An empty stream.

> toList nil
[]

Since: 0.1.0

Construct a stream by adding a pure value at the head of an existing stream. For serial streams this is the same as (return a) `consM` r but more efficient. For concurrent streams this is not concurrent whereas consM is concurrent. For example:

> toList $ 1 `cons` 2 `cons` 3 `cons` nil
[1,2,3]

Since: 0.1.0

Operator equivalent of cons.

> toList $ 1 .: 2 .: 3 .: nil
[1,2,3]

Since: 0.1.1

Constructs a stream by adding a monadic action at the head of an existing stream. For example:

> toList $ getLine `consM` getLine `consM` nil
hello
world
["hello","world"]

Concurrent (do not use fromParallel to construct infinite streams)

Since: 0.2.0

Operator equivalent of consM. We can read it as "parallel colon" to remember that | comes before :.

> toList $ getLine |: getLine |: nil
hello
world
["hello","world"]

let delay = threadDelay 1000000 >> print 1
drain $ fromSerial  $ delay |: delay |: delay |: nil
drain $ fromParallel $ delay |: delay |: delay |: nil

Concurrent (do not use fromParallel to construct infinite streams)

Since: 0.2.0

Convert an Unfold into a stream by supplying it an input seed.

>>> Stream.drain $ Stream.unfold (Unfold.replicateM 3) (putStrLn "hello")
hello
hello
hello

Since: 0.7.0

unfoldr step s =
    case step s of
        Nothing -> nil
        Just (a, b) -> a `cons` unfoldr step b

Build a stream by unfolding a pure step function step starting from a seed s. The step function returns the next element in the stream and the next seed value. When it is done it returns Nothing and the stream ends. For example,

>>> :{
let f b =
        if b > 3
        then Nothing
        else Just (b, b + 1)
in Stream.toList $ Stream.unfoldr f 0
:}
[0,1,2,3]

Since: 0.1.0

Build a stream by unfolding a monadic step function starting from a seed. The step function returns the next element in the stream and the next seed value. When it is done it returns Nothing and the stream ends. For example,

>>> :{
let f b =
        if b > 3
        then return Nothing
        else return (Just (b, b + 1))
in Stream.toList $ Stream.unfoldrM f 0
:}
[0,1,2,3]

When run concurrently, the next unfold step can run concurrently with the processing of the output of the previous step. Note that more than one step cannot run concurrently as the next step depends on the output of the previous step.

(fromAsync $ S.unfoldrM (\n -> liftIO (threadDelay 1000000) >> return (Just (n, n + 1))) 0)
    & S.foldlM' (\_ a -> threadDelay 1000000 >> print a) ()

Concurrent

Since: 0.1.0

fromPure a = a `cons` nil

Create a singleton stream from a pure value.

The following holds in monadic streams, but not in Zip streams:

fromPure = pure
fromPure = fromEffect . pure

In Zip applicative streams fromPure is not the same as pure because in that case pure is equivalent to repeat instead. fromPure and pure are equally efficient, in other cases fromPure may be slightly more efficient than the other equivalent definitions.

Since: 0.8.0 (Renamed yield to fromPure)

fromEffect m = m `consM` nil

Create a singleton stream from a monadic action.

> Stream.toList $ Stream.fromEffect getLine
hello
["hello"]

Since: 0.8.0 (Renamed yieldM to fromEffect)

Generate an infinite stream by repeating a pure value.

Since: 0.4.0

repeatM = fix . consM
repeatM = cycle1 . fromEffect

Generate a stream by repeatedly executing a monadic action forever.

drain $ fromSerial $ S.take 10 $ S.repeatM $ (threadDelay 1000000 >> print 1)
drain $ fromAsync  $ S.take 10 $ S.repeatM $ (threadDelay 1000000 >> print 1)

Concurrent, infinite (do not use with fromParallel)

Since: 0.2.0

replicate = take n . repeat

Generate a stream of length n by repeating a value n times.

Since: 0.6.0

replicateM = take n . repeatM

Generate a stream by performing a monadic action n times. Same as:

drain $ fromSerial $ S.replicateM 10 $ (threadDelay 1000000 >> print 1)
drain $ fromAsync  $ S.replicateM 10 $ (threadDelay 1000000 >> print 1)

Concurrent

Since: 0.1.1

Types that can be enumerated as a stream. The operations in this type class are equivalent to those in the Enum type class, except that these generate a stream instead of a list. Use the functions in Streamly.Internal.Data.Stream.Enumeration module to define new instances.

Since: 0.6.0

enumerate = enumerateFrom minBound

Enumerate a Bounded type from its minBound to maxBound

Since: 0.6.0

enumerateTo = enumerateFromTo minBound

Enumerate a Bounded type from its minBound to specified value.

Since: 0.6.0

iterate f x = x `cons` iterate f x

Generate an infinite stream with x as the first element and each successive element derived by applying the function f on the previous element.

>>> Stream.toList $ Stream.take 5 $ Stream.iterate (+1) 1
[1,2,3,4,5]

Since: 0.1.2

iterateM f m = m >>= a -> return a `consM` iterateM f (f a)

Generate an infinite stream with the first element generated by the action m and each successive element derived by applying the monadic function f on the previous element.

When run concurrently, the next iteration can run concurrently with the processing of the previous iteration. Note that more than one iteration cannot run concurrently as the next iteration depends on the output of the previous iteration.

drain $ fromSerial $ S.take 10 $ S.iterateM
     (\x -> threadDelay 1000000 >> print x >> return (x + 1)) (return 0)

drain $ fromAsync  $ S.take 10 $ S.iterateM
     (\x -> threadDelay 1000000 >> print x >> return (x + 1)) (return 0)

Concurrent

Since: 0.1.2

Since: 0.7.0 (signature change)

fromIndices f = fmap f $ Stream.enumerateFrom 0
fromIndices f = let g i = f i `cons` g (i + 1) in g 0

Generate an infinite stream, whose values are the output of a function f applied on the corresponding index. Index starts at 0.

>>> Stream.toList $ Stream.take 5 $ Stream.fromIndices id
[0,1,2,3,4]

Since: 0.6.0

fromIndicesM f = Stream.mapM f $ Stream.enumerateFrom 0
fromIndicesM f = let g i = f i `consM` g (i + 1) in g 0

Generate an infinite stream, whose values are the output of a monadic function f applied on the corresponding index. Index starts at 0.

