Module documentation for 0.8.1.1
Streamly: Idiomatic Haskell with the Performance of C
Streamly is a Haskell library that provides the building blocks to build safe, scalable, modular and high performance software. Streamly offers:
- The type safety of Haskell.
- The performance of C programs.
- Powerful abstractions for structuring your code.
- Idiomatic functional programming.
- Declarative concurrency for the seamless use of multiprocessing hardware.
About This Document
This guide introduces programming with Streamly using a few practical examples:
- We will start with a simple program that counts the number of words in a text. We will then transform this program into a concurrent program that can efficiently use multiprocessing hardware.
- Next, we will create a concurrent network server. We then show how to write a network server that merges multiple streams concurrently.
- Our third example shows how to list a directory tree concurrently, by reading multiple directories in parallel.
- Finally, we will look at how to rate limit stream processing.
If you wish to follow along with this guide, you will need to have Streamly installed.
If you wish to run benchmarks, please be sure to build your application using the instructions in the Build Guide.
An overview of the types used in these examples
As an expository device, we have indicated the types at the intermediate stages of stream computations as comments in the examples below. The meaning of these types are:
SerialT IO ais a serial stream of values of type
ain the IO Monad.
AsyncT IO ais a concurrent (asynchronous) stream of values of type
ain the IO Monad.
Unfold IO a bis a representation of a function that converts a seed value of type
ainto a stream of values of type
bin the IO Monad.
Fold IO a bis a representation of a function that converts a stream of type
ato a final accumulator of type
bin the IO Monad.
A Note on Module Naming
Some of the examples below use modules from the
Internal Streamly package
hierarchy. These are not really internal to the library. We classify
Streamly modules into two categories:
- Released modules and APIs: These modules and APIs are stable. Significant changes to these modules and APIs will cause Streamly’s version number to change according to the package versioning policy.
- Pre-release modules and APIs: These modules and APIs have not been
formally released yet. They may change in the near future, and such
changes will not necessarily be reflected in Streamly’s package
version number. As yet unreleased modules and APIs reside in the
Please use a minor release upper bound to adhere to the Haskell PVP when using the pre-release (internal) modules.
Modular Word Counting
Fold in Streamly is a composable stream consumer. For our first
example, we will use
Folds to count the number of bytes, words and lines
present in a file. We will then compose individual
Folds together to
count words, bytes and lines at the same time.
Please see the file WordCountModular.hs for the complete example program, including the imports that we have omitted here.
Count Bytes (wc -c)
We start with a code fragment that counts the number of bytes in a file:
import qualified Streamly.Data.Fold as Fold import qualified Streamly.Internal.FileSystem.File as File import qualified Streamly.Prelude as Stream wcb :: String -> IO Int wcb file = File.toBytes file -- SerialT IO Word8 & Stream.fold Fold.length -- IO Int
Count Lines (wc -l)
The next code fragment shows how to count the number of lines in a file:
-- ASCII character 10 is a newline. countl :: Int -> Word8 -> Int countl n ch = if ch == 10 then n + 1 else n -- The fold accepts a stream of `Word8` and returns a line count (`Int`). nlines :: Monad m => Fold m Word8 Int nlines = Fold.foldl' countl 0 wcl :: String -> IO Int wcl file = File.toBytes file -- SerialT IO Word8 & Stream.fold nlines -- IO Int
Count Words (wc -w)
Our final code fragment counts the number of whitespace-separated words in a stream:
countw :: (Int, Bool) -> Word8 -> (Int, Bool) countw (n, wasSpace) ch = if isSpace $ chr $ fromIntegral ch then (n, True) else (if wasSpace then n + 1 else n, False) -- The fold accepts a stream of `Word8` and returns a word count (`Int`). nwords :: Monad m => Fold m Word8 Int nwords = fst <$> Fold.foldl' countw (0, True) wcw :: String -> IO Int wcw file = File.toBytes file -- SerialT IO Word8 & Stream.fold nwords -- IO Int
Counting Bytes, Words and Lines Together
By using the
Tee combinator we can compose the three folds that count
bytes, lines and words individually into a single fold that counts all
three at once. The applicative instance of
Tee distributes its input
to all the supplied folds (
then combines the outputs from the folds using the supplied combiner
import qualified Streamly.Internal.Data.Fold.Tee as Tee -- The fold accepts a stream of `Word8` and returns the three counts. countAll :: Fold IO Word8 (Int, Int, Int) countAll = Tee.toFold $ (,,) <$> Tee Fold.length <*> Tee nlines <*> Tee nwords wc :: String -> IO (Int, Int, Int) wc file = File.toBytes file -- SerialT IO Word8 & Stream.fold countAll -- IO (Int, Int, Int)
This example demonstrates the excellent modularity offered by
Streamly’s simple and concise API. Experienced Haskellers will
notice that we have not used bytestrings—we instead used a stream of
Word8 values, simplifying our program.
