Back in March, I mentioned that we'd be using conduit for high performance analyses. We've been busy working on various aspects of this behind the scenes. This is the first publicly available follow-up since then.

One issue with financial analyses is bridging the gap between in-memory representations and streaming data. The former allows for higher performance for many forms of analysis, while the latter allows us to deal with far larger data sets and to generate output more quickly for certain analyses.

We'll be using the vector package almost exclusively for efficient in-memory representations in IAP, and conduit for streaming data. The question comes: what happens when we need to transition from one to the other? There are already functions like yieldMany to yield values from a Vector as a Conduit , and conduitVector or sinkVector to consume values from a Conduit into a packed representation.

While these solutions are always sufficient, they aren't always optimal. When we're going for high speed analyses, we don't want to waste time boxing unpacked values or allocating extra constructors unnecessarily. This blog post introduces a new tool we're adding to the IAP toolchain (and the conduit toolchain in general): vectorBuilder .

The problem

Often times when working with Haskell in a high performance context, the overhead introduced by a linked list representation can be too high. Having an extra constructor around each value, a constructor for each cons cell, and the indirection introduced by having to follow pointers, can completely kill performance. The most common examples of this are the high speedup you can often achieve by replacing String with Text (or sometimes ByteString ), or by using Vector s- especially unboxed or storable Vector s.

conduit has a similar representation of a stream as a list, including the constructor overheads just mentioned. It's not surprising, therefore, that in a situation that a list would be a poor representation, conduits will often suffer similar performance problems. Like lists, some of this overhead is mitigated by shortcut fusion (a.k.a., rewrite rules). But this isn't always the case.

conduit-combinators provides a helper function which allows us to take back performance, by working with a packed representation instead of creating a bunch of cons cells. It does this by using the vector package's generic mutable interface under the surface, while at a user-facing level providing a simple yield-like function, avoiding the need to muck around with mutable buffers.

This article will cover how to use this function, some implementation details, and comparisons to other approaches.

NOTE: At the time of writing, the version of conduit-combinators provided on School of Haskell does not contain the vectorBuilder function, and therefore the active code below will not run.

Motivating use case

Let's start with a simple goal: we have chunks of bytes coming in, and we want to (1) duplicate each successive byte so that, e.g. [1, 2, 3] becomes [1, 1, 2, 2, 3, 3] and (2) rechunk the values into vectors of size 512. The original data could be chunked in any way, so we can rely on any specific incoming chunk size (in this case, a known 256 chunk size would be convenient).

Likely the easiest approach is to convert our stream of chunked values (e.g., ByteString or Vector Word8 ) into a stream of elements (e.g., Word8 ), duplicate the individual values, then chunk those back up. Such a solution would look like:

rechunk1 = concatC =$= concatMapC ( \ x -> [ x , x ] ) =$= conduitVector 512

This uses the concatC combinator to "flatten out" the input stream, concatMapC to duplicate each individual Word8 , and then conduitVector to create a stream of 512-sized Vector s. In my simple benchmark, this function took 13.06ms.

But as we can probably guess, this falls into the problem zone described in our introduction. So instead of dealing with things on the individual byte level, let's try to use some higher-level functions operating on Vector s of values instead. Our new approach will be to first mapC over the stream and use vector's concatMap to double each value, and then use takeCE and foldC to extract successive chunks of size 4096. In code:

rechunk2 = mapC ( concatMap $ replicate 2 ) =$= loop where loop = do x <- takeCE 512 =$= foldC unless ( null x ) $ yield x >> loop

In the same benchmark, this performed at 8.83ms, a 32% speedup. While respectable, we can do better.

Buffer copying

Our first approach is optimal in one way: it avoids needless buffer copying. Each Word8 is copied precisely once into an output Vector by conduitVector . Unfortunately, this advantage is killed by the overhead of boxing the Word8 s and allocating constructors for conduit. Our second approach avoids the boxing and constructors by always operating on Vector s, but we end up copying buffers multiple times: from the original Vector to the doubled Vector , and then when folding together multiple Vector s into a single Vector of size 512.

