{-# OPTIONS_GHC -fno-warn-unused-imports #-}
-- |
-- Module      : User.Tutorials.ConcurrentStreams
-- Copyright   : (c) 2017 Composewell Technologies
--
-- License     : BSD3
-- Maintainer  : streamly@composewell.com
--
-- THIS TUTORIAL IS OBSOLETE.
--
-- In this tutorial we will show how streamly can be used for idiomatic and
-- declarative concurrent programming.  Before you go through this tutorial we
-- recommend that you take a look at the Streamly serial streams tutorial.

module User.Tutorials.ConcurrentStreams
    (
    -- * Imports
    -- $setup

    -- * Concurrent Streams
    -- $concurrentStreams

    -- * Combining Streams
    -- $flavors

    -- * Imports and Supporting Code
    -- $imports

    -- * Generating Streams Concurrently
    -- $generatingConcurrently

    -- * Concurrent Pipeline Stages
    -- $concurrentApplication

    -- * Mapping Concurrently
    -- $concurrentTransformation

    -- * Merging Streams

    -- ** Semigroup Style

    -- *** Deep Speculative Composition ('Ahead')
    -- $ahead

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

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

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

    -- XXX we should deprecate and remove the mkAsync API
    -- Custom composition
    -- custom

    -- ** Monoid Style
    -- $monoid

    -- * Nesting Streams

    -- ** Monad
    -- *** Deep Speculative Nesting ('Ahead')
    -- $aheadNesting

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

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

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

    -- ** Applicative
    -- $applicative

    -- * Zipping Streams

    -- ** Parallel Zipping
    -- $parallelzip

    -- * Concurrent Programming
    -- $concurrent

    -- * Writing Concurrent Programs
    -- $programs

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

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

-- CAUTION: please keep setup and imports sections in sync

-- XXX This tutorial has to be rewritten.

-- $setup
-- In most of example snippets we do not repeat the imports. Where imports are
-- not explicitly specified use the imports shown below.
--
-- >>> :m
-- >>> :set -fno-warn-deprecations
-- >>> import Data.Function ((&))
-- >>> import Streamly.Prelude ((|:), (|&))
-- >>> import Streamly.Internal.Data.Stream.Cross (CrossStream(..))
-- >>> import qualified Streamly.Prelude as Stream
-- >>> import qualified Streamly.Data.Fold as Fold
--
-- 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.
--
-- >>> import Control.Concurrent (threadDelay, myThreadId)
-- >>> :{
--   delay n = Stream.fromEffect $ do
--      threadDelay (n * 1000000)
--      tid <- myThreadId
--      putStrLn (show tid ++ ": Delay " ++ show n)
-- :}
--
-- For concurrent examples, use line buffering, otherwise output from different
-- threads may get mixed:
--
-- >>> import System.IO (stdout, hSetBuffering, BufferMode(LineBuffering))
-- >>> hSetBuffering stdout LineBuffering
--

-- $concurrentStreams
--
-- Many stream operations can be done concurrently:
--
-- * Streams can be generated concurrently.
--
-- * Streams can be merged concurrently.
--
-- * Multiple stages in a streaming pipeline can run concurrently.
--
-- * Streams can be mapped and zipped concurrently.
--
-- * In monadic composition they combine like a list transformer,
--   providing concurrent non-determinism.
--
-- There are three basic concurrent stream styles, 'Ahead', 'Async', and
-- 'Parallel'. The 'Ahead' style streams are similar to 'Serial' except that
-- they can speculatively execute multiple stream actions concurrently in
-- advance. 'Ahead' would return exactly the same stream as 'Serial' except
-- that it may execute the actions concurrently. The 'Async' style streams,
-- like 'Ahead', speculatively execute multiple stream actions in advance but
-- return the results in their finishing order rather than in the stream
-- traversal order.  'Parallel' is like 'Async' except that it provides
-- unbounded parallelism instead of controlled parallelism.
--
-- For easy reference, we can classify the stream types based on /execution order/,
-- /consumption order/, and /bounded or unbounded/ concurrency.
-- Execution could be serial (i.e. synchronous) or asynchronous. In serial
-- execution we execute the next action in the stream only after the previous
-- one has finished executing. In asynchronous execution multiple actions in
-- the stream can be executed asynchronously i.e. the next action can start
-- executing even before the first one has finished. Consumption order
-- determines the order in which the outputs generated by the composition are
-- 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.
--
-- +------------+--------------+--------------+--------------+
-- | Type       | Execution    | Consumption  | Concurrency  |
-- +============+==============+==============+==============+
-- | 'Serial'   | Serial       | Serial       | None         |
-- +------------+--------------+--------------+--------------+
-- | 'Ahead'    | Asynchronous | Serial       | bounded      |
-- +------------+--------------+--------------+--------------+
-- | 'Async'    | Asynchronous | Asynchronous | bounded      |
-- +------------+--------------+--------------+--------------+
-- | 'Parallel' | Asynchronous | Asynchronous | unbounded    |
-- +------------+--------------+--------------+--------------+
--
-- All these types can be freely inter-converted using type conversion
-- combinators or type annotations, without any cost, to achieve the desired
-- composition style.  To force a particular type of composition, we coerce the
-- stream type using the corresponding type adapting combinator from
-- 'fromSerial', 'fromAhead', 'fromAsync', or 'fromParallel'.  The default stream type
-- is inferred as 'Serial' unless you change it by using one of the combinators
-- or by using a type annotation.

