{-# OPTIONS_GHC -fno-warn-unused-imports #-}
-- |
-- Module      : Streamly.Tutorial
-- Copyright   : (c) 2017 Harendra Kumar
--
-- License     : BSD3
-- Maintainer  : harendra.kumar@gmail.com
--
-- Streamly is a general computing framework based on streaming IO. The IO
-- monad and pure lists are a special case of streamly. On one hand, streamly
-- extends the lists of pure values to lists of monadic actions, on the other
-- hand it extends the IO monad with concurrrent non-determinism. In simple
-- imperative terms we can say that streamly extends the IO monad with @for@
-- loops and nested @for@ loops with concurrency support. You can understand
-- this analogy better once you can go through this tutorial.
--
-- Streaming in general enables writing modular, composable and scalable
-- applications with ease, and concurrency allows you to make them scale and
-- perform well.  Streamly enables writing scalable concurrent applications
-- without being aware of threads or synchronization. No explicit thread
-- control is needed, where applicable the concurrency rate is automatically
-- controlled based on the demand by the consumer. However, combinators can be
-- used to fine tune the concurrency control.
--
-- Streaming and concurrency together enable expressing reactive applications
-- conveniently. See the @CirclingSquare@ example in the examples directory for
-- a simple SDL based FRP example. To summarize, streamly provides a unified
-- computing framework for streaming, non-determinism and functional reactive
-- programming in an elegant and simple API that is a natural extension of pure
-- lists to monadic streams.
--
-- In this tutorial we will go over the basic concepts and how to
-- use the library. See the last section for further reading resources.

module Streamly.Tutorial
    (
    -- * Streams
    -- $streams

    -- * Flavors of Streams
    -- $flavors

    -- * Imports and Supporting Code
    -- $imports

    -- * Generating Streams
    -- $generating

    -- * Eliminating Streams
    -- $eliminating

    -- * Transforming Streams
    -- $transformation

    -- * Merging Streams

    -- ** Semigroup Style
    -- $semigroup

    -- *** Deep Serial Composition ('Serial')
    -- $serial

    -- *** Wide Serial Composition ('WSerial')
    -- $interleaved

    -- *** Deep Asynchronous Composition ('Async')
    -- $async

    -- *** Wide Asynchronous Composition ('WAsync')
    -- $wasync

    -- *** Parallel Asynchronous Composition ('Parallel')
    -- $parallel

    -- *** Custom composition
    -- $custom

    -- ** Monoid Style
    -- $monoid

    -- * Nesting Streams
    -- $nesting

    -- ** Monad
    -- $monad

    -- *** Deep Serial Nesting ('Serial')
    -- $regularSerial

    -- *** Wide Serial Nesting ('WSerial')
    -- $interleavedNesting

    -- *** Deep Asynchronous Nesting ('Async')
    -- $concurrentNesting

    -- *** Wide Asynchronous Nesting ('WAsync')
    -- $wasyncNesting

    -- *** Parallel Asynchronous Nesting ('Parallel')
    -- $parallelNesting

    -- *** Exercise
    -- $monadExercise

    -- ** Applicative
    -- $applicative

    -- ** Functor
    -- $functor

    -- * Zipping Streams
    -- $zipping

    -- ** Serial Zipping
    -- $serialzip

    -- ** Parallel Zipping
    -- $parallelzip

    -- * Monad transformers
    -- $monadtransformers

    -- * Concurrent Programming
    -- $concurrent

    -- * Reactive Programming
    -- $reactive

    -- * Performance
    -- $performance

    -- * Interoperation with Streaming Libraries
    -- $interop

    -- * Comparison with Existing Packages
    -- $comparison

    -- * Where to go next?
    -- $furtherReading
    )
where

import Streamly
import Streamly.Prelude
import Data.Semigroup
import Control.Applicative
import Control.Monad
import Control.Monad.IO.Class      (MonadIO(..))
import Control.Monad.Trans.Class   (MonadTrans (lift))

-- $streams
--
-- The way a list represents a sequence of pure values, a stream represents a
-- sequence of monadic actions. The monadic stream API offered by Streamly is
-- very close to the Haskell "Prelude" pure lists' API, it can be considered as a
-- natural extension of lists to monadic actions. Streamly streams provide
-- concurrent composition and merging of streams. It can be considered as a
-- concurrent list transformer. In contrast to the "Prelude" lists, merging or
-- appending streams of arbitrary length is scalable and inexpensive.
--
-- The basic stream type is 'Serial', it represents a sequence of IO actions,
-- and is a 'Monad'.  The 'Serial' monad is almost a drop in replacement for
-- the 'IO' monad, IO monad is a special case of the 'Serial' monad; IO monad
-- represents a single IO action whereas the 'Serial' monad represents a series
-- of IO actions.  The only change you need to make to go from 'IO' to 'Serial'
-- is to use 'runStream' to run the monad and to prefix the IO actions with
-- either 'once' or 'liftIO'.  If you use liftIO you can switch from 'Serial'
-- to IO monad by simply removing the 'runStream' function; no other changes
-- are needed unless you have used some stream specific composition or
-- combinators.
--
-- Similarly, the 'Serial' type is almost a drop in replacement for pure lists,
-- pure lists are a special case of monadic streams. If you use 'nil' in place
-- of '[]' and '|:' in place ':' you can replace a list with a 'Serial' The
-- only difference is that the elements must be monadic type and to operate on
-- the streams we must use the corresponding functions from "Streamly.Prelude"
-- instead of using the base "Prelude".

