3-- Copyright : (c) 2017 Harendra Kumar
6-- Maintainer : streamly@composewell.com
7-- Stability : experimental
10-- Streamly is a general purpose programming framework using cocnurrent data
11-- flow programming paradigm. It can be considered as a generalization of
12-- Haskell lists to monadic streaming with concurrent composition capability.
13-- The serial stream type in streamly @SerialT m a@ is like the list type @[a]@
14-- parameterized by the monad @m@. For example, @SerialT IO a@ is a moral
15-- equivalent of @[a]@ in the IO monad. Streams are constructed very much like
16-- lists, except that they use 'nil' and 'cons' instead of '[]' and ':'.
20-- > import "Streamly.Prelude" (cons, consM)
21-- > import qualified "Streamly.Prelude" as S
23-- > S.toList $ 1 \`cons` 2 \`cons` 3 \`cons` nil
27-- Unlike lists, streams can be constructed from monadic effects:
30-- > S.'toList' $ 'getLine' \`consM` 'getLine' \`consM` S.'nil'
36-- Streams are processed just like lists, with list like combinators, except
37-- that they are monadic and work in a streaming fashion. Here is a simple
38-- console echo program example:
41-- > S.drain $ S.repeatM getLine & S.mapM putStrLn
44-- @SerialT Identity a@ is a moral equivalent of pure lists. Streamly utilizes
45-- fusion for high performance, therefore, we can represent and process strings
46-- as streams of 'Char', encode and decode the streams to/from UTF8 and
47-- serialize them to @Array Word8@ obviating the need for special purpose
48-- libraries like @bytestring@ and @text@.
51-- please see the "Streamly.Tutorial" module and the examples directory in this
55{-# LANGUAGE MultiParamTypeClasses #-}
57#if __GLASGOW_HASKELL__ >= 800
58{-# OPTIONS_GHC -Wno-orphans #-}
65 -- -- * Concepts Overview
81 -- * Stream transformers
82 -- | A stream represents a sequence of pure or effectful actions. The
83 -- `cons` and `consM` operations and the corresponding operators '.:' and
84 -- '|:' can be used to join pure values or effectful actions in a sequence.
85 -- The effects in the stream can be executed in many different ways
86 -- depending on the type of stream. In other words, the behavior of 'consM'
87 -- depends on the type of the stream.
89 -- There are three high level categories of streams, /spatially ordered/
90 -- streams, /speculative/ streams and /time ordered/ streams. Spatially
91 -- ordered streams, 'SerialT' and 'WSerialT', execute the effects in serial
92 -- order i.e. one at a time and present the outputs of those effects to the
93 -- consumer in the same order. Speculative streams, 'AheadT', may execute
94 -- many effects concurrently but present the outputs to the consumer in the
95 -- specified spatial order. Time ordered streams, 'AsyncT', 'WAsyncT' and
96 -- 'ParallelT', may execute many effects concurrently and present the
97 -- outputs of those effects to the consumer in time order i.e. as soon as
98 -- the output is generated.
100 -- We described above how the effects in a sequence are executed for
101 -- different types of streams. The behvavior of the 'Semigroup' and 'Monad'
102 -- instances follow the behavior of 'consM'. Stream generation operations
103 -- like 'repeatM' also execute the effects differently for different
104 -- streams, providing a concurrent generation capability when used with
105 -- stream types that execute effects concurrently. Similarly, effectful
106 -- transformation operations like 'mapM' also execute the transforming
107 -- effects differently for different types of streams.
114 -- ** Speculative Streams
118 -- ** Asynchronous Streams
124 -- ** Zipping Streams
129 -- * Parallel Function Application
146 -- * Concurrency Control
159 -- * Stream Type Adapters
183 -- ** Folding Containers of Streams
184 -- | These are variants of standard 'Foldable' fold functions that use a
185 -- polymorphic stream sum operation (e.g. 'async' or 'wSerial') to fold a
186 -- finite container of streams. Note that these are just special cases of
187 -- the more general 'concatMapWith' operation.
216 -- * Deprecated/Moved
217 -- | These APIs have been moved to other modules
225import Data.Semigroup (Semigroup(..))
