Stream is a Swift library that enables you to create scalable data pipelines for medium or large datasets.
Stream pipelines allow you to process large or even infinite collections efficiently by:
- Performing computation in parallel within each Stream.
- Running each Stream concurrently within a pipeline.
- Providing back-pressure mechanisms to control memory growth.
You can install it via SwiftPM via:
.package(url: "https://github.com/cgarciae/Stream", from: "0.0.7")
It might work on other compatible package managers.
Any Sequence
can be converted into a Stream
via the .stream
property, after that you can use its custom functional methods like map
, filter
, etc, to process the data in parallel / concurrently:
import Stream
_ = getURLs()
.stream
.map {
downloadImage($0)
}
.filter {
validateImage($0)
}
.flatMap {
getMultipleImageSizes($0)
}
.forEach {
storeImage($0)
}
Stream
inherits from LazySequence
so you can treat it like a normal Sequence for other purposes. By default the results of each stream may come in any order which has better performance, but if you do want to preserve order you can turn a Stream
into an OrderedStream
via the .inOrder
property.
import Stream
_ = getURLs()
.stream
.inOrder
.map {
downloadImage($0)
}
.filter {
validateImage($0)
}
.flatMap {
getMultipleImageSizes($0)
}
.forEach {
storeImage($0)
}
To manage resources you can use the maxTasks
and queueMax
parameters:
import Stream
_ = getURLs()
.stream
.map(maxTasks: 4, queueMax: 10) {
downloadImage($0)
}
.filter(maxTasks: 2, queueMax: 15) {
validateImage($0)
}
.flatMap(maxTasks: 5, queueMax: 25) {
getMultipleImageSizes($0)
}
.forEach(maxTasks: 3,queueMax: 20) {
storeImage($0)
}
maxTasks
will control the number of GCD Tasks created by the Stream, and queueMax
will limit maximum amount of elements allowed to live in the output queue simultaneously. If the output queue is full tasks will eventually block and the Stream will halt until its consumer requests more elements.
map
flatMap
filter
forEach
Cristian Garcia – cgarcia.e88@gmail.com
Distributed under the MIT license. See LICENSE for more information.