-- $flavors
--
-- There are a few more types similar to 'Serial' that all represent streams
-- and differ only in the 'Semigroup', 'Applicative' and 'Monad' compositions
-- of the streams.
--
-- The composition of two or more streams is distinguished based on three
-- characterstics, /traversal order/, /execution order/ and
-- /consumption order/. Traversal of a composition of streams could be @deep@
-- or @wide@.  Deep goes depth first i.e.  each stream is traversed fully
-- before we traverse the next stream. Wide goes breadth first i.e. one element
-- from each stream is traversed before coming back to the first stream again.
-- Execution could be serial (i.e. synchronous) or asynchronous. In serial
-- execution we execute an action in the next stream only after the first has
-- finished executing. In asynchronous execution actions in both streams can be
-- executed asynchronously i.e. the next action can start executing even before
-- the first one has finished. The third parameter is consumption order that is
-- in what order the output generated by the composition is consumed.
-- Consumption could be serial or asynchronous. In serial consumption, the
-- outputs are consumed in the traversal order, in asynchronous consumption the
-- outputs are consumed as they arrive i.e. first come first serve order.
--
-- The following table summarizes different styles of streams based on how they
-- compose. All these types are monads and they differ in 'Semigroup',
-- 'Applicative' and 'Monad' compositions:
--
-- @
-- +------------+-----------+--------------+--------------+
-- | Type       | Traversal | Execution    | Consumption  |
-- +============+===========+==============+==============+
-- | 'Serial'     | Deep      | Serial       | Serial       |
-- +------------+-----------+--------------+--------------+
-- | 'WSerial'    | Wide      | Serial       | Serial       |
-- +------------+-----------+--------------+--------------+
-- | 'Async'      | Deep      | Asynchronous | Asynchronous |
-- +------------+-----------+--------------+--------------+
-- | 'WAsync'     | Wide      | Asynchronous | Asynchronous |
-- +------------+-----------+--------------+--------------+
-- | 'Parallel'   | Parallel  | Asynchronous | Asynchronous |
-- +------------+-----------+--------------+--------------+
-- @
--
-- Other than these types there are also 'ZipSerial' and 'ZipAsync' types that
-- zip streams serially or concurrently using 'Applicative' operation. These
-- types are not monads they are only applicatives and they do not differ in
-- 'Semigroup' composition.
--
-- All these types can be freely inter-converted using type conversion
-- combinators or type annotations without any cost, to acheive the desired
-- composition style.  To force a particular type of composition we coerce the
-- stream type using the corresponding type adapting combinator from
-- 'serially', 'wSerially', 'asyncly', 'wAsyncly', 'parallely', 'zipSerially'
-- or 'zipAsyncly'. The default stream type is inferred as 'Serial' unless you
-- change it by using one of the combinators or using a type annotation.

-- $monadtransformers
--
-- To represent streams in an arbitrary monad use the more general monad
-- transformer types for example the monad transformer type corresponding to
-- the 'Serial' type is 'SerialT'.  @SerialT m a@ represents a stream of values
-- of type 'a' in some underlying monad 'm'. For example, @SerialT IO Int@ is a
-- stream of 'Int' in 'IO' monad.  In fact, the type 'Serial' is a synonym for
-- @SerialT IO@.
--
-- Similarly we have monad transformer types for other stream types as well viz.
-- 'WSerialT', 'AsyncT', 'WAsyncT' and 'ParallelT'.
--
-- To lift a value from an underlying monad in a monad transformer stack into a
-- singleton stream use 'lift' and to lift from an IO action use 'liftIO'.
--
-- @
-- > 'runStream' $ liftIO $ putStrLn "Hello world!"
-- Hello world!
-- > 'runStream' $ lift $ putStrLn "Hello world!"
-- Hello world!
-- @
--

-- $generating
--
-- We will assume the following imports in this tutorial. Go ahead, fire up a
-- GHCi session and import these lines to start playing.
--
-- @
-- > import "Streamly"
-- > import "Streamly.Prelude" ((|:))
-- > import qualified "Streamly.Prelude" as S
-- @
--
-- 'nil' represents an empty stream and 'consM' or its operator form '|:' adds
-- a monadic action at the head of the stream.
--
-- @
-- > S.'toList' S.'nil'
-- []
-- > S.'toList' $ 'getLine' |: 'getLine' |: S.'nil'
-- hello
-- world
-- ["hello","world"]
-- @
--
-- To create a singleton stream from a pure value use 'pure' and to create a
-- singleton stream from a monadic action use 'once'.
--
-- @
-- > S.'toList' $ 'pure' 1
-- [1]
-- > S.'toList' $ S.'once' 'getLine'
-- hello
-- ["hello"]
-- @
--
-- To create a stream from pure values in a 'Foldable' container use
-- 'fromFoldable' which is equivalent to a fold using 'cons' and 'nil':
--
-- @
-- > S.'toList' $ S.'fromFoldable' [1..3]
-- [1,2,3]
-- > S.'toList' $ 'Prelude.foldr' S.'cons' S.'nil' [1..3]
-- [1,2,3]
-- @
--
-- To create a stream from monadic actions in a 'Foldable' container just use a
-- right fold using 'consM' and 'nil':
--
-- @
-- > 'runStream' $ 'Prelude.foldr' ('|:') S.'nil' ['putStr' "Hello ", 'putStrLn' "world!"]
-- Hello world!
-- @
--
-- For more ways to construct a stream see the module "Streamly.Prelude".

-- $eliminating
--
-- We have already seen 'runStream' and 'toList' to eliminate a stream in the
-- examples above.  'runStream' runs a stream discarding the results i.e. only
-- for effects.  'toList' runs the stream and collects the results in a list.
--
-- For other ways to eliminate a stream see the @Folding@ section in
-- "Streamly.Prelude" module.

-- $transformation
--
-- Transformation over a stream is the equivalent of a @for@ loop construct in
-- imperative paradigm. We iterate over every element in the stream and perform
-- certain transformations for each element.  Transformations may involve
-- mapping functions over the elements, filtering elements from the stream or
-- folding all the elements in the stream into a single value. Streamly streams
-- are exactly like lists and you can perform all the transformations in the
-- same way as you would on lists.
--
-- Here is a simple console echo program that just echoes every input line,
-- forever:
--
-- @
-- > 'runStream' $ S.'repeatM' getLine & S.'mapM' putStrLn
-- @
--
-- The following code snippet reads lines from standard input, filters blank
-- lines, drops the first non-blank line, takes the next two, up cases them,
-- numbers them and prints them:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Data.Char (toUpper)
-- import Data.Function ((&))
--
-- main = 'runStream' $
--        S.'repeatM' getLine
--      & S.'filter' (not . null)
--      & S.'drop' 1
--      & S.'take' 2
--      & fmap (map toUpper)
--      & S.'zipWith' (\\n s -> show n ++ " " ++ s) (S.'fromFoldable' [1..])
--      & S.'mapM' putStrLn
-- @

-- $semigroup
--
-- We can combine two streams into a single stream using semigroup composition
-- operation '<>'.  Streams can be combined in many different ways as described
-- in the following sections, the '<>' operation behaves differently depending
-- on the stream type in effect. The stream type and therefore the composition
-- style can be changed at any point using one of the type combinators as
-- discussed earlier.