Concurrent

Since: 0.6.0

fromList = foldr cons nil

Construct a stream from a list of pure values. This is more efficient than fromFoldable for serial streams.

Since: 0.4.0

fromListM = foldr consM nil

Construct a stream from a list of monadic actions. This is more efficient than fromFoldableM for serial streams.

Since: 0.4.0

fromFoldable = foldr cons nil

Construct a stream from a Foldable containing pure values:

Since: 0.2.0

fromFoldableM = foldr consM nil

Construct a stream from a Foldable containing monadic actions.

drain $ fromSerial $ S.fromFoldableM $ replicateM 10 (threadDelay 1000000 >> print 1)
drain $ fromAsync  $ S.fromFoldableM $ replicateM 10 (threadDelay 1000000 >> print 1)

Concurrent (do not use with fromParallel on infinite containers)

Since: 0.3.0

Fold a stream using the supplied left Fold and reducing the resulting expression strictly at each step. The behavior is similar to foldl'. A Fold can terminate early without consuming the full stream. See the documentation of individual Folds for termination behavior.

>>> Stream.fold Fold.sum (Stream.enumerateFromTo 1 100)
5050

Folds never fail, therefore, they produce a default value even when no input is provided. It means we can always fold an empty stream and get a valid result. For example:

>>> Stream.fold Fold.sum Stream.nil
0

However, foldMany on an empty stream results in an empty stream. Therefore, Stream.fold f is not the same as Stream.head . Stream.foldMany f.

fold f = Stream.parse (Parser.fromFold f)

Since: 0.7.0

Decompose a stream into its head and tail. If the stream is empty, returns Nothing. If the stream is non-empty, returns Just (a, ma), where a is the head of the stream and ma its tail.

This is a brute force primitive. Avoid using it as long as possible, use it when no other combinator can do the job. This can be used to do pretty much anything in an imperative manner, as it just breaks down the stream into individual elements and we can loop over them as we deem fit. For example, this can be used to convert a streamly stream into other stream types.

All the folds in this module can be expressed in terms of uncons, however the specific implementations are generally more efficient.

Since: 0.1.0

tail = fmap (fmap snd) . Stream.uncons

Extract all but the first element of the stream, if any.

Since: 0.1.1

Extract all but the last element of the stream, if any.

Since: 0.5.0

Right associative/lazy pull fold. foldrM build final stream constructs an output structure using the step function build. build is invoked with the next input element and the remaining (lazy) tail of the output structure. It builds a lazy output expression using the two. When the "tail structure" in the output expression is evaluated it calls build again thus lazily consuming the input stream until either the output expression built by build is free of the "tail" or the input is exhausted in which case final is used as the terminating case for the output structure. For more details see the description in the previous section.

Example, determine if any element is odd in a stream:

>>> Stream.foldrM (\x xs -> if odd x then return True else xs) (return False) $ Stream.fromList (2:4:5:undefined)
True

Since: 0.7.0 (signature changed)

Since: 0.2.0 (signature changed)

Since: 0.1.0

Right fold, lazy for lazy monads and pure streams, and strict for strict monads.

Please avoid using this routine in strict monads like IO unless you need a strict right fold. This is provided only for use in lazy monads (e.g. Identity) or pure streams. Note that with this signature it is not possible to implement a lazy foldr when the monad m is strict. In that case it would be strict in its accumulator and therefore would necessarily consume all its input.

Since: 0.1.0

Left associative/strict push fold. foldl' reduce initial stream invokes reduce with the accumulator and the next input in the input stream, using initial as the initial value of the current value of the accumulator. When the input is exhausted the current value of the accumulator is returned. Make sure to use a strict data structure for accumulator to not build unnecessary lazy expressions unless that's what you want. See the previous section for more details.

Since: 0.2.0

Strict left fold, for non-empty streams, using first element as the starting value. Returns Nothing if the stream is empty.

Since: 0.5.0

Like foldl' but with a monadic step function.

Since: 0.2.0

Since: 0.8.0 (signature change)

drain = mapM_ (\_ -> return ())
drain = Stream.fold Fold.drain

Run a stream, discarding the results. By default it interprets the stream as SerialT, to run other types of streams use the type adapting combinators for example Stream.drain . fromAsync.

Since: 0.7.0

Extract the last element of the stream, if any.

last xs = xs !! (Stream.length xs - 1)
last = Stream.fold Fold.last

Since: 0.1.1

Determine the length of the stream.

Since: 0.1.0

Determine the sum of all elements of a stream of numbers. Returns 0 when the stream is empty. Note that this is not numerically stable for floating point numbers.

sum = Stream.fold Fold.sum

Since: 0.1.0

Determine the product of all elements of a stream of numbers. Returns 1 when the stream is empty.

product = Stream.fold Fold.product

Since: 0.1.1

Determine the maximum element in a stream using the supplied comparison function.

maximumBy = Stream.fold Fold.maximumBy

Since: 0.6.0

maximum = maximumBy compare
maximum = Stream.fold Fold.maximum

Determine the maximum element in a stream.

Since: 0.1.0

Determine the minimum element in a stream using the supplied comparison function.

minimumBy = Stream.fold Fold.minimumBy

Since: 0.6.0

minimum = minimumBy compare
minimum = Stream.fold Fold.minimum

Determine the minimum element in a stream.

Since: 0.1.0

Ensures that all the elements of the stream are identical and then returns that unique element.

Since: 0.6.0

drainN n = Stream.drain . Stream.take n
drainN n = Stream.fold (Fold.take n Fold.drain)

Run maximum up to n iterations of a stream.

Since: 0.7.0

drainWhile p = Stream.drain . Stream.takeWhile p

Run a stream as long as the predicate holds true.

Since: 0.7.0

Lookup the element at the given index.

