Where to get it
To get the latest version of the library, add the following to your SBT build:
// available for Scala 2.10.6, 2.11.8, 2.12.0-RC2 libraryDependencies += "org.scalaz.stream" %% "scalaz-stream" % "0.8.5"
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
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!
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
Processto 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.
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