226import Streamly.Internal.Data.SVar (MonadAsync, Rate(..))
227import Streamly.Internal.Data.Stream.Ahead
228import Streamly.Internal.Data.Stream.Async
229import Streamly.Internal.Data.Stream.Combinators
230import Streamly.Internal.Data.Stream.Parallel
231import Streamly.Internal.Data.Stream.Serial
232import Streamly.Internal.Data.Stream.StreamK hiding (serial)
233import Streamly.Internal.Data.Stream.Zip
235import qualified Streamly.Prelude as P
236import qualified Streamly.Internal.Prelude as IP
237import qualified Streamly.Internal.Data.Stream.StreamK as K
239-- XXX provide good succinct examples of pipelining, merging, splitting etc.
244-- A program is expressed as a network of streams and folds. A stream is a
245-- source or generator of data elements and a fold is a consumer of data elements that
246-- reduces multiple input elements to a single value.
248-- In the following example, a 'Word8' stream is generated by using
249-- 'Streamly.FileSystem.Handle.read' on a file handle, then the
250-- 'Streamly.Prelude.splitBySuffix' transformation splits the stream on
251-- newlines (ascii value 10); it uses the 'Streamly.Data.Fold.drain' fold to reduce
252-- the resulting lines to unit values (@()@), 'Streamly.Prelude.length' fold
253-- then counts the unit elements in the resulting stream which gives us the
254-- number of lines in the file:
256-- > S.length $ S.splitOnSuffix FL.drain 10 $ FH.read fh
258-- The following example folds the lines to arrays of 'Word8' using the
259-- 'Streamly.Memory.Array.writeF' fold and then wraps the lines in square
260-- brackets before writing them to standard output using
261-- 'Streamly.FileSystem.Handle.write':
263-- > wrapLine ln = S.fromList "[" <> A.read ln <> S.fromList "]\n"
264-- > readLines = S.splitOnSuffix A.writeF 10
265-- > FH.write stdout $ S.concatMap wrapLine $ readLines fh1
267-- One stream can be appended after another:
269-- > FH.write stdout $ S.concatMap wrapLine $ readLines fh1 <> readLines fh2
271-- The following example reads two files concurrently, merges the lines from
272-- the two streams and writes the resulting stream to another file:
274-- > FH.write stdout $ S.concatMap wrapLine $ readLines fh1 `parallel` readLines fh2
276-- There are many ways to generate, merge, zip, transform and fold data
277-- streams. Many transformations can be chained in a stream pipeline. See
278-- "Streamly.Prelude" module for combinators to manipulate streams.
282-- The way stream types in this module like 'SerialT' represent data sources,
283-- the same way the 'Fold' type from "Streamly.Data.Fold" represents data sinks or
284-- reducers of streams. Reducers can be combined to consume a stream source in
285-- many ways. The simplest is to reduce a stream source using a fold e.g.:
287-- > S.runFold FL.length $ S.enumerateTo 100
289-- Folds are consumers of streams and can be used to split a stream into
290-- multiple independent flows. Grouping transforms a stream by applying a fold
291-- on segments of a stream, distributing applies multiple folds in parallel on
292-- the same stream and combines them, partitioning sends different elements of
293-- a stream to different folds, unzipping divides the elements of a stream into
294-- parts and sends them through different folds. Parsers are nothing but a
295-- particular type of folds. Transformations can be applied contravariantly on
296-- the input of a fold.
300-- |---transform----Fold m a b--------|
301-- ---stream m a-->transform-->| |---f b c ...
302-- |---transform----Fold m a c--------|
310-- Streamly arrays (See "Streamly.Memory.Array") complement streams to provide an
311-- efficient computing paradigm. Streams are suitable for immutable
312-- transformations of /potentially infinite/ data using /sequential access/ and
313-- pipelined transformations whereas arrays are suitable for in-place
314-- transformations of /necessarily finite/ data using /random access/. Streams
315-- are synonymous with /sequential pipelined processing/ whereas arrays are
316-- synonymous with /efficient buffering and random access/.
318-- In general, a data processing pipeline reads data from some IO device, does
319-- some processing on it and finally writes the output to another IO device.