Since: 0.6.0

Extract the first element of the stream, if any.

head = (!! 0)
head = Stream.fold Fold.head

Since: 0.1.0

Returns the first element that satisfies the given predicate.

findM = Stream.fold Fold.findM

Since: 0.6.0

Like findM but with a non-monadic predicate.

find p = findM (return . p)
find = Stream.fold Fold.find

Since: 0.5.0

In a stream of (key-value) pairs (a, b), return the value b of the first pair where the key equals the given value a.

lookup = snd <$> Stream.find ((==) . fst)
lookup = Stream.fold Fold.lookup

Since: 0.5.0

Returns the first index that satisfies the given predicate.

findIndex = Stream.fold Fold.findIndex

Since: 0.5.0

Returns the first index where a given value is found in the stream.

elemIndex a = Stream.findIndex (== a)

Since: 0.5.0

Determine whether the stream is empty.

null = Stream.fold Fold.null

Since: 0.1.1

Determine whether an element is present in the stream.

elem = Stream.fold Fold.elem

Since: 0.1.0

Determine whether an element is not present in the stream.

notElem = Stream.fold Fold.length

Since: 0.1.0

Determine whether all elements of a stream satisfy a predicate.

all = Stream.fold Fold.all

Since: 0.1.0

Determine whether any of the elements of a stream satisfy a predicate.

any = Stream.fold Fold.any

Since: 0.1.0

Determines if all elements of a boolean stream are True.

and = Stream.fold Fold.and

Since: 0.5.0

Determines whether at least one element of a boolean stream is True.

or = Stream.fold Fold.or

Since: 0.5.0

toList = Stream.foldr (:) []

Convert a stream into a list in the underlying monad. The list can be consumed lazily in a lazy monad (e.g. Identity). In a strict monad (e.g. IO) the whole list is generated and buffered before it can be consumed.

Warning! working on large lists accumulated as buffers in memory could be very inefficient, consider using Streamly.Array instead.

Since: 0.1.0

Parallel fold application operator; applies a fold function t m a -> m b to a stream t m a concurrently; The the input stream is evaluated asynchronously in an independent thread yielding elements to a buffer and the folding action runs in another thread consuming the input from the buffer.

If you read the signature as (t m a -> m b) -> (t m a -> m b) you can look at it as a transformation that converts a fold function to a buffered concurrent fold function.

The . at the end of the operator is a mnemonic for termination of the stream.

In the example below, each stage introduces a delay of 1 sec but output is printed every second because both stages are concurrent.

>>> import Control.Concurrent (threadDelay)
>>> import Streamly.Prelude ((|$.))
>>> :{
 Stream.foldlM' (\_ a -> threadDelay 1000000 >> print a) (return ())
     |$. Stream.replicateM 3 (threadDelay 1000000 >> return 1)
:}
1
1
1

Concurrent

Since: 0.3.0 (Streamly)

Since: 0.8.0

Same as |$. but with arguments reversed.

(|&.) = flip (|$.)

Concurrent

Since: 0.3.0 (Streamly)

Since: 0.8.0

Compare two streams for equality using an equality function.

Since: 0.6.0

Compare two streams lexicographically using a comparison function.

Since: 0.6.0

Returns True if the first stream is the same as or a prefix of the second. A stream is a prefix of itself.

>>> Stream.isPrefixOf (Stream.fromList "hello") (Stream.fromList "hello" :: SerialT IO Char)
True

Since: 0.6.0

Returns True if all the elements of the first stream occur, in order, in the second stream. The elements do not have to occur consecutively. A stream is a subsequence of itself.

>>> Stream.isSubsequenceOf (Stream.fromList "hlo") (Stream.fromList "hello" :: SerialT IO Char)
True

Since: 0.6.0

stripPrefix prefix stream strips prefix from stream if it is a prefix of stream. Returns Nothing if the stream does not start with the given prefix, stripped stream otherwise. Returns Just nil when the prefix is the same as the stream.

See also "Streamly.Internal.Data.Stream.IsStream.Nesting.dropPrefix".

Space: O(1)

Since: 0.6.0

map = fmap

Same as fmap.

> S.toList $ S.map (+1) $ S.fromList [1,2,3]
[2,3,4]

Since: 0.4.0

sequence = mapM id

Replace the elements of a stream of monadic actions with the outputs of those actions.

>>> drain $ Stream.sequence $ Stream.fromList [putStr "a", putStr "b", putStrLn "c"]
abc

>>> :{
drain $ Stream.replicateM 10 (return $ threadDelay 1000000 >> print 1)
 & (fromSerial . Stream.sequence)
:}
1
...
1

>>> :{
drain $ Stream.replicateM 10 (return $ threadDelay 1000000 >> print 1)
 & (fromAsync . Stream.sequence)
:}
1
...
1

Concurrent (do not use with fromParallel on infinite streams)

Since: 0.1.0

mapM f = sequence . map f

Apply a monadic function to each element of the stream and replace it with the output of the resulting action.

>>> drain $ Stream.mapM putStr $ Stream.fromList ["a", "b", "c"]
abc

>>> :{
   drain $ Stream.replicateM 10 (return 1)
     & (fromSerial . Stream.mapM (x -> threadDelay 1000000 >> print x))
:}
1
...
1

> drain $ Stream.replicateM 10 (return 1)
 & (fromAsync . Stream.mapM (x -> threadDelay 1000000 >> print x))

Concurrent (do not use with fromParallel on infinite streams)

Since: 0.1.0

mapM_ = Stream.drain . Stream.mapM

Apply a monadic action to each element of the stream and discard the output of the action. This is not really a pure transformation operation but a transformation followed by fold.

Since: 0.1.0

Apply a monadic function to each element flowing through the stream and discard the results.

>>> Stream.drain $ Stream.trace print (Stream.enumerateFromTo 1 2)
1
2

Compare with tap.

Since: 0.7.0

Tap the data flowing through a stream into a Fold. For example, you may add a tap to log the contents flowing through the stream. The fold is used only for effects, its result is discarded.

                  Fold m a b
                      |
-----stream m a ---------------stream m a-----

>>> Stream.drain $ Stream.tap (Fold.drainBy print) (Stream.enumerateFromTo 1 2)
1
2

Compare with trace.

Since: 0.7.0

Introduce a delay of specified seconds before consuming an element of the stream except the first one.

>>> Stream.mapM_ print $ Stream.timestamped $ Stream.delay 1 $ Stream.enumerateFromTo 1 3
(AbsTime (TimeSpec {sec = ..., nsec = ...}),1)
(AbsTime (TimeSpec {sec = ..., nsec = ...}),2)
(AbsTime (TimeSpec {sec = ..., nsec = ...}),3)

Since: 0.8.0

Strict left scan. Like map, scanl' too is a one to one transformation, however it adds an extra element.

