Parallel operations on Kotlin collections?

Question

In Scala, one can easily do a parallel map, forEach, etc, with:

collection.par.map(..)

Is there an equivalent in Kotlin?

Answer 1

The Kotlin standard library has no support for parallel operations. However, since Kotlin uses the standard Java collection classes, you can use the Java 8 stream API to perform parallel operations on Kotlin collections as well.

e.g.

myCollection.parallelStream()
        .map { ... }
        .filter { ... }

Answer 2

As of Kotlin 1.1, parallel operations can also be expressed quite elegantly in terms of coroutines. Here is a custom pmap helper function for lists:

fun <A, B>List<A>.pmap(f: suspend (A) -> B): List<B> = runBlocking {
    map { async(Dispatchers.Default) { f(it) } }.map { it.await() }
}

Answer 3

You can use this extension method:

suspend fun <A, B> Iterable<A>.pmap(f: suspend (A) -> B): List<B> = coroutineScope {
    map { async { f(it) } }.awaitAll()
}

See Parallel Map in Kotlin for more info

Answer 4

There is no official support in Kotlin's stdlib yet, but you could define an extension function to mimic par.map:

fun <T, R> Iterable<T>.pmap(
          numThreads: Int = Runtime.getRuntime().availableProcessors() - 2, 
          exec: ExecutorService = Executors.newFixedThreadPool(numThreads),
          transform: (T) -> R): List<R> {

    // default size is just an inlined version of kotlin.collections.collectionSizeOrDefault
    val defaultSize = if (this is Collection<*>) this.size else 10
    val destination = Collections.synchronizedList(ArrayList<R>(defaultSize))

    for (item in this) {
        exec.submit { destination.add(transform(item)) }
    }

    exec.shutdown()
    exec.awaitTermination(1, TimeUnit.DAYS)

    return ArrayList<R>(destination)
}

(github source)

Here's a simple usage example

val result = listOf("foo", "bar").pmap { it+"!" }.filter { it.contains("bar") }

If needed it allows to tweak threading by providing the number of threads or even a specific java.util.concurrent.Executor. E.g.

listOf("foo", "bar").pmap(4, transform = { it + "!" })

Please note, that this approach just allows to parallelize the map operation and does not affect any downstream bits. E.g. the filter in the first example would run single-threaded. However, in many cases just the data transformation (ie. map) requires parallelization. Furthermore, it would be straightforward to extend the approach from above to other elements of Kotlin collection API.

Answer 5

From 1.2 version, kotlin added a stream feature which is compliant with JRE8

So, iterating over a list asynchronously could be done like bellow:

fun main(args: Array<String>) {
  val c = listOf("toto", "tata", "tutu")
  c.parallelStream().forEach { println(it) }
}

Answer 6

Kotlin wants to be idiomatic but not too much synthetic to be hard to understand at a first glance.

Parallel computation trough Coroutines is no exception. They want it to be easy but not implicit with some pre-built method, allowing to branch the computation when needed.

In your case:

collection.map { 
        async{ produceWith(it) } 
    }
    .forEach { 
        consume(it.await()) 
    }

Notice that to call async and await you need to be inside a so called Context, you cannot make suspending calls or launching a coroutine from a non-coroutine context. To enter one you can either:

• runBlocking { /* your code here */ }: it will suspend the current thread until the lambda returns.
• GlobalScope.launch { }: it will execute the lambda in parallel; if your main finishes executing while your coroutines have not bad things will happen, in that case better use runBlocking.

Hope it may helps :)

Answer 7

At the present moment no. The official Kotlin comparison to Scala mentions:

Things that may be added to Kotlin later:

• Parallel collections

Answer 8

This solution assumes that your project is using coroutines:

implementation( "org.jetbrains.kotlinx:kotlinx-coroutines-core:1.3.2")

The functions called parallelTransform don't retain the order of elements and return a Flow<R>, while the function parallelMap retains the order and returns a List<R>.

Create a threadpool for multiple invocations:

val numberOfCores = Runtime.getRuntime().availableProcessors()
val executorDispatcher: ExecutorCoroutineDispatcher =
    Executors.newFixedThreadPool(numberOfCores ).asCoroutineDispatcher()

use that dispatcher (and call close() when it's no longer needed):

inline fun <T, R> Iterable<T>.parallelTransform(
    dispatcher: ExecutorDispatcher,
    crossinline transform: (T) -> R
): Flow<R> = channelFlow {

    val items: Iterable<T> = this@parallelTransform
    val channelFlowScope: ProducerScope<R> = this@channelFlow

    launch(dispatcher) {
        items.forEach {item ->
            launch {
                channelFlowScope.send(transform(item))
            }
        }
    }
}

