{-# LANGUAGE CPP                       #-}
{-# LANGUAGE ConstraintKinds           #-}
{-# LANGUAGE FlexibleContexts          #-}
{-# LANGUAGE FlexibleInstances         #-}
{-# LANGUAGE GeneralizedNewtypeDeriving#-}
{-# LANGUAGE InstanceSigs              #-}
{-# LANGUAGE MultiParamTypeClasses     #-}
{-# LANGUAGE UndecidableInstances      #-} -- XXX

#include "inline.hs"

-- |
-- Module      : Streamly.Internal.Data.Stream.Parallel
-- Copyright   : (c) 2017 Harendra Kumar
--
-- License     : BSD3
-- Maintainer  : streamly@composewell.com
-- Stability   : experimental
-- Portability : GHC
--
--
module Streamly.Internal.Data.Stream.Parallel
    (
    -- * Parallel Stream Type
      ParallelT
    , Parallel
    , parallely

    -- * Merge Concurrently
    , parallel
    , parallelFst
    , parallelMin

    -- * Evaluate Concurrently
    , mkParallel

    -- * Tap Concurrently
    , tapAsync
    , distributeAsync_
    )
where

import Control.Concurrent (myThreadId, takeMVar)
import Control.Monad (when)
import Control.Monad.Base (MonadBase(..), liftBaseDefault)
import Control.Monad.Catch (MonadThrow, throwM)
-- import Control.Monad.Error.Class   (MonadError(..))
import Control.Monad.IO.Class (MonadIO(..))
import Control.Monad.Reader.Class (MonadReader(..))
import Control.Monad.State.Class (MonadState(..))
import Control.Monad.Trans.Class (MonadTrans(lift))
import Data.Functor (void)
import Data.IORef (readIORef, writeIORef)
import Data.Maybe (fromJust)
#if __GLASGOW_HASKELL__ < 808
import Data.Semigroup (Semigroup(..))
#endif
import Prelude hiding (map)

import qualified Data.Set as Set

import Streamly.Internal.Data.Stream.SVar
       (fromSVar, fromProducer, fromConsumer, pushToFold)
import Streamly.Internal.Data.Stream.StreamK
       (IsStream(..), Stream, mkStream, foldStream, foldStreamShared, adapt)

import Streamly.Internal.Data.SVar

import qualified Streamly.Internal.Data.Stream.StreamK as K
import qualified Streamly.Internal.Data.Stream.StreamD as D

#include "Instances.hs"

-------------------------------------------------------------------------------
-- Parallel
-------------------------------------------------------------------------------

-------------------------------------------------------------------------------
-- StreamK based worker routines
-------------------------------------------------------------------------------

{-# NOINLINE runOne #-}
runOne
    :: MonadIO m
    => State Stream m a -> Stream m a -> Maybe WorkerInfo -> m ()
runOne st m0 winfo =
    case getYieldLimit st of
        Nothing -> go m0
        Just _  -> runOneLimited st m0 winfo

    where

    go m = do
        liftIO $ decrementBufferLimit sv
        foldStreamShared st yieldk single stop m

    sv = fromJust $ streamVar st

    stop = liftIO $ do
        incrementBufferLimit sv
        sendStop sv winfo
    sendit a = liftIO $ void $ send sv (ChildYield a)
    single a = sendit a >> (liftIO $ sendStop sv winfo)
    yieldk a r = sendit a >> go r

runOneLimited
    :: MonadIO m
    => State Stream m a -> Stream m a -> Maybe WorkerInfo -> m ()
runOneLimited st m0 winfo = go m0

    where

    go m = do
        yieldLimitOk <- liftIO $ decrementYieldLimit sv
        if yieldLimitOk
        then do
            liftIO $ decrementBufferLimit sv
            foldStreamShared st yieldk single stop m
        else do
            liftIO $ cleanupSVarFromWorker sv
            liftIO $ sendStop sv winfo

    sv = fromJust $ streamVar st

    stop = liftIO $ do
        incrementBufferLimit sv
        incrementYieldLimit sv
        sendStop sv winfo
    sendit a = liftIO $ void $ send sv (ChildYield a)
    single a = sendit a >> (liftIO $ sendStop sv winfo)
    yieldk a r = sendit a >> go r

-------------------------------------------------------------------------------
-- Consing and appending a stream in parallel style
-------------------------------------------------------------------------------

-- Note that consing and appending requires StreamK as it would not scale well
-- with StreamD unless we are only consing a very small number of streams or
-- elements in a stream. StreamK allows us to manipulate control flow in a way
-- which StreamD cannot allow. StreamK can make a jump without having to
-- remember the past state.

