FS2: Functional Streams for Scala (previously 'Scalaz-Stream')

We are currently in the process of completing a major reworking of this library. The new version, likely 0.9, should hopefully see a release in the next few months, and along with this release we are renaming the project to FS2: Functional Streams for Scala, or just FS2 for short (official pronunciation 'FS two'). The name is being changed as the new version will not depend on scalaz and will have a core with zero dependencies. Versions prior to 0.9 are still called scalaz-stream.

The 0.9 release will include major new capabilities:

A new Pull datatype for building arbitrary transformations of any number of streams.
Much richer support for concurrency and parallelism. Operations that previously needed special library support can now be defined with regular 'userland' FS2 code, using the existing primitive operations.
The ability to read whole chunks on each step when transforming streams. This enables much higher performance. Existing transforms like take, takeWhile, etc are all being rewritten to take full advantage of this capability.
Pushback and peeking of streams, useful for streaming parsing tasks.
A simpler core, consisting of 22 primitive operations, from which all functionality in the library is derived.

There's an incomplete and slightly out of date guide for the new design if you'd like to get a feel for what the new version will be like.

The rest of these docs pertain to the 0.8 scalaz-stream release. If you'd like to follow FS2 development, see the 0.9 milestone.

Where to get the latest stable version

The latest stable release is 0.8 (source). To get it, add the following to your SBT build:

// available for Scala 2.10.5, 2.11.7, 2.12.0-M1, 2.12.0-M2
libraryDependencies += "org.scalaz.stream" %% "scalaz-stream" % "0.8"

As of version 0.8, scalaz-stream is solely published against scalaz 7.1.x. The most recent build for 7.0.x is scalaz-stream 0.7.3.

If you were using a previous version of scalaz-stream, you may have a resolvers entry for the Scalaz Bintray repository. This is no longer required, as scalaz-stream is now published to Maven Central. It won't hurt you though.

About the library

scalaz-stream is a streaming I/O library. The design goals are compositionality, expressiveness, resource safety, and speed. The design is meant to supersede or replace older iteratee or iteratee-style libraries. Here's a simple example of its use:

import scalaz.stream._
import scalaz.concurrent.Task

val converter: Task[Unit] =
  io.linesR("testdata/fahrenheit.txt")
    .filter(s => !s.trim.isEmpty && !s.startsWith("//"))
    .map(line => fahrenheitToCelsius(line.toDouble).toString)
    .intersperse("\n")
    .pipe(text.utf8Encode)
    .to(io.fileChunkW("testdata/celsius.txt"))
    .run

// at the end of the universe...
val u: Unit = converter.run

This will construct a Task, converter, which reads lines incrementally from testdata/fahrenheit.txt, skipping blanklines and commented lines. It then parses temperatures in degrees fahrenheit, converts these to celsius, UTF-8 encodes the output and writes incrementally to testdata/celsius.txt, using constant memory. The input and output files will be closed in the event of normal termination or exceptions.

The library supports a number of other interesting use cases:

Zipping and merging of streams: A streaming computations may read from multiple sources in a streaming fashion, zipping or merging their elements using a arbitrary Tee. In general, clients have a great deal of flexibility in what sort of topologies they can define--source, sinks, and effectful channels are all first-class concepts in the library.
Dynamic resource allocation: A streaming computation may allocate resources dynamically (for instance, reading a list of files to process from a stream built off a network socket), and the library will ensure these resources get released in the event of normal termination or when errors occur.
Nondeterministic and concurrent processing: A computation may read from multiple input streams simultaneously, using whichever result comes back first, and a pipeline of transformation can allow for nondeterminism and queueing at each stage.
Streaming parsing (UPCOMING): A separate layer handles constructing streaming parsers, for instance, for streaming JSON, XML, or binary parsing. See the roadmap for more information on this and other upcoming work.

Documentation and getting help

There are examples (with commentary) in the test directory scalaz.stream.examples. Also see the wiki for more documentation. If you use scalaz.stream, you're strongly encouraged to submit additional examples and add to the wiki!

For questions about the library, use the scalaz mailing list or the scalaz-stream tag on StackOverflow.

Blog posts and other external resources are listed on the Additional Resources page.

Projects using scalaz-stream

If you have a project you'd like to include in this list, send a message to the scalaz mailing list and we'll add a link to it here.

http4s: Minimal, idiomatic Scala interface for HTTP services using scalaz-stream
scodec-stream: A library for streaming binary decoding and encoding, built using scalaz-stream and scodec
streamz: A library that allows a Process to consume from and produce to Apache Camel endpoints, Akka Persistence journals and snapshot stores and Akka Stream flows (reactive streams) with full back-pressure support.

Related projects

Machines is a Haskell library with the same basic design as scalaz-stream, though some of the particulars differ. There is also scala-machines, which is an older, deprecated version of the basic design of scalaz-stream.