Pipes: Streaming Huffman Compression in Haskell (Part 3)

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Let’s finally finish up our Streaming Huffman Compression project by actually implementing the “streaming” part :) In part 1 we looked at the data structures which we used to implement our compression logic; in part 2 we looked at the actual compression/decompression algorithm and implemented it. Finally, let’s wrap it all up and actually implement a streaming interface!

If we were using an imperative approach, this would usually involve some sort of loop — read a byte, process it, write the resulting byte, read the next, process it, write it…it’s a step of instructions that a computer will be able to perform step-by-step.

In Haskell, when we can, we try to look for a pure, declarative approach based on compositions of abstractions. That’s what Haskell does best, after all. So let’s see what we can do!

(All of the code in this article and the ones before can be downloaded from github, so you can download it and try it out yourself!)

Pipes

Choosing Pipes

So we are searching for an abstraction to handle constant-space IO streaming — that is, we only ever have in memory exactly what we are processing at that moment, and nothing else. For this, there are a couple go-to abstractions we can use that provide this (at the low level).

We can use lazy IO, which basically relies on Haskell’s built in laziness semantics that we know and love to control when IO happens. The problem here is that your IO actions are no longer first-class members of the language — they are “runtime magic”. You can no longer really reason about when file handles are closed and exactly when reads happen. This really is a bit antithetical to Haskell, a language where we actually have the ability to move IO into a first-class member of the language and make it something that we can actually reason about.

There have been many solutions developed to this problem and in modern times, conduit and pipes have emerged, built on the backs of early coroutine-based libraries. These libraries are built on the idea of purely assembling and “declaring” the IO pipeline that you want, with each pipeline component having very explicit and comparable and able-to-reason-with IO read/write/close semantics.

The choice between conduit and pipes depends a lot on what you want to accomplish. There was a very nice Haskell Cast episode on this matter (and more) that I would highly recommend. Both libraries come from very different backgrounds and histories.

This picture is slightly simplified, but conduit focuses around safe resource handling, and pipes focuses on equational reasoning and applied mathematical abstractions.

I’m picking pipes for this tutorial, for no major reason. All of this could be written in conduit with little difference in code size or expressiveness, I’m sure. I mostly chose pipes because I wanted to demonstrate some of the nice reasoning that pipes enables that Haskell is so famous for. I also just wanted to learn it, myself :)

Before we go

Before you proceed, it is recommended that you read over or are at least somewhat familiar with the excellent pipes tutorial, which is a part of the actual pipes documentation. This post does not attempt to be a substitute for it, only a “what’s next?”.

Now, we are going to be using a bit more than just plain old pipes for our program. In addition to the libraries used in our previous parts, we’re going to be using:

  1. pipes-parse, for leftover support. We’re going to be using limited leftover handling for this project in a couple of situations.
  2. pipes-bytestring, which provides lenses for us to manipulate bytestring and byte producers in efficient and expressive ways.

Today, our work with pipes will revolve around a couple of main concepts:

  • Taking two or more pipes and chaining them together to make new ones; hooking up input generators (“sources”, or Producers) to pipes and to data consumers (“sinks”, or Consumers)

  • Taking producers and pipes and chains of pipes (which are themselves just pipes, by the way) and transforming them into new producers and pipes.

If you’ve ever used bash/unix, the first concept is like using unix pipes to “declare” a chain of tools. You can do powerful things by just chaining simple components.

The second concept relates to things to sudo or time; they take normal bash commands and “transform” them into super user commands, or “timeable” commands.

And without any further delay, let’s write encode.hs!

Encoding

(Remember that you can download encode.hs from github and try it out yourself; just remember to also grab Huffman.hs, PQueue.hs, and PreTree.hs, and Weighted.hs from the previous parts of this tutorial!)

Design

Okay, so with the above in mind, let’s sketch out a rough plan. We’ll talk about the holes in the plan later, but it’s useful to see exactly what won’t work, or what is a bad idea :)

We can envision this all as a big single giant pipeline of atomic components.