-- $flavors
--
-- Streams can be combined using '<>' or 'mappend' to form a
-- composite. Composite streams can be interpreted in a depth first or
-- breadth first manner using an appropriate type conversion before
-- consumption. Deep (e.g. 'Serial') stream type variants traverse a
-- composite stream in a depth first manner, such that each stream is
-- traversed fully before traversing the next stream. Wide
-- (e.g. 'WSerial') stream types traverse it in a breadth first
-- manner, such that one element from each stream is traversed before
-- coming back to the first stream again.
--
-- Each stream type has a wide traversal variant prefixed by 'W'. The wide
-- variant differs only in the Semigroup\/Monoid, Applicative\/Monad
-- compositions of the streams.
-- The following table summarizes the basic types and the corresponding wide
-- variants:
--
-- @
-- +------------+-----------+
-- | Deep       | Wide      |
-- +============+===========+
-- | 'Serial'     | 'WSerial'   |
-- +------------+-----------+
-- | 'Ahead'      | 'WAhead'    |
-- +------------+-----------+
-- | 'Async'      | 'WAsync'    |
-- +------------+-----------+
-- @
--
-- 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.
--

-- $programs
--
-- When writing concurrent programs it is advised to not use the concurrent
-- style stream combinators blindly at the top level. That might create too
-- much concurrency where it is not even required, and can even degrade
-- performance in some cases. In some cases it can also lead to surprising
-- behavior because of some code that is supposed to be serial becoming
-- concurrent. Please be aware that all concurrency capable APIs that you may
-- have used under the scope of a concurrent stream combinator will become
-- concurrent. For example if you have a 'repeatM' somewhere in your program
-- and you use 'fromParallel' on top, the 'repeatM' becomes fully parallel,
-- resulting into an infinite parallel execution . Instead, use the
-- /Keep It Serial and Stupid/ principle, start with the default serial
-- composition and enable concurrent combinators only when and where necessary.
-- When you use a concurrent combinator you can use an explicit 'fromSerial'
-- combinator to suppress any unnecessary concurrency under the scope of that
-- combinator.

-- $generatingConcurrently
--
-- Monadic construction and generation functions like 'consM', 'unfoldrM',
-- 'replicateM', 'repeatM', 'iterateM' and 'fromFoldableM' work concurrently
-- when used with appropriate stream type combinator. The pure versions of
-- these APIs are not concurrent, however you can use the monadic versions even
-- for pure computations by wrapping the pure value in a monad to get the
-- concurrent generation capability where required.
--
-- The following code finishes in 3 seconds (6 seconds when serial):
--
-- >>> let p n = threadDelay (n * 1000000) >> return n
-- >>> Stream.toList $ Stream.fromParallel $ p 3 |: p 2 |: p 1 |: Stream.nil
-- [1,2,3]
--
-- >>> Stream.toList $ Stream.fromAhead $ p 3 |: p 2 |: p 1 |: Stream.nil
-- [3,2,1]
--
-- The following finishes in 10 seconds (100 seconds when serial):
--
-- >>> Stream.drain $ Stream.fromAsync $ Stream.replicateM 10 $ p 10
--