-- $imports
--
-- In most of example snippets we do not repeat the imports. Where imports are
-- not explicitly specified use the imports shown below.
--
-- @
-- import "Streamly"
-- import "Streamly.Prelude" ((|:), nil)
-- import qualified "Streamly.Prelude" as S
--
-- import Control.Concurrent
-- import Control.Monad (forever)
-- @
--
-- To illustrate concurrent vs serial composition aspects, we will use the
-- following @delay@ function to introduce a sleep or delay specified in
-- seconds. After the delay it prints the number of seconds it slept.
--
-- @
-- delay n = S.'once' $ do
--  threadDelay (n * 1000000)
--  tid \<- myThreadId
--  putStrLn (show tid ++ ": Delay " ++ show n)
-- @

-- $serial
--
-- The 'Semigroup' operation '<>' of the 'Serial' type combines the two streams
-- in a /serial depth first/ manner. We use the 'serially' type combinator to
-- effect 'Serial' style of composition. We can also use an explicit 'Serial'
-- type annotation for the stream to acheive the same effect.  However, since
-- 'Serial' is the default type unless explicitly specified by using a
-- combinator, we can omit using an explicit combinator or type annotation for
-- this style of composition.
--
-- When two streams with multiple elements are combined in this manner, the
-- monadic actions in the two streams are performed sequentially i.e. first all
-- actions in the first stream are performed sequentially and then all actions
-- in the second stream are performed sequentially. We call it
-- /serial depth first/ as the full depth of one stream is fully traversed
-- before we move to the next. The following example prints the sequence 1, 2,
-- 3, 4:
--
-- @
-- main = 'runStream' $ (print 1 |: print 2 |: nil) <> (print 3 |: print 4 |: nil)
-- @
-- @
-- 1
-- 2
-- 3
-- 4
-- @
--
-- All actions in both the streams are performed serially in the same thread.
-- In the following example we can see that all actions are performed in the
-- same thread and take a combined total of @3 + 2 + 1 = 6@ seconds:
--
-- @
-- main = 'runStream' $ delay 3 <> delay 2 <> delay 1
-- @
-- @
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 2
-- ThreadId 36: Delay 1
-- @
--
-- The polymorphic version of the binary operation '<>' of the 'Serial' type is
-- 'serial'. We can use 'serial' to join streams in a sequential manner
-- irrespective of the type of stream:
--
-- @
-- main = 'runStream' $ (print 1 |: print 2 |: nil) \`serial` (print 3 |: print 4 |: nil)
-- @

-- $interleaved
--
-- The 'Semigroup' operation '<>' of the 'WSerial' type combines the two
-- streams in a /serial breadth first/ manner. We use the 'wSerially' type
-- combinator to effect 'WSerial' style of composition. We can also use the
-- 'WSerial' type annotation for the stream to acheive the same effect.
--
-- When two streams with multiple elements are combined in this manner, we
-- traverse all the streams in a breadth first manner i.e. one action from each
-- stream is peformed and yielded to the resulting stream before we come back
-- to the first stream again and so on.
-- The following example prints the sequence 1, 3, 2, 4
--
-- @
-- main = 'runStream' . 'wSerially' $ (print 1 |: print 2 |: nil) <> (print 3 |: print 4 |: nil)
-- @
-- @
-- 1
-- 3
-- 2
-- 4
-- @
--
-- Even though the monadic actions of the two streams are performed in an
-- interleaved manner they are all performed serially in the same thread. In
-- the following example we can see that all actions are performed in the same
-- thread and take a combined total of @3 + 2 + 1 = 6@ seconds:
--
-- @
-- main = 'runStream' . 'wSerially' $ delay 3 <> delay 2 <> delay 1
-- @
-- @
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 2
-- ThreadId 36: Delay 1
-- @
--
-- The polymorphic version of the 'WSerial' binary operation '<>' is called
-- 'wSerial'. We can use 'wSerial' to join streams in an interleaved manner
-- irrespective of the type, notice that we have not used the 'wSerially'
-- combinator in the following example:
--
-- @
-- main = 'runStream' $ (print 1 |: print 2 |: nil) \`wSerial` (print 3 |: print 4 |: nil)
-- @
-- @
-- 1
-- 3
-- 2
-- 4
-- @
--
-- Note that this composition cannot be used to fold infinite number of streams
-- since it requires preserving the state until a stream is finished.

-- $async
--
-- The 'Semigroup' operation '<>' of the 'Async' type combines the two
-- streams in a depth first manner with parallel look ahead. We use the
-- 'asyncly' type combinator to effect 'Async' style of composition. We
-- can also use the 'Async' type annotation for the stream type to acheive
-- the same effect.
--
-- When two streams with multiple elements are combined in this manner, the
-- streams are traversed in depth first manner just like 'Serial', however it
-- can execute the next stream concurrently and return the results from it
-- as they arrive i.e. the results from the next stream may be yielded even
-- before the results from the first stream. Concurrent execution of the next
-- stream(s) is performed if the first stream blocks or if it cannot produce
-- output at the rate that is enough to meet the consumer demand. Multiple
-- streams can be executed concurrently to meet the demand.
-- In the following example the first stream does not block,
-- therefore the first stream is completely exhausted before the second.
--
-- @
-- main = 'runStream' . 'asyncly' $ (print 1 |: print 2 |: nil) <> (print 3 |: print 4 |: nil)
-- @
-- @
-- 1
-- 2
-- 3
-- 4
-- @
--
-- If the first stream blocks, we can yield from the second. In the example
-- below each yield in the stream has a constant delay of 1 second therefore 1
-- and 3 would be yielded first and then 2 and 4 would be yielded.
--
-- @
-- main = 'runStream' . 'asyncly' $ (p 1 |: p 2 |: nil) <> (p 3 |: p 4 |: nil)
--  where p n = threadDelay 1000000 >> print n
-- @
-- @
-- 1
-- 3
-- 2
-- 4
-- @
--
-- In the following example we can see that new threads are started when a
-- computation blocks.  Notice that the output from the stream with the
-- shortest delay is printed first. The whole computation takes @maximum of
-- (3, 2, 1) = 3@ seconds:
--
-- @
-- main = 'runStream' . 'asyncly' $ delay 3 '<>' delay 2 '<>' delay 1
-- @
-- @
-- ThreadId 42: Delay 1
-- ThreadId 41: Delay 2
-- ThreadId 40: Delay 3
-- @
--
-- When we have a tree of computations composed using this style, the tree is
-- traversed in DFS style just like the 'Serial' style, the only difference is
-- that here we can move on to executing the next stream if a stream blocks.
-- However, we will not start new threads if we have sufficient output to
-- saturate the consumer.  This is why we call it left-biased demand driven or
-- adaptive concurrency style, the concurrency tends to stay on the left side
-- of the composition as long as possible. More threads are started based on
-- the pull rate of the consumer. The following example prints an output every
-- second as all of the actions are concurrent.
--
-- @
-- main = 'runStream' . 'asyncly' $ (delay 1 <> delay 2) <> (delay 3 <> delay 4)
-- @
-- @
-- 1
-- 2
-- 3
-- 4
-- @
--
-- All the computations may even run in a single thread when more threads are
-- not needed. As you can see, in the following example the computations are
-- run in a single thread one after another, because none of them blocks.
-- However, if the thread consuming the stream were faster than the producer
-- then it would have started parallel threads for each computation to keep up
-- even if none of them blocks:
--
-- @
-- main = 'runStream' . 'asyncly' $ traced (sqrt 9) '<>' traced (sqrt 16) '<>' traced (sqrt 25)
--  where traced m = S.'once' (myThreadId >>= print) >> return m
-- @
-- @
-- ThreadId 40
-- ThreadId 40
-- ThreadId 40
-- @
--
-- Note that the order of printing in the above examples may change due to
-- variations in scheduling latencies for concurrent threads.
--
-- The polymorphic version of the 'Async' binary operation '<>' is called
-- 'async'. We can use 'async' to join streams in a left biased
-- adaptively concurrent manner irrespective of the type, notice that we have
-- not used the 'asyncly' combinator in the following example:
--
-- @
-- main = 'runStream' $ delay 3 \`async` delay 2 \`async` delay 1
-- @
-- @
-- ThreadId 42: Delay 1
-- ThreadId 41: Delay 2
-- ThreadId 40: Delay 3
-- @
--
-- Since the concurrency provided by this operator is demand driven it cannot
-- be used when the composed computations start timers that are relative to
-- each other because all computations may not be started at the same time and
-- therefore timers in all of them may not start at the same time.  When
-- relative timing among all computations is important or when we need to start
-- all computations at once for any reason 'Parallel' style must be used
-- instead.
--
-- 'Async' style should be preferred over 'Parallel' or 'WAsync' unless you
-- really need those. It utilizes the resources optimally. It should be used
-- when we know that the computations can run in parallel but we do not care if
-- they actually run in parallel or not, that decision can be left to the
-- scheduler based on demand. Also, note that this operator can be used to fold
-- infinite number of streams in contrast to the 'Parallel' or 'WAsync' styles,
-- because it does not require us to run all of them at the same time in a fair
-- manner.

