This blog post is a short response to my MITH colleague Jim Smith, who several weeks ago published a blog post about a stream processing language that he’s developing. His post walks through an example of how this language could allow you to take a stream of characters, add some location metadata to each, and then group them into words, while still holding onto the location metadata about the characters that make up the words.
The process he describes sounds a little like the functionality that iteratees provide, so I decided I’d take a quick stab at writing up an iteratee implementation of his example in Haskell. I’m using John Millikin’s enumerator package, since that’s the iteratee library that I’m most comfortable with.
We’ll start with some imports, which you don’t need to worry too much about at this point:
We’ll need a few simple data structures for this program. We’ll model locations as pairs of integers representing page and line numbers. A located character will be a character paired with a location, and a located word will be a string paired with a list of locations (one for each character in the string). None of these types need any explicit definition here, although in a more complex program we’d probably want to give them names for clarity.
Our first two functions will be very simple—they just tell us how to move to the next page or line:
Each of these takes a location and returns a location. We could write out the type signatures, but we don’t have to (thanks to Haskell’s type inference), and these functions are pretty simple, so we won’t.
Now for the first interesting part:
I’ve skipped the unnecessary type signature, again—this time because it contains the word “monad”. Monads are neat, but they aren’t terribly relevant here, and I’m not writing a monad tutorial.
The important thing to know about the type of
locator is that it’s an enumeratee. An enumeratee is just a stream transformer—it plugs into a streaming source (or enumerator) on one end, changes the items from that source in some way, and feeds them to a stream consumer (or iteratee).
The transformation doesn’t have to be a one-to-one mapping—the enumeratee could take a dozen items from its source and only feed one to its iteratee, for example. In this case, though, the enumeratee is a simple mapping that takes each character and pairs it with its location. Note that the nature of this task means that the enumeratee needs to maintain some state, which is why we use the
mapAccum combinator instead of just
Next we’ll write another enumeratee to perform our tokenization:
Here we’ve composed three different enumeratees with the
=$= combinator. The first uses
splitWhen to group incoming located characters into words, the second drops empty words from the output stream, and the third “unzips” a list of located characters into our located word type. The result is a stream transformer that takes located characters on one end and outputs located words on the other.
Next we need a source (or enumerator) that will stream characters into our enumeratee. Let’s use a few lines from Shelley:
Here we’re enumerating characters from a string, but they could just as well be coming from a file, a directory full of files, a network resource, etc.
Finally we can tie it all together:
That’s a lot of combinators, but the idea is simple: we’re running a pipeline that has our poem as its source of characters, adds location metadata to each one, breaks them into words, and feeds them to a consumer that just prints them to the screen.
The output looks like this:
("It",[(0,0),(0,0)]) ("is",[(0,0),(0,0)]) ("the",[(0,0),(0,0),(0,0)]) ("same!",[(0,0),(0,0),(0,0),(0,0),(0,0)]) ("\8212",[(0,0)]) ("For,",[(0,0),(0,0),(0,0),(0,0)]) ("be",[(0,0),(0,0)]) ("it",[(0,0),(0,0)]) ("joy",[(0,0),(0,0),(0,0)]) ("or",[(0,0),(0,0)]) ("sorrow,",[(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0)]) ("The",[(0,1),(0,1),(0,1)]) ("path",[(0,1),(0,1),(0,1),(0,1)]) ("of",[(0,1),(0,1)]) ("its",[(0,1),(0,1),(0,1)]) ("departure",[(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1)]) ...
I think it’s pretty neat that we’ve been able to do this in a few lines of reasonably simple code, but the concision isn’t the best part—I’d guess we could do as well or better in languages like Python or Ruby. The best part is how generic, composable, safe, and efficient these components are. They’re simple enough to write for a little one-off parsing problem, but they can scale like crazy when your one-off solution becomes a library. We could stream gigabytes of text through the transformers we’ve defined here without worrying about memory usage at all. If our enumerators are reading from the file system or the network, we get lots of nice guarantees about resource management. If any part of our pipeline can fail, we get nice clean ways to handle that failure. And so on.
And not a “monad” in sight.