>>> Stream.toList $ Stream.scanl' (+) 0 $ fromList [1,2,3,4]
[0,1,3,6,10]

>>> Stream.toList $ Stream.scanl' (flip (:)) [] $ Stream.fromList [1,2,3,4]
[[],[1],[2,1],[3,2,1],[4,3,2,1]]

The output of scanl' is the initial value of the accumulator followed by all the intermediate steps and the final result of foldl'.

By streaming the accumulated state after each fold step, we can share the state across multiple stages of stream composition. Each stage can modify or extend the state, do some processing with it and emit it for the next stage, thus modularizing the stream processing. This can be useful in stateful or event-driven programming.

Consider the following monolithic example, computing the sum and the product of the elements in a stream in one go using a foldl':

>>> Stream.foldl' ((s, p) x -> (s + x, p * x)) (0,1) $ Stream.fromList 1,2,3,4

Using scanl' we can make it modular by computing the sum in the first stage and passing it down to the next stage for computing the product:

>>> :{
  Stream.foldl' ((_, p) (s, x) -> (s, p * x)) (0,1)
  $ Stream.scanl' ((s, _) x -> (s + x, x)) (0,1)
  $ Stream.fromList [1,2,3,4]
:}
(10,24)

IMPORTANT: scanl' evaluates the accumulator to WHNF. To avoid building lazy expressions inside the accumulator, it is recommended that a strict data structure is used for accumulator.

See also: usingStateT

Since: 0.2.0

Like scanl' but with a monadic step function and a monadic seed.

Since: 0.4.0

Since: 0.8.0 (signature change)

Like scanl' but does not stream the initial value of the accumulator.

postscanl' f z xs = Stream.drop 1 $ Stream.scanl' f z xs

Since: 0.7.0

Like postscanl' but with a monadic step function and a monadic seed.

Since: 0.7.0

Since: 0.8.0 (signature change)

Like scanl' but for a non-empty stream. The first element of the stream is used as the initial value of the accumulator. Does nothing if the stream is empty.

>>> Stream.toList $ Stream.scanl1' (+) $ fromList [1,2,3,4]
[1,3,6,10]

Since: 0.6.0

Like scanl1' but with a monadic step function.

Since: 0.6.0

Scan a stream using the given monadic fold.

>>> Stream.toList $ Stream.takeWhile (< 10) $ Stream.scan Fold.sum (Stream.fromList [1..10])
[0,1,3,6]

Since: 0.7.0

Postscan a stream using the given monadic fold.

The following example extracts the input stream up to a point where the running average of elements is no more than 10:

>>> import Data.Maybe (fromJust)
>>> let avg = Fold.teeWith (/) Fold.sum (fmap fromIntegral Fold.length)
>>> :{
 Stream.toList
  $ Stream.map (fromJust . fst)
  $ Stream.takeWhile (\(_,x) -> x <= 10)
  $ Stream.postscan (Fold.tee Fold.last avg) (Stream.enumerateFromTo 1.0 100.0)
:}
[1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0]

Since: 0.7.0

Deletes the first occurrence of the element in the stream that satisfies the given equality predicate.

>>> Stream.toList $ Stream.deleteBy (==) 3 $ Stream.fromList [1,3,3,5]
[1,3,5]

Since: 0.6.0

Include only those elements that pass a predicate.

Since: 0.1.0

Same as filter but with a monadic predicate.

Since: 0.4.0

Drop repeated elements that are adjacent to each other.

Since: 0.6.0

Take first n elements from the stream and discard the rest.

Since: 0.1.0

End the stream as soon as the predicate fails on an element.

Since: 0.1.0

Same as takeWhile but with a monadic predicate.

Since: 0.4.0

Discard first n elements from the stream and take the rest.

Since: 0.1.0

Drop elements in the stream as long as the predicate succeeds and then take the rest of the stream.

Since: 0.1.0

Same as dropWhile but with a monadic predicate.

Since: 0.4.0

insertBy cmp elem stream inserts elem before the first element in stream that is less than elem when compared using cmp.

insertBy cmp x = mergeBy cmp (fromPure x)

>>> Stream.toList $ Stream.insertBy compare 2 $ Stream.fromList [1,3,5]
[1,2,3,5]

Since: 0.6.0

Insert an effect and its output before consuming an element of a stream except the first one.

>>> Stream.toList $ Stream.trace putChar $ Stream.intersperseM (putChar '.' >> return ',') $ Stream.fromList "hello"
h.,e.,l.,l.,o"h,e,l,l,o"

Since: 0.5.0

Insert a pure value between successive elements of a stream.

>>> Stream.toList $ Stream.intersperse ',' $ Stream.fromList "hello"
"h,e,l,l,o"

Since: 0.7.0

Returns the elements of the stream in reverse order. The stream must be finite. Note that this necessarily buffers the entire stream in memory.

Since 0.7.0 (Monad m constraint)

Since: 0.1.1

indexed = Stream.postscanl' (\(i, _) x -> (i + 1, x)) (-1,undefined)
indexed = Stream.zipWith (,) (Stream.enumerateFrom 0)

Pair each element in a stream with its index, starting from index 0.

>>> Stream.toList $ Stream.indexed $ Stream.fromList "hello"
[(0,'h'),(1,'e'),(2,'l'),(3,'l'),(4,'o')]

Since: 0.6.0

indexedR n = Stream.postscanl' (\(i, _) x -> (i - 1, x)) (n + 1,undefined)
indexedR n = Stream.zipWith (,) (Stream.enumerateFromThen n (n - 1))

Pair each element in a stream with its index, starting from the given index n and counting down.

>>> Stream.toList $ Stream.indexedR 10 $ Stream.fromList "hello"
[(10,'h'),(9,'e'),(8,'l'),(7,'l'),(6,'o')]

Since: 0.6.0

Find all the indices where the element in the stream satisfies the given predicate.

findIndices = fold Fold.findIndices

Since: 0.5.0

Find all the indices where the value of the element in the stream is equal to the given value.

elemIndices a = findIndices (== a)

Since: 0.5.0

Map a Maybe returning function to a stream, filter out the Nothing elements, and return a stream of values extracted from Just.

Equivalent to:

mapMaybe f = Stream.map fromJust . Stream.filter isJust . Stream.map f

Since: 0.3.0

Like mapMaybe but maps a monadic function.

