Streamly.Internal.Data.Stream.Concurrent

Non-parallelizable stream combinators like unfoldrM, iterateM etc. can be evaluated concurrently with the stream consumer by using eval. Parallelizable combinators like repeatM, replicateM can generate the stream concurrently using concatMap.

Imports

Imports for example snippets in this module.

>>> :m
>>> {-# LANGUAGE FlexibleContexts #-}
>>> import Control.Concurrent (threadDelay)
>>> import qualified Streamly.Data.Array as Array
>>> import qualified Streamly.Data.Fold as Fold
>>> import qualified Streamly.Data.Parser as Parser
>>> import qualified Streamly.Internal.Data.Stream as Stream hiding (append2)
>>> import qualified Streamly.Internal.Data.Stream.Concurrent as Stream
>>> import Prelude hiding (concatMap, concat, zipWith)
>>> :{
 delay n = do
     threadDelay (n * 1000000)   -- sleep for n seconds
     putStrLn (show n ++ " sec") -- print "n sec"
     return n                    -- IO Int
:}

Types

type MonadAsync m = (MonadIO m, MonadBaseControl IO m, MonadThrow m)

A monad that can perform concurrent or parallel IO operations. Streams that can be composed concurrently require the underlying monad to be MonadAsync.

Configuration

data Config

An abstract type for specifying the configuration parameters of a Channel. Use Config -> Config modifier functions to modify the default configuration. See the individual modifier documentation for default values.

maxThreads :: Int -> Config -> Config

Specify the maximum number of threads that can be spawned by the channel. A value of 0 resets the thread limit to default, a negative value means there is no limit. The default value is 1500.

When the actions in a stream are IO bound, having blocking IO calls, this option can be used to control the maximum number of in-flight IO requests. When the actions are CPU bound this option can be used to control the amount of CPU used by the stream.

maxBuffer :: Int -> Config -> Config

Specify the maximum size of the buffer for storing the results from concurrent computations. If the buffer becomes full we stop spawning more concurrent tasks until there is space in the buffer. A value of 0 resets the buffer size to default, a negative value means there is no limit. The default value is 1500.

CAUTION! using an unbounded maxBuffer value (i.e. a negative value) coupled with an unbounded maxThreads value is a recipe for disaster in presence of infinite streams, or very large streams. Especially, it must not be used when pure is used in ZipAsyncM streams as pure in applicative zip streams generates an infinite stream causing unbounded concurrent generation with no limit on the buffer or threads.

eager :: Bool -> Config -> Config

By default, processing of output from the worker threads is given priority over dispatching new workers. More workers are dispatched only when there is no output to process. With eager on workers are dispatched aggresively as long as there is more work to do irrespective of whether there is output pending to be processed. However, dispatching may stop if maxThreads or maxBuffer is reached.

Note: This option has no effect when rate has been specified.

Note: Not supported with interleaved.

data StopWhen

Specify when the Channel should stop.

Constructors

FirstStops

Stop when the first stream ends.

AllStop

Stop when all the streams end.

AnyStops

Stop when any one stream ends.

stopWhen :: StopWhen -> Config -> Config

Specify when the Channel should stop.

ordered :: Bool -> Config -> Config

When enabled the streams may be evaluated cocnurrently but the results are produced in the same sequence as a serial evaluation would produce.

Note: Not supported with interleaved.

interleaved :: Bool -> Config -> Config

Interleave the streams fairly instead of prioritizing the left stream. This schedules all streams in a round robin fashion over limited number of threads.

Note: Can only be used on finite number of streams.

Note: Not supported with ordered.

data Rate

Specifies the stream yield rate in yields per second (Hertz). We keep accumulating yield credits at rateGoal. At any point of time we allow only as many yields as we have accumulated as per rateGoal since the start of time. If the consumer or the producer is slower or faster, the actual rate may fall behind or exceed rateGoal. We try to recover the gap between the two by increasing or decreasing the pull rate from the producer. However, if the gap becomes more than rateBuffer we try to recover only as much as rateBuffer.

rateLow puts a bound on how low the instantaneous rate can go when recovering the rate gap. In other words, it determines the maximum yield latency. Similarly, rateHigh puts a bound on how high the instantaneous rate can go when recovering the rate gap. In other words, it determines the minimum yield latency. We reduce the latency by increasing concurrency, therefore we can say that it puts an upper bound on concurrency.