What we want to do is to be able to yield a Word8 and have it fill up an output buffer, and once that buffer is filled, yield that buffer downstream and start working on a new one. We could do that by directly dealing with mutable Vector s, but that's error-prone and tedious. Instead, let's introduce our new combinator function: vectorBuilder (or its unqualified name, vectorBuilderC ).

The idea is simple. vectorBuilder will allocate an output buffer for you. It provides you with a special yield -like function that fills up this buffer, and when it's full, yields the entire buffer downstream for you.

To use it, we're going to use one other combinator function: mapM_CE , which performs an action for every value in a chunked input stream (in our case, for each Word8 in our input Vector Word8 s). Altogether, this looks like:

rechunk3 = vectorBuilderC 512 $ \ yield' -> mapM_CE ( \ x -> yield' x >> yield' x )

We call yield' twice to double our bytes. vectorBuilder ensures that each output buffer is of size 512. mapM_CE efficiently traverses the incoming Vector s without creating intermediate data structures.

This version benchmarks at 401.12us. This is approximately 95% faster than our previous attempt!

Avoiding transformers

There's something tricky about the yield' function above. Notice how it's not being used in the Conduit monad transformer, but is instead living the base monad (e.g., IO ). This is not accidental. Not only does this allow us to use existing monadic combinators like mapM_CE , it also allows for far more efficient code. To demonstrate, let's look at two different ways of doing the same thing:

bgroup "transformers" $ let src = return () in [ bench "single" $ nfIO $ do ref <- newIORef 0 let incr = modifyIORef ref succ src $$ liftIO ( replicateM_ 1000 incr ) , bench "multi" $ nfIO $ do ref <- newIORef 0 let incr = liftIO $ modifyIORef ref succ src $$ replicateM_ 1000 incr ]

Both of these benchmarks use no conduit features. They both create an IORef , then increment it 1000 times. The difference is that the first calls liftIO once, while the second calls liftIO 1000 times. Let's see the difference in benchmark results:

benchmarking transformers/single mean: 4.292891 us, lb 4.285319 us, ub 4.303626 us, ci 0.950 std dev: 45.83832 ns, lb 35.04324 ns, ub 59.43617 ns, ci 0.950 benchmarking transformers/multi mean: 93.10228 us, lb 92.95708 us, ub 93.30159 us, ci 0.950 std dev: 869.6636 ns, lb 673.8342 ns, ub 1.090044 us, ci 0.950

Avoiding extra liftIO calls has a profound performance impact. The reason for this is somewhat similar to what we've been discussing up until now about extra cons cells. In our case, it's extra PipeM constructors used by conduit's MonadIO instance. I don't want to dwell on those details too much right now, as that's a whole separate topic of analysis, involving looking at GHC core output. But let's take it as a given right now.

The question is: how does vectorBuilder allow you to live in the base monad, but still yield values downstream, which requires access to the Conduit transformer? There's a trick here using mutable variables. The implementation essentially works like this:

Allocate a new, empty mutable vector.

Allocate a mutable variable holding an empty list.

Start running the user-supplied Conduit function, providing it with a specialized yield function.

function, providing it with a specialized function. The specialized yield function- which lives in the base monad- will write values into the mutable vector. Once that mutable vector is filled, the vector is frozen and added to the end of the mutable variable's list, and a new mutable vector is allocated.

function- which lives in the base monad- will write values into the mutable vector. Once that mutable vector is filled, the vector is frozen and added to the end of the mutable variable's list, and a new mutable vector is allocated. The next time the user's function await s for values from upstream, we jump into action. Since we're already forced to be in the Conduit transformer at that point, this is our chance to yield . We grab all of the frozen vectors from the mutable variable and yield them downstream. Once that's done, we await for new data from upstream, and provide it to the user's function.

s for values from upstream, we jump into action. Since we're already forced to be in the transformer at that point, this is our chance to . We grab all of the frozen vectors from the mutable variable and them downstream. Once that's done, we for new data from upstream, and provide it to the user's function. When the user's function is finished, we freeze the last bit of data from the mutable vector and yield that downstream too.

