I’ll start with the story of how I got saved, since it’s kind of relevant. Back when I was an English Ph.D. student, I worked on a number of projects that involved natural language processing, which meant doing a lot of counting trigrams or whatever in tens of thousands of text files in giant messy directory trees. I was working primarily in Ruby at the time, after years of Java, and at least back in 2008 it was a pain in the ass to do this kind of thing in either Ruby or Java. You really want a library that provides the following features:
- Resource management: you don’t want to have to worry about running out of file handles.
- Streaming: you shouldn’t ever have to have all of the data in memory at once.
- Fusion: two successive mapping operations shouldn’t need to traverse the data twice.
- Graceful error recovery: these tasks are all off-line, but you still don’t want to have to restart a computation that’s been running for ten minutes just because the formatting in one file is wrong.
Maybe there was such a library for Ruby or Java back then, but if there was I didn’t know about it. I did have some experience with Haskell, though, and at some point in 2010 I heard about iteratees, and they were exactly what I’d always wanted. I didn’t really understand how they worked at first, but with iteratee (and later John Millikin’s enumerator) I was able to write code that did what I wanted and didn’t make me think about stuff I didn’t want to think about. I started picking Haskell instead of Ruby for new projects, and that’s how I accepted statically-typed functional programming into my life.
Iteratees in Scala
Eventually I started using more Scala than Haskell, and one of the things I always missed in Scala was a good iteratee library. The implementation in Scalaz 6 was kind of a mess, at least compared to the two most popular Haskell implementations, and using it with Scalaz’s effect package for I/O was incredibly slow. Since then scalaz-iteratee has been overhauled and play-iteratee and scalaz-stream (now “fs2”) got created, but I never really found any of them as satisfying as the iteratee libraries in Haskell I was used to.
Even after the rewrite in Scalaz 7, scalaz-iteratee is still too inconsistent, slow, and buggy for my taste. As one example, this is just about the most basic thing you can imagine doing with
scala> import scalaz._, Scalaz._, scalaz.iteratee._ import scalaz._ import Scalaz._ import scalaz.iteratee._ scala> (Iteratee.take[Int, List](5) &= EnumeratorT.iterate(_ + 1, 0)).run res0: scalaz.Id.Id[List[Int]] = List(0, 0, 0, 0, 0)
Play’s iteratee implementation is a big improvement over Scalaz’s in many ways—it actually feels like people actually use it, for one thing—but it has several serious limitations for me. Most importantly, it’s not generic over the computational context that processing happens in—you can use any monad you like as long as the monad you like is the standard library’s future. I also just don’t prefer the way it mixes purity and impurity—e.g. I’ve gotten burned a couple of times because I didn’t know or remember that the enumerator you get from
enumerate is side-effecting and not reusable. I still think it’s pretty good, though—I’ve chosen it over Scalaz’s iteratees or nothing fairly often (given that I don’t use Play), especially before Scalaz Stream came around.
Scalaz Stream isn’t an iteratee implementation, but it does the same kind of stream-y, ARM-y stuff, and the model is similar. I like Scalaz Stream a lot—I’ve used it for a handful of side projects, wrote a little port of Haskell’s split library for it, etc. I still find it extremely complicated, though (the variance gymnastics, the use of exceptions for control flow). I’ve repeatedly run into issues with e.g.
gatherMap and I still don’t have a clear sense of whether I was seeing bugs or misusing the library.
Building a library around the fact that
Any are (accidentally?) kind-polymorphic in Scala is great (I love that kind of thing), but it feels like the opposite of the simplicity of iteratees. One of the things I like most about iteratees is that if you’ve heard the word “monad” before, you’ve got a reasonable shot at understanding the basic model in an afternoon. This just isn’t the case with Scalaz Stream (or fs2, from what I’ve seen so far).
