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Within LTS Haskell 24.35 (ghc-9.10.3)

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  1. pooledMapConcurrentlyIO_ :: Foldable t => Int -> (a -> IO b) -> t a -> IO ()

    unliftio UnliftIO.Internals.Async

    No documentation available.

  2. pooledMapConcurrentlyIO_' :: Foldable t => Int -> (a -> IO ()) -> t a -> IO ()

    unliftio UnliftIO.Internals.Async

    No documentation available.

  3. pooledMapConcurrentlyN :: (MonadUnliftIO m, Traversable t) => Int -> (a -> m b) -> t a -> m (t b)

    unliftio UnliftIO.Internals.Async

    Like mapConcurrently from async, but instead of one thread per element, it does pooling from a set of threads. This is useful in scenarios where resource consumption is bounded and for use cases where too many concurrent tasks aren't allowed.

    Example usage

    import Say

action :: Int -> IO Int
action n = do
tid <- myThreadId
sayString $ show tid
threadDelay (2 * 10^6) -- 2 seconds
return n

main :: IO ()
main = do
yx <- pooledMapConcurrentlyN 5 (\x -> action x) [1..5]
print yx
    On executing you can see that five threads have been spawned: 
    $ ./pool
ThreadId 36
ThreadId 38
ThreadId 40
ThreadId 42
ThreadId 44
[1,2,3,4,5]
    Let's modify the above program such that there are less threads than the number of items in the list: 
    import Say

action :: Int -> IO Int
action n = do
tid <- myThreadId
sayString $ show tid
threadDelay (2 * 10^6) -- 2 seconds
return n

main :: IO ()
main = do
yx <- pooledMapConcurrentlyN 3 (\x -> action x) [1..5]
print yx
    On executing you can see that only three threads are active totally: 
    $ ./pool
ThreadId 35
ThreadId 37
ThreadId 39
ThreadId 35
ThreadId 39
[1,2,3,4,5]

  4. pooledMapConcurrentlyN_ :: (MonadUnliftIO m, Foldable f) => Int -> (a -> m b) -> f a -> m ()

    unliftio UnliftIO.Internals.Async

    Like pooledMapConcurrentlyN but with the return value discarded.

  5. pooledMapConcurrently_ :: (MonadUnliftIO m, Foldable f) => (a -> m b) -> f a -> m ()

    unliftio UnliftIO.Internals.Async

    Like pooledMapConcurrently but with the return value discarded.

  6. bmap :: FunctorB b => (forall (a :: k) . () => f a -> g a) -> b f -> b g

    hedgehog Hedgehog

    No documentation available.

  7. bmap :: FunctorB b => (forall (a :: k) . () => f a -> g a) -> b f -> b g

    hedgehog Hedgehog.Internal.Barbie

    No documentation available.

  8. concatMap :: Foldable t => (a -> [b]) -> t a -> [b]

    hedgehog Hedgehog.Internal.Prelude

    Map a function over all the elements of a container and concatenate the resulting lists.

    Examples

    Basic usage: 
    >>> concatMap (take 3) [[1..], [10..], [100..], [1000..]]
[1,2,3,10,11,12,100,101,102,1000,1001,1002]

    >>> concatMap (take 3) (Just [1..])
[1,2,3]

  9. fmap :: Functor f => (a -> b) -> f a -> f b

    hedgehog Hedgehog.Internal.Prelude

    fmap is used to apply a function of type (a -> b) to a value of type f a, where f is a functor, to produce a value of type f b. Note that for any type constructor with more than one parameter (e.g., Either), only the last type parameter can be modified with fmap (e.g., b in `Either a b`). Some type constructors with two parameters or more have a Bifunctor instance that allows both the last and the penultimate parameters to be mapped over.

    Examples

    Convert from a Maybe Int to a Maybe String using show: 
    >>> fmap show Nothing
Nothing

>>> fmap show (Just 3)
Just "3"
    Convert from an Either Int Int to an Either Int String using show: 
    >>> fmap show (Left 17)
Left 17

>>> fmap show (Right 17)
Right "17"
    Double each element of a list: 
    >>> fmap (*2) [1,2,3]
[2,4,6]
    Apply even to the second element of a pair: 
    >>> fmap even (2,2)
(2,True)
    It may seem surprising that the function is only applied to the last element of the tuple compared to the list example above which applies it to every element in the list. To understand, remember that tuples are type constructors with multiple type parameters: a tuple of 3 elements (a,b,c) can also be written (,,) a b c and its Functor instance is defined for Functor ((,,) a b) (i.e., only the third parameter is free to be mapped over with fmap). It explains why fmap can be used with tuples containing values of different types as in the following example: 
    >>> fmap even ("hello", 1.0, 4)
("hello",1.0,True)

  10. foldMap :: (Foldable t, Monoid m) => (a -> m) -> t a -> m

    hedgehog Hedgehog.Internal.Prelude

    Map each element of the structure into a monoid, and combine the results with (<>). This fold is right-associative and lazy in the accumulator. For strict left-associative folds consider foldMap' instead.

    Examples

    Basic usage: 
    >>> foldMap Sum [1, 3, 5]
Sum {getSum = 9}

    >>> foldMap Product [1, 3, 5]
Product {getProduct = 15}

    >>> foldMap (replicate 3) [1, 2, 3]
[1,1,1,2,2,2,3,3,3]
    When a Monoid's (<>) is lazy in its second argument, foldMap can return a result even from an unbounded structure. For example, lazy accumulation enables Data.ByteString.Builder to efficiently serialise large data structures and produce the output incrementally: 
    >>> import qualified Data.ByteString.Lazy as L

>>> import qualified Data.ByteString.Builder as B

>>> let bld :: Int -> B.Builder; bld i = B.intDec i <> B.word8 0x20

>>> let lbs = B.toLazyByteString $ foldMap bld [0..]

>>> L.take 64 lbs
"0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24"

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