pull-stream

pull-stream

Minimal Pipeable Pull-stream

In classic-streams, streams push data to the next stream in the pipeline. In new-streams, data is pulled out of the source stream, into the destination.

pull-stream is a minimal take on pull streams, optimized for "object" streams, but still supporting text streams.

Quick Example

Stat some files:

pull.values(['file1', 'file2', 'file3'])
.pipe(pull.asyncMap(fs.stat))
.pipe(pull.collect(function (err, array) {
  console.log(array)
})

The best thing about pull-stream is that it can be completely lazy. This is perfect for async traversals where you might want to stop early.

Examples

What if implementing a stream was this simple:

Pipeable Streams

pull.{Source,Through,Sink} just wrap a function and give it a .pipe(dest)!

var pull = require('pull-stream')

var createSourceStream = pull.Source(function () {
  return function (end, cb) {
    return cb(end, Math.random())
  }
})

var createThroughStream = pull.Through(function (read) {
  return function (end, cb) {
    read(end, cb)
  }
})

var createSinkStream = pull.Sink(function (read) {
  read(null, function next (end, data) {
    if(end) return
    console.log(data)
    read(null, next)
  })
})

createSourceStream().pipe(createThroughStream()).pipe(createSinkStream())

Readable & Reader vs. Readable & Writable

Instead of a readable stream, and a writable stream, there is a readable stream, and a reader stream.

See also:

• Sources
• Throughs
• Sinks

Readable

The readable stream is just a function(end, cb), that may be called many times, and will (asynchronously) callback(null, data) once for each call.

The readable stream eventually callback(err) if there was an error, or callback(true) if the stream has no more data.

if the user passes in end = true, then stop getting data from wherever.

All Sources and Throughs are readable streams.

var i = 100
var randomReadable = pull.Source(function () {
  return function (end, cb) {
    if(end) return cb(end)
    //only read 100 times
    if(i-- < 0) return cb(true)
    cb(null, Math.random())
  }
})

Reader (aka, "writable")

A reader, is just a function that calls a readable, until it decideds to stop, or the readable cb(err || true)

All Throughs and Sinks are reader streams.

var logger = pull.Sink(function (read) {
  read(null, function next(end, data) {
    if(end === true) return
    if(end) throw end

    console.log(data)
    read(null, next)
  })
})

These can be connected together by passing the readable to the reader

logger()(randomReadable())

Or, if you prefer to read things left-to-right

randomReadable().pipe(logger())

Through / Duplex

A duplex/through stream is both a reader that is also readable

A duplex/through stream is just a function that takes a read function, and returns another read function.

var map = pull.Through(function (read, map) {
  //return a readable function!
  return function (end, cb) {
    read(end, function (end, data) {
      cb(end, data != null ? map(data) : null)
    })
  }
})

pipeability

Every pipeline must go from a source to a sink. Data will not start moving until the whole thing is connected.

source.pipe(through).pipe(sink)

When setting up pipeability, you must use the right function, so pipe has the right behavior.

Use Source, Through and Sink, to add pipeability to your pull-streams.

More Cool Stuff

What if you could do this?

var tripleThrough =
  through1().pipe(through2()).pipe(through3())
//THE THREE THROUGHS BECOME ONE

source().pipe(tripleThrough).pipe(sink())

Design Goals & Rationale

There is a deeper, platonic abstraction, where a streams is just an array in time, instead of in space. And all the various streaming "abstractions" are just crude implementations of this abstract idea.

classic-streams, new-streams, reducers

The objective here is to find a simple realization of the best features of the above.

Type Agnostic

A stream abstraction should be able to handle both streams of text and streams of objects.

A pipeline is also a stream.

This should work: a.pipe(x.pipe(y).pipe(z)).pipe(b) this makes it possible to write a custom stream simply by combining a few available streams.

Propagate End/Error conditions.

If a stream ends in an unexpected way (error), then other streams in the pipeline should be notified. (this is a problem in node streams - when an error occurs, the stream is disconnected, and the user must handle that specially)

Also, the stream should be able to be ended from either end.

Transparent Backpressure & Laziness

Very simple transform streams must be able to transfer back pressure instantly.

This is a problem in node streams, pause is only transfered on write, so on a long chain (a.pipe(b).pipe(c)), if c pauses, b will have to write to it to pause, and then a will have to write to b to pause. If b only transforms a's output, then a will have to write to b twice to find out that c is paused.

reducers reducers has an interesting method, where synchronous tranformations propagate back pressure instantly!

This means you can have two "smart" streams doing io at the ends, and lots of dumb streams in the middle, and back pressure will work perfectly, as if the dumb streams are not there.

This makes laziness work right.

License

MIT