-- $wasync
--
-- The 'Semigroup' operation '<>' of the 'WAsync' type combines two streams in
-- a concurrent manner using /breadth first traversal/. We use the 'wAsyncly'
-- type combinator to effect 'WAsync' style of composition. We can also use the
-- 'WAsync' type annotation for the stream to acheive the same effect.
--
-- When streams with multiple elements are combined in this manner, we traverse
-- all the streams concurrently in a breadth first manner i.e. one action from
-- each stream is peformed and yielded to the resulting stream before we come
-- back to the first stream again and so on. Even though we execute the actions
-- in a breadth first order the outputs may be consumed in a different order
-- because they are consumed on a first come first serve basis.
--
-- In the following example we can see that outputs are produced in the breadth
-- first travresal order but this is not guaranteed.
--
-- @
-- main = 'runStream' . 'wAsyncly' $ (print 1 |: print 2 |: nil) <> (print 3 |: print 4 |: nil)
-- @
-- @
-- 1
-- 3
-- 2
-- 4
-- @
--
-- The polymorphic version of the binary operation '<>' of the 'WAsync' type is
-- 'wAsync'.  We can use 'wAsync' to join streams using a breadth first
-- concurrent traversal irrespective of the type, notice that we have not used
-- the 'wAsyncly' combinator in the following example:
--
-- @
-- main = 'runStream' $ delay 3 \`wAsync` delay 2 \`wAsync` delay 1
-- @
-- @
-- ThreadId 42: Delay 1
-- ThreadId 41: Delay 2
-- ThreadId 40: Delay 3
-- @
--
-- Since the concurrency provided by this style is demand driven it may not
-- be used when the composed computations start timers that are relative to
-- each other because all computations may not be started at the same time and
-- therefore timers in all of them may not start at the same time.  When
-- relative timing among all computations is important or when we need to start
-- all computations at once for any reason 'Parallel' style must be used
-- instead.
--

-- $parallel
--
-- The 'Semigroup' operation '<>' of the 'Parallel' type combines the two
-- streams in a fairly concurrent manner with round robin scheduling. We use
-- the 'parallely' type combinator to effect 'Parallel' style of composition.
-- We can also use the 'Parallel' type annotation for the stream type to
-- acheive the same effect.
--
-- When two streams with multiple elements are combined in this manner, the
-- monadic actions in both the streams are performed concurrently with a fair
-- round robin scheduling.  The outputs are yielded in the order in which the
-- actions complete. This is pretty similar to the 'WAsync' type, the
-- difference is that 'WAsync' is adaptive to the consumer demand and may or
-- may not execute all actions in parallel depending on the demand, whereas
-- 'Parallel' runs all the streams in parallel irrespective of the demand.
--
-- The following example sends a query to all the three search engines in
-- parallel and prints the name of the search engines in the order in which the
-- responses arrive:
--
-- @
-- import "Streamly"
-- import qualified Streamly.Prelude as S
-- import Network.HTTP.Simple
--
-- main = 'runStream' . 'parallely' $ google \<> bing \<> duckduckgo
--     where
--         google     = get "https://www.google.com/search?q=haskell"
--         bing       = get "https://www.bing.com/search?q=haskell"
--         duckduckgo = get "https://www.duckduckgo.com/?q=haskell"
--         get s = S.'once' (httpNoBody (parseRequest_ s) >> putStrLn (show s))
-- @
--
-- The polymorphic version of the binary operation '<>' of the 'Parallel' type
-- is 'parallel'. We can use 'parallel' to join streams in a fairly concurrent
-- manner irrespective of the type, notice that we have not used the
-- 'parallely' combinator in the following example:
--
-- @
-- main = 'runStream' $ delay 3 \`parallel` delay 2 \`wAsync` delay 1
-- @
-- @
-- ThreadId 42: Delay 1
-- ThreadId 41: Delay 2
-- ThreadId 40: Delay 3
-- @
--
-- Note that this style of composition cannot be used to combine infinite
-- number of streams, as it will lead to an infinite sized scheduling queue.
--

-- $custom
--
-- The 'mkAsync' API can be used to create references to asynchronously running
-- stream computations. We can then use 'uncons' to explore the streams
-- arbitrarily and then recompose individual elements to create a new stream.
-- This way we can dynamically decide which stream to explore at any given
-- time.  Take an example of a merge sort of two sorted streams. We need to
-- keep consuming items from the stream which has the lowest item in the sort
-- order.  This can be achieved using async references to streams. See
-- "MergeSort.hs" in the examples directory.