If the rateGoal is 0 or negative the stream never yields a value. If the rateBuffer is 0 or negative we do not attempt to recover.

Constructors

Rate

Fields

• rateLow :: Double

  The lower rate limit

• rateGoal :: Double

  The target rate we want to achieve

• rateHigh :: Double

  The upper rate limit

• rateBuffer :: Int

  Maximum slack from the goal

rate :: Maybe Rate -> Config -> Config

Specify the stream evaluation rate of a channel.

A Nothing value means there is no smart rate control, concurrent execution blocks only if maxThreads or maxBuffer is reached, or there are no more concurrent tasks to execute. This is the default.

When rate (throughput) is specified, concurrent production may be ramped up or down automatically to achieve the specified stream throughput. The specific behavior for different styles of Rate specifications is documented under Rate. The effective maximum production rate achieved by a channel is governed by:

• The maxThreads limit
• The maxBuffer limit
• The maximum rate that the stream producer can achieve
• The maximum rate that the stream consumer can achieve

Maximum production rate is given by:

\(rate = \frac{maxThreads}{latency}\)

If we know the average latency of the tasks we can set maxThreads accordingly.

avgRate :: Double -> Config -> Config

Same as rate (Just $ Rate (r/2) r (2*r) maxBound)

Specifies the average production rate of a stream in number of yields per second (i.e. Hertz). Concurrent production is ramped up or down automatically to achieve the specified average yield rate. The rate can go down to half of the specified rate on the lower side and double of the specified rate on the higher side.

minRate :: Double -> Config -> Config

Same as rate (Just $ Rate r r (2*r) maxBound)

Specifies the minimum rate at which the stream should yield values. As far as possible the yield rate would never be allowed to go below the specified rate, even though it may possibly go above it at times, the upper limit is double of the specified rate.

maxRate :: Double -> Config -> Config

Same as rate (Just $ Rate (r/2) r r maxBound)

Specifies the maximum rate at which the stream should yield values. As far as possible the yield rate would never be allowed to go above the specified rate, even though it may possibly go below it at times, the lower limit is half of the specified rate. This can be useful in applications where certain resource usage must not be allowed to go beyond certain limits.

constRate :: Double -> Config -> Config

Same as rate (Just $ Rate r r r 0)

Specifies a constant yield rate. If for some reason the actual rate goes above or below the specified rate we do not try to recover it by increasing or decreasing the rate in future. This can be useful in applications like graphics frame refresh where we need to maintain a constant refresh rate.

inspect :: Bool -> Config -> Config

Print debug information about the Channel when the stream ends.

Combinators

Stream combinators using a concurrent channel

Evaluate

Evaluates a stream concurrently using a channel.

parEval :: MonadAsync m => (Config -> Config) -> Stream m a -> Stream m a

Evaluate a stream asynchronously. In a serial stream, each element of the stream is generated as it is demanded by the consumer. parEval evaluates multiple elements of the stream ahead of time and serves the results from a buffer.

Note that the evaluation requires only one thread as only one stream needs to be evaluated. Therefore, the concurrency options that are relevant to multiple streams won't apply here e.g. maxThreads, eager, interleaved, ordered, stopWhen options won't have any effect.

Generate

Uses a single channel to evaluate all actions.

parRepeatM :: MonadAsync m => (Config -> Config) -> m a -> Stream m a

Definition:

>>> parRepeatM cfg = Stream.parSequence cfg . Stream.repeat

Generate a stream by repeatedly executing a monadic action forever.

parReplicateM :: MonadAsync m => (Config -> Config) -> Int -> m a -> Stream m a

Generate a stream by concurrently performing a monadic action n times.

Definition:

>>> parReplicateM cfg n = Stream.parSequence cfg . Stream.replicate n

Example, parReplicateM in the following example executes all the replicated actions concurrently, thus taking only 1 second:

>>> Stream.fold Fold.drain $ Stream.parReplicateM id 10 $ delay 1
...