320-- Streams provide the overall framework of sequential processing pipeline in
321-- which arrays are used as buffering elements in the middle. In addition to
322-- buffering in the middle, arrays can also be used at the boundaries of the
323-- pipeline to efficiently interface with external storage systems like memory,
324-- files and network. If streams are the pipes in a water pipeline network
325-- then arrays are like the storage tanks in the middle. On the input side,
326-- think of arrays as buckets to fetch water to feed the pipeline and on the
327-- output side buckets to remove the processed water.
329-- 'ByteString' data type from the 'bytestring' package and the 'Text' data
330-- type from the 'text' package are special cases of arrays. 'ByteString' is
331-- like @Array Word8@ and 'Text' is like @utf16@ encoded @Array Word8@.
332-- Streamly arrays can be transformed as efficiently as @bytestring@ or @text@
333-- by using stream operations on them.
335-- Streams and arrays are equally important in computing. They are computing
336-- duals of each other.
339-- The basic stream type is 'Serial', it represents a sequence of IO actions,
340-- and is a 'Monad'. The type 'SerialT' is a monad transformer that can
341-- represent a sequence of actions in an arbitrary monad. The type 'Serial' is
342-- in fact a synonym for @SerialT IO@. There are a few more types similar to
343-- 'SerialT', all of them represent a stream and differ only in the
344-- 'Semigroup', 'Applicative' and 'Monad' compositions of the stream. 'Serial'
345-- and 'WSerial' types compose serially whereas 'Async' and 'WAsync'
346-- types compose concurrently. All these types can be freely inter-converted
347-- using type combinators without any cost. You can freely switch to any type
348-- of composition at any point in the program. When no type annotation or
349-- explicit stream type combinators are used, the default stream type is
350-- inferred as 'Serial'.
352-- This module exports stream types, instances and combinators for:
354-- * converting between different stream types
355-- * appending and concurrently merging streams
356-- * Concurrency control
357-- * Concurrent function application
358-- * Stream rate control
360-- This module is designed to be imported unqualified:
366-- See the "Streamly.Prelude" module for APIs for construction,
367-- generation, elimination and transformation of streams.
369------------------------------------------------------------------------------
370-- Eliminating a stream
371------------------------------------------------------------------------------
373-- | Same as 'runStream'
376{-# DEPRECATED runStreaming "Please use runStream instead." #-}
377runStreaming :: (Monad m, IsStream t) => t m a -> m ()
378runStreaming = P.drain . K.adapt
380-- | Same as @runStream@.
383{-# DEPRECATED runStreamT "Please use runStream instead." #-}
384runStreamT :: Monad m => SerialT m a -> m ()
387-- | Same as "Streamly.Prelude.runStream".
389{-# DEPRECATED runStream "Please use Streamly.Prelude.drain instead." #-}
390runStream :: Monad m => SerialT m a -> m ()
393-- | Same as @runStream . wSerially@.
396{-# DEPRECATED runInterleavedT "Please use 'runStream . interleaving' instead." #-}
397runInterleavedT :: Monad m => WSerialT m a -> m ()
398runInterleavedT = P.drain . K.adapt
400-- | Same as @runStream . parallely@.
403{-# DEPRECATED runParallelT "Please use 'runStream . parallely' instead." #-}
404runParallelT :: Monad m => ParallelT m a -> m ()
405runParallelT = P.drain . K.adapt
407-- | Same as @runStream . asyncly@.
410{-# DEPRECATED runAsyncT "Please use 'runStream . asyncly' instead." #-}
411runAsyncT :: Monad m => AsyncT m a -> m ()
412runAsyncT = P.drain . K.adapt
414-- | Same as @runStream . zipping@.
417{-# DEPRECATED runZipStream "Please use 'runStream . zipSerially instead." #-}
418runZipStream :: Monad m => ZipSerialM m a -> m ()
419runZipStream = P.drain . K.adapt
421-- | Same as @runStream . zippingAsync@.
424{-# DEPRECATED runZipAsync "Please use 'runStream . zipAsyncly instead." #-}
425runZipAsync :: Monad m => ZipAsyncM m a -> m ()
426runZipAsync = P.drain . K.adapt
429-- | Same as "Streamly.Prelude.foldWith".