As a Producer, we have fromHandle, which emits ByteStrings read from a given file handle. As a Consumer, we have toHandle, which takes in ByteStrings and writes them to the given file handle.

Those in hand, we’ll need:

  1. A pipe that turns incoming ByteStrings into bytes1 (Word8s), emitting one at a time.
  2. A pipe that turns incoming Word8s into Directions, by looking up each Word8 in an encoding table to get a list of Directions and emitting them one at a time.
  3. A pipe that takes in Directions and “chunks them up” 8-at-a-time, and re-emits those chunks of 8 as bytes/Word8’s.
  4. A pipe that takes incoming Word8s and wraps them each back into ByteStrings and emits them.

Sounds simple enough, right? Basically like using unix pipes!

We’ll be making two modifications to this plan before we go forward.

Leftovers

The first hole: vanilla pipes does not have leftover support. That is, the stream terminates as soon as the producer terminates.

To put more technically: when a pipe is awaiting something, there is no guarantee that it’ll ever get anything — if the producer it is awaiting from terminates, then that’s that; no chance to respond.

This is normally not a problem, and it won’t be an issue for our decoding program. However, we run into a problem for pipe #3 above: we need to “clump up” incoming Directions and emit them in groups of 8.

This spells trouble for us, because our pipe will be merrily be waiting for eight Directions before clumping them up — until our producer terminates mid-clump. Then what? That final in-progress clump will be lost…forever!

The problem is in the semantics of pipe composition with (>->).

So it’s clear that using normal pipe composition ((>->)) doesn’t work. We’re going to have to transform our Direction producer in another way.

Luckily for us, this is precisely the problem that pipes-parse was made to solve.

We’ll go into more detail about just how it solves this later. At the high level, instead of composing pipes with (>->), we’ll transform pipes by using pipe transformers/functions.

So we’ll modify our plan. We’ll have our “Direction producer”, which consists of:

  1. Our ByteString producer.
  2. Our ByteString to Word8 pipe.
  3. Our Word8 to Direction pipe.

And then we “transform” that Direction producer into a Word8 producer, which we’ll call dirsBytes:

dirsBytes :: Producer Direction m r -> Producer Word8 m r

which turns a Direction producer into a Word8 producer that clumps up the Directions into groups of 8 — and if the directions run out, pad the rest of the byte with 0’s.

pipes-parse gives us the ability to write dirsBytes.

Perfect Packing

The next problem.

If you’ve ever worked with ByteStrings, you might have noted an asymmetry to what we are doing. Look closely — do you see it?

We read ByteStrings from the file — entire big chunks of bytes/Word8s.

We write individual bytes, one at a time. That is, we emit individual Word8s, which we each individually wrap into singleton ByteStrings one at a time, which we write to the file one at a time.

This is bad!

As you might have guessed, the solution is to not use (>->) and instead use a pipe transformer.

We’re not going to write it ourselves using pipes-parse; pipes-bytestring (which we will import qualified as PB) actually comes with such a transformer for us.

The only hitch is that it’s “trapped” in a “lens”, called PB.pack.

PB.pack :: Lens' (Producer Word8 m r) (Producer ByteString m r)

If you are still learning lens, this basically means that PB.pack contains, among other things, a function that allows you to go from a Word8 producer to a ByteString producer. The function view lets us unlock that pipe transformer from the lens.

view :: Lens' a b -> (a -> b)       -- in our case

So,

view PB.pack :: Producer Word8      m r
             -> Producer ByteString m r

Cool. Anyways, pipes-bytestring implements view pack (the conversion function) in a way that does “smart chunking” — it waits until an appropriate amount of Word8s have accumulated in a buffer before packing them all into a big fat ByteString.

And that should be the last hole in our puzzle!

Down to it

Let’s just get down to it!