-- $monoid
--
-- We can use 'Monoid' instances to fold a container of streams in the desired
-- style using 'fold' or 'foldMap'.  We have also provided some fold utilities
-- to fold streams using the polymorphic combine operations:
--
-- * 'foldWith' is like 'fold', it folds a 'Foldable' container of streams
-- using the given composition operator.
-- * 'foldMapWith' is like 'foldMap', it folds like @foldWith@ but also maps a
-- function before folding.
-- * 'forEachWith' is like @foldMapwith@ but the container argument comes before
-- the function argument.
--
-- All of the following are equivalent and start ten concurrent tasks each with
-- a delay from 1 to 10 seconds, resulting in the printing of each number every
-- second:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Control.Concurrent
--
-- main = do
--  'runStream' $ 'asyncly' $ foldMap delay [1..10]
--  'runStream' $ 'foldWith'    'async' (map delay [1..10])
--  'runStream' $ 'foldMapWith' 'async' delay [1..10]
--  'runStream' $ 'forEachWith' 'async' [1..10] delay
--  where delay n = S.'once' $ threadDelay (n * 1000000) >> print n
-- @

-- $nesting
--
-- Till now we discussed ways to apply transformations on a stream or to merge
-- streams together to create another stream. We mentioned earlier that
-- transforming a stream is similar to a @for@ loop in the imperative paradigm.
-- We will now discuss the concept of a nested composition of streams which is
-- analogous to nested @for@ loops in the imperative paradigm. Functional
-- programmers call this style of composition a list transformer or @ListT@.
-- Logic programmers call it a logic monad or non-deterministic composition,
-- but for ordinary imperative minded people like me it is easier to think in
-- terms of good old nested @for@ loops.
--
-- $monad
--
-- In functional programmer's parlance the 'Monad' instances of different
-- 'IsStream' types implement non-determinism, exploring all possible
-- combination of choices from both the streams. From an imperative
-- programmer's point of view it behaves like nested loops i.e.  for each
-- element in the first stream and for each element in the second stream
-- execute the body of the loop.
--
-- The 'Monad' instances of 'Serial', 'WSerial', 'Async' and 'WAsync'
-- stream types support different flavors of nested looping.  In other words,
-- they are all variants of list transformer.  The nesting behavior of these
-- types correspond exactly to the way they merge streams as we discussed in
-- the previous section.
--

-- $regularSerial
--
-- The 'Monad' composition of the 'Serial' type behaves like a standard list
-- transformer. This is the default when we do not use an explicit type
-- combinator. However, the 'serially' type combinator can be used to switch to
-- this style of composition. We will see how this style of composition works
-- in the following examples.
--
-- Let's start with an example with a simple @for@ loop without any nesting.
-- For simplicity of illustration we are using streams of pure values in all
-- the examples.  However, the streams could also be made of monadic actions
-- instead.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' $ do
--     x <- S.'fromFoldable' [3,2,1]
--     delay x
-- @
-- @
-- ThreadId 30: Delay 3
-- ThreadId 30: Delay 2
-- ThreadId 30: Delay 1
-- @
--
-- As we can see, the code after the @fromFoldable@ statement is run three
-- times, once for each value of @x@ drawn from the stream. All the three
-- iterations are serial and run in the same thread one after another. In
-- imperative terms this is equivalent to a @for@ loop with three iterations.
--
-- A console echo loop copying standard input to standard output can simply be
-- written like this:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' $ forever $ S.once getLine >>= S.once . putStrLn
-- @
--
-- When multiple streams are composed using this style they nest in a DFS
-- manner i.e. nested iterations of a loop are executed before we proceed to
-- the next iteration of the parent loop. This behaves just like nested @for@
-- loops in imperative programming.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' $ do
--     x <- S.'fromFoldable' [1,2]
--     y <- S.'fromFoldable' [3,4]
--     S.'once' $ putStrLn $ show (x, y)
-- @
-- @
-- (1,3)
-- (1,4)
-- (2,3)
-- (2,4)
-- @
--
-- Notice that this is analogous to merging streams of type 'Serial' or merging
-- streams using 'serial'.

-- $concurrentNesting
--
-- The 'Monad' composition of 'Async' type can perform the iterations of a
-- loop concurrently.  Concurrency is demand driven i.e. more concurrent
-- iterations are started only if the previous iterations are not able to
-- produce enough output for the consumer of the output stream.  This works
-- exactly the same way as the merging of two streams 'asyncly' works.
-- This is the concurrent analogue of 'Serial' style monadic composition.
--
-- The 'asyncly' type combinator can be used to switch to this style of
-- composition. Alternatively, a type annotation can be used to specify the
-- type of the stream as 'Async'.
--
-- @
-- import "Streamly"
-- import "Streamly.Prelude"
--
-- main = 'runStream' . 'asyncly' $ do
--     x <- S.'fromFoldable' [3,2,1]
--     delay x
-- @
-- @
-- ThreadId 40: Delay 1
-- ThreadId 39: Delay 2
-- ThreadId 38: Delay 3
-- @
--
-- As we can see the code after the @fromFoldable@ statement is run three
-- times, once for each value of @x@. All the three iterations are concurrent
-- and run in different threads. The iteration with least delay finishes first.
-- When compared to imperative programming, this can be viewed as a @for@ loop
-- with three concurrent iterations.
--
-- Concurrency is demand driven just as in the case of 'async' merging.
-- When multiple streams are composed using this style, the iterations are
-- triggered in a depth first manner just like 'Serial' i.e. nested iterations are
-- executed before we proceed to the next iteration at higher level. However,
-- unlike 'Serial' more than one iterations may be started concurrently based
-- on the demand from the consumer of the stream.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' . 'asyncly' $ do
--     x <- S.'fromFoldable' [1,2]
--     y <- S.'fromFoldable' [3,4]
--     S.'once' $ putStrLn $ show (x, y)
-- @
-- @
-- (1,3)
-- (1,4)
-- (2,3)
-- (2,4)
-- @