Map

Uses a single channel to evaluate all actions.

parMapM :: MonadAsync m => (Config -> Config) -> (a -> m b) -> Stream m a -> Stream m b

Definition:

>>> parMapM modifier f = Stream.parConcatMap modifier (Stream.fromEffect . f)

Example, the following example finishes in 1 second as all actions run in parallel. Even though results are available out of order they are ordered due to the config option::

>>> f x = delay x >> return x
>>> Stream.fold Fold.toList $ Stream.parMapM (Stream.ordered True) f $ Stream.fromList [3,2,1]
1 sec
2 sec
3 sec
[3,2,1]

parSequence :: MonadAsync m => (Config -> Config) -> Stream m (m a) -> Stream m a

>>> parSequence modifier = Stream.parMapM modifier id

Combine two

Use a channel for each pair.

parTwo :: MonadAsync m => (Config -> Config) -> Stream m a -> Stream m a -> Stream m a

Binary operation to evaluate two streams concurrently using a channel.

If you want to combine more than two streams you almost always want the parList or parConcat operation instead. The performance of this operation degrades rapidly when more streams are combined as each operation adds one more concurrent channel. On the other hand, parConcat uses a single channel for all streams. However, with this operation you can precisely control the scheduling by creating arbitrary shape expression trees.

Definition:

>>> parTwo cfg x y = Stream.parList cfg [x, y]

Example, the following code finishes in 4 seconds:

>>> async = Stream.parTwo id
>>> stream1 = Stream.fromEffect (delay 4)
>>> stream2 = Stream.fromEffect (delay 2)
>>> Stream.fold Fold.toList $ stream1 `async` stream2
2 sec
4 sec
[2,4]

parZipWithM :: MonadAsync m => (Config -> Config) -> (a -> b -> m c) -> Stream m a -> Stream m b -> Stream m c

Evaluates the streams being zipped in separate threads than the consumer. The zip function is evaluated in the consumer thread.

>>> parZipWithM cfg f m1 m2 = Stream.zipWithM f (Stream.parEval cfg m1) (Stream.parEval cfg m2)

Multi-stream concurrency options won't apply here, see the notes in parEval.

If you want to evaluate the zip function as well in a separate thread, you can use a parEval on parZipWithM.

parZipWith :: MonadAsync m => (Config -> Config) -> (a -> b -> c) -> Stream m a -> Stream m b -> Stream m c

>>> parZipWith cfg f = Stream.parZipWithM cfg (\a b -> return $ f a b)

>>> m1 = Stream.fromList [1,2,3]
>>> m2 = Stream.fromList [4,5,6]
>>> Stream.fold Fold.toList $ Stream.parZipWith id (,) m1 m2
[(1,4),(2,5),(3,6)]

parMergeByM :: MonadAsync m => (Config -> Config) -> (a -> a -> m Ordering) -> Stream m a -> Stream m a -> Stream m a

Like mergeByM but evaluates both the streams concurrently.

Definition:

>>> parMergeByM cfg f m1 m2 = Stream.mergeByM f (Stream.parEval cfg m1) (Stream.parEval cfg m2)

parMergeBy :: MonadAsync m => (Config -> Config) -> (a -> a -> Ordering) -> Stream m a -> Stream m a -> Stream m a

Like mergeBy but evaluates both the streams concurrently.

Definition:

>>> parMergeBy cfg f = Stream.parMergeByM cfg (\a b -> return $ f a b)

List of streams

Shares a single channel across many streams.

parListLazy :: MonadAsync m => [Stream m a] -> Stream m a

Like concat but works on a list of streams.

>>> parListLazy = Stream.parList id

parListOrdered :: MonadAsync m => [Stream m a] -> Stream m a

Like parListLazy but with ordered on.

>>> parListOrdered = Stream.parList (Stream.ordered True)

parListInterleaved :: MonadAsync m => [Stream m a] -> Stream m a

Like parListLazy but interleaves the streams fairly instead of prioritizing the left stream. This schedules all streams in a round robin fashion over limited number of threads.

>>> parListInterleaved = Stream.parList (Stream.interleaved True)

parListEager :: MonadAsync m => [Stream m a] -> Stream m a

Like parListLazy but with eager on.

>>> parListEager = Stream.parList (Stream.eager True)

parListEagerFst :: MonadAsync m => [Stream m a] -> Stream m a

Like parListEager but stops the output as soon as the first stream stops.