If threadpool reuse is of no concern (threadpools aren't cheap), you can use this version:

inline fun <T, R> Iterable<T>.parallelTransform(
    numberOfThreads: Int,
    crossinline transform: (T) -> R
): Flow<R> = channelFlow {

    val items: Iterable<T> = this@parallelTransform
    val channelFlowScope: ProducerScope<R> = this@channelFlow

    Executors.newFixedThreadPool(numberOfThreads).asCoroutineDispatcher().use { dispatcher ->
        launch( dispatcher ) {
            items.forEach { item ->
                launch {
                    channelFlowScope.send(transform(item))
                }
            }
        }
    }
}

if you need a version that retains the order of elements:

inline fun <T, R> Iterable<T>.parallelMap(
    dispatcher: ExecutorDispatcher,
    crossinline transform: (T) -> R
): List<R> = runBlocking {

    val items: Iterable<T> = this@parallelMap
    val result = ConcurrentSkipListMap<Int, R>()

    launch(dispatcher) {
        items.withIndex().forEach {(index, item) ->
            launch {
                result[index] = transform(item)
            }
        }
    }

    // ConcurrentSkipListMap is a SortedMap
    // so the values will be in the right order
    result.values.toList()
}

Answer 9

I found this:

implementation 'com.github.cvb941:kotlin-parallel-operations:1.3'

details:

https://github.com/cvb941/kotlin-parallel-operations

Answer 10

I've come up with a couple of extension functions:

1. The suspend extension function on Iterable<T> type, which does a parallel processing of items and returns some result of processing each item. By default it uses Dispatchers.IO dispatcher to offload blocking tasks to a shared pool of threads. Must be called from a coroutine (including a coroutine with Dispatchers.Main dispatcher) or another suspend function.

   suspend fun <T, R> Iterable<T>.processInParallel(
       dispatcher: CoroutineDispatcher = Dispatchers.IO,
       processBlock: suspend (v: T) -> R,
   ): List<R> = coroutineScope { // or supervisorScope
       map {
           async(dispatcher) { processBlock(it) }
       }.awaitAll()
   }
   

   Example of calling from a coroutine:

   val collection = listOf("A", "B", "C", "D", "E")
   
   someCoroutineScope.launch {
       val results = collection.processInParallel {
           process(it)
       }
       // use processing results
   }
   

where someCoroutineScope is an instance of CoroutineScope.

2. Launch and forget extension function on CoroutineScope, which doesn't return any result. It also uses Dispatchers.IO dispatcher by default. Can be called using CoroutineScope or from another coroutine.

   fun <T> CoroutineScope.processInParallelAndForget(
       iterable: Iterable<T>,
       dispatcher: CoroutineDispatcher = Dispatchers.IO,
       processBlock: suspend (v: T) -> Unit
   ) = iterable.forEach {
       launch(dispatcher) { processBlock(it) }
   }
   

   Example of calling:

   someoroutineScope.processInParallelAndForget(collection) {
       process(it)
   }
   
   // OR from another coroutine:
   
   someCoroutineScope.launch {
       processInParallelAndForget(collection) {
           process(it)
       }
   }
   

2a. Launch and forget extension function on Iterable<T>. It's almost the same as previous, but the extension type is different. CoroutineScope must be passed as argument to the function.

fun <T> Iterable<T>.processInParallelAndForget(
    scope: CoroutineScope,
    dispatcher: CoroutineDispatcher = Dispatchers.IO,
    processBlock: suspend (v: T) -> Unit
) = forEach {
    scope.launch(dispatcher) { processBlock(it) }
}

Calling:

collection.processInParallelAndForget(someCoroutineScope) {
    process(it)
}

// OR from another coroutine:

someScope.launch {
    collection.processInParallelAndForget(this) {
        process(it)
    }
}

Answer 11

You can mimic the Scala API by using extension properties and inline classes. Using the coroutine solution from @Sharon answer, you can write it like this

val <A> Iterable<A>.par get() = ParallelizedIterable(this)

@JvmInline
value class ParallelizedIterable<A>(val iter: Iterable<A>) {
    suspend fun <B> map(f: suspend (A) -> B): List<B> = coroutineScope {
        iter.map { async { f(it) } }.awaitAll()
    }
}

with this, now your code can change from

anIterable.map { it.value } 

to

anIterable.par.map { it.value } 

also you can change the entry point as you like other than using extension properties, e.g.

fun <A> Iterable<A>.parallel() = ParallelizedIterable(this)

anIterable.parallel().map { it.value } 

You can also use another parallel solution and implement the rest of iterable methods inside ParallelizedIterable while still having the same method names for the operations

The drawback is that this implementation can only parallelize one operation after it, to make it so that it parallelize every subsequent operation, you may need to modify ParallelizedIterable further so it return its own type instead of returning back to List<A>