{-# LANGUAGE CPP #-} -- | -- Module : Streamly.Internal.Data.Stream.Container -- Copyright : (c) 2019 Composewell Technologies -- License : BSD-3-Clause -- Maintainer : streamly@composewell.com -- Stability : experimental -- Portability : GHC -- -- Stream operations that require transformers or containers like Set or Map. module Streamly.Internal.Data.Stream.Container ( -- * Deduplication ordNub -- * Joins , leftJoin , outerJoin -- * Ord Joins , innerOrdJoin , leftOrdJoin , outerOrdJoin ) where #include "inline.hs" import Control.Monad.IO.Class (MonadIO) import Control.Monad.Trans.State.Strict (get, put) import Data.Function ((&)) import Data.Maybe (isJust) import Streamly.Internal.Data.Stream.Step (Step(..)) import Streamly.Internal.Data.Stream.Type (Stream(..), mkCross, unCross) import qualified Data.Map.Strict as Map import qualified Data.Set as Set import qualified Streamly.Data.Fold as Fold import qualified Streamly.Internal.Data.Array.Generic as Array import qualified Streamly.Internal.Data.MutArray.Type as MA import qualified Streamly.Internal.Data.Stream.Type as Stream import qualified Streamly.Internal.Data.Stream.Nesting as Stream import qualified Streamly.Internal.Data.Stream.Generate as Stream import qualified Streamly.Internal.Data.Stream.Transform as Stream import qualified Streamly.Internal.Data.Stream.Transformer as Stream #include "DocTestDataStream.hs" -- | @nub@ specialized to 'Ord' types for better performance. Returns a -- subsequence of the stream removing any duplicate elements. -- -- The memory used is proportional to the number of unique elements in the -- stream. One way to limit the memory is to use @take@ on the resulting -- stream to limit the unique elements in the stream. {-# INLINE_NORMAL ordNub #-} ordNub :: (Monad m, Ord a) => Stream m a -> Stream m a ordNub (Stream step1 state1) = Stream step (Set.empty, state1) where step gst (set, st) = do r <- step1 gst st return $ case r of Yield x s -> if Set.member x set then Skip (set, s) else Yield x (Set.insert x set, s) Skip s -> Skip (set, s) Stop -> Stop -- XXX Generate error if a duplicate insertion is attempted? toMap :: (Monad m, Ord k) => Stream m (k, v) -> m (Map.Map k v) toMap = let f = Fold.foldl' (\kv (k, b) -> Map.insert k b kv) Map.empty in Stream.fold f -- If the second stream is too big it can be partitioned based on hashes and -- then we can process one parition at a time. -- -- XXX An IntMap may be faster when the keys are Int. -- XXX Use hashmap instead of map? -- | 'innerJoin' specialized to 'Ord' types for better performance. -- -- If the input streams have duplicate keys, the behavior is undefined. -- -- For space efficiency use the smaller stream as the second stream. -- -- Space: O(n) -- -- Time: O(m + n) -- -- /Pre-release/ {-# INLINE innerOrdJoin #-} innerOrdJoin :: (Monad m, Ord k) => Stream m (k, a) -> Stream m (k, b) -> Stream m (k, a, b) innerOrdJoin s1 s2 = Stream.concatEffect $ do km <- toMap s2 pure $ Stream.mapMaybe (joinAB km) s1 where joinAB kvm (k, a) = case k `Map.lookup` kvm of Just b -> Just (k, a, b) Nothing -> Nothing -- XXX We can do this concurrently. -- XXX Check performance of StreamD vs StreamK -- XXX If the second stream is sorted and passed as an Array or a seek capable -- stream then we could use binary search if we have an Ord instance or -- Ordering returning function. The time complexity would then become (m x log -- n). -- | Like 'innerJoin' but emits @(a, Just b)@ whenever a and b are equal, for -- those @a@'s that are not equal to any @b@ emits @(a, Nothing)@. -- -- This is a generalization of 'innerJoin' to include all elements from the -- left stream and not just those which have an equal in the right stream. This -- is not a commutative operation, the order of the stream arguments matters. -- -- All the caveats mentioned in 'innerJoin' apply here as well. Right join is -- not provided because it is just a flipped left join: -- -- >>> rightJoin eq = flip (Stream.leftJoin eq) -- -- Space: O(n) assuming the second stream is cached in memory. -- -- Time: O(m x n) -- -- /Unimplemented/ {-# INLINE leftJoin #-} leftJoin :: Monad m => (a -> b -> Bool) -> Stream m a -> Stream m b -> Stream m (a, Maybe b) leftJoin eq s1 s2 = Stream.evalStateT (return False) $ unCross $ do a <- mkCross (Stream.liftInner s1) -- XXX should we use StreamD monad here? -- XXX Is there a better way to perform some action at the end of a loop -- iteration? mkCross (Stream.fromEffect $ put False) let final = Stream.concatEffect $ do r <- get if r then pure Stream.nil else pure (Stream.fromPure Nothing) b <- mkCross (fmap Just (Stream.liftInner s2) `Stream.append` final) case b of Just b1 -> if a `eq` b1 then do mkCross (Stream.fromEffect $ put True) return (a, Just b1) else mkCross Stream.nil Nothing -> return (a, Nothing) -- | 'leftJoin' specialized to 'Ord' types for better performance. -- -- Space: O(n) -- -- Time: O(m + n) -- -- /Pre-release/ {-# INLINE leftOrdJoin #-} leftOrdJoin :: (Ord k, Monad m) => Stream m (k, a) -> Stream m (k, b) -> Stream m (k, a, Maybe b) leftOrdJoin s1 s2 = Stream.concatEffect $ do km <- toMap s2 return $ fmap (joinAB km) s1 where joinAB km (k, a) = case k `Map.lookup` km of Just b -> (k, a, Just b) Nothing -> (k, a, Nothing) -- XXX We can do this concurrently. -- XXX Check performance of StreamD vs StreamK cross operation. -- | Like 'leftJoin' but emits a @(Just a, Just b)@. Like 'leftJoin', for those -- @a@'s that are not equal to any @b@ emit @(Just a, Nothing)@, but -- additionally, for those @b@'s that are not equal to any @a@ emit @(Nothing, -- Just b)@. -- -- This is a generalization of left join to include all the elements from the -- right stream as well, in other words it is a combination of left and right -- joins. This is a commutative operation. The order of stream arguments can be -- changed without affecting results, except for the ordering of elements in -- the resulting tuple. -- -- For space efficiency use the smaller stream as the second stream. -- -- Space: O(n) -- -- Time: O(m x n) -- -- /Pre-release/ {-# INLINE outerJoin #-} outerJoin :: MonadIO m => (a -> b -> Bool) -> Stream m a -> Stream m b -> Stream m (Maybe a, Maybe b) outerJoin eq s1 s2 = Stream.concatEffect $ do inputArr <- Array.fromStream s2 let len = Array.length inputArr foundArr <- Stream.fold (MA.writeN len) (Stream.fromList (Prelude.replicate len False)) return $ go inputArr foundArr `Stream.append` leftOver inputArr foundArr where leftOver inputArr foundArr = let stream1 = Array.read inputArr stream2 = Stream.unfold MA.reader foundArr in Stream.filter isJust ( Stream.zipWith (\x y -> if y then Nothing else Just (Nothing, Just x) ) stream1 stream2 ) & Stream.catMaybes evalState = Stream.evalStateT (return False) . unCross go inputArr foundArr = evalState $ do a <- mkCross (Stream.liftInner s1) -- XXX should we use StreamD monad here? -- XXX Is there a better way to perform some action at the end of a loop -- iteration? mkCross (Stream.fromEffect $ put False) let final = Stream.concatEffect $ do r <- get if r then pure Stream.nil else pure (Stream.fromPure Nothing) (i, b) <- let stream = Array.read inputArr in mkCross (Stream.indexed $ fmap Just (Stream.liftInner stream) `Stream.append` final) case b of Just b1 -> if a `eq` b1 then do mkCross (Stream.fromEffect $ put True) MA.putIndex i foundArr True return (Just a, Just b1) else mkCross Stream.nil Nothing -> return (Just a, Nothing) -- Put the b's that have been paired, in another hash or mutate the hash to set -- a flag. At the end go through @Stream m b@ and find those that are not in that -- hash to return (Nothing, b). -- | 'outerJoin' specialized to 'Ord' types for better performance. -- -- Space: O(m + n) -- -- Time: O(m + n) -- -- /Pre-release/ {-# INLINE outerOrdJoin #-} outerOrdJoin :: (Ord k, MonadIO m) => Stream m (k, a) -> Stream m (k, b) -> Stream m (k, Maybe a, Maybe b) outerOrdJoin s1 s2 = Stream.concatEffect $ do km1 <- kvFold s1 km2 <- kvFold s2 -- XXX Not sure if toList/fromList would fuse optimally. We may have to -- create a fused Map.toStream function. let res1 = fmap (joinAB km2) $ Stream.fromList $ Map.toList km1 where joinAB km (k, a) = case k `Map.lookup` km of Just b -> (k, Just a, Just b) Nothing -> (k, Just a, Nothing) -- XXX We can take advantage of the lookups in the first pass above to -- reduce the number of lookups in this pass. If we keep mutable cells -- in the second Map, we can flag it in the first pass and not do any -- lookup in the second pass if it is flagged. let res2 = Stream.mapMaybe (joinAB km1) $ Stream.fromList $ Map.toList km2 where joinAB km (k, b) = case k `Map.lookup` km of Just _ -> Nothing Nothing -> Just (k, Nothing, Just b) return $ Stream.append res1 res2 where -- XXX Generate error if a duplicate insertion is attempted? kvFold = let f = Fold.foldl' (\kv (k, b) -> Map.insert k b kv) Map.empty in Stream.fold f