431{-# DEPRECATED foldWith "Please use Streamly.Prelude.foldWith instead." #-}
432{-# INLINABLE foldWith #-}
433foldWith :: (IsStream t, Foldable f)
434 => (t m a -> t m a -> t m a) -> f (t m a) -> t m a
437-- | Same as "Streamly.Prelude.foldMapWith".
439{-# DEPRECATED foldMapWith "Please use Streamly.Prelude.foldMapWith instead." #-}
440{-# INLINABLE foldMapWith #-}
441foldMapWith :: (IsStream t, Foldable f)
442 => (t m b -> t m b -> t m b) -> (a -> t m b) -> f a -> t m b
443foldMapWith = P.foldMapWith
445-- | Same as "Streamly.Prelude.forEachWith".
447{-# DEPRECATED forEachWith "Please use Streamly.Prelude.forEachWith instead." #-}
448{-# INLINABLE forEachWith #-}
449forEachWith :: (IsStream t, Foldable f)
450 => (t m b -> t m b -> t m b) -> f a -> (a -> t m b) -> t m b
451forEachWith = P.forEachWith
454------------------------------------------------------------------------------
456------------------------------------------------------------------------------
460-- When a stream consumer demands an element from a serial stream constructed
461-- as @a \`consM` b \`consM` ... nil@, the action @a@ at the head of the stream
462-- sequence is executed and the result is supplied to the consumer. When the
463-- next element is demanded, the action @b@ is executed and its result is
464-- supplied. Thus, the effects are performed and results are consumed strictly
465-- in a serial order. Serial streams can be considered as /spatially ordered/
466-- streams as the order of execution and consumption is the same as the spatial
467-- order in which the actions are composed by the programmer.
469-- Serial streams enforce the side effects as well as the results of the
470-- actions to be in the same order in which the actions are added to the
471-- stream. Therefore, the semigroup operation for serial streams is not
475-- a <> b is not the same as b <> a
478-- There are two serial stream types 'SerialT' and 'WSerialT'. The stream
479-- evaluation of both the variants works in the same way as described above,
480-- they differ only in the 'Semigroup' and 'Monad' implementaitons.
484-- When a stream consumer demands an element from a speculative stream
485-- constructed as @a \`consM` b \`consM` ... nil@, the action @a@ at the head
486-- of the stream is executed and the output of the action is supplied to the
487-- consumer. However, in addition to the action at the head multiple actions
488-- following it may also be executed concurrently and the results buffered.
489-- When the next element is demanded it may be served from the buffer and we
490-- may execute the next action in the sequence to keep the buffer adequately
491-- filled. Thus, the actions are executed concurrently but results consumed in
492-- serial order just like serial streams. `consM` can be used to fold an
493-- infinite lazy container of effects, as the number of concurrent executions
496-- Similar to 'consM', the monadic stream generation (e.g. replicateM) and
497-- transformation operations (e.g. mapM) on speculative streams can execute
498-- multiple effects concurrently in a speculative manner.
500-- How many effects can be executed concurrently and how many results can be
501-- buffered are controlled by 'maxThreads' and 'maxBuffer' combinators
502-- respectively. The actual number of concurrent threads is adjusted according
503-- to the rate at which the consumer is consuming the stream. It may even
504-- execute actions serially in a single thread if that is enough to match the
507-- Speculative streams enforce ordering of the results of actions in the stream
508-- but the side effects are only partially ordered. Therefore, the semigroup
509-- operation for speculative streams is not commutative from the pure outputs
510-- perspective but commutative from side effects perspective.
514-- When a stream consumer demands an element from an asynchronous stream,
515-- constructed as @a \`consM` b \`consM` ... nil@, the action @a@ along with
516-- multiple following at the head of the stream sequence are executed
517-- concurrently and the output of the one that completes first is supplied to
518-- the consumer. As more actions complete, their results are buffered in the
519-- order of completion. When the next element is demanded it may be served
520-- from the buffer and we may initiate execution of more actions in the
521-- sequence to keep the buffer adequately filled. Thus, the actions are
522-- executed concurrently and their results are consumed in the order of
523-- completion. `consM` can be used to fold an infinite lazy container of
524-- effects, as the number of concurrent executions is limited.