A few months ago I was starting to think about using Scalaz Stream for streaming JSON parsing in circe, but then Twitter very generously decided to pay me to take a long vacation, and it seemed reasonable to use some of my new free time to write the Scala iteratee library I’d always wished existed. I’d already ported the Scalaz implementation to Cats, and I was happy enough with the way that had gone to take it as a starting point, so last month I published my port as an 0.1 release and set myself the following goals for 0.2:
- Usage should look at least as clean as Scalaz Stream.
- Performance should be within an order of magnitude of the standard collections library.
An 0.2.0 snapshot of this new library (iteratee.io) is available now, and I’ll be publishing a release as soon as Cats 0.4.0 is out. The rest of this post is an introduction to the library, with some discussion of how it differs from other iteratee implementations, what the performance looks like, and my longer-term goals for the project.
There are three important types in any iteratee implementation:
- Enumerators, which are sources of data.
- Iteratees, which are computations that reduce streams of data to a value (think sum, length, fold, etc.).
- Enumeratees, which are computations that transform streams of data (think map or filter).
These types can be combined in various ways:
- An iteratee can be applied to an enumerator, resulting in a new iteratee.
- An iteratee can draw data through an enumeratee, also resulting in a new iteratee.
- Enumerators can be concatenated, resulting in a bigger enumerator.
- An enumerator can be “wrapped” with an enumeratee to create a new enumerator.
- Iteratees can be composed sequentially.
- Enumeratees can be composed sequentially.
And so on.
All three types are parametrized on a computational context (usually monadic, if you want to do anything interesting), and none of the operations in the list above actually performs any processing—they simply define new computations. There’s exactly one way to make processing happen: you “run” an iteratee, which results in a value in the iteratee’s monadic context.
We can make this more concrete with a simple example. We’ll solve the first Project Euler problem, which asks for the sum of the natural numbers smaller than a thousand that are multiples of three or five.
import io.iteratee.pure._ val naturals = iterate(1)(_ + 1) val mult3or5 = filter[Int](i => i % 3 == 0 || i % 5 == 0) val multsUnder1000 = takeWhile[Int](_ < 1000).through(mult3or5).map(_.sum)
naturals is an enumerator (in this case representing an infinite stream),
mult3or5 is an enumeratee that filters the numbers we care about, and
multsUnder1000 is an iteratee representing a computation that performs this filtering for the relevant part of the stream and takes the sum of the resulting values.
Now we can apply the iteratee to our source and run the resulting computation:
scala> val computation = multsUnder1000(naturals) computation: io.iteratee.Iteratee[cats.Id,Int,Int] = io.iteratee.Iteratee@3d111cde scala> computation.run res0: cats.Id[Int] = 233168
There’s also shorthand for this, since applying an iteratee to an enumerator and running it is something you do all the time:
scala> naturals.run(multsUnder1000) res1: cats.Id[Int] = 233168
As a side note, stack safety is BYOB, so you generally don’t want to use
Id as your monad outside of simple examples like this. The library provides “modules” for several monads, including identity (which we get above from
Eval (which is stack-safe), and Scalaz’s
Task (in a separate task subproject). You just import the contents of the module for the monad you wish to work in, and you get a bunch of useful enumerators, enumeratees, and iteratees (most of which have names that look like methods from collection library classes and companion objects). For example, using the module for the
Eval monad this time:
scala> import io.iteratee.eval._ import io.iteratee.eval._ scala> iterate("a")(_ + "a") res0: io.iteratee.Enumerator[cats.Eval,String] = io.iteratee.Enumerator$$anon$15@335372c9 scala> map[Int, String](_.toString) res1: io.iteratee.Enumeratee[cats.Eval,Int,String] = io.iteratee.Enumeratee$$anon$7@4216e8c6 scala> fold[Int, List[Int]](Nil)(_ :+ _) res2: io.iteratee.Iteratee[cats.Eval,Int,List[Int]] = io.iteratee.Iteratee@6d848fab
In general, an operation that would be a method on e.g. the
List companion object is an enumerator here, while operations that are methods on
List instances are either enumeratees (if they return another list) or iteratees (if they return a single value).