Equivalent to:

mapMaybeM f = Stream.map fromJust . Stream.filter isJust . Stream.mapM f

Concurrent (do not use with fromParallel on infinite streams)

Since: 0.3.0

Parallel transform application operator; applies a stream transformation function t m a -> t m b to a stream t m a concurrently; the input stream is evaluated asynchronously in an independent thread yielding elements to a buffer and the transformation function runs in another thread consuming the input from the buffer. |$ is just like regular function application operator $ except that it is concurrent.

If you read the signature as (t m a -> t m b) -> (t m a -> t m b) you can look at it as a transformation that converts a transform function to a buffered concurrent transform function.

The following code prints a value every second even though each stage adds a 1 second delay.

>>> :{
Stream.drain $
   Stream.mapM (\x -> threadDelay 1000000 >> print x)
     |$ Stream.replicateM 3 (threadDelay 1000000 >> return 1)
:}
1
1
1

Concurrent

Since: 0.3.0 (Streamly)

Since: 0.8.0

Same as |$ but with arguments reversed.

(|&) = flip (|$)

Concurrent

Since: 0.3.0 (Streamly)

Since: 0.8.0

Make the stream producer and consumer run concurrently by introducing a buffer between them. The producer thread evaluates the input stream until the buffer fills, it terminates if the buffer is full and a worker thread is kicked off again to evaluate the remaining stream when there is space in the buffer. The consumer consumes the stream lazily from the buffer.

Since: 0.2.0 (Streamly)

Since: 0.8.0

Specify the maximum number of threads that can be spawned concurrently for any concurrent combinator in a stream. A value of 0 resets the thread limit to default, a negative value means there is no limit. The default value is 1500. maxThreads does not affect ParallelT streams as they can use unbounded number of threads.

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.

Since: 0.4.0 (Streamly)

Since: 0.8.0

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.

Since: 0.4.0 (Streamly)

Since: 0.8.0

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.

Since: 0.5.0 (Streamly)

Since: 0.8.0

Specify the pull rate of a stream. A Nothing value resets the rate to default which is unlimited. When the rate is specified, concurrent production may be ramped up or down automatically to achieve the specified yield rate. The specific behavior for different styles of Rate specifications is documented under Rate. The effective maximum production rate achieved by a stream 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

Since: 0.5.0 (Streamly)

Since: 0.8.0

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.

Since: 0.5.0 (Streamly)

Since: 0.8.0

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.

Since: 0.5.0 (Streamly)

Since: 0.8.0

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.

Since: 0.5.0 (Streamly)

Since: 0.8.0

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.

Since: 0.5.0 (Streamly)

Since: 0.8.0

Appends two streams sequentially, yielding all elements from the first stream, and then all elements from the second stream.

>>> import Streamly.Prelude (serial)
>>> stream1 = Stream.fromList [1,2]
>>> stream2 = Stream.fromList [3,4]
>>> Stream.toList $ stream1 `serial` stream2
[1,2,3,4]

This operation can be used to fold an infinite lazy container of streams.

Since: 0.2.0 (Streamly)

Since: 0.8.0

Interleaves two streams, yielding one element from each stream alternately. When one stream stops the rest of the other stream is used in the output stream.

>>> import Streamly.Prelude (wSerial)
>>> stream1 = Stream.fromList [1,2]
>>> stream2 = Stream.fromList [3,4]
>>> Stream.toList $ Stream.fromWSerial $ stream1 `wSerial` stream2
[1,3,2,4]

Note, for singleton streams wSerial and serial are identical.

Note that this operation cannot be used to fold a container of infinite streams but it can be used for very large streams as the state that it needs to maintain is proportional to the logarithm of the number of streams.

Since: 0.2.0 (Streamly)

Since: 0.8.0

Appends two streams, both the streams may be evaluated concurrently but the outputs are used in the same order as the corresponding actions in the original streams, side effects will happen in the order in which the streams are evaluated:

>>> import Streamly.Prelude (ahead, SerialT)
>>> stream1 = Stream.fromEffect (delay 4) :: SerialT IO Int
>>> stream2 = Stream.fromEffect (delay 2) :: SerialT IO Int
>>> Stream.toList $ stream1 `ahead` stream2 :: IO [Int]
2 sec
4 sec
[4,2]

Multiple streams can be combined. With enough threads, all of them can be scheduled simultaneously:

>>> stream3 = Stream.fromEffect (delay 1)
>>> Stream.toList $ stream1 `ahead` stream2 `ahead` stream3
1 sec
2 sec
4 sec
[4,2,1]

With 2 threads, only two can be scheduled at a time, when one of those finishes, the third one gets scheduled:

>>> Stream.toList $ Stream.maxThreads 2 $ stream1 `ahead` stream2 `ahead` stream3
2 sec
1 sec
4 sec
[4,2,1]

Only streams are scheduled for ahead evaluation, how actions within a stream are evaluated depends on the stream type. If it is a concurrent stream they will be evaluated concurrently. It may not make much sense combining serial streams using ahead.

ahead can be safely used to fold an infinite lazy container of streams.

Since: 0.3.0 (Streamly)

Since: 0.8.0

Merges two streams, both the streams may be evaluated concurrently, outputs from both are used as they arrive:

>>> import Streamly.Prelude (async)
>>> stream1 = Stream.fromEffect (delay 4)
>>> stream2 = Stream.fromEffect (delay 2)
>>> Stream.toList $ stream1 `async` stream2
2 sec
4 sec
[2,4]

Multiple streams can be combined. With enough threads, all of them can be scheduled simultaneously:

>>> stream3 = Stream.fromEffect (delay 1)
>>> Stream.toList $ stream1 `async` stream2 `async` stream3
...
[1,2,4]

With 2 threads, only two can be scheduled at a time, when one of those finishes, the third one gets scheduled:

>>> Stream.toList $ Stream.maxThreads 2 $ stream1 `async` stream2 `async` stream3
...
[2,1,4]

With a single thread, it becomes serial:

>>> Stream.toList $ Stream.maxThreads 1 $ stream1 `async` stream2 `async` stream3
...
[4,2,1]

Only streams are scheduled for async evaluation, how actions within a stream are evaluated depends on the stream type. If it is a concurrent stream they will be evaluated concurrently.