526-- Similar to 'consM', the monadic stream generation (e.g. replicateM) and
527-- transformation operations (e.g. mapM) on asynchronous streams can execute
528-- multiple effects concurrently in an asynchronous manner.
530-- How many effects can be executed concurrently and how many results can be
531-- buffered are controlled by 'maxThreads' and 'maxBuffer' combinators
532-- respectively. The actual number of concurrent threads is adjusted according
533-- to the rate at which the consumer is consuming the stream. It may even
534-- execute actions serially in a single thread if that is enough to match the
537-- Asynchronous streams do not enforce any spatial order on the side effects or
538-- on the results of the actions. However there is a partial ordering as the
539-- actions to be executed are picked from the head of stream. The results are
540-- presented to the consumer in the completion time order. Therefore, the
541-- semigroup operation for asynchronous streams is commutative i.e. the stream
542-- is considered unordered.
544-- There are two asynchronous stream types 'AsyncT' and 'WAsyncT'. The stream
545-- evaluation of both the variants works in the same way as described above,
546-- they differ only in the 'Semigroup' and 'Monad' implementaitons.
550-- 'ZipSerialM' and 'ZipAsyncM', provide 'Applicative' instances for zipping the
551-- corresponding elements of two streams together. Note that these types are
556-- Stream processing functions can be composed in a chain using function
557-- application with or without the '$' operator, or with reverse function
558-- application operator '&'. Streamly provides concurrent versions of these
559-- operators applying stream processing functions such that each stage of the
560-- stream can run in parallel. The operators start with a @|@; we can read '|$'
561-- as "@parallel dollar@" to remember that @|@ comes before '$'.
563-- Imports for the code snippets below:
567-- import qualified Streamly.Prelude as S
568-- import Control.Concurrent
572-- The 'Semigroup' operation '<>' of each stream type combines two streams in a
573-- type specific manner. This section provides polymorphic versions of '<>'
574-- which can be used to combine two streams in a predetermined way irrespective
577-- XXX An alternative design choice would be to let a control parameter affect
578-- the nearest SVar only and then it gets cleared. The benefit of the current
579-- choice is that it is simply just like global configuration, just like state
580-- behaves, so should be easy to comprehend. But it has the downside of leaking
581-- to undesired actions, that is we can forget to reset it.
585-- These combinators can be used at any point in a stream composition to set
586-- parameters to control the concurrency of the /argument stream/. A control
587-- parameter set at any point remains effective for any concurrent combinators
588-- used in the argument stream until it is reset by using the combinator again.
589-- These control parameters have no effect on non-concurrent combinators in the
590-- stream, or on non-concurrent streams.
592-- /Pitfall:/ Remember that 'maxBuffer' in the following example applies to
593-- 'mapM' and any other combinators that may follow it, and it does not apply
594-- to the combinators before it:
603-- If we use '&' instead of '$' the situation will reverse, in the following
604-- example, 'maxBuffer' does not apply to 'mapM', it applies to combinators
605-- that come before it, because those are the arguments to 'maxBuffer':
616-- You may want to use different stream composition styles at different points
617-- in your program. Stream types can be freely converted or adapted from one
618-- type to another. The 'IsStream' type class facilitates type conversion of
619-- one stream type to another. It is not used directly, instead the type
620-- combinators provided below are used for conversions.
622-- To adapt from one monomorphic type (e.g. 'AsyncT') to another monomorphic
623-- type (e.g. 'SerialT') use the 'adapt' combinator. To give a polymorphic code
624-- a specific interpretation or to adapt a specific type to a polymorphic type
625-- use the type specific combinators e.g. 'asyncly' or 'wSerially'. You
626-- cannot adapt polymorphic code to polymorphic code, as the compiler would not know
627-- which specific type you are converting from or to. If you see a an
628-- @ambiguous type variable@ error then most likely you are using 'adapt'
629-- unnecessarily on polymorphic code.
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