This probably isn’t terribly interesting yet—we’re doing stuff that we could have done just as easily with the standard library’s streams. The nicest part is probably the automatic resource management. Suppose for example that I want to know how many times I’ve written “flatMap” in the source for the core and task subprojects of this library. The task subproject provides enumerators for listing directory contents and reading lines from files that make this easy:
import io.iteratee._, io.iteratee.task._, java.io.File, scalaz.concurrent.Task val words: Enumerator[Task, String] = listAllFiles(new File("core/src")).append(listAllFiles(new File("task/src"))) .flatMap(lines) .flatMap(line => enumVector(line.split("\\W").toVector))
Now we’ve got an enumerator that will produce every word in these source trees. We can then count the flatMaps:
scala> val countingComputation = words.mapE(filter(_ == "flatMap")).run(length) countingComputation: scalaz.concurrent.Task[Int] = scalaz.concurrent.Task@5d1fe005 scala> countingComputation.run res0: Int = 72
We could also ask for the first five words that start with “e”:
scala> words.mapE(filter(_.startsWith("e"))).run(take(5)).run res1: Vector[String] = Vector(exists, experiments, element, extends, elements)
It’s also possible to describe two computations and zip them together so that they’ll both be computed on a single pass through the stream. For example, if we rephrase our two computations a bit, we can have them run together:
scala> val count = length[String].through(filter(_ == "flatMap")) count: io.iteratee.Iteratee[scalaz.concurrent.Task,String,Int] = io.iteratee.Iteratee@744dbfe scala> val es = take[String](5).through(filter(_.startsWith("e"))) es: io.iteratee.Iteratee[scalaz.concurrent.Task,String,Vector[String]] = io.iteratee.Iteratee@6dbf8bba scala> words.run(count.zip(es)).run res2: (Int, Vector[String]) = (72,Vector(exists, experiments, element, extends, elements))
We don’t have to worry about resource management in any of these cases because the
words enumerator is doing that for us. When we run the computation, it keeps track of which files it opens, and it will always close them, even if we don’t use all of the words, if an
IOException gets thrown, etc. It also won’t open files unnecessarily—if we ask for the first five words that start with “e” and they occur in the first file, then that’s the only file that will be opened.
What about performance?
Right now the performance situation is looking better than I’d hoped—I’m well within an order of magnitude of the standard collection library. In the following benchmarks
I stands for this library,
S for Scalaz Stream,
Z for scalaz-iteratee,
P for play-iteratee, and
C for the collection library. Each benchmark is intended to reflect idiomatic use of the library it uses. For iteratee.io, Scalaz Stream, and scalaz-iteratee, all computations are in the
The source for these benchmarks is available in the GitHub repository, and of course I’d welcome improvements for any of the implementations.
In the first benchmark we take the sum of 10,000 integers in an in-memory collection (if such a thing is available for the library). iteratee.io has an advantage over the Scalaz libraries here because it supports chunked inputs, and it actually beats the standard library, since
sum on a vector (which uses Scala’s
Numeric) is slower than the
combine on Algebra’s monoid that the iteratee.io operation ends up using on the chunk.
Benchmark Mode Cnt Score Error Units InMemoryBenchmark.sumIntsI thrpt 80 15105.537 ± 25.871 ops/s InMemoryBenchmark.sumIntsS thrpt 80 78.947 ± 0.510 ops/s InMemoryBenchmark.sumIntsZ thrpt 80 296.223 ± 1.971 ops/s InMemoryBenchmark.sumIntsP thrpt 80 57.355 ± 0.745 ops/s InMemoryBenchmark.sumIntsC thrpt 80 13056.163 ± 22.790 ops/s
The results report throughput, so higher numbers are better.
In the second benchmark we collect the first 10,000 values from an infinite stream of non-negative long integers into a sequence.