{-# NOINLINE forkSVarPar #-}
forkSVarPar :: (IsStream t, MonadAsync m)
    => SVarStopStyle -> t m a -> t m a -> t m a
forkSVarPar ss m r = mkStream $ \st yld sng stp -> do
    sv <- newParallelVar ss st
    pushWorkerPar sv (runOne st{streamVar = Just sv} $ toStream m)
    case ss of
        StopBy -> liftIO $ do
            set <- readIORef (workerThreads sv)
            writeIORef (svarStopBy sv) $ Set.elemAt 0 set
        _ -> return ()
    pushWorkerPar sv (runOne st{streamVar = Just sv} $ toStream r)
    foldStream st yld sng stp (fromSVar sv)

{-# INLINE joinStreamVarPar #-}
joinStreamVarPar :: (IsStream t, MonadAsync m)
    => SVarStyle -> SVarStopStyle -> t m a -> t m a -> t m a
joinStreamVarPar style ss m1 m2 = mkStream $ \st yld sng stp ->
    case streamVar st of
        Just sv | svarStyle sv == style && svarStopStyle sv == ss -> do
            -- Here, WE ARE IN THE WORKER/PRODUCER THREAD, we know that because
            -- the SVar exists. We are running under runOne and the output we
            -- produce ultimately will be sent to the SVar by runOne.
            --
            -- If we came here the worker/runOne is evaluating a `parallel`
            -- combinator. In this case, we always fork a new worker for the
            -- first component (m1) in the parallel composition and continue to
            -- evaluate the second component (m2) in the current worker thread.
            --
            -- When m1 is serially composed, the worker would evaluate it
            -- without any further forks and the resulting output is sent to
            -- the SVar and the evaluation terminates. If m1 is a `parallel`
            -- composition of two streams the worker would again recurses here.
            --
            -- Similarly, when m2 is serially composed it gets evaluated here
            -- and the resulting output is sent to the SVar by the runOne
            -- wrapper. When m2 is composed with `parallel` it will again
            -- recurse here and so on until it finally terminates.
            --
            -- When we create a right associated expression using `parallel`,
            -- then m1 would always terminate without further forks or
            -- recursion into this routine, therefore, the worker returns
            -- immediately after evaluating it. And m2 would continue to
            -- fork/recurse, therefore, the current thread always recurses and
            -- forks new workers one after the other.  This is a tail recursive
            -- style execution, m2, the recursive expression always executed at
            -- the tail.
            --
            -- When the expression is left associated, the worker spawned would
            -- get the forking/recursing responsibility and then again the
            -- worker spawned by that worker would fork, thus creating layer
            -- over layer of workers and a chain of threads leading to a very
            -- inefficient execution.
            pushWorkerPar sv (runOne st $ toStream m1)
            foldStreamShared st yld sng stp m2
        _ ->
            -- Here WE ARE IN THE CONSUMER THREAD, we create a new SVar, fork
            -- worker threads to execute m1 and m2 and this thread starts
            -- pulling the stream from the SVar.
            foldStreamShared st yld sng stp (forkSVarPar ss m1 m2)

-------------------------------------------------------------------------------
-- User facing APIs
-------------------------------------------------------------------------------

-- | XXX we can implement it more efficienty by directly implementing instead
-- of combining streams using parallel.
{-# INLINE consMParallel #-}
{-# SPECIALIZE consMParallel :: IO a -> ParallelT IO a -> ParallelT IO a #-}
consMParallel :: MonadAsync m => m a -> ParallelT m a -> ParallelT m a
consMParallel m r = fromStream $ K.yieldM m `parallel` (toStream r)

-- | Polymorphic version of the 'Semigroup' operation '<>' of 'ParallelT'
-- Merges two streams concurrently.
--
-- @since 0.2.0
{-# INLINE parallel #-}
parallel :: (IsStream t, MonadAsync m) => t m a -> t m a -> t m a
parallel = joinStreamVarPar ParallelVar StopNone