-- $interleavedNesting
--
-- The 'Monad' composition of 'WSerial' type interleaves the iterations of
-- outer and inner loops in a nested loop composition. This works exactly the
-- same way as the merging of two streams in 'wSerially' fashion works.  The
-- 'wSerially' type combinator can be used to switch to this style of
-- composition. Alternatively, a type annotation can be used to specify the
-- type of the stream as 'WSerial'.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' . 'wSerially' $ do
--     x <- S.'fromFoldable' [1,2]
--     y <- S.'fromFoldable' [3,4]
--     S.once $ putStrLn $ show (x, y)
-- @
-- @
-- (1,3)
-- (2,3)
-- (1,4)
-- (2,4)
-- @
--

-- $wasyncNesting
--
-- Just like 'Async' the 'Monad' composition of 'WAsync' runs the iterations of
-- a loop concurrently. The difference is in the nested loop behavior. The
-- nested loops in this type are traversed and executed in a breadth first
-- manner rather than the depth first manner of 'Async' style.
-- The loop nesting works exactly the same way as the merging of streams
-- 'wAsyncly' works.  The 'wAsyncly' type combinator can be used to switch to
-- this style of composition. Alternatively, a type annotation can be used to
-- specify the type of the stream as 'WAsync'.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' . 'wAsyncly' $ do
--     x <- S.'fromFoldable' [1,2]
--     y <- S.'fromFoldable' [3,4]
--     S.'once' $ putStrLn $ show (x, y)
-- @
-- @
-- (1,3)
-- (2,3)
-- (1,4)
-- (2,4)
-- @

-- $parallelNesting
--
-- Just like 'Async' or 'WAsync' the 'Monad' composition of 'Parallel' runs the
-- iterations of a loop concurrently. The difference is in the nested loop
-- behavior. The streams at each nest level is run fully concurrently
-- irrespective of the demand.  The loop nesting works exactly the same way as
-- the merging of streams 'parallely' works.  The 'parallely' type combinator
-- can be used to switch to this style of composition. Alternatively, a type
-- annotation can be used to specify the type of the stream as 'Parallel'.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = 'runStream' . 'parallely' $ do
--     x <- S.'fromFoldable' [3,2,1]
--     delay x
-- @
-- @
-- ThreadId 40: Delay 1
-- ThreadId 39: Delay 2
-- ThreadId 38: Delay 3
-- @

-- $monadExercise
--
-- Streamly code is usually written in a way that is agnostic of the
-- specific monadic composition type. We use a polymorphic type with a
-- 'IsStream' type class constraint. When running the stream we can choose the
-- specific mode of composition. For example take a look at the following code.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- composed :: (IsStream t, Monad (t IO)) => t IO ()
-- composed = do
--     sz <- sizes
--     cl <- colors
--     sh <- shapes
--     S.'once' $ putStrLn $ show (sz, cl, sh)
--
--     where
--
--     sizes  = S.'fromFoldable' [1, 2, 3]
--     colors = S.'fromFoldable' ["red", "green", "blue"]
--     shapes = S.'fromFoldable' ["triangle", "square", "circle"]
-- @
--
-- Now we can interpret this in whatever way we want:
--
-- @
-- main = 'runStream' . 'serially'  $ composed
-- main = 'runStream' . 'wSerially' $ composed
-- main = 'runStream' . 'asyncly'   $ composed
-- main = 'runStream' . 'wAsyncly'  $ composed
-- main = 'runStream' . 'parallely' $ composed
-- @
--
--  As an exercise try to figure out the output of this code for each mode of
--  composition.

-- $functor
--
-- 'fmap' transforms a stream by mapping a function on all elements of the
-- stream. 'fmap' behaves in the same way for all stream types, it is always
-- serial.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
--
-- main = (S.'toList' $ fmap show $ S.'fromFoldable' [1..10]) >>= print
-- @
--
-- Also see the 'mapM' and 'sequence' functions for mapping actions, in the
-- "Streamly.Prelude" module.

-- $applicative
--
-- Applicative is precisely the same as the 'ap' operation of 'Monad'. For
-- zipping applicatives separate types 'ZipSerial' and 'ZipAsync' are
-- provided.
--
-- The following example uses the 'Serial' applicative, it runs all iterations
-- serially and takes a total 17 seconds (1 + 3 + 4 + 2 + 3 + 4):
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Control.Concurrent
--
-- s1 = d 1 <> d 2
-- s2 = d 3 <> d 4
-- d n = delay n >> return n
--
-- main = (S.'toList' . 'serially' $ (,) \<$> s1 \<*> s2) >>= print
-- @
-- @
-- ThreadId 36: Delay 1
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 4
-- ThreadId 36: Delay 2
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 4
-- [(1,3),(1,4),(2,3),(2,4)]
-- @
--
-- Similalrly 'WSerial' applicative runs the iterations in an interleaved
-- order but since it is serial it takes a total of 17 seconds:
--
-- @
-- main = (S.'toList' . 'wSerially' $ (,) \<$> s1 \<*> s2) >>= print
-- @
-- @
-- ThreadId 36: Delay 1
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 2
-- ThreadId 36: Delay 3
-- ThreadId 36: Delay 4
-- ThreadId 36: Delay 4
-- [(1,3),(2,3),(1,4),(2,4)]
-- @
--
-- 'Async' can run the iterations concurrently and therefore takes a total
-- of 10 seconds (1 + 2 + 3 + 4):
--
-- @
-- main = (S.'toList' . 'asyncly' $ (,) \<$> s1 \<*> s2) >>= print
-- @
-- @
-- ThreadId 34: Delay 1
-- ThreadId 36: Delay 2
-- ThreadId 35: Delay 3
-- ThreadId 36: Delay 3
-- ThreadId 35: Delay 4
-- ThreadId 36: Delay 4
-- [(1,3),(2,3),(1,4),(2,4)]
-- @
--
-- Similalrly 'WAsync' as well can run the iterations concurrently and
-- therefore takes a total of 10 seconds (1 + 2 + 3 + 4):
--
-- @
-- main = (S.'toList' . 'wAsyncly' $ (,) \<$> s1 \<*> s2) >>= print
-- @
-- @
-- ThreadId 34: Delay 1
-- ThreadId 36: Delay 2
-- ThreadId 35: Delay 3
-- ThreadId 36: Delay 3
-- ThreadId 35: Delay 4
-- ThreadId 36: Delay 4
-- [(1,3),(2,3),(1,4),(2,4)]
-- @

-- $zipping
--
-- Zipping is a special transformation where the corresponding elements of two
-- streams are combined together using a zip function producing a new stream of
-- outputs. Two different types are provided for serial and concurrent zipping.
-- These types provide an applicative instance that can be used to lift
-- functions to zip the argument streams.
-- Also see the zipping functions in the "Streamly.Prelude" module.