-- $concurrentTransformation
--
-- Monadic transformation functions 'mapM' and 'sequence' work concurrently
-- when used with appropriate stream type combinators. The pure versions do not
-- work concurrently, however you can use the monadic versions even for pure
-- computations to get the concurrent transformation capability where required.
--
-- This would print a value every second (2 seconds when serial):
--
-- >>> let p n = threadDelay (n * 1000000) >> return n
-- >>> :{
-- parMap =
--       Stream.repeatM (p 1)
--     & Stream.fromSerial   -- repeatM is serial
--     & Stream.mapM (\x -> p 1 >> print x)
--     & Stream.fromAhead    -- mapM is cocnurrent using Ahead style
--     & Stream.drain
-- :}
--

-- $concurrentApplication
--
-- The concurrent function application operators '|$' and '|&' apply a stream
-- argument to a stream function concurrently to compose a concurrent pipeline
-- of stream processing functions:
--
-- Because both the stages run concurrently, we would see a delay of only 1
-- second instead of 2 seconds in the following:
--
-- >>> let p n = threadDelay (n * 1000000) >> return n
-- >>> :{
-- parApp =
--        Stream.repeatM (p 1)
--     |& Stream.mapM (\x -> p 1 >> print x)
--      & Stream.drain
-- :}

-- $ahead
--
-- The 'Semigroup' operation '<>' of the 'Ahead' type combines two streams in a
-- /serial depth first/ manner with concurrent lookahead. We use the 'fromAhead'
-- type combinator to effect 'Ahead' style of composition. We can also use an
-- explicit 'Ahead' type annotation for the stream to achieve the same effect.
--
-- When two streams 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 keep the results ready when its turn arrives.
-- 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.  The following example would print the result in a second even
-- though each action in each stream takes one second:
--
-- >>> p n = threadDelay 1000000 >> return n
-- >>> stream1 = p 1 |: p 2 |: Stream.nil
-- >>> stream2 = p 3 |: p 4 |: Stream.nil
-- >>> Stream.toList $ Stream.fromAhead $ stream1 <> stream2
-- [1,2,3,4]
--
-- Each stream is constructed 'fromAhead' and then both the streams are merged
-- 'fromAhead', therefore, all the actions can run concurrently but the result is
-- presented in serial order.
--
-- You can also use the polymorphic combinator 'ahead' in place of '<>' to
-- compose any type of streams in this manner.

-- $async
--
-- The 'Semigroup' operation '<>' of the 'Async' type combines the two
-- streams in a depth first manner with parallel look ahead. We use the
-- 'fromAsync' type combinator to effect 'Async' style of composition. We
-- can also use the 'Async' type annotation for the stream type to achieve
-- 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 example below each element in the stream introduces a constant delay
-- of 1 second, however, it takes just one second to produce all the results.
-- The results are not guaranteed to be in any particular order:
--
-- >>> p n = threadDelay 1000000 >> return n
-- >>> stream1 = p 1 |: p 2 |: Stream.nil
-- >>> stream2 = p 3 |: p 4 |: Stream.nil
-- >>> Stream.toList $ Stream.fromAsync $ stream1 <> stream2
-- ...
--
-- The constituent streams are also composed in 'Async' manner and the
-- composition of streams too. We can compose the constituent streams to run
-- serially, in that case it would take 2 seconds to produce all the results.
-- The elements in the serial streams would be in serial order in the results:
--
-- >>> p n = threadDelay 1000000 >> return n
-- >>> stream = (Stream.fromSerial stream1) <> (Stream.fromSerial stream2)
-- >>> Stream.toList $ Stream.fromAsync stream
-- ...
--
-- 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:
--
-- >>> Stream.drain $ Stream.fromAsync $ delay 3 <> delay 2 <> delay 1
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: 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.
--
-- >>> Stream.drain $ Stream.fromAsync $ (delay 1 <> delay 2) <> (delay 3 <> delay 4)
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: Delay 3
-- ThreadId ...: Delay 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:
--
-- >>> :{
-- traced m = Stream.fromEffect (myThreadId >>= print) >> return m
-- stream = traced (sqrt 9) <> traced (sqrt 16) <> traced (sqrt 25)
-- main = Stream.drain $ Stream.fromAsync stream
-- :}
--
-- 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 'fromAsync' combinator in the following example:
--
-- >>> Stream.drain $ delay 3 `Stream.async` delay 2 `Stream.async` delay 1
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: 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 utilizes resources optimally and should be preferred over
-- 'Parallel' or 'WAsync' unless you really need those. 'Async' 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 'async' 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 'fromWAsync'
-- type combinator to effect 'WAsync' style of composition. We can also use the
-- 'WAsync' type annotation for the stream to achieve 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 performed 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 are consumed on a first come first
-- serve basis.
--
-- In the following example we can see that outputs are produced in the breadth
-- first traversal order but this is not guaranteed.
--
-- >>> stream1 = print 1 |: print 2 |: Stream.nil
-- >>> stream2 = print 3 |: print 4 |: Stream.nil
-- >>> Stream.drain $ Stream.fromWAsync $ stream1 <> stream2
-- 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 'fromWAsync' combinator in the following example:
--
-- >>> Stream.drain $ delay 3 `Stream.wAsync` delay 2 `Stream.wAsync` delay 1
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: 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 'fromParallel' type combinator to effect 'Parallel' style of composition.
-- We can also use the 'Parallel' type annotation for the stream type to
-- achieve 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 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
-- 'fromParallel' combinator in the following example:
--
-- >>> Stream.drain $ delay 3 `Stream.parallel` delay 2 `Stream.wAsync` delay 1
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: 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.
--