-- This is a co-parallel like combinator for streams, where first stream is the
-- main stream and the rest are just supporting it, when the first ends
-- everything ends.
--
-- | Like `parallel` but stops the output as soon as the first stream stops.
--
-- @since 0.7.0
{-# INLINE parallelFst #-}
parallelFst :: (IsStream t, MonadAsync m) => t m a -> t m a -> t m a
parallelFst = joinStreamVarPar ParallelVar StopBy

-- This is a race like combinator for streams.
--
-- | Like `parallel` but stops the output as soon as any of the two streams
-- stops.
--
-- @since 0.7.0
{-# INLINE parallelMin #-}
parallelMin :: (IsStream t, MonadAsync m) => t m a -> t m a -> t m a
parallelMin = joinStreamVarPar ParallelVar StopAny

------------------------------------------------------------------------------
-- Convert a stream to parallel
------------------------------------------------------------------------------

-- | Generate a stream asynchronously to keep it buffered, lazily consume
-- from the buffer.
--
-- /Internal/
--
mkParallel :: (IsStream t, MonadAsync m) => t m a -> t m a
mkParallel m = mkStream $ \st yld sng stp -> do
    sv <- newParallelVar StopNone (adaptState st)
    -- pushWorkerPar sv (runOne st{streamVar = Just sv} $ toStream m)
    D.toSVarParallel st sv $ D.toStreamD m
    foldStream st yld sng stp $ fromSVar sv

------------------------------------------------------------------------------
-- Clone and distribute a stream in parallel
------------------------------------------------------------------------------

-- Tap a stream and send the elements to the specified SVar in addition to
-- yielding them again.
--
-- XXX this could be written in StreamD style for better efficiency with fusion.
--
{-# INLINE teeToSVar #-}
teeToSVar :: (IsStream t, MonadAsync m) => SVar Stream m a -> t m a -> t m a
teeToSVar svr m = mkStream $ \st yld sng stp -> do
    foldStreamShared st yld sng stp (go False m)

    where

    go False m0 = mkStream $ \st yld _ stp -> do
        let drain = do
                -- In general, a Stop event would come equipped with the result
                -- of the fold. It is not used here but it would be useful in
                -- applicative and distribute.
                done <- fromConsumer svr
                when (not done) $ do
                    liftIO $ withDiagMVar svr "teeToSVar: waiting to drain"
                           $ takeMVar (outputDoorBellFromConsumer svr)
                    drain

            stopFold = do
                liftIO $ sendStop svr Nothing
                -- drain/wait until a stop event arrives from the fold.
                drain

            stop       = stopFold >> stp
            single a   = do
                done <- pushToFold svr a
                yld a (go done (K.nilM stopFold))
            yieldk a r = pushToFold svr a >>= \done -> yld a (go done r)
         in foldStreamShared st yieldk single stop m0

    go True m0 = m0

-- In case of folds the roles of worker and parent on an SVar are reversed. The
-- parent stream pushes values to an SVar instead of pulling from it and a
-- worker thread running the fold pulls from the SVar and folds the stream. We
-- keep a separate channel for pushing exceptions in the reverse direction i.e.
-- from the fold to the parent stream.
--
-- Note: If we terminate due to an exception, we do not actively terminate the
-- fold. It gets cleaned up by the GC.

-- | Create an SVar with a fold consumer that will fold any elements sent to it
-- using the supplied fold function.
{-# INLINE newFoldSVar #-}
newFoldSVar :: (IsStream t, MonadAsync m)
    => State Stream m a -> (t m a -> m b) -> m (SVar Stream m a)
newFoldSVar stt f = do
    -- Buffer size for the SVar is derived from the current state
    sv <- newParallelVar StopAny (adaptState stt)

    -- Add the producer thread-id to the SVar.
    liftIO myThreadId >>= modifyThread sv

    void $ doFork (void $ f $ fromStream $ fromProducer sv)
                  (svarMrun sv)
                  (handleFoldException sv)
    return sv

