>= print
Socket IO
Its easy to build concurrent client and server programs using streamly.
Streamly.Network.*
modules provide easy combinators to build network servers
and client programs using streamly. See
FromFileClient.hs,
EchoServer.hs,
FileSinkServer.hs
in the examples directory.
Exceptions
Exceptions can be thrown at any point using the MonadThrow
instance. Standard
exception handling combinators like bracket
, finally
, handle
,
onException
are provided in Streamly.Prelude
module.
In presence of concurrency, synchronous exceptions work just the way they are
supposed to work in 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.
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:
For additional unreleased/experimental APIs, build the haddock docs using:
$ cabal haddock --haddock-option="--show-all"
$ stack haddock --haddock-arguments "--show-all" --no-haddock-deps
Support
Please feel free to ask questions on the
streamly gitter channel.
If you require professional support, consulting, training or timely
enhancements to the library please contact
support@composewell.com.
Credits
The following authors/libraries have influenced or inspired this library in a
significant way:
- Roman Leshchinskiy (vector)
- Gabriel Gonzalez (foldl)
- Alberto G. Corona (transient)
See the credits
directory for full list of contributors, credits and licenses.
Contributing
The code is available under BSD-3 license
on github. Join the gitter
chat channel for discussions. Please
ask any questions on the gitter channel or contact the maintainer
directly. All contributions are welcome!