First, our imports:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L19-46
-- General imports
import Control.Applicative              ((<$>))
import Control.Monad.Trans.State.Strict (evalState)
import Data.Foldable                    (sum)
import Data.Map.Strict                  (Map, (!))
import Lens.Family2                     (view)
import Prelude hiding                   (sum)
import System.Environment               (getArgs)
import System.IO                        (withFile, IOMode(..))
import qualified Data.Map.Strict        as M

-- Pipes imports
import Pipes
import Pipes.Parse
import qualified Pipes.ByteString as PB
import qualified Pipes.Prelude    as PP

-- Working with Binary
import Data.Binary hiding             (encodeFile)
import Data.Bits                      (setBit)
import Data.ByteString                (ByteString)
import qualified Data.ByteString      as B
import qualified Data.ByteString.Lazy as BL

-- Huffman imports
import Huffman
import PQueue
import PreTree

It’s a doozy, admittedly!

Now main:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L48-60
main :: IO ()
main = do
    args     <- getArgs
    let (inp, out)  = case args of
                        i:o:_      -> (i,o)
                        _          -> error "Give input and output files."

    metadata <- analyzeFile inp
    let (len, tree) = case metadata of
                        Just (l, t) -> (l, t)
                        Nothing     -> error "Empty File"

    encodeFile inp out len tree

Just straight-forward, more or less. The error handling is kind of not too great, but we won’t go into that too deeply here :)

File metadata

analyzeFile is going to be how we build the Huffman Tree for the encoding, as discussed in part 1. It’ll go through an entire pass of the file and count up the number of occurrences for each byte and build a Huffman encoding tree out of it. It’ll also give us the length of the file in bytes; this is actually necessary for decoding the file later, because it tells us where to stop decoding (lest we begin decoding the leftover padding bits).

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L63-74
analyzeFile :: FilePath -> IO (Maybe (Int, PreTree Word8))
analyzeFile fp = withFile fp ReadMode $ \hIn -> do
    let byteProducer = PB.fromHandle hIn >-> bsToBytes
    fqs <- freqs byteProducer
    let len  = sum fqs
        tree = evalState (listQueueStateTable fqs >> buildTree) emptyPQ
    return $ fmap (len,) tree
  where
    freqs :: (Monad m, Ord a) => Producer a m () -> m (M.Map a Int)
    freqs = PP.fold f M.empty id
      where
        f m x = M.insertWith (+) x 1 m

First, we use withFile from System.IO, which gives us a file handler for a given filepath; we can pass this handler onto functions that take file handlers. withFile actually handles most of the IO-based error handling and cleanup we would ever need for our simple use cases of pipes.

Now we run into real pipes for the first time!

We’ll assemble our producer of bytes by using PB.fromHandle hIn — a producer of ByteStrings — and chaining it to bsToBytes, a pipe that takes incoming ByteStrings and emits their constituent, unpacked Word8s one-by-one:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L96-97
bsToBytes :: Monad m => Pipe ByteString Word8 m r
bsToBytes = PP.mapFoldable B.unpack

Our implementation uses B.unpack :: ByteString -> [Word8] from pipes-bytestring, which turns a ByteString into a list of its constituent Word8s. We use PP.mapFoldable, which is sort of like concatMap — it applies the given function to every incoming element in the stream, and emits the items in the resulting list2 one-by-one. So bsToBytes is a Pipe that takes in ByteStrings and emits each contained Word8 one-by-one.

Then with our pipe ready, we “run”/“use” it, using PP.fold, from the pipes Prelude. This basically runs a giant “foldl” all over the incoming items of the given producer.

The fold is identical in logic to listFreq from a Part 2:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/Huffman.hs#L22-25
listFreq :: Ord a => [a] -> FreqTable a
listFreq = foldr f M.empty
  where
    f x m = M.insertWith (+) x 1 m

Except instead of folding over a list, we fold over the elements of the producer. Note that the helper function has its arguments reversed. This whole thing, then, will fold over all of the items produced by the given producer (all of the Word8s) with our frequency-table-building.