The Performance of Word Counting
We compare two equivalent implementations: one using Streamly, and the other using C.
The performance of the Streamly word counting implementation is:
$ time WordCount-hs gutenberg-500MB.txt 11242220 97050938 574714449 gutenberg-500MB.txt real 0m1.825s user 0m1.697s sys 0m0.128s
The performance of an equivalent wc implementation in C is:
$ time WordCount-c gutenberg-500MB.txt 11242220 97050938 574714449 gutenberg-500MB.txt real 0m2.100s user 0m1.935s sys 0m0.165s
Concurrent Word Counting
In our next example we show how the task of counting words, lines, and bytes could be done in parallel on multiprocessor hardware.
To count words in parallel we first divide the stream into chunks (arrays), do the counting within each chunk, and then add all the counts across chunks. We use the same code as above except that we use arrays for our input data.
Please see the file WordCountParallel.hs for the complete working code for this example, including the imports that we have omitted below.
countArray function counts the line, word, char counts in one chunk:
import qualified Streamly.Data.Array.Foreign as Array countArray :: Array Word8 -> IO Counts countArray arr = Stream.unfold Array.read arr -- SerialT IO Word8 & Stream.decodeLatin1 -- SerialT IO Char & Stream.foldl' count (Counts 0 0 0 True) -- IO Counts
Here the function
count and the
Counts data type are defined in the
WordCount helper module defined in WordCount.hs.
When combining the counts in two contiguous chunks, we need to check
whether the first element of the next chunk is a whitespace character
in order to determine if the same word continues in the next chunk or
whether the chunk starts with a new word. The
Bool flag to
Counts returned by
countArray to indicate
whether the first character in the chunk is a space.
partialCounts :: Array Word8 -> IO (Bool, Counts) partialCounts arr = do let r = Array.getIndex arr 0 case r of Just x -> do counts <- countArray arr return (isSpace (chr (fromIntegral x)), counts) Nothing -> return (False, Counts 0 0 0 True)
addCounts then adds the counts from two consecutive chunks:
addCounts :: (Bool, Counts) -> (Bool, Counts) -> (Bool, Counts) addCounts (sp1, Counts l1 w1 c1 ws1) (sp2, Counts l2 w2 c2 ws2) = let wcount = if not ws1 && not sp2 -- No space between two chunks. then w1 + w2 - 1 else w1 + w2 in (sp1, Counts (l1 + l2) wcount (c1 + c2) ws2)
To count in parallel we now only need to divide the stream into arrays, apply our counting function to each array, and then combine the counts from each chunk.
wc :: String -> IO (Bool, Counts) wc file = do Stream.unfold File.readChunks file -- AheadT IO (Array Word8) & Stream.mapM partialCounts -- AheadT IO (Bool, Counts) & Stream.maxThreads numCapabilities -- AheadT IO (Bool, Counts) & Stream.fromAhead -- SerialT IO (Bool, Counts) & Stream.foldl' addCounts (False, Counts 0 0 0 True) -- IO (Bool, Counts)
Please note that the only difference between a concurrent and a
non-concurrent program lies in the use of the
combinator. If we remove the call to
Stream.fromAhead, we would
still have a perfectly valid and performant serial program. Notice
how succinctly and idiomatically we have expressed the concurrent word
A benchmark with 2 CPUs:
$ time WordCount-hs-parallel gutenberg-500MB.txt 11242220 97050938 574714449 gutenberg-500MB.txt real 0m1.284s user 0m1.952s sys 0m0.140s
These example programs have assumed ASCII encoded input data. For UTF-8
streams, we have a concurrent wc implementation
with UTF-8 decoding. This concurrent implementation performs as well
as the standard
wc program in serial benchmarks. In concurrent mode
Streamly’s implementation can utilise multiple processing cores if
these are present, and can thereby run much faster than the standard
Streamly provides concurrency facilities similar to OpenMP and Cilk but with a more declarative style of expression. With Streamly you can write concurrent programs with ease, with support for different types of concurrent scheduling.