-- XXX to be removed
-- $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 <https://github.com/composewell/streamly-examples Streamly Examples>.

-- $monoid
--
-- 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:
--
-- >>> :{
-- main = do
--  Stream.drain $ Stream.fromAsync $ foldMap delay [1..10]
--  Stream.drain $ Stream.concatFoldableWith Stream.async (map delay [1..10])
--  Stream.drain $ Stream.concatMapFoldableWith Stream.async delay [1..10]
--  Stream.drain $ Stream.concatForFoldableWith Stream.async [1..10] delay
-- :}
--

-- $aheadNesting
--
-- The 'Monad' composition of 'Ahead' type behaves just like 'Serial' except
-- that it can speculatively perform a bounded number of next iterations of a
-- loop concurrently.
--
-- >>> :{
-- Stream.toList $ Stream.fromAhead $ do
--     x <- Stream.fromFoldable [3,2,1]
--     delay x
--     return x
-- :}
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: Delay 3
-- [3,2,1]
--
-- This code finishes in 3 seconds, 'Serial' would take 6 seconds.  As we can
-- see all the three iterations are concurrent and run in different threads,
-- however, the results are returned in the serial order.
--
-- Concurrency is demand driven, when multiple streams are composed using this
-- style, the iterations are executed in a depth first manner just like
-- 'Serial' i.e. nested iterations are executed before we proceed to the next
-- outer iteration. The only difference is that we may execute multiple future
-- iterations concurrently and keep the results ready.
--
-- The 'fromAhead' 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 'Ahead'.
--

-- $concurrentNesting
--
-- The 'Monad' composition of 'Async' type can perform the iterations of a
-- loop concurrently.
--
-- >>> :{
-- Stream.drain $ Stream.fromAsync $ do
--      x <- Stream.fromFoldable [3,2,1]
--      delay x
-- :}
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: 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 i.e. more concurrent iterations are started
-- only if the previous iterations are not able to saturate the consumer of the
-- output stream.  This works exactly the same way as the merging of two
-- streams using 'async' works.
--
-- The 'fromAsync' 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'.
--
-- When multiple streams are nested using this style, the iterations are
-- concurrently evaluated in a depth first manner:
--
--
-- >>> :{
-- Stream.drain $ Stream.fromAsync $ do
--     x <- Stream.fromFoldable [1,2]
--     y <- Stream.fromFoldable [3,4]
--     Stream.fromEffect $ putStrLn $ show (x, y)
-- :}
-- (1,3)
-- ...
-- ...
-- ...
--
-- Nested iterations are given preference for concurrent evaluation i.e.
-- (1,4) will be scheduled in preference to (2,3).

