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!)
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:
- pipes-parse, for leftover support. We’re going to be using limited leftover handling for this project in a couple of situations.
- 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!
(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!)
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:
- A pipe that turns incoming
ByteStrings into bytes1 (
Word8s), emitting one at a time.
- A pipe that turns incoming
Directions, by looking up each
Word8in an encoding table to get a list of
Directions and emitting them one at a time.
- A pipe that takes in
Directions and “chunks them up” 8-at-a-time, and re-emits those chunks of 8 as bytes/
- 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.
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:
And then we “transform” that
Direction producer into a
Word8 producer, which we’ll call
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
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?
ByteStrings from the file — entire big chunks of bytes/
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
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.
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
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-L46 -- 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!
-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L48-L60 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 :)
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-L74 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
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
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:
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
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 is a
Maybe (PreTree Word8); we then tag on the length to our
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-L92 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.
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
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,
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
[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
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
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-L117 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
Remember that in pipes-parse:
runStateT parser takes a
Producer a and “parses” a value out of it, returning the parsed value and the “leftover/used”
In our case:
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
-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/encode.hs#L123-L137 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
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
And finally, we use the “smart chunking” provided by pipes-bytestring by transforming our bytes stream:
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!
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!
Let’s try to see the plan for our decoding script, applying what we learned before. What components do we need?
- First, a component producing decoded
Word8s (that will be
view PB.pack’d into a component producing decoded
ByteStrings with smart chunking)
- A producer that reads in
ByteStrings from a file and sends them downstream.
- A pipe that unpacks those
Word8s and sends each one down.
- A pipe that “unpacks” those
Directions and sends those down.
- A pipe that traverses down the Huffman encoding tree following the incoming
Directions, and emits a decoded
Word8every time it decodes a value.
- A producer that reads in
- 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-L37 -- 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:
And now on to the juicy parts:
-- source: https://github.com/mstksg/inCode/tree/master/code-samples/huffman/decode.hs#L48-L69 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 the metadata is a snap, and it uses what we learned earlier from
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:
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!
decodingPipe is the leftover producer after the parse of the metadata.
bsToBytes is the same as from our encoder.
bytesToDirs is implemented “exactly” like
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-L104 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.
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
Word8s, and then stop.
And so now we have our
One could actually have written
searchPT like this:
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 with the ends not sealed off) that consumes
Directions and returns resulting
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-L86 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
(>~) is a pretty useful thing. Basically, when you say
it is like saying “Every time
awaits, just use the result returned by
We can look at
Which just simply echoes/sends back down whatever it receives.
When we say:
We basically say “every time we
await something in
cat, just use
consumer’s return value”:
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
Pipes and seeing what it does to it; you might have some fun.
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
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
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!
And the rest is…well, we already know it!
(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!
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.
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
We can just write
Okay, one last thing.
With lens, we not only have the ability to “view” the
ByteString producer “as a”
We also have the ability to modify the
Word8 producer that we “see”…and put it back into the
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”
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,
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
over PB.unpack handles the unpacking (to get
bytesOut) and the re-packing (after the result of
dirsBytes) for us, in one fell swoop.
Okay now, good bye, for reals!
ByteStringis an efficiently packed “chunk”/“list” of
Word8/bytes; we can use functions like
ByteString.packto turn a
ByteStringinto a list of
Word8s or go backwards.↩
It actually works on all
Foldables, not just
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.↩