>>> parListEagerFst = Stream.parList (Stream.eager True . Stream.stopWhen Stream.FirstStops)

parListEagerMin :: MonadAsync m => [Stream m a] -> Stream m a

Like parListEager but stops the output as soon as any of the two streams stops.

Definition:

>>> parListEagerMin = Stream.parList (Stream.eager True . Stream.stopWhen Stream.AnyStops)

parList :: MonadAsync m => (Config -> Config) -> [Stream m a] -> Stream m a

Like parConcat but works on a list of streams.

>>> parList modifier = Stream.parConcat modifier . Stream.fromList

Stream of streams

Apply

parApply :: MonadAsync m => (Config -> Config) -> Stream m (a -> b) -> Stream m a -> Stream m b

Apply an argument stream to a function stream concurrently. Uses a shared channel for all individual applications within a stream application.

Concat

Shares a single channel across many streams.

parConcat :: MonadAsync m => (Config -> Config) -> Stream m (Stream m a) -> Stream m a

Evaluate the streams in the input stream concurrently and combine them.

>>> parConcat modifier = Stream.parConcatMap modifier id

parConcatMap :: MonadAsync m => (Config -> Config) -> (a -> Stream m b) -> Stream m a -> Stream m b

Map each element of the input to a stream and then concurrently evaluate and concatenate the resulting streams. Multiple streams may be evaluated concurrently but earlier streams are perferred. Output from the streams are used as they arrive.

Definition:

>>> parConcatMap modifier f stream = Stream.parConcat modifier $ fmap f stream

Examples:

>>> f cfg xs = Stream.fold Fold.toList $ Stream.parConcatMap cfg id $ Stream.fromList xs

The following streams finish in 4 seconds:

>>> stream1 = Stream.fromEffect (delay 4)
>>> stream2 = Stream.fromEffect (delay 2)
>>> stream3 = Stream.fromEffect (delay 1)
>>> f id [stream1, stream2, stream3]
1 sec
2 sec
4 sec
[1,2,4]

Limiting threads to 2 schedules the third stream only after one of the first two has finished, releasing a thread:

>>> f (Stream.maxThreads 2) [stream1, stream2, stream3]
...
[2,1,4]

When used with a Single thread it behaves like serial concatMap:

>>> f (Stream.maxThreads 1) [stream1, stream2, stream3]
...
[4,2,1]

>>> stream1 = Stream.fromList [1,2,3]
>>> stream2 = Stream.fromList [4,5,6]
>>> f (Stream.maxThreads 1) [stream1, stream2]
[1,2,3,4,5,6]

Schedule all streams in a round robin fashion over the available threads:

>>> f cfg xs = Stream.fold Fold.toList $ Stream.parConcatMap (Stream.interleaved True . cfg) id $ Stream.fromList xs

>>> stream1 = Stream.fromList [1,2,3]
>>> stream2 = Stream.fromList [4,5,6]
>>> f (Stream.maxThreads 1) [stream1, stream2]
[1,4,2,5,3,6]

ConcatIterate

parConcatIterate :: MonadAsync m => (Config -> Config) -> (a -> Stream m a) -> Stream m a -> Stream m a

Same as concatIterate but concurrent.

Pre-release

Reactive

fromCallback :: MonadAsync m => ((a -> m ()) -> m ()) -> Stream m a

Supplies a stream generating callback to a callback setter function. Each invocation of the callback results in a value being generated in the resulting stream.

Pre-release

tapCountD :: MonadAsync m => (a -> Bool) -> (Stream m Int -> m b) -> Stream m a -> Stream m a

tapCount :: MonadAsync m => (a -> Bool) -> (Stream m Int -> m b) -> Stream m a -> Stream m a

tapCount predicate fold stream taps the count of those elements in the stream that pass the predicate. The resulting count stream is sent to another thread which folds it using fold.

For example, to print the count of elements processed every second:

>>> rate = Stream.rollingMap2 (flip (-)) . Stream.delayPost 1
>>> report = Stream.fold (Fold.drainMapM print) . rate
>>> tap = Stream.tapCount (const True) report
>>> go = Stream.fold Fold.drain $ tap $ Stream.enumerateFrom 0

Note: This may not work correctly on 32-bit machines because of Int overflow.

Pre-release