We then use sum from Data.Foldable, which sums up all the numbers in our frequency map. Then we use what we learned about the State monad in Part 1 to build our tree (review Part 1 if you do not understand the declaration of tree). tree is a Maybe (PreTree Word8); we then tag on the length to our tree using fmap and the TupleSections extension. (That is, (,y) is sugar for (\x -> (x,y))).

The Encoding Pipeline

Once we have that, we can get onto the actual encoding process: the second pass.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L78-92
encodeFile :: FilePath -> FilePath -> Int -> PreTree Word8 -> IO ()
encodeFile inp out len tree =
    withFile inp ReadMode  $ \hIn  ->
    withFile out WriteMode $ \hOut -> do
      BL.hPut hOut $ encode (len, tree)
      let dirsOut   = PB.fromHandle hIn
                  >-> bsToBytes
                  >-> encodeByte encTable
          bsOut     = view PB.pack . dirsBytes $ dirsOut
          pipeline  = bsOut
                  >-> PB.toHandle hOut

      runEffect pipeline
  where
    encTable  = ptTable tree

First, we open our file handles for our input and output files. Then, we use what we learned in Part 2 to get binary serializations of our length and our tree using encode, and use BL.hPut to write it to our file, as the metadata. BL.hPut from Data.ByteString.Lazy takes a file handle and a lazy ByteString, and writes that ByteString out to the file. We use the lazy version because encode gives us a lazy ByteString by default.

Note that we can “put” (len, tree) together as a tuple instead of putting len and tree one after the other. This is because (a, b) has a Binary instance. We’ll read it back in later as a tuple, but it actually doesn’t matter, because the Binary instance for tuples is just putting/getting each item one after the other.

Now, we get to our actual pipes. The first “pipeline” is dirsOut, which is our stream (producer) of Directions encoding the input file. As can be read, dirStream is PB.fromHandle hIn (a ByteString producer from the given handle) piped into our old friend bsToBytes piped into encodeByte encTable, which is a pipe taking in bytes (Word8), looks them up in encTable (the table mapping Word8 to [Direction], which we built in Part 2), and spits out the resulting Directions one at a time.

encodeByte encTable is implemented “exactly the same” as bsToBytes:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L101-104
encodeByte :: (Ord a, Monad m)
           => Map a Encoding
           -> Pipe a Direction m r
encodeByte encTable = PP.mapFoldable (encTable !)

instead of using mapFoldable with a ByteString -> [Word8], we use mapFoldable with a Word8 -> [Direction], which does the same thing — apply the function to every incoming item, and spit out the items in the resulting list one at a time.

(!) :: Map k v -> k -> v is the lookup function for Maps.

Parser

So now we have dirsOut :: Producer Direction IO r, which is a producer of Directions drawn from the file. It’s now time to “group up” the directions, using the “producer transformer” tactic we discussed earlier.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L108-117
dirsBytes :: (MonadIO m, Functor m)
          => Producer Direction m r
          -> Producer Word8     m ()
dirsBytes p = do
    (result, leftovers) <- lift $ runStateT dirsBytesP p
    case result of
      Just byte -> do
        yield byte
        dirsBytes leftovers
      Nothing   -> return ()

dirsBytes turns out Direction producer into a Word8 producer by running the parser dirsBytesP onto the producer, and looping onto itself. We’ll look at dirsBytesP later, but for now, know that it is a parser that attempts to consume eight Directions and returns them together in a Just byte with zero padding if the stream runs out; if the stream is already empty to start with, it returns Nothing.

Remember that in pipes-parse:

runStateT :: Parser a m b -> Producer a m r -> m (b, Producer a m r)

Basically, runStateT parser takes a Producer a and “parses” a value out of it, returning the parsed value and the “leftover/used” Producer.