A Concurrent Network Server
We now move to a slightly more complicated example: we simulate a
dictionary lookup server which can serve word meanings to multiple
clients concurrently. This example demonstrates the use of the concurrent
Please see the file WordServer.hs for the complete code for this example, including the imports that we have omitted below.
import qualified Streamly.Data.Fold as Fold import qualified Streamly.Network.Inet.TCP as TCP import qualified Streamly.Network.Socket as Socket import qualified Streamly.Unicode.Stream as Unicode -- Simulate network/db query by adding a delay. fetch :: String -> IO (String, String) fetch w = threadDelay 1000000 >> return (w,w) -- Read lines of whitespace separated list of words from a socket, fetch the -- meanings of each word concurrently and return the meanings separated by -- newlines, in same order as the words were received. Repeat until the -- connection is closed. lookupWords :: Socket -> IO () lookupWords sk = Stream.unfold Socket.read sk -- SerialT IO Word8 & Unicode.decodeLatin1 -- SerialT IO Char & Stream.wordsBy isSpace Fold.toList -- SerialT IO String & Stream.fromSerial -- AheadT IO String & Stream.mapM fetch -- AheadT IO (String, String) & Stream.fromAhead -- SerialT IO (String, String) & Stream.map show -- SerialT IO String & Stream.intersperse "\n" -- SerialT IO String & Unicode.encodeStrings Unicode.encodeLatin1 -- SerialT IO (Array Word8) & Stream.fold (Socket.writeChunks sk) -- IO () serve :: Socket -> IO () serve sk = finally (lookupWords sk) (close sk) -- | Run a server on port 8091. Accept and handle connections concurrently. The -- connection handler is "serve" (i.e. lookupWords). You can use "telnet" or -- "nc" as a client to try it out. main :: IO () main = Stream.unfold TCP.acceptOnPort 8091 -- SerialT IO Socket & Stream.fromSerial -- AsyncT IO () & Stream.mapM serve -- AsyncT IO () & Stream.fromAsync -- SerialT IO () & Stream.drain -- IO ()
Merging Incoming Streams
In the next example, we show how to merge logs coming from multiple
nodes in your network. These logs are merged at line boundaries and
the merged logs are written to a file or to a network destination.
This example uses the
concatMapWith combinator to merge multiple
Please see the file MergeServer.hs for the complete working code, including the imports that we have omitted below.
import qualified Streamly.Data.Unfold as Unfold import qualified Streamly.Network.Socket as Socket -- | Read a line stream from a socket. -- Note: lines are buffered, and we could add a limit to the -- buffering for safety. readLines :: Socket -> SerialT IO (Array Char) readLines sk = Stream.unfold Socket.read sk -- SerialT IO Word8 & Unicode.decodeLatin1 -- SerialT IO Char & Stream.splitWithSuffix (== '\n') Array.write -- SerialT IO String recv :: Socket -> SerialT IO (Array Char) recv sk = Stream.finally (liftIO $ close sk) (readLines sk) -- | Starts a server at port 8091 listening for lines with space separated -- words. Multiple clients can connect to the server and send streams of lines. -- The server handles all the connections concurrently, merges the incoming -- streams at line boundaries and writes the merged stream to a file. server :: Handle -> IO () server file = Stream.unfold TCP.acceptOnPort 8090 -- SerialT IO Socket & Stream.concatMapWith Stream.parallel recv -- SerialT IO (Array Char) & Stream.unfoldMany Array.read -- SerialT IO Char & Unicode.encodeLatin1 -- SerialT IO Word8 & Stream.fold (Handle.write file) -- IO () main :: IO () main = withFile "output.txt" AppendMode server
Listing Directories Recursively/Concurrently
Our next example lists a directory tree recursively, reading multiple directories concurrently.
This example uses the tree traversing combinator
This combinator maps a stream generator on the
Left values in its
input stream (directory names in this case), feeding the resulting
values back to the input, while it lets the
Right values (file names
in this case) pass through to the output. The
joining combinator then makes it iterate on the directories concurrently.