-- NOTE: In regular pull style streams, the consumer stream is pulling elements
-- from the SVar and we have several workers producing elements and pushing to
-- SVar. In case of folds, we, the parent stream driving the fold, are the
-- stream producing worker, we start an SVar and start pushing to the SVar, the
-- fold on the other side of the SVar is the consumer stream.
--
-- In the pull stream case exceptions are propagated from the producing workers
-- to the consumer stream, the exceptions are propagated on the same channel as
-- the produced stream elements. However, in case of push style folds the
-- current stream itself is the worker and the fold is the consumer, in this
-- case we have to propagate the exceptions from the consumer to the producer.
-- This is reverse of the pull case and we need a reverse direction channel
-- to propagate the exception.
--
-- | Redirect a copy of the stream to a supplied fold and run it concurrently
-- in an independent thread. The fold may buffer some elements. The buffer size
-- is determined by the prevailing 'maxBuffer' setting.
--
-- @
--               Stream m a -> m b
--                       |
-- -----stream m a ---------------stream m a-----
--
-- @
--
-- @
-- > S.drain $ S.tapAsync (S.mapM_ print) (S.enumerateFromTo 1 2)
-- 1
-- 2
-- @
--
-- Exceptions from the concurrently running fold are propagated to the current
-- computation.  Note that, because of buffering in the fold, exceptions may be
-- delayed and may not correspond to the current element being processed in the
-- parent stream, but we guarantee that before the parent stream stops the tap
-- finishes and all exceptions from it are drained.
--
--
-- Compare with 'tap'.
--
-- @since 0.7.0
{-# INLINE tapAsync #-}
tapAsync :: (IsStream t, MonadAsync m) => (t m a -> m b) -> t m a -> t m a
tapAsync f m = mkStream $ \st yld sng stp -> do
    sv <- newFoldSVar st f
    foldStreamShared st yld sng stp (teeToSVar sv m)

-- | Concurrently distribute a stream to a collection of fold functions,
-- discarding the outputs of the folds.
--
-- >>> S.drain $ distributeAsync_ [S.mapM_ print, S.mapM_ print] (S.enumerateFromTo 1 2)
--
-- @
-- distributeAsync_ = flip (foldr tapAsync)
-- @
--
-- /Internal/
--
{-# INLINE distributeAsync_ #-}
distributeAsync_ :: (Foldable f, IsStream t, MonadAsync m)
    => f (t m a -> m b) -> t m a -> t m a
distributeAsync_ = flip (foldr tapAsync)

------------------------------------------------------------------------------
-- ParallelT
------------------------------------------------------------------------------

-- | Async composition with strict concurrent execution of all streams.
--
-- The 'Semigroup' instance of 'ParallelT' executes both the streams
-- concurrently without any delay or without waiting for the consumer demand
-- and /merges/ the results as they arrive. If the consumer does not consume
-- the results, they are buffered upto a configured maximum, controlled by the
-- 'maxBuffer' primitive. If the buffer becomes full the concurrent tasks will
-- block until there is space in the buffer.
--
-- Both 'WAsyncT' and 'ParallelT', evaluate the constituent streams fairly in a
-- round robin fashion. The key difference is that 'WAsyncT' might wait for the
-- consumer demand before it executes the tasks whereas 'ParallelT' starts
-- executing all the tasks immediately without waiting for the consumer demand.
-- For 'WAsyncT' the 'maxThreads' limit applies whereas for 'ParallelT' it does
-- not apply. In other words, 'WAsyncT' can be lazy whereas 'ParallelT' is
-- strict.
--
-- 'ParallelT' is useful for cases when the streams are required to be
-- evaluated simultaneously irrespective of how the consumer consumes them e.g.
-- when we want to race two tasks and want to start both strictly at the same
-- time or if we have timers in the parallel tasks and our results depend on
-- the timers being started at the same time. If we do not have such
-- requirements then 'AsyncT' or 'AheadT' are recommended as they can be more
-- efficient than 'ParallelT'.
--
-- @
-- main = ('toList' . 'parallely' $ (fromFoldable [1,2]) \<> (fromFoldable [3,4])) >>= print
-- @
-- @
-- [1,3,2,4]
-- @
--
-- When streams with more than one element are merged, it yields whichever
-- stream yields first without any bias, unlike the 'Async' style streams.
--
-- Any exceptions generated by a constituent stream are propagated to the
-- output stream. The output and exceptions from a single stream are guaranteed
-- to arrive in the same order in the resulting stream as they were generated
-- in the input stream. However, the relative ordering of elements from
-- different streams in the resulting stream can vary depending on scheduling
-- and generation delays.
--
-- Similarly, the 'Monad' instance of 'ParallelT' runs /all/ iterations
-- of the loop concurrently.
--
-- @
-- import "Streamly"
-- import qualified "Streamly.Prelude" as S
-- import Control.Concurrent
--
-- main = 'drain' . 'parallely' $ do
--     n <- return 3 \<\> return 2 \<\> return 1
--     S.yieldM $ do
--          threadDelay (n * 1000000)
--          myThreadId >>= \\tid -> putStrLn (show tid ++ ": Delay " ++ show n)
-- @
-- @
-- ThreadId 40: Delay 1
-- ThreadId 39: Delay 2
-- ThreadId 38: Delay 3
-- @
--
-- Note that parallel composition can only combine a finite number of
-- streams as it needs to retain state for each unfinished stream.
--
-- /Since: 0.7.0 (maxBuffer applies to ParallelT streams)/
--
-- /Since: 0.1.0/
newtype ParallelT m a = ParallelT {getParallelT :: Stream m a}
    deriving (MonadTrans)