In our case:

runStateT :: Parser   Direction IO (Maybe Word8)
          -> Producer Direction IO r
          -> IO (Maybe Word8, Producer Direction IO r)

So we use the dirsBytesP parser onto the producer we are given. If it doesn’t parse any bytes (Nothing), then we stop. If it does (Just byte), then we yield the parsed Word8 and then start over again with the leftovers producer.

Let’s take a look at the dirsBytesP parser, which parses Directions into a Word8:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L123-137
dirsBytesP :: (Monad m, Functor m) => Parser Direction m (Maybe Word8)
dirsBytesP = do
    isEnd <- isEndOfInput
    if isEnd
      then return Nothing
      else Just <$> go 0 0
  where
    go :: Monad m => Word8 -> Int -> Parser Direction m Word8
    go b 8 = return b
    go b i = do
      dir <- draw
      case dir of
        Just DLeft  -> go     b            (i + 1)
        Just DRight -> go     (setBit b i) (i + 1)
        Nothing     -> return b

This implementation is pretty straightforward — “if the producer is empty, return Nothing. Otherwise, start with 00000000 and draw Directions one at a time, flipping the appropriate bit when you get a Right.” For more information on the exact functions for bitwise operators, look into the bits package, where they come from.

Note the usage of draw, which “returns” a Nothing if you draw from the end of the producer, and a Just x if there is something to draw. draw is special to parsers, because it lets you react on end-of-input as a Nothing (as opposed to await). In go, we loop drawing until we either get all eight bits (and return the resulting byte) or run out of inputs (and return the byte that we have so far).

We get our direction producer by doing dirsBytes dirsOut.

Smart Chunker

And finally, we use the “smart chunking” provided by pipes-bytestring by transforming our bytes stream:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L86-86
          bsOut     = view PB.pack . dirsBytes $ dirsOut

All together

That gives us our final pipeline; we lay out a series of pipes and pipes transformers that takes our file and streamingly processes the data and writes it into the output file.

Once we have our pipeline, we use runEffect to “run” it; then…that’s it!

Testing it out

Cool, let’s try it out with Leo Tolstoy’s great classic War and Peace from Project Gutenberg!

$ ghc -O2 encode.hs
$ ./encode warandpeace.txt warandpeace.enc
$ du -h warandpeace.*
# 1.8M warandpeace.enc
# 3.1M warandpeace.txt

Cool, we compressed it to 58% of the original file size. Not bad! Using gzip with default settings gives a compression of 39%, so it’s not the best, but it’s something. If we take out the encoding part of the script, we can see that the metadata (the length and the dictionary) itself only takes 259 bytes (which is negligible) — so 58% is pretty much the asymptotic compression rate.

At this point it’s not as snappy (performance wise) as we’d like; a compressing a 3.1M file is not “super slow” (it takes about seven seconds on my computer), but you probably won’t be compressing a gigabyte. We’ll look into performance in a later post!

Decoding

(Remember again that download decode.hs is also available online from github! Again, be sure to also grab Huffman.hs, PQueue.hs, and PreTree.hs, and Weighted.hs from posts past.)

Design

Let’s try to see the plan for our decoding script, applying what we learned before. What components do we need?

  1. First, a component producing decoded Word8s (that will be view PB.pack’d into a component producing decoded ByteStrings with smart chunking)
    1. A producer that reads in ByteStrings from a file and sends them downstream.
    2. A pipe that unpacks those ByteStrings into Word8s and sends each one down.
    3. A pipe that “unpacks” those Word8s into Directions and sends those down.
    4. A pipe that traverses down the Huffman encoding tree following the incoming Directions, and emits a decoded Word8 every time it decodes a value.
  2. A component consuming the incoming ByteStrings, and writing them to our output file.

Down to it

Luckily we can use most of the concepts we learned in writing the encoding script to write the decoding script; we also have a less imports, so it’s a sign that decoding is going to be slightly simpler than encoding.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L18-37
-- General imports
import Lens.Family2       (view)
import System.Environment (getArgs)
import System.IO          (withFile, IOMode(..))