In the following example, both the streams are scheduled for concurrent evaluation but each individual stream is evaluated serially:

>>> stream1 = Stream.fromListM $ Prelude.map delay [3,3] -- SerialT IO Int
>>> stream2 = Stream.fromListM $ Prelude.map delay [1,1] -- SerialT IO Int
>>> Stream.toList $ stream1 `async` stream2 -- IO [Int]
...
[1,1,3,3]

If total threads are 2, the third stream is scheduled only after one of the first two has finished:

>>> stream3 = Stream.fromListM $ Prelude.map delay [2,2] -- SerialT IO Int
>>> Stream.toList $ Stream.maxThreads 2 $ stream1 `async` stream2 `async` stream3 -- IO [Int]
...
[1,1,3,2,3,2]

Thus async goes deep in first few streams rather than going wide in all streams. It prefers to evaluate the leftmost streams as much as possible. Because of this behavior, async can be safely used to fold an infinite lazy container of streams.

Since: 0.2.0 (Streamly)

Since: 0.8.0

For singleton streams, wAsync is the same as async. See async for singleton stream behavior. For multi-element streams, while async is left biased i.e. it tries to evaluate the left side stream as much as possible, wAsync tries to schedule them both fairly. In other words, async goes deep while wAsync goes wide. However, outputs are always used as they arrive.

With a single thread, async starts behaving like serial while wAsync starts behaving like wSerial.

>>> import Streamly.Prelude (wAsync)
>>> stream1 = Stream.fromList [1,2,3]
>>> stream2 = Stream.fromList [4,5,6]
>>> Stream.toList $ Stream.fromAsync $ Stream.maxThreads 1 $ stream1 `async` stream2
[1,2,3,4,5,6]

>>> Stream.toList $ Stream.fromWAsync $ Stream.maxThreads 1 $ stream1 `wAsync` stream2
[1,4,2,5,3,6]

With two threads available, and combining three streams:

>>> stream3 = Stream.fromList [7,8,9]
>>> Stream.toList $ Stream.fromAsync $ Stream.maxThreads 2 $ stream1 `async` stream2 `async` stream3
[1,2,3,4,5,6,7,8,9]

>>> Stream.toList $ Stream.fromWAsync $ Stream.maxThreads 2 $ stream1 `wAsync` stream2 `wAsync` stream3
[1,4,2,7,5,3,8,6,9]

This operation cannot be used to fold an infinite lazy container of streams, because it schedules all the streams in a round robin manner.

Note that WSerialT and single threaded WAsyncT both interleave streams but the exact scheduling is slightly different in both cases.

Since: 0.2.0 (Streamly)

Since: 0.8.0

Like async except that the execution is much more strict. There is no limit on the number of threads. While async may not schedule a stream if there is no demand from the consumer, parallel always evaluates both the streams immediately. The only limit that applies to parallel is maxBuffer. Evaluation may block if the output buffer becomes full.

>>> import Streamly.Prelude (parallel)
>>> stream = Stream.fromEffect (delay 2) `parallel` Stream.fromEffect (delay 1)
>>> Stream.toList stream -- IO [Int]
1 sec
2 sec
[1,2]

parallel guarantees that all the streams are scheduled for execution immediately, therefore, we could use things like starting timers inside the streams and relying on the fact that all timers were started at the same time.

Unlike async this operation cannot be used to fold an infinite lazy container of streams, because it schedules all the streams strictly concurrently.

Since: 0.2.0 (Streamly)

Since: 0.8.0

Merge two streams using a comparison function. The head elements of both the streams are compared and the smaller of the two elements is emitted, if both elements are equal then the element from the first stream is used first.

If the streams are sorted in ascending order, the resulting stream would also remain sorted in ascending order.

>>> Stream.toList $ Stream.mergeBy compare (Stream.fromList [1,3,5]) (Stream.fromList [2,4,6,8])
[1,2,3,4,5,6,8]

Since: 0.6.0

Like mergeBy but with a monadic comparison function.

Merge two streams randomly:

> randomly _ _ = randomIO >>= x -> return $ if x then LT else GT
> Stream.toList $ Stream.mergeByM randomly (Stream.fromList [1,1,1,1]) (Stream.fromList [2,2,2,2])
[2,1,2,2,2,1,1,1]

Merge two streams in a proportion of 2:1:

>>> :{
do    
 let proportionately m n = do
      ref <- newIORef $ cycle $ Prelude.concat [Prelude.replicate m LT, Prelude.replicate n GT]
      return $ _ _ -> do
         r <- readIORef ref
         writeIORef ref $ Prelude.tail r
         return $ Prelude.head r  
 f <- proportionately 2 1
 xs <- Stream.toList $ Stream.mergeByM f (Stream.fromList [1,1,1,1,1,1]) (Stream.fromList [2,2,2])
 print xs
:}
[1,1,2,1,1,2,1,1,2]

Since: 0.6.0

Like mergeBy but merges concurrently (i.e. both the elements being merged are generated concurrently).

Since: 0.6.0

Like mergeByM but merges concurrently (i.e. both the elements being merged are generated concurrently).

Since: 0.6.0

Zip two streams serially using a pure zipping function.

> S.toList $ S.zipWith (+) (S.fromList [1,2,3]) (S.fromList [4,5,6])
[5,7,9]

Since: 0.1.0

Like zipWith but using a monadic zipping function.

Since: 0.4.0

Like zipWith but zips concurrently i.e. both the streams being zipped are generated concurrently.

Since: 0.1.0

Like zipWithM but zips concurrently i.e. both the streams being zipped are generated concurrently.

Since: 0.4.0

Like concatMap but uses an Unfold for stream generation. Unlike concatMap this can fuse the Unfold code with the inner loop and therefore provide many times better performance.

Since: 0.8.0

intersperse followed by unfold and concat.

intercalate unf a str = unfoldMany unf $ intersperse a str
intersperse = intercalate (Unfold.function id)
unwords = intercalate Unfold.fromList " "

>>> Stream.toList $ Stream.intercalate Unfold.fromList " " $ Stream.fromList ["abc", "def", "ghi"]
"abc def ghi"

Since: 0.8.0

intersperseSuffix followed by unfold and concat.

intercalateSuffix unf a str = unfoldMany unf $ intersperseSuffix a str
intersperseSuffix = intercalateSuffix (Unfold.function id)
unlines = intercalateSuffix Unfold.fromList "\n"

>>> Stream.toList $ Stream.intercalateSuffix Unfold.fromList "\n" $ Stream.fromList ["abc", "def", "ghi"]
"abc\ndef\nghi\n"

Since: 0.8.0

concatMapWith mixer generator stream is a two dimensional looping combinator. The generator function is used to generate streams from the elements in the input stream and the mixer function is used to merge those streams.