-- | A parallely composing IO stream of elements of type @a@.
-- See 'ParallelT' documentation for more details.
--
-- @since 0.2.0
type Parallel = ParallelT IO

-- | Fix the type of a polymorphic stream as 'ParallelT'.
--
-- @since 0.1.0
parallely :: IsStream t => ParallelT m a -> t m a
parallely = adapt

instance IsStream ParallelT where
    toStream = getParallelT
    fromStream = ParallelT

    {-# INLINE consM #-}
    {-# SPECIALIZE consM :: IO a -> ParallelT IO a -> ParallelT IO a #-}
    consM = consMParallel

    {-# INLINE (|:) #-}
    {-# SPECIALIZE (|:) :: IO a -> ParallelT IO a -> ParallelT IO a #-}
    (|:) = consM

------------------------------------------------------------------------------
-- Semigroup
------------------------------------------------------------------------------

{-# INLINE mappendParallel #-}
{-# SPECIALIZE mappendParallel :: ParallelT IO a -> ParallelT IO a -> ParallelT IO a #-}
mappendParallel :: MonadAsync m => ParallelT m a -> ParallelT m a -> ParallelT m a
mappendParallel m1 m2 = fromStream $ parallel (toStream m1) (toStream m2)

instance MonadAsync m => Semigroup (ParallelT m a) where
    (<>) = mappendParallel

------------------------------------------------------------------------------
-- Monoid
------------------------------------------------------------------------------

instance MonadAsync m => Monoid (ParallelT m a) where
    mempty = K.nil
    mappend = (<>)

------------------------------------------------------------------------------
-- Applicative
------------------------------------------------------------------------------

{-# INLINE apParallel #-}
{-# SPECIALIZE apParallel :: ParallelT IO (a -> b) -> ParallelT IO a -> ParallelT IO b #-}
apParallel :: MonadAsync m => ParallelT m (a -> b) -> ParallelT m a -> ParallelT m b
apParallel (ParallelT m1) (ParallelT m2) =
    let f x1 = K.concatMapBy parallel (pure . x1) m2
    in ParallelT $ K.concatMapBy parallel f m1

instance (Monad m, MonadAsync m) => Applicative (ParallelT m) where
    {-# INLINE pure #-}
    pure = ParallelT . K.yield
    {-# INLINE (<*>) #-}
    (<*>) = apParallel

------------------------------------------------------------------------------
-- Monad
------------------------------------------------------------------------------

{-# INLINE bindParallel #-}
{-# SPECIALIZE bindParallel :: ParallelT IO a -> (a -> ParallelT IO b) -> ParallelT IO b #-}
bindParallel :: MonadAsync m => ParallelT m a -> (a -> ParallelT m b) -> ParallelT m b
bindParallel m f = fromStream $ K.bindWith parallel (K.adapt m) (\a -> K.adapt $ f a)

instance MonadAsync m => Monad (ParallelT m) where
    return = pure
    (>>=) = bindParallel

------------------------------------------------------------------------------
-- Other instances
------------------------------------------------------------------------------

MONAD_COMMON_INSTANCES(ParallelT, MONADPARALLEL)