Please see the file ListDir.hs for the complete working code, including the imports that we have omitted below.
import Streamly.Internal.Data.Stream.IsStream (iterateMapLeftsWith) import qualified Streamly.Prelude as Stream import qualified Streamly.Internal.FileSystem.Dir as Dir (toEither) -- Lists a directory as a stream of (Either Dir File). listDir :: String -> SerialT IO (Either String String) listDir dir = Dir.toEither dir -- SerialT IO (Either String String) & Stream.map (bimap mkAbs mkAbs) -- SerialT IO (Either String String) where mkAbs x = dir ++ "/" ++ x -- | List the current directory recursively using concurrent processing. main :: IO () main = do hSetBuffering stdout LineBuffering let start = Stream.fromPure (Left ".") Stream.iterateMapLeftsWith Stream.ahead listDir start & Stream.mapM_ print
For bounded concurrent streams, a stream yield rate can be specified easily. For example, to print “tick” once every second you can simply write:
main :: IO () main = Stream.repeatM (pure "tick") -- AsyncT IO String & Stream.timestamped -- AsyncT IO (AbsTime, String) & Stream.avgRate 1 -- AsyncT IO (AbsTime, String) & Stream.fromAsync -- SerialT IO (AbsTime, String) & Stream.mapM_ print -- IO ()
Please see the file Rate.hs for the complete working code.
The concurrency of the stream is automatically controlled to match the specified rate. Streamly’s rate control works precisely even at throughputs as high as millions of yields per second.
For more sophisticated rate control needs please see the Streamly reference documentation.
Streamly supports reactive (time domain) programming because of its
support for declarative concurrency. Please see the
module for time-specific combinators like
Please also see the pre-release sampling combinators in the
Streamly.Internal.Data.Stream.IsStream.Top module for
debounce like operations.
If you would like to view more examples, please visit the Streamly Examples web page.
- Streaming Benchmarks
- Concurrency Benchmarks
- Functional Conf 2019 Video | Slides
- Other Guides
- Streamly Homepage
As you have seen in the word count example above, Streamly offers highly modular abstractions for building programs while also offering the performance close to an equivalent (imperative) C program.
Streamly offers excellent performance even for byte-at-a-time stream
operations using efficient abstractions like
Unfolds and terminating
Folds. Byte-at-a-time stream operations can simplify programming
because the developer does not have to deal explicitly with chunking
and re-combining data.
Streamly exploits GHC’s stream fusion optimizations (
spec-constr) aggressively to achieve C-like speed, while also offering
highly modular abstractions to developers.
Streamly will usually perform very well without any compiler plugins. However, we have fixed some deficiencies that we had noticed in GHC’s optimizer using a compiler plugin. We hope to fold these optimizations into GHC in the future; until then we recommend that you use this plugin for applications that are performance sensitive.
We measured several Haskell streaming implementations using various micro-benchmarks. Please see the streaming benchmarks page for a detailed comparison of Streamly against other streaming libraries.
Our results show that Streamly is the fastest effectful streaming implementation on almost all the measured microbenchmarks. In many cases it runs up to 100x faster, and in some cases even 1000x faster than some of the tested alternatives. In some composite operation benchmarks Streamly turns out to be significantly faster than Haskell’s list implementation.
Note: If you can write a program in some other way or with some other language that runs significantly faster than what Streamly offers, please let us know and we will improve.
Streamly comes equipped with a very powerful set of abstractions to
accomplish many kinds of programming tasks: it provides support for
programming with streams and arrays, for reading and writing from the
file system and from the network, for time domain programming (reactive
programming), and for reacting to file system events using
Please view Streamly’s documentation for more information about Streamly’s features.
Streamly uses lock-free synchronization for achieving concurrent operation with low overheads. The number of tasks performed concurrently are determined automatically based on the rate at which a consumer is consuming the results. In other words, you do not need to manage thread pools or decide how many threads to use for a particular task. For CPU-bound tasks Streamly will try to keep the number of threads close to the number of CPUs available; for IO-bound tasks it will utilize more threads.
The parallelism available during program execution can be utilized with
very little overhead even where the task size is very
small, because Streamly will automatically switch between
serial or batched execution of tasks on the same CPU depending
on whichever is more efficient. Please see our concurrency
benchmarks for more detailed performance
measurements, and for a comparison with the
Our goals for Streamly from the very beginning have been:
- To achieve simplicity by unifying abstractions.
- To offer high performance.
These goals are hard to achieve simultaneously because they are usually inversely related. We have spent many years trying to get the abstractions right without compromising performance.
Unfold is an example of an abstraction that we have created to achieve
high performance when mapping streams on streams.
Unfold allows stream
generation to be optimized well by the compiler through stream fusion.