-- Pipes imports
import Pipes
import Pipes.Parse
import qualified Pipes.Binary     as PB
import qualified Pipes.ByteString as PB
import qualified Pipes.Prelude    as PP

-- Working with Binary
import Data.Bits                 (testBit)
import Data.ByteString           (ByteString)
import Data.Word                 (Word8)
import qualified Data.ByteString as B

-- Huffman imports
import PreTree

main should seem very familiar:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L39-45
main :: IO ()
main = do
    args     <- getArgs
    let (inp, out)  = case args of
                        i:o:_      -> (i,o)
                        _          -> error "Give input and output files."
    decodeFile inp out

And now on to the juicy parts:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L48-69
decodeFile :: FilePath -> FilePath -> IO ()
decodeFile inp out =
    withFile inp ReadMode  $ \hIn  ->
    withFile out WriteMode $ \hOut -> do
      let metadataPipe = PB.fromHandle hIn

      -- consume metapipe to read in the tree/metadata
      (metadata, decodingPipe) <- runStateT PB.decode metadataPipe

      case metadata of
        Left   _          ->
          error "Corrupt metadata."
        Right (len, tree) -> do
          -- do everything with the rest
          let bytesOut  = decodingPipe >-> bsToBytes
                      >-> bytesToDirs  >-> searchPT tree
                      >-> PP.take len
              bsOut     = (view PB.pack) bytesOut
              pipeline  = bsOut
                      >-> PB.toHandle hOut

          runEffect pipeline

Loading metadata

Loading the metadata is a snap, and it uses what we learned earlier from runStateT and Parsers.

Here, our Parser is PB.decode, from the pipes-binary package (and not from binary), and it does more or less exactly what you’d expect: it reads in binary data from the source stream, consuming it until it has a successful (or unsuccessful) parse, as given by the binary package talked about in Part 2. The “result” is the Either containing the success or failure, and the “leftover”, consumed source stream.

In our case:

runStateT
  :: Parser   ByteString IO (Either DecodingError (Int, PreTree Word8))
  -> Producer ByteString IO r
  -> IO (Either DecodingError (Int, PreTree Word8), Producer ByteString IO r)

So metadata is Either DecodingError (Int, PreTree Word8). If we get a Left e, then we throw an error for unparseable/corrupted metadata. If we get a Right (len, tree), then we are good to go.

The Decoding Pipeline

The rest just reads like poetry!

let byteStream = decodingPipe >-> bsToBytes
             >-> bytesToDirs  >-> searchPT tree
             >-> PP.take len

Beautiful! decodingPipe is the leftover producer after the parse of the metadata. bsToBytes is the same as from our encoder. bytesToDirs is implemented “exactly” like bsToBytes and encodeByte (from encode.hs) — using PP.mapFoldable and a Word8 -> [Direction] function.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L96-104
bytesToDirs :: Monad m => Pipe Word8 Direction m r
bytesToDirs = PP.mapFoldable byteToDirList
  where
    -- Turns a byte into a list of directions
    byteToDirList :: Word8 -> [Direction]
    byteToDirList b = map f [0..7]
      where
        f i | testBit b i = DRight
            | otherwise   = DLeft

It uses the bits package to turn an incoming Word8 into a list of its constituent bits (in the form of Directions), and yields each of them in turn.

We have searchPT tree, which is a pipe turning incoming Directions into aggregate/outgoing Word8s by finding them on the given PreTree. The implementation is a bit tricky so we’re going to go into it in more detail later.

PP.take len is new; it’s from Pipes.Prelude, and it basically says “take len items from the source, then stop drawing.” This is necessary because, because of the padding of 0’s we did from the encoding script, there will be more bits in the file than are actually a part of the encoding; using PP.take ensures that we don’t try to read the extra padding bits. It’ll take up to len Word8s, and then stop.