-- $serialzip
--
-- The applicative instance of 'ZipSerial' type zips streams serially.
-- 'zipSerially' type combinator can be used to switch to serial applicative
-- zip composition:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Control.Concurrent
--
-- d n = delay n >> return n
-- s1 = 'serially' $ d 1 <> d 2
-- s2 = 'serially' $ d 3 <> d 4
--
-- main = (S.'toList' . 'zipSerially' $ (,) \<$> s1 \<*> s2) >>= print
-- @
--
-- This takes total 10 seconds to zip, which is (1 + 2 + 3 + 4) since
-- everything runs serially:
--
-- @
-- ThreadId 29: Delay 1
-- ThreadId 29: Delay 3
-- ThreadId 29: Delay 2
-- ThreadId 29: Delay 4
-- [(1,3),(2,4)]
-- @

-- $parallelzip
--
-- The applicative instance of 'ZipAsync' type zips streams concurrently.
-- 'zipAsyncly' type combinator can be used to switch to parallel applicative
-- zip composition:
--
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Control.Concurrent
-- import System.IO (stdout, hSetBuffering, BufferMode(LineBuffering))
--
-- d n = delay n >> return n
-- s1 = 'serially' $ d 1 <> d 2
-- s2 = 'serially' $ d 3 <> d 4
--
-- main = do
--     hSetBuffering stdout LineBuffering
--     (S.'toList' . 'zipAsyncly' $ (,) \<$> s1 \<*> s2) >>= print
-- @
--
-- This takes 7 seconds to zip, which is max (1,3) + max (2,4) because 1 and 3
-- are produced concurrently, and 2 and 4 are produced concurrently:
--
-- @
-- ThreadId 32: Delay 1
-- ThreadId 32: Delay 2
-- ThreadId 33: Delay 3
-- ThreadId 33: Delay 4
-- [(1,3),(2,4)]
-- @

-- $concurrent
--
-- When writing concurrent programs there are two distinct places where the
-- programmer can control the concurrency. First, when /composing/ a stream by
-- merging multiple streams we can choose an appropriate sum style operators to
-- combine them concurrently or serially. Second, when /processing/ a stream in
-- a monadic composition we can choose one of the monad composition types to
-- choose the desired type of concurrency.
--
-- In the following example the squares of @x@ and @y@ are computed
-- concurrently using the 'async' operation and the square roots of their
-- sum are computed serially because of the 'streamly' combinator. We can
-- choose different combinators for the monadic processing and the stream
-- generation, to control the concurrency.  We can also use the 'asyncly'
-- combinator instead of explicitly folding with 'async'.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Data.List (sum)
--
-- main = do
--     z \<-   S.'toList'
--          $ 'serially'     -- Serial monadic processing (sqrt below)
--          $ do
--              x2 \<- 'forEachWith' 'async' [1..100] $ -- Concurrent @"for"@ loop
--                          \\x -> return $ x * x  -- body of the loop
--              y2 \<- 'forEachWith' 'async' [1..100] $
--                          \\y -> return $ y * y
--              return $ sqrt (x2 + y2)
--     print $ sum z
-- @
--
-- We can see how this directly maps to the imperative style
-- <https://en.wikipedia.org/wiki/OpenMP OpenMP> model, we use combinators
-- and operators instead of the ugly pragmas.
--
-- For more concurrent programming examples see,
-- "ListDir.hs", "MergeSort.hs" and "SearchQuery.hs" in the examples directory.

-- $reactive
--
-- Reactive programming is nothing but concurrent streaming which is what
-- streamly is all about. With streamly we can generate streams of events,
-- merge streams that are generated concurrently and process events
-- concurrently. We can do all this without any knowledge about the specifics
-- of the implementation of concurrency. In the following example you will see
-- that the code is just regular Haskell code without much streamly APIs used
-- (active hyperlinks are the streamly APIs) and yet it is a reactive
-- application.
--
-- This application has two independent and concurrent sources of event
-- streams, @acidRain@ and @userAction@. @acidRain@ continuously generates
-- events that deteriorate the health of the character in the game.
-- @userAction@ can be "potion" or "quit". When the user types "potion" the
-- health improves and the game continues.
--
-- @
-- {-\# LANGUAGE FlexibleContexts #-}
--
-- import "Streamly"
-- import Control.Concurrent (threadDelay)
-- import Control.Monad (when)
-- import Control.Monad.IO.Class (MonadIO(..))
-- import Control.Monad.State (MonadState, get, modify, runStateT)
-- import Data.Semigroup (cycle1)
--
-- data Event = Harm Int | Heal Int | Quit deriving (Show)
--
-- userAction :: MonadIO m => 'SerialT' m Event
-- userAction = cycle1 $ liftIO askUser
--     where
--     askUser = do
--         command <- getLine
--         case command of
--             "potion" -> return (Heal 10)
--             "quit"   -> return  Quit
--             _        -> putStrLn "What?" >> askUser
--
-- acidRain :: MonadIO m => 'SerialT' m Event
-- acidRain = cycle1 $ liftIO (threadDelay 1000000) >> return (Harm 1)
--
-- game :: ('MonadAsync' m, MonadState Int m) => 'SerialT' m ()
-- game = do
--     event \<- userAction \`parallel` acidRain
--     case event of
--         Harm n -> modify $ \\h -> h - n
--         Heal n -> modify $ \\h -> h + n
--         Quit   -> fail "quit"
--
--     h <- get
--     when (h <= 0) $ fail "You die!"
--     liftIO $ putStrLn $ "Health = " ++ show h
--
-- main = do
--     putStrLn "Your health is deteriorating due to acid rain,\\
--              \\ type \\"potion\\" or \\"quit\\""
--     _ <- runStateT ('runStream' game) 60
--     return ()
-- @
--
-- You can also find the source of this example in the examples directory as
-- "AcidRain.hs". It has been adapted from Gabriel's
-- <https://hackage.haskell.org/package/pipes-concurrency-2.0.8/docs/Pipes-Concurrent-Tutorial.html pipes-concurrency>
-- package.
-- This is much simpler compared to the pipes version because of the builtin
-- concurrency in streamly. You can also find a SDL based reactive programming
-- example adapted from Yampa in "Streamly.Examples.CirclingSquare".