Note we can merge streams concurrently by using a concurrent merge function.

Since: 0.7.0

Since: 0.8.0 (signature change)

Map a stream producing function on each element of the stream and then flatten the results into a single stream.

concatMap f = concatMapM (return . f)
concatMap = concatMapWith serial
concatMap f = 'concat . map f'
concatMap f = unfoldMany (UF.lmap f UF.fromStream)

Since: 0.6.0

Map a stream producing monadic function on each element of the stream and then flatten the results into a single stream. Since the stream generation function is monadic, unlike concatMap, it can produce an effect at the beginning of each iteration of the inner loop.

Since: 0.6.0

A variant of fold that allows you to fold a Foldable container of streams using the specified stream sum operation.

concatFoldableWith async $ map return [1..3]

Equivalent to:

concatFoldableWith f = Prelude.foldr f S.nil
concatFoldableWith f = S.concatMapFoldableWith f id

Since: 0.8.0 (Renamed foldWith to concatFoldableWith)

Since: 0.1.0 (Streamly)

A variant of foldMap that allows you to map a monadic streaming action on a Foldable container and then fold it using the specified stream merge operation.

concatMapFoldableWith async return [1..3]

Equivalent to:

concatMapFoldableWith f g = Prelude.foldr (f . g) S.nil
concatMapFoldableWith f g xs = S.concatMapWith f g (S.fromFoldable xs)

Since: 0.8.0 (Renamed foldMapWith to concatMapFoldableWith)

Since: 0.1.0 (Streamly)

Like concatMapFoldableWith but with the last two arguments reversed i.e. the monadic streaming function is the last argument.

Equivalent to:

concatForFoldableWith f xs g = Prelude.foldr (f . g) S.nil xs
concatForFoldableWith = flip S.concatMapFoldableWith

Since: 0.8.0 (Renamed forEachWith to concatForFoldableWith)

Since: 0.1.0 (Streamly)

Apply a Fold repeatedly on a stream and emit the fold outputs in the output stream.

To sum every two contiguous elements in a stream:

>>> f = Fold.take 2 Fold.sum
>>> Stream.toList $ Stream.foldMany f $ Stream.fromList [1..10]
[3,7,11,15,19]

On an empty stream the output is empty:

>>> Stream.toList $ Stream.foldMany f $ Stream.fromList []
[]

Note Stream.foldMany (Fold.take 0) would result in an infinite loop in a non-empty stream.

Since: 0.8.0

Group the input stream into groups of n elements each and then fold each group using the provided fold function.

>>> Stream.toList $ Stream.chunksOf 2 Fold.sum (Stream.enumerateFromTo 1 10)
[3,7,11,15,19]

This can be considered as an n-fold version of take where we apply take repeatedly on the leftover stream until the stream exhausts.

chunksOf n f = foldMany (FL.take n f)

Since: 0.7.0

Group the input stream into windows of n second each and then fold each group using the provided fold function.

>>> Stream.toList $ Stream.take 5 $ Stream.intervalsOf 1 Fold.sum $ Stream.constRate 2 $ Stream.enumerateFrom 1
[...,...,...,...,...]

Since: 0.7.0

Split on an infixed separator element, dropping the separator. The supplied Fold is applied on the split segments. Splits the stream on separator elements determined by the supplied predicate, separator is considered as infixed between two segments:

>>> splitOn' p xs = Stream.toList $ Stream.splitOn p Fold.toList (Stream.fromList xs)
>>> splitOn' (== '.') "a.b"
["a","b"]

An empty stream is folded to the default value of the fold:

>>> splitOn' (== '.') ""
[""]

If one or both sides of the separator are missing then the empty segment on that side is folded to the default output of the fold:

>>> splitOn' (== '.') "."
["",""]

>>> splitOn' (== '.') ".a"
["","a"]

>>> splitOn' (== '.') "a."
["a",""]

>>> splitOn' (== '.') "a..b"
["a","","b"]

splitOn is an inverse of intercalating single element:

Stream.intercalate (Stream.fromPure '.') Unfold.fromList . Stream.splitOn (== '.') Fold.toList === id

Assuming the input stream does not contain the separator:

Stream.splitOn (== '.') Fold.toList . Stream.intercalate (Stream.fromPure '.') Unfold.fromList === id

Since: 0.7.0

Split on a suffixed separator element, dropping the separator. The supplied Fold is applied on the split segments.

>>> splitOnSuffix' p xs = Stream.toList $ Stream.splitOnSuffix p Fold.toList (Stream.fromList xs)
>>> splitOnSuffix' (== '.') "a.b."
["a","b"]

>>> splitOnSuffix' (== '.') "a."
["a"]

An empty stream results in an empty output stream:

>>> splitOnSuffix' (== '.') ""
[]

An empty segment consisting of only a suffix is folded to the default output of the fold:

>>> splitOnSuffix' (== '.') "."
[""]

>>> splitOnSuffix' (== '.') "a..b.."
["a","","b",""]

A suffix is optional at the end of the stream:

>>> splitOnSuffix' (== '.') "a"
["a"]

>>> splitOnSuffix' (== '.') ".a"
["","a"]

>>> splitOnSuffix' (== '.') "a.b"
["a","b"]

lines = splitOnSuffix (== '\n')

splitOnSuffix is an inverse of intercalateSuffix with a single element:

Stream.intercalateSuffix (Stream.fromPure '.') Unfold.fromList . Stream.splitOnSuffix (== '.') Fold.toList === id

Assuming the input stream does not contain the separator:

Stream.splitOnSuffix (== '.') Fold.toList . Stream.intercalateSuffix (Stream.fromPure '.') Unfold.fromList === id

Since: 0.7.0

Like splitOnSuffix but keeps the suffix attached to the resulting splits.

>>> splitWithSuffix' p xs = Stream.toList $ splitWithSuffix p Fold.toList (Stream.fromList xs)

>>> splitWithSuffix' (== '.') ""
[]

>>> splitWithSuffix' (== '.') "."
["."]