Fold with termination capability is another example which modularizes
stream elimination operations through stream fusion. Terminating folds
can perform many simple parsing tasks that do not require backtracking.
Parsers are a natural extension to terminating
Parsers add the ability to backtrack to
Folds. Unification leads
to simpler abstractions and lower cognitive overheads while also not
The following authors/libraries have influenced or inspired this library in a significant way:
Please see the
credits directory for a full
list of contributors, credits and licenses.
Contributing to Streamly
As an open project we welcome contributions:
You can also join our community chat channel on Gitter.
0.8.1.1 (Dec 2021)
- Disable building FileSystem.Events where FS Events isn’t supported.
0.8.1 (Nov 2021)
See docs/API-changelog.txt for new APIs introduced.
- Several bug fixes in the Array module:
- Fix writeN fold eating away one element when applied multiple times #1258.
- Fix potentially writing beyond allocated memory when shrinking. Likely cause of #944.
- Fix potentially writing beyond allocated memory when writing the last element. Likely cause of #944.
- Fix missing pointer touch could potentially cause use of freed memory.
- Fix unnecessary additional allocation due to a bug
- Fix a bug in classifySessionsBy, see PR #1311. The bug could cause premature ejection of a session when input events with the same key are split into multiple sessions.
Notable Internal API Changes
Streamly.Internal.Data.Stream.Parallelhas been moved to
Streamly.Internal.Data.Stream.IsStreamand renamed to
Fold2has now been renamed to
Refoldand the corresponding
Fold2combinators have been either renamed or removed.
0.8.0 (Jun 2021)
See API Changelog for a complete list of signature changes and new APIs introduced.
fold: this function may now terminate early without consuming the entire stream. For example,
fold Fold.head streamwould now terminate immediately after consuming the head element from
stream. This may result in change of behavior in existing programs if the program relies on the evaluation of the full stream.
- The following APIs no longer throw errors on invalid input, use new
APIs suffixed with a prime for strict behavior:
- The following APIs no longer throw errors on invalid input, use new APIs suffixed with a prime for strict behavior:
- Several instances have been moved to the
Streamly.Data.Fold.Teemodule, please use the
Teetype to adapt to the changes.
- Several instances have been moved to the
- Concurrent Streams: The monadic state for the stream is now propagated across threads. Please refer to #369 for more info.
finallynow also work correctly on streams that aren’t fully drained. Also, the resource acquisition and release is atomic with respect to async exceptions.
iterateMnow consume O(1) space instead of O(n).
fromFoldableMis fixed to be concurrent.
connectAPIs now close the socket if an exception is thrown.
acceptnow closes the socket if an exception is thrown.
- See API Changelog for a complete list of new modules and APIs introduced.
- The Fold type is now more powerful, the new termination behavior allows to express basic parsing of streams using folds.
- Many new Fold and Unfold APIs are added.
- A new module for console IO APIs is added.
- Experimental modules for the following are added:
- File system event handling (fsnotify/inotify)
- Folds for streams of arrays
use-c-mallocbuild flag to use the c library
mallocfor array allocations. This could be useful to avoid pinned memory fragmentation.
Notable Internal/Pre-release API Changes
Foldtype has changed to accommodate terminating folds.
- Several other internal modules have been renamed and re-factored.
- A bug was fixed in the conversion of
- Bug fix:
classifySessionsBynow flushes sessions at the end and terminates.
- Drop support for GHC 7.10.3.
- The examples in this package are moved to a new github repo streamly-examples
0.7.3 (February 2021)
- Fix build issues with primitive package version >= 0.7.1.
- Fix build issues on armv7.
0.7.2 (April 2020)
- Fix a bug in the
Functorinstances of the
- Fix a bug that occasionally caused a build failure on windows when
- Now builds on 32-bit machines.
- Now builds with
primitivepackage version >= 0.5.4 && <= 0.6.4.0
- Now builds with newer
QuickCheckpackage version >= 2.14 && < 2.15.
- Now builds with GHC 8.10.
0.7.1 (February 2020)
- Fix a bug that caused
findIndicesto return wrong indices in some cases.
- Fix a bug in
chunksOfthat caused memory consumption to increase in some cases.
- Fix a space leak in concurrent streams (
ahead) that caused memory consumption to increase with the number of elements in the stream, especially when built with
-threadedand used with
-NRTS option. The issue occurs only in cases when a worker thread happens to be used continuously for a long time.
- Fix scheduling of WAsyncT stream style to be in round-robin fashion.
