Dylan's current streams library has served us moderately well over the years. However, it has some issues which can be addressed by a new design, expanding the range of problems for which it is suited.

How things are now According to the current streams library's documentation, the design goals were: A generic, easy-to-use interface for streaming over sequences and files. The same high-level interface for consuming or producing is available irrespective of the type of stream, or the types of the elements being streamed over.

Efficiency, especially for the common case of file I/O.

Access to an underlying buffer management protocol. One of the things it was explicitly not designed to handle was, again, according to the design goals in the documentation: A comprehensive range of I/O facilities for using memory-mapped files, network connections, and so on. Unfortunately, the primary interface to our current network library is based on these very streams for which network connections were not a design goal. While this works in practice, it imposes some important limitations on our networking code. The biggest of these is that sockets can not be non-blocking as it is expected that reads and writes will complete. This prevents us from effectively utilizing event loops and largely requires that networking code require a thread per connection. This is only one of the ways in which the current design and implementation of the streams library is limiting us.

The present and future? The state of the art in stream libraries has advanced in the last 15+ years. Most recently, stream processing and data flow have been the focus of libraries in Haskell such as pipes, conduit, and Machines. Similar libraries exist in Scala, like scalaz-stream. There is a new effort afoot to provide similar capabilities for Java and Scala via Reactive Streams. A library that implements some similar concepts for networking code, although not in a functional programming manner, is Netty. Another approach is taken in the Play framework with Iteratees. Some of the primary concerns driving these more recent stream processing libraries are: Functional in nature. A stream isn't an object, but rather a pipeline of functions.

A stream isn't an object, but rather a pipeline of functions. Type safety. Each stage of the stream processing has typed inputs and outputs. Some systems provide additional guarantees.

Each stage of the stream processing has typed inputs and outputs. Some systems provide additional guarantees. Compositional. Streams can be composed with each other, just as functions can be.

Streams can be composed with each other, just as functions can be. Event driven. Part of being CPU friendly is that streams only execute when data is available or something has changed.

Part of being CPU friendly is that streams only execute when data is available or something has changed. Resource management friendly. They should be CPU efficient, memory friendly (not requiring an entire dataset to be loaded into memory), and they should free resources (like closing open files) once they're completed.

They should be CPU efficient, memory friendly (not requiring an entire dataset to be loaded into memory), and they should free resources (like closing open files) once they're completed. Not focused around I/O itself. Many stream usages might just be processing data.

Many stream usages might just be processing data. Unbounded. The stream may be unbounded in size.

The stream may be unbounded in size. Lazy. Streams shouldn't do work unless there is demand.

Streams shouldn't do work unless there is demand. No storage. Streams typically don't store data, although they may perform some buffering. This similar to the general concept of pipes, except that these are typically both push and pull. When the consumer is faster than the producer, the producer is pushing to the consumer. When the consumer is slower than the producer, then the consumer is pulling from the producer.

Examples from other frameworks What does code look like using some of these frameworks? This is a very simple network server using Haskell's conduit and some additional libraries: {-# LANGUAGE OverloadedStrings #-} import Conduit import Data.Conduit.Network import Data.Word8 ( toUpper ) main :: IO () main = runTCPServer ( serverSettings 4000 "*" ) $ \ appData -> appSource appData $$ omapCE toUpper =$ appSink appData It is explained in a blog post about network-conduit examples: runTCPServer takes two parameters. The first is the server settings, which indicates how to listen for incoming connections. Our two parameters say to listen on port 4000 and that the server should answer on all network interfaces. The second parameter is an Application, which takes some AppData and runs some action. Importantly, our app data provides a Source to read data from the client, and a Sink to write data to the client. (There's also information available such as the SockAddr of the client.) The next line is a very trivial conduit pipeline: we take all data from the source, pump it through omapCE toUpper , and send it back to the client. omapCE is our first taste of conduit-combinators: omap means we're doing a monomorphic map (on a ByteString ), and C means conduit, and E means "do it to each element in the container." Hopefully that is fairly clear without necessarily having a lot of Haskell knowledge. This is an example of a program that loads a file, converts values from Fahrenheit to Celsius and saves the results in a new file, using the scalaz-stream framework in Scala: 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 ( "

" ). pipe ( text . utf8Encode ). to ( io . fileChunkW ( "testdata/celsius.txt" )). run val u : Unit = converter . run This example is explained in depth in the scalaz-stream examples. An interesting thing about this example is that the entire file is not read into memory to convert it into lines. Instead, it is streamed through memory bit by bit, keeping memory consumption to a reasonable and hopefully constant amount.

Callbacks? No! One thing that we definitely want to avoid is the phenomenon known as "callback hell". This is common in some frameworks such as Node.js (without using their stream libraries) and Python's Twisted. In frameworks using callbacks, the flow of control is often difficult to visualize from the code and the flow of the code is often confusing or inverted. There are ways to avoid callbacks in these frameworks, such as using defer.inlineCallbacks in Twisted. But the overall pattern of relying upon chains of callbacks is something that we wish to avoid.