The upsides of this approach are ease-of-use and performance. There is one downside you should be aware of: if you generate a large amount of output without await ing for more data from upstream, you can begin to accumulate more memory. You can force the collection of frozen Vector s to be flushed using the following helper function:

forceFlush :: Monad m => ConduitM i o m () forceFlush = await >>= maybe ( return () ) leftover

This simply await s for a value, allowing vectorBuilder to clear its cache, and then gives the new value back as a leftover.

Overall, your goal should be to have a decent trade-off between memory and time efficiency. To demonstrate, try playing around with the functions f1, f2, and f3 in the following code snippet:

{-# LANGUAGE NoImplicitPrelude #-} {-# LANGUAGE FlexibleContexts #-} import ClassyPrelude.Conduit forceFlush :: Monad m => ConduitM i o m () forceFlush = await >>= maybe (return ()) leftover -- Memory inefficient, time efficient f1 :: (Int -> IO ()) -> Sink () IO () f1 f = liftIO $ forM_ [1..1000000] f -- Memory efficient, time inefficient f2 :: (Int -> Sink () IO ()) -> Sink () IO () f2 f = forM_ [1..1000000] $ \i -> do f i forceFlush -- Good trade-off f3 f = forM_ (chunksOf 10000 [1..1000000]) $ \is -> do liftIO $ mapM_ f is forceFlush where chunksOf _ [] = [] chunksOf i x = y : chunksOf i z where (y, z) = splitAt i x main = vectorBuilderC 4096 f3 $$ (sinkNull :: Sink (Vector Int) IO ())

ByteString and Vector

It may be surprising to have seen an entire article on packed representations of bytes, and not yet seen ByteString . As a matter of fact, the original use case I started working on this for had nothing to do with the vector package. However, I decided to focus on vector for two reasons:

Unlike bytestring, it provides a well developed mutable interface. Not only that, but the mutable interface is optimized for storable, unboxed, and generic vectors, plus existing helper packages like hybrid-vectors. In other words, this is a far more general-purpose solution. It's trivial and highly efficient to convert a ByteString to and from a storable Vector .

To demonstrate that second point, let's try to read a file, duplicate all of its bytes as we did above, and write it back to a separate file. We'll use the toByteVector and fromByteVector functions, which I recently added to mono-traversable for just this purpose:

{-# LANGUAGE NoImplicitPrelude #-} import ClassyPrelude.Conduit import System.IO (IOMode (ReadMode, WriteMode), withBinaryFile) double :: (Word8 -> IO ()) -> Sink (SVector Word8) IO () double yield' = mapM_CE $ \w -> yield' w >> yield' w main :: IO () main = withBinaryFile "input.txt" ReadMode $ \inH -> withBinaryFile "output.txt" WriteMode $ \outH -> sourceHandle inH $$ mapC toByteVector =$ vectorBuilderC 4096 double =$ mapC fromByteVector =$ sinkHandle outH

Comparison with blaze-builder

There's a strong overlap between what vectorBuilder does, and how blaze-builder (and more recently, bytestring's Builder type) are intended to be used. I unfortunately can't give any conclusive comparisons between these two techniques right now. What I can say is that there are cases where using a Builder has proven to be inefficient, and vectorBuilder provides a large performance improvement. I can also say that vectorBuilder addresses many more use cases that Builder . For example, at FP Complete we're planning to use this in financial analyses for creating time series data.