-- $wasyncNesting
--
-- Like 'Async', the 'Monad' composition of 'WAsync' runs the iterations of a
-- loop concurrently. It differs from 'Async' in the nested loop behavior. Like
-- 'WSerial', the nested loops in this type are traversed and executed in a
-- breadth first manner rather than the depth first manner of 'Async' style.
--
-- >>> :{
-- Stream.drain $ Stream.fromWAsync $ do
--     x <- Stream.fromSerial $ Stream.fromFoldable [1,2]
--     y <- Stream.fromSerial $ Stream.fromFoldable [3,4]
--     Stream.fromEffect $ putStrLn $ show (x, y)
-- :}
-- (1,3)
-- (1,4)
-- (2,3)
-- (2,4)
--
-- Note that (2,3) is preferred to (1,4) when evaluating the iterations
-- concurrently.  This works exactly the same way as the merging of two streams
-- using 'wAsync' works.
--
-- The 'fromWAsync' 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'.
--

-- $parallelNesting
--
-- Just like 'Async' or 'WAsync' the 'Monad' composition of 'Parallel' runs the
-- iterations of a loop concurrently.
--
-- >>> :{
-- Stream.drain $ Stream.fromParallel $ do
--    x <- Stream.fromFoldable [3,2,1]
--    delay x
-- :}
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 2
-- ThreadId ...: Delay 3
--
-- It differs from 'Async' and 'WAsync' in the nested loop behavior. All
-- iterations of the loop are run fully concurrently irrespective of the
-- demand.  This works exactly the same way as the merging of streams using
-- 'parallel' works.
--
-- The 'fromParallel' 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'.
--

-- $applicative
--
-- 'Async' can run the iterations concurrently, therefore, it takes a total
-- of 6 seconds which is max (1, 2) + max (3, 4):
--
-- >>> (Stream.toList $ Stream.fromAsync $ (,) <$> 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)]
-- @
--
-- Similarly, 'WAsync' as well can run the iterations concurrently, but with a
-- different style of scheduling than 'Async' as explained in the Monad
-- section, therefore, it too takes a total of 6 seconds (2 + 4):
--
-- >>> (Stream.toList $ Stream.fromWAsync $ (,) <$> 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)]
-- @

-- $parallelzip
--
-- The applicative instance of 'ZipAsync' type zips streams concurrently.
-- 'fromZipAsync' type combinator can be used to switch to parallel applicative
-- zip composition:
--
-- >>> d n = unCrossStream (CrossStream (delay n) >> return n)
-- >>> s1 = Stream.fromSerial $ d 2 <> d 4
-- >>> s2 = Stream.fromSerial $ d 3 <> d 1
-- >>> (Stream.toList $ Stream.fromZipAsync $ (,) <$> s1 <*> s2) >>= print
-- ThreadId ...: Delay 2
-- ThreadId ...: Delay 3
-- ThreadId ...: Delay 1
-- ThreadId ...: Delay 4
-- [(2,3),(4,1)]
--
-- The actions within each stream are executed serially, however, the two
-- streams are run concurrently with respect to each other. Therefore, it takes
-- 6 seconds to zip the two streams, which is the maximum of the time to
-- evaluate stream s1 (2 + 4 = 6 seconds) and stream s2 (3 + 1 = 4 seconds).

-- $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 'fromSerial' combinator. We can
-- choose different combinators for the monadic processing and the stream
-- generation, to control the concurrency.  We can also use the 'fromAsync'
-- combinator instead of explicitly folding with 'async'.
--
-- >>> import Data.List (sum)
-- >>> :{
-- main = do
--     z <- Stream.toList
--          $ Stream.fromSerial     -- Serial monadic processing (sqrt below)
--          $ unCrossStream $ do
--              -- Concurrent @"for"@ loop
--              x2 <- CrossStream (Stream.concatForFoldableWith Stream.async [1..100] $
--                          \x -> Stream.fromPure $ x * x)  -- body of the loop
--              y2 <- CrossStream (Stream.concatForFoldableWith Stream.async [1..100] $
--                          \y -> Stream.fromPure $ 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,
-- <https://github.com/composewell/streamly-examples>.

-- $furtherReading
--
-- * Read the reactive programming tutorial
-- * See the examples in <https://github.com/composewell/streamly-examples streamly-examples> repo.