- Now builds with
containerspackage version < 0.5.8.
- Now builds with
networkpackage version >= 184.108.40.206 && < 220.127.116.11.
- Combinators in
Streamly.Network.Inet.TCPno longer use TCP
ReuseAddrsocket options by default. These options can now be specified using appropriate combinators.
- Now uses
fusion-pluginpackage for predictable stream fusion optimizations
- Significant improvement in performance of concurrent stream operations.
- Improved space and time performance of
0.7.0 (November 2019)
- Change the signature of
foldrMto ensure that it is lazy
- Change the signature of
iterateMto ensure that it is lazy.
scanxwould now require an additional
ParallelTwas unaffected by
maxBuffercan limit the buffer of a
ParallelTstream as well. When the buffer becomes full, the producer threads block.
ParallelTstreams no longer have an unlimited buffer by default. Now the buffer for parallel streams is limited to 1500 by default, the same as other concurrent stream types.
runStreamhas been replaced by
runNhas been replaced by
runWhilehas been replaced by
fromHandlehas been deprecated. Please use
Streamly.Data.Fold.toListto split the stream to a stream of
Stringseparated by a newline.
toHandlehas been deprecated. Please use
concatUnfoldto add newlines to a stream,
Streamly.Data.Unicode.Stream.encodeUtf8for encoding and
Streamly.FileSystem.Handle.writefor writing to a file handle.
- Remove deprecated APIs
- Replace deprecated API
scanwith a new signature, to scan using Fold.
runStreamhas been deprecated, please use
Remove deprecated module
Streamly.Time(moved to Streamly.Internal.Data.Time)
Streamly.Internal(functionality moved to the Internal hierarchy)
- Fix a bug that caused
uniqfunction to yield the same element twice.
- Fix a bug that caused “thread blocked indefinitely in an MVar operation” exception in a parallel stream.
- Fix unbounded memory usage (leak) in
parallelcombinator. The bug manifests when large streams are combined using
This release contains a lot of new features and major enhancements. For more details on the new features described below please see the haddock docs of the modules on hackage.
Streamly.Prelude for new exception handling combinators like
Streamly.Data.Fold module provides composable folds (stream consumers). Folds
allow splitting, grouping, partitioning, unzipping and nesting a stream onto
multiple folds without breaking the stream. Combinators are provided for
temporal and spatial window based fold operations, for example, to support
folding and aggregating data for timeout or inactivity based sessions.
Streamly.Data.Unfold module provides composable stream generators. Unfolds allow
high performance merging/flattening/combining of stream generators.
Streaming File IO
Streamly.FileSystem.Handle provides handle based streaming file IO
Streaming Network IO
Streamly.Network.Socketprovides socket based streaming network IO operations.
Streamly.Network.Inet.TCPprovides combinators to build Inet/TCP clients and servers.
Streamly.Prelude combinator performs a
concatMap using a supplied merge/concat strategy. This is a very
powerful combinator as you can, for example, concat streams
concurrently using this.
Add the following new features/modules:
- Unicode Strings:
Streamly.Data.Unicode.Streammodule provides encoding/decoding of character streams and other character stream operations.
Streamly.Memory.Arraymodule provides arrays for efficient in-memory buffering and efficient interfacing with IO.
- Unicode Strings:
Add the following to
concatUnfoldto concat a stream after unfolding each element
intersperseintersperse an element in between consecutive elements in stream
tracecombinator maps a monadic function on a stream just for side effects
tapredirects a copy of the stream to a
0.6.1 (March 2019)
- Fix a bug that caused
maxThreadsdirective to be ignored when rate control was not used.
- Add GHCJS support
- Remove dependency on “clock” package
0.6.0 (December 2018)
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
0.5.2 (October 2018)
- 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
0.5.1 (September 2018)
- Performance improvements, especially space consumption, for concurrent streams
0.5.0 (September 2018)
- 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.
0.4.1 (July 2018)
- foldxM was not fully strict, fixed.
0.4.0 (July 2018)
- 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’
0.3.0 (June 2018)
- 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.
0.2.1 (June 2018)
- Fixed a bug that caused some transformation ops to return incorrect results
when used with concurrent streams. The affected ops are
0.2.0 (May 2018)
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
0.1.2 (March 2018)
- Fixed a bug that caused unexpected behavior when
purewas used to inject values in Applicative composition of
0.1.1 (March 2018)
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
0.1.0 (December 2017)
- Initial release