And so now we have our Word8/byte Producer/stream!

searchPT

One could actually have written searchPT like this:

searchPT :: PreTree a -> Pipe Direction Word8 m r
searchPT pt0 = go pt0
  where
    go (PTLeaf x) = do
        yield x
        go pt0
    go (PTNode pt1 pt2) = do
        dir <- await
        go $ case dir of
               DLeft  -> pt1
               DRight -> pt2

which looks a lot like the logic of our decoder functions from Part 2.

However, we can do better. This way sort of mixes together the “logic” of decoding from the yielding/continuation/recursion/pipe-ness of it all. Ideally we’d like to be able to separate the logic. This isn’t too necessary, but doing this will expose us to some nice pipes idioms :)

One way we can do it is to turn searchPT into a Consumer' (a Consumer with the ends not sealed off) that consumes Directions and returns resulting Word8s.

Then we use (>~ cat), which turns a Consumer' into something that is forever consuming and re-yielding — in essence, it turns a Consumer' returning values into a Pipe repeatedly yielding the returned values.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L74-86
searchPT :: forall a m r. Monad m
         => PreTree a
         -> Pipe Direction a m r
searchPT t = searchPT' t >~ cat
  where
    searchPT' :: PreTree a -> Consumer' Direction m a
    searchPT' (PTLeaf x)       =
        return x
    searchPT' (PTNode pt1 pt2) = do
        dir <- await
        searchPT' $ case dir of
                      DLeft  -> pt1
                      DRight -> pt2

The logic is slightly cleaner; the gain isn’t that much, but just being able to have this separation is nice. Also, we get rid of explicit recursion. And everybody knows that every time you can get rid of explicit recursion, you get a big win — in lack of potential bugs, in more concise code, and in leveraging higher order functions. In any case, this is also a good exposure to (>~)!

Aside

(>~) is a pretty useful thing. Basically, when you say

consumer >~ pipe

it is like saying “Every time pipe awaits, just use the result returned by consumer instead”.

We can look at cat:

cat :: Pipe a a m r
cat = forever $ do
        a <- await
        yield a

Which just simply echoes/sends back down whatever it receives.

When we say:

consumer >~ cat

We basically say “every time we await something in cat, just use consumer’s return value”:

consumer >~ cat
    = forever $ do
        a <- consumer
        yield a

Basically, consumer >~ cat repeatedly consumes the input and yields downstream the return of the consuming.

(Remember, the value the pipe returns (the r) is different than the value the pipe “sends downstream”; the downstream values are used in connecting with (>->) and the like, and the return value is just the value that the specific thing returns when ran, to the thing doing the running.)

Play around with (>~)-ifying different Pipes and seeing what it does to it; you might have some fun.

Why Consumer' and not Consumer? Well, remember that all lines of the do block have to have the same “yield” type (because the Monad is Pipe a b m, so all lines have to be Pipe a b m r for different r’s — the a’s and b’s (the yield type) and m’s have to be the same), so Consumer' lets the yield type be whatever it needs to be to match with the rest of the do block.

Don’t worry if this is a bit complicated; you don’t need to really undersatnd this to use pipes :)

Admittedly, my description isn’t too great, so if anyone has a better one, I’d be happy to use it here!

The Rest

And the rest is…well, we already know it!

We use (view PB.pack) byteStream like last time to turn our stream of Word8 into a stream of ByteString, with “smart chunking”. Then we pipe that in to PB.toHandle, like we did last time, and have it all flow into the output file.

We have assembled our pipeline; all we have to do now is runEffect, to “run” it. And again, that’s it!

Testing

$ ghc -O2 decode.hs
$ ./decode warandpeace.enc warandpeace.dec
$ md5sum warandpeace.txt
# 3c8168e48f49784ac3c2c25d15388e96  warandpeace.txt
$ md5sum warandpeace.dec
# 3c8168e48f49784ac3c2c25d15388e96  warandpeace.dec

And yup, we get an exact, lossless decompression.