-- $performance
--
-- Streamly is highly optimized for performance, it is designed for serious
-- high performing, concurrent and scalable applications. We have created the
-- <https://hackage.haskell.org/package/streaming-benchmarks streaming-benchmarks>
-- package which is specifically and carefully designed to measure the
-- performance of Haskell streaming libraries fairly and squarely in the right
-- way. Streamly performs at par or even better than most streaming libraries
-- for serial operations even though it needs to deal with the concurrency
-- capability.

-- $interop
--
-- We can use @unfoldr@ and @uncons@ to convert one streaming type to another.
--
--  Interop with @vector@:
--
-- @
-- import Streamly
-- import qualified Streamly.Prelude as S
-- import qualified Data.Vector.Fusion.Stream.Monadic as V
--
-- main = do
--     -- streamly to vector
--     V.toList (V.unfoldrM S.uncons (S.fromFoldable [1..3])) >>= print
--
--     -- vector to streamly
--     S.toList (S.unfoldrM unconsV (V.fromList [1..3])) >>= print
--
--     where
--     unconsV v = do
--         r <- V.null v
--         if r
--         then return Nothing
--         else do
--             h <- V.head v
--             return $ Just (h, V.tail v)
-- @
--
--  Interop with @pipes@:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import qualified Pipes as P
-- import qualified Pipes.Prelude as P
--
-- main = do
--     -- streamly to pipe
--     P.toListM (P.unfoldr unconsS (S.'fromFoldable' [1..3])) >>= print
--
--     -- pipe to streamly
--     S.'toList' (S.'unfoldrM' unconsP (P.each [1..3])) >>= print
--
--     where
--     -- Adapt P.next to return a Maybe instead of Either
--     unconsP p = P.next p >>= either (\\_ -> return Nothing) (return . Just)
--
--     -- Adapt S.uncons to return an Either instead of Maybe
--     unconsS s = S.'uncons' s >>= maybe (return $ Left ()) (return . Right)
-- @
--
-- Interop with @streaming@:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import qualified Streaming as SG
-- import qualified Streaming.Prelude as SG
--
-- main = do
--     -- streamly to streaming
--     SG.toList (SG.unfoldr unconsS (S.'fromFoldable' [1..3])) >>= print
--
--     -- streaming to streamly
--     S.'toList' (S.unfoldrM SG.uncons (SG.each [1..3])) >>= print
--
--     where
--
--     -- Adapt S.uncons to return an Either instead of Maybe
--     unconsS s = S.'uncons' s >>= maybe (return $ Left ()) (return . Right)
-- @
--
-- Interop with @conduit@:
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import qualified Data.Conduit as C
-- import qualified Data.Conduit.List as C
-- import qualified Data.Conduit.Combinators as C
--
-- main = do
--  -- streamly to conduit
--  C.runConduit (C.unfoldM S.'uncons' (S.'fromFoldable' [1..3]) C..| C.sinkList) >>= print
--
--  -- It seems there is no way out of a conduit as it does not provide an
--  -- uncons or a tail function.
-- @

-- $comparison
--
-- List transformers and logic programming monads also provide a product style
-- composition similar to streamly, however streamly generalizes it with the
-- time dimension; allowing streams to be composed in an asynchronous and
-- concurrent fashion in many different ways.  It also provides multiple
-- alternative ways of composing streams e.g.  serial, interleaved or
-- concurrent.
--
-- This seemingly simple addition of asynchronicity and concurrency to product
-- style streaming composition unifies a number of disparate abstractions into
-- one powerful, concise and elegant abstraction.  A wide variety of
-- programming problems can be solved elegantly with this abstraction. In
-- particular, it unifies three major programming domains namely
-- non-deterministic (logic) programming, concurrent programming and functional
-- reactive programming. In other words, you can do everything with this one
-- abstraction that you could do with the popular libraries listed under these
-- categories in the list below.
--
-- @
-- +-----------------+----------------+
-- | Non-determinism | <https://hackage.haskell.org/package/pipes pipes>          |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/list-t list-t>         |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/logict logict>         |
-- +-----------------+----------------+
-- | Streaming       | <https://hackage.haskell.org/package/vector vector>         |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/streaming streaming>      |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/pipes pipes>          |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/conduit conduit>        |
-- +-----------------+----------------+
-- | Concurrency     | <https://hackage.haskell.org/package/async async>          |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/transient transient>      |
-- +-----------------+----------------+
-- | FRP             | <https://hackage.haskell.org/package/Yampa Yampa>          |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/dunai dunai>          |
-- |                 +----------------+
-- |                 | <https://hackage.haskell.org/package/reflex reflex>         |
-- +-----------------+----------------+
-- @
--
-- Streamly is a list-transformer. It provides all the functionality provided
-- by any of the list transformer and logic programming packages listed above.
-- In addition, Streamly naturally integrates the concurrency dimension to the
-- basic list transformer functionality.
--
-- When it comes to streaming, in terms of the streaming API streamly is almost
-- identical to the vector package. Streamly, vector and streaming packages all
-- represent a stream as data and are therefore similar in the fundamental
-- approach to streaming. The fundamental difference is that streamly adds
-- concurrency support and the monad instance provides concurrent looping.
-- Other streaming libraries like pipes, conduit and machines represent and
-- compose stream processors rather than the stream data and therefore fall in
-- another class of streaming libraries and have comparatively more complicated
-- types.
--
-- When it comes to concurrency, streamly can do everything that the @async@
-- package can do and more. async provides applicative concurrency whereas
-- streamly provides both applicative and monadic concurrency. The
-- 'ZipAsync' type behaves like the applicative instance of async.  In
-- comparison to transient streamly has a first class streaming interface and
-- is a monad transformer that can be used universally in any Haskell monad
-- transformer stack.  Streamly was in fact originally inspired by the
-- concurrency implementation in @transient@ though it has no resemblence with
-- that and takes a lazy pull approach versus transient's strict push approach.
--
-- The non-determinism, concurrency and streaming combination make streamly a
-- strong FRP capable library as well. FRP is fundamentally stream of events
-- that can be processed concurrently. The example in this tutorial as well as
-- the "Streamly.Examples.CirclingSquare" example from Yampa demonstrate the
-- basic FRP capability of streamly. In core concepts streamly is strikingly
-- similar to @dunai@. dunai was designed from a FRP perspective and streamly
-- was originally designed from a concurrency perspective. However, both have
-- similarity at the core.

-- $furtherReading
--
-- * Read the documentation of "Streamly" module
-- * Read the documentation of "Streamly.Prelude" module
-- * See the examples in the "examples" directory of the package
-- * See the tests in the "test" directory of the package