>>> splitWithSuffix' (== '.') "a"
["a"]

>>> splitWithSuffix' (== '.') ".a"
[".","a"]

>>> splitWithSuffix' (== '.') "a."
["a."]

>>> splitWithSuffix' (== '.') "a.b"
["a.","b"]

>>> splitWithSuffix' (== '.') "a.b."
["a.","b."]

>>> splitWithSuffix' (== '.') "a..b.."
["a.",".","b.","."]

Since: 0.7.0

Like splitOn after stripping leading, trailing, and repeated separators. Therefore, ".a..b." with . as the separator would be parsed as ["a","b"]. In other words, its like parsing words from whitespace separated text.

>>> wordsBy' p xs = Stream.toList $ Stream.wordsBy p Fold.toList (Stream.fromList xs)

>>> wordsBy' (== ',') ""
[]

>>> wordsBy' (== ',') ","
[]

>>> wordsBy' (== ',') ",a,,b,"
["a","b"]

words = wordsBy isSpace

Since: 0.7.0

groups = groupsBy (==)
groups = groupsByRolling (==)

Groups contiguous spans of equal elements together in individual groups.

>>> Stream.toList $ Stream.groups Fold.toList $ Stream.fromList [1,1,2,2]
[[1,1],[2,2]]

Since: 0.7.0

groupsBy cmp f $ S.fromList [a,b,c,...] assigns the element a to the first group, if b `cmp` a is True then b is also assigned to the same group. If c `cmp` a is True then c is also assigned to the same group and so on. When the comparison fails a new group is started. Each group is folded using the fold f and the result of the fold is emitted in the output stream.

>>> Stream.toList $ Stream.groupsBy (>) Fold.toList $ Stream.fromList [1,3,7,0,2,5]
[[1,3,7],[0,2,5]]

Since: 0.7.0

Unlike groupsBy this function performs a rolling comparison of two successive elements in the input stream. groupsByRolling cmp f $ S.fromList [a,b,c,...] assigns the element a to the first group, if a `cmp` b is True then b is also assigned to the same group. If b `cmp` c is True then c is also assigned to the same group and so on. When the comparison fails a new group is started. Each group is folded using the fold f.

>>> Stream.toList $ Stream.groupsByRolling (\a b -> a + 1 == b) Fold.toList $ Stream.fromList [1,2,3,7,8,9]
[[1,2,3],[7,8,9]]

Since: 0.7.0

Run the action m b before the stream yields its first element.

before action xs = 'nilM' action <> xs

Since: 0.7.0

Run the action m b whenever the stream t m a stops normally, or if it is garbage collected after a partial lazy evaluation.

The semantics of the action m b are similar to the semantics of cleanup action in bracket.

See also after_

Since: 0.7.0

Run the alloc action m b with async exceptions disabled but keeping blocking operations interruptible (see mask). Use the output b as input to b -> t m a to generate an output stream.

b is usually a resource under the state of monad m, e.g. a file handle, that requires a cleanup after use. The cleanup action b -> m c, runs whenever the stream ends normally, due to a sync or async exception or if it gets garbage collected after a partial lazy evaluation.

bracket only guarantees that the cleanup action runs, and it runs with async exceptions enabled. The action must ensure that it can successfully cleanup the resource in the face of sync or async exceptions.

When the stream ends normally or on a sync exception, cleanup action runs immediately in the current thread context, whereas in other cases it runs in the GC context, therefore, cleanup may be delayed until the GC gets to run.

See also: bracket_

Inhibits stream fusion

Since: 0.7.0

Run the action m b if the stream aborts due to an exception. The exception is not caught, simply rethrown.

Inhibits stream fusion

Since: 0.7.0

Run the action m b whenever the stream t m a stops normally, aborts due to an exception or if it is garbage collected after a partial lazy evaluation.

The semantics of running the action m b are similar to the cleanup action semantics described in bracket.

finally release = bracket (return ()) (const release)

See also finally_

Inhibits stream fusion

Since: 0.7.0

When evaluating a stream if an exception occurs, stream evaluation aborts and the specified exception handler is run with the exception as argument.

Inhibits stream fusion

Since: 0.7.0

Lift the inner monad m of a stream t m a to tr m using the monad transformer tr.

Since: 0.8.0

Evaluate the inner monad of a stream as ReaderT.

Since: 0.8.0

Evaluate the inner monad of a stream as StateT and emit the resulting state and value pair after each step.

This is supported only for SerialT as concurrent state updation may not be safe.

Since: 0.8.0

For SerialT streams:

(<>) = serial                       -- Semigroup
(>>=) = flip . concatMapWith serial -- Monad

A single Monad bind behaves like a for loop:

>>> :{
Stream.toList $ do
     x <- Stream.fromList [1,2] -- foreach x in stream
     return x
:}
[1,2]

Nested monad binds behave like nested for loops:

>>> :{
Stream.toList $ do
    x <- Stream.fromList [1,2] -- foreach x in stream
    y <- Stream.fromList [3,4] -- foreach y in stream
    return (x, y)
:}
[(1,3),(1,4),(2,3),(2,4)]

Since: 0.2.0 (Streamly)

Since: 0.8.0

For WSerialT streams:

(<>) = wSerial                       -- Semigroup
(>>=) = flip . concatMapWith wSerial -- Monad

Note that <> is associative only if we disregard the ordering of elements in the resulting stream.

A single Monad bind behaves like a for loop:

>>> :{
Stream.toList $ Stream.fromWSerial $ do
     x <- Stream.fromList [1,2] -- foreach x in stream
     return x
:}
[1,2]

Nested monad binds behave like interleaved nested for loops:

>>> :{
Stream.toList $ Stream.fromWSerial $ do
    x <- Stream.fromList [1,2] -- foreach x in stream
    y <- Stream.fromList [3,4] -- foreach y in stream
    return (x, y)
:}
[(1,3),(2,3),(1,4),(2,4)]

It is a result of interleaving all the nested iterations corresponding to element 1 in the first stream with all the nested iterations of element 2:

>>> import Streamly.Prelude (wSerial)
>>> Stream.toList $ Stream.fromList [(1,3),(1,4)] `wSerial` Stream.fromList [(2,3),(2,4)]
[(1,3),(2,3),(1,4),(2,4)]

The W in the name stands for wide or breadth wise scheduling in contrast to the depth wise scheduling behavior of SerialT.

Since: 0.2.0 (Streamly)

Since: 0.8.0