Decompression is faster than compression, as you’d expect; on my computer it takes about two seconds to decompress the 3.1M file. Still a bit slower than we’d like, but not too bad. Well. Maybe.

Conclusion

Hopefully from this all, you can see pipes as a beautiful abstraction for chaining together and transforming streaming computations in a pure, constant-space way. I hope that looking back on it all you can see everything as either a transformation of pipes, or a chaining of pipes.

I recommend looking more into the great pipes documentation, joining the pipes mailing list, and trying your own streaming data projects with pipes to see what you can do with it.

You should also checkout conduit and try to implement this streaming logic in that framework. Let me know how it turns out!

As always the great people of freenode’s #haskell are always free to answer any questions you might have, and also of course the haskell tag on Stack Overflow. And I’ll try to address as many questions as I can in the comments!

Keep in mind that I’m still a new user of pipes myself; if I’ve made any huge mistakes in style or idiomaticness, I’m available here in the comments and I’d appreciate any corrections y’all can offer.

So ends the “pipes tutorial” section of this series; tune in next time and we’ll try our best to optimize this baby! 3

Bonus Round: Full Lens

Hey guess what! Let’s try and go full lens :)

(This section does not invalidate anything you learned already, so if you have problems with it, it’s okay :) )

Now, you might have thought, “Hey, we used view PB.pack to turn our Word8 producer into a ByteString producer…couldn’t we just use view PB.unpack to turn our ByteString producer into a Word8 producer in the first place???”

Yup! In fact, this takes us into a…“pipe transformer style” of pipes code, as opposed to a “pipe composition style” of pipes code. Both ways are considered “idiomatic”, and it’s up to you to decide what suits you more.

Basically, we don’t ever need bsToBytes; instead of

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L65-65
    let byteProducer = PB.fromHandle hIn >-> bsToBytes

We can just write

let byteProducer = (view PB.unpack) (PB.fromHandle hIn)

Okay, one last thing.

With lens, we not only have the ability to “view” the ByteString producer “as a” Word8 producer.

We also have the ability to modify the Word8 producer that we “see”…and put it back into the ByteString producer!

That is, if I have a ByteString producer, I can see the Word8 producer, modify it, and “stick it back into” the ByteString producer…to basically create a new ByteString producer that instead outputs our “modified” Word8 producer.

It’s like a fancy fmap. And like how view was how we “unlocked” the viewer from the lens, we use over to “unlock” the “pull out, edit, and stick back in”.

That is, in our case,

over :: Lens' (Producer ByteString m r) (Producer Word8 m r)
     -> (Producer Word8 m r -> Producer Word8 m r)
     -> Producer ByteString m r
     -> Producer ByteString m r

What does this mean, in practice?

That means that we can use over, apply a function to the Word8 producer, and over will handle the re-packing (with the smart chunking) for us, all in one swoop.

So, we can rewrite bsOut:

bsIn      = PB.fromHandle hIn
bsOut     = flip (over PB.unpack) bsIn $ \bytesOut ->
                dirsBytes ( bytesOut
                        >-> encodeByte encTable )
pipeline  = bsOut
        >-> PB.toHandle hOut

So over PB.unpack handles the unpacking (to get bytesOut) and the re-packing (after the result of dirsBytes) for us, in one fell swoop.

Neat!

Okay now, good bye, for reals!


  1. Remember, a ByteString is an efficiently packed “chunk”/“list” of Word8/bytes; we can use functions like ByteString.unpack and ByteString.pack to turn a ByteString into a list of Word8s or go backwards.

  2. It actually works on all Foldables, not just [].

  3. Hopefully you aren’t holding your breath on this one :) This next part is not scheduled any ime soon and might not come for a while, as I’ll be pursuing some other things in the near future — I apologize for any disappointment/inconvenience this may cause.

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