Benchmark Mode Cnt Score Error Units StreamingBenchmark.takeLongsI thrpt 80 1146.021 ± 6.539 ops/s StreamingBenchmark.takeLongsS thrpt 80 65.916 ± 0.182 ops/s StreamingBenchmark.takeLongsZ thrpt 80 198.919 ± 2.097 ops/s StreamingBenchmark.takeLongsP thrpt 80 1.447 ± 0.082 ops/s StreamingBenchmark.takeLongsC thrpt 80 3286.878 ± 37.967 ops/s
In both benchmarks iteratee.io is within a factor of three of the standard library, and at least an order of magnitude faster than the other libraries.
To be fair, in many cases these differences will be irrelevant, since the network or disk will be the bottleneck. My goal is to make it possible and attractive to use the same tools for most or all of the collections-like operations in your program, though, so that you don’t have to switch between your stream library’s combinators and standard collection library operations for the sake of performance (or anything else).
With most iteratee libraries it’s very easy to construct “bad” iteratees that violate the monad laws. For example, here’s Haskell’s iteratee:
Prelude> import Data.Iteratee Prelude Data.Iteratee> let leftover = idone () $ Chunk  Prelude Data.Iteratee> let ended = idone () $ EOF Nothing Prelude Data.Iteratee> run $ (leftover >> ended) >> stream2list  Prelude Data.Iteratee> run $ leftover >> (ended >> stream2list) 
So much for bind’s associativity. One of my goals for iteratee.io was to make it harder to end up with these bad iteratees. As part of this effort (and for the sake of performance), some of the work that’s normally done by the “input” representation is done instead by the “step” representation in iteratee.io.
Usually the input type is an ADT that’s either an end-of-stream signal or a collection (possibly empty) of elements. In iteratee.io, the input type is isomorphic to a non-empty list—there’s no end-of-stream input and no empty input—and the step type has three states instead of the usual two, with the “extra” state representing an iteratee that’s finished with no leftover input.
It’s still possible to violate the monad laws:
scala> import cats.syntax.flatMap._, io.iteratee.eval._ import cats.syntax.flatMap._ import io.iteratee.eval._ scala> ((head[Int] >> done((), Vector(0))) >> head).run.value res0: Option[Int] = None scala> (head[Int] >> (done((), Vector(0)) >> head)).run.value res1: Option[Int] = Some(0)
But there are fewer ways for this to happen, and if you don’t inject values into the stream by creating finished iteratees with leftovers they didn’t actually receive, you’re safe.
So far this model seems to work well for my use cases, but it’s still an experiment and is subject to change.
The creation of iteratee.io was motivated in part by the need for streaming parsing in circe, and it follows the design guidelines I wrote for circe. The 0.3.0 release of circe will include a circe-streaming module with generic enumeratees for streaming JSON processing—you bring your own
MonadError and enumerators and you get nice, fast, streaming parsing and decoding (powered by Jawn’s asynchronous parser). You can currently build this subproject yourself, and there’s a circe tutorial with examples of how you can use it, but it’s not yet in the circe 0.3.0 snapshots.
iteratee.io is a small library, with the iteratee-core jar currently weighing in at 324K, with cats-core as its only runtime dependency. Neither of these things are likely to change in the near future, although I’m planning to add several new subprojects which may have additional dependencies (for I/O operations in the standard library’s
Future, more generic zipping with Shapeless, etc.).
Test coverage for iteratee.io is currently at 100%, in part thanks to Discipline-powered law checking, and my goal is to keep 100% coverage a requirement for at least the core project. Oh, and iteratee.io supports and is published for Scala.js, if you’re into that kind of thing.
The library is still very young, but I think it’s stable enough to play with, and the API isn’t likely to change much before 0.2.0. If you have any questions, there’s a new Gitter channel for the project, or you can file an issue on GitHub or contact me on Twitter.