On the other hand, blaze-builder and bytestring's Builder have both had far more real-world tuning than vectorBuilder . They also have support for things such as copying existing ByteString s into the output stream, whereas vectorBuilder always works by copying a single element at a time.

So for now, if you have a use case and you're uncertain whether to use vectorBuilder to blaze-builder, I recommend either trying both approaches, or discussing it on one of the Haskell mailing lists to get more feedback.

Complete code

The code for most of the blog post above is below. Sorry that it's a bit messy:

{-# LANGUAGE FlexibleContexts #-} {-# LANGUAGE NoImplicitPrelude #-} {-# LANGUAGE NoMonomorphismRestriction #-} import ClassyPrelude.Conduit import Control.Monad.Primitive (PrimMonad) import Control.Monad.ST (runST) import Criterion.Main (bench, bgroup, defaultMain, nfIO, whnfIO) import qualified Data.Vector.Generic as VG import qualified System.Random.MWC as MWC import Test.Hspec (hspec, shouldBe) import Test.Hspec.QuickCheck (prop) rechunk1 :: ( Monad m , VG.Vector vector (Element input) , PrimMonad base , MonadBase base m , MonoFoldable input ) => Conduit input m (vector (Element input)) rechunk1 = concatC =$= concatMapC (\x -> [x, x]) =$= conduitVector 512 {-# INLINE rechunk1 #-} rechunk2 :: (Monad m, IsSequence a) => Conduit a m a rechunk2 = mapC (concatMap $ replicate 2) =$= loop where loop = do x <- takeCE 512 =$= foldC unless (null x) $ yield x >> loop {-# INLINE rechunk2 #-} rechunk3 :: ( MonadBase base m , PrimMonad base , MonoFoldable input , VG.Vector vector (Element input) ) => Conduit input m (vector (Element input)) rechunk3 = vectorBuilderC 512 $ \yield' -> mapM_CE (\x -> yield' x >> yield' x) {-# INLINE rechunk3 #-} main :: IO () main = do hspec $ prop "rechunking" $ \ws -> do let src = yield (pack ws :: UVector Word8) doubled = concatMap (\w -> [w, w]) ws res1 = runST $ src $$ rechunk1 =$ sinkList res2 = runST $ src $$ rechunk2 =$ sinkList res3 = runST $ src $$ rechunk3 =$ sinkList res1 `shouldBe` (res2 :: [UVector Word8]) (res3 :: [UVector Word8]) `shouldBe` (res2 :: [UVector Word8]) (unpack $ (mconcat res2 :: UVector Word8)) `shouldBe` (doubled :: [Word8]) case reverse res1 :: [UVector Word8] of [] -> return () x:xs -> do (length x <= 512) `shouldBe` True all ((== 512) . length) xs `shouldBe` True gen <- MWC.createSystemRandom bytes <- replicateM 20 $ MWC.uniformR (12, 1024) gen >>= MWC.uniformVector gen defaultMain [ bgroup "copy bytes" [ bench "rechunk1" $ whnfIO $ yieldMany (bytes :: [UVector Word8]) $$ (rechunk1 :: Conduit (UVector Word8) IO (UVector Word8)) =$ sinkNull , bench "rechunk2" $ whnfIO $ yieldMany (bytes :: [UVector Word8]) $$ (rechunk2 :: Conduit (UVector Word8) IO (UVector Word8)) =$ sinkNull , bench "rechunk3" $ whnfIO $ yieldMany (bytes :: [UVector Word8]) $$ (rechunk3 :: Conduit (UVector Word8) IO (UVector Word8)) =$ sinkNull ] , bgroup "transformers" $ let src = return () in [ bench "single" $ nfIO $ do ref <- newIORef (0 :: Int) let incr = modifyIORef ref succ src $$ liftIO (replicateM_ 1000 incr) , bench "multi" $ nfIO $ do ref <- newIORef (0 :: Int) let incr = liftIO $ modifyIORef ref succ src $$ replicateM_ 1000 incr ] ]

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