In my version of alphaBeta I'm doing something like this. (moves is initially a Seq[Move].)
val alphaMovePairs: Stream[(Score, Move)] =
moves.toStream.scanLeft {
// Use moves.head as the default best move and 0 as the default best score.
(0, moves.head)
} {
case ((alpha, bestMove), nextMove) => ...
// evaluate nextMove; compare the result to alpha; update if it's better.
(newAlpha, newBestMove)
}
val validAlpha: Stream[(Score, Move)] = alphaMovePairs.takeWhile {
case (alpha, move) => alpha <= beta }
My intent was to use the Stream and takeWhile to avoid evaluating the entire Stream of Moves if I encounter one that was greater than beta. But my trace shows that the entire Stream is examined before takeWhile is run.
What am I missing?
Thanks.
Related
I am trying to loop over inputs and process them to produce scores.
Just for the first input, I want to do some processing that takes a while.
The function ends up returning just the values from the 'else' part. The 'if' part is done executing after the function returns the value.
I am new to Scala and understand the behavior but not sure how to fix it.
I've tried inputs.zipWithIndex.map instead of foreach but the result is the same.
def getscores(
inputs: inputs
): Future[Seq[scoreInfo]] = {
var scores: Seq[scoreInfo] = Seq()
inputs.zipWithIndex.foreach {
case (f, i) => {
if (i == 0) {
// long operation that returns Future[Option[scoreInfo]]
getgeoscore(f).foreach(gso => {
gso.foreach(score => {
scores = scores.:+(score)
})
})
} else {
scores = scores.:+(
scoreInfo(
id = "",
score = 5
)
)
}
}
}
Future {
scores
}
}
For what you need, I would drop the mutable variable and replace foreach with map to obtain an immutable list of Futures and recover to handle exceptions, followed by a sequence like below:
def getScores(inputs: Inputs): Future[List[ScoreInfo]] = Future.sequence(
inputs.zipWithIndex.map{ case (input, idx) =>
if (idx == 0)
getGeoScore(input).map(_.getOrElse(defaultScore)).recover{ case e => errorHandling(e) }
else
Future.successful(ScoreInfo("", 5))
})
To capture/print the result, one way is to use onComplete:
getScores(inputs).onComplete(println)
The part your missing is understanding a tricky element of concurrency, and that is that the order of execution when using multiple futures is not guaranteed.
If your block here is long running, it will take a while before appending the score to scores
// long operation that returns Future[Option[scoreInfo]]
getgeoscore(f).foreach(gso => {
gso.foreach(score => {
// stick a println("here") in here to see what happens, for demonstration purposes only
scores = scores.:+(score)
})
})
Since that executes concurrently, your getscores function will also simultaneously continue its work iterating over the rest of inputs in your zipWithindex. This iteration, especially since it's trivial work, likely finishes well before the long-running getgeoscore(f) completes the execution of the Future it scheduled, and the code will exit the function, moving on to whatever code is next after you called getscores
val futureScores: Future[Seq[scoreInfo]] = getScores(inputs)
futureScores.onComplete{
case Success(scoreInfoSeq) => println(s"Here's the scores: ${scoreInfoSeq.mkString(",")}"
}
//a this point the call to getgeoscore(f) could still be running and finish later, but you will never know
doSomeOtherWork()
Now to clean this up, since you can run a zipWithIndex on your inputs parameter, I assume you mean it's something like a inputs:Seq[Input]. If all you want to do is operate on the first input, then use the head function to only retrieve the first option, so getgeoscores(inputs.head) , you don't need the rest of the code you have there.
Also, as a note, if using Scala, get out of the habit of using mutable vars, especially if you're working with concurrency. Scala is built around supporting immutability, so if you find yourself wanting to use a var , try using a val and look up how to work with the Scala's collection library to make it work.
In general, that is when you have several concurrent futures, I would say Leo's answer describes the right way to do it. However, you want only the first element transformed by a long running operation. So you can use the future return by the respective function and append the other elements when the long running call returns by mapping the future result:
def getscores(inputs: Inputs): Future[Seq[ScoreInfo]] =
getgeoscore(inputs.head)
.map { optInfo =>
optInfo ++ inputs.tail.map(_ => scoreInfo(id = "", score = 5))
}
So you neither need zipWithIndex nor do you need an additional future or join the results of several futures with sequence. Mapping the future just gives you a new future with the result transformed by the function passed to .map().
How do I replace my first conditional with the require function in the context of a Future? Should I wrap the entire inRange method in a Future, and if I do that, how do I handle the last Future so that it doesn't return a Future[Future[List[UserId]], or is there a better way?
I have a block of code that looks something like this:
class RetrieveHomeownersDefault(depA: DependencyA, depB: DependencyB) extends RetrieveHomeowners {
def inRange(range: GpsRange): Future[List[UserId]] = {
// I would like to replace this conditional with `require(count >= 0, "The offset…`
if (count < 0) {
Future.failed(new IllegalArgumentException("The offset must be a positive integer.")
} else {
val retrieveUsers: Future[List[UserId]] = depA.inRange(range)
for (
userIds <- retrieveUsers
homes <- depB.homesForUsers(userIds)
) yield FilterUsers.withoutHomes(userIds, homes)
}
}
}
I started using the require function in other areas of my code, but when I tried to use it in the context of Futures I ran into some hiccups.
class RetrieveHomeownersDefault(depA: DependencyA, depB: DependencyB) extends RetrieveHomeowners {
// Wrapped the entire method with Future, but is that the correct approach?
def inRange(range: GpsRange): Future[List[UserId]] = Future {
require(count >= 0, "The offset must be a positive integer.")
val retrieveUsers: Future[List[UserId]] = depA.inRange(range)
// Now I get Future[Future[List[UserId]]] error in the compiler.
for (
userIds <- retrieveUsers
homes <- depB.homesForUsers(userIds)
) yield FilterUsers.withoutHomes(userIds, homes)
}
}
Any tips, feedback, or suggestions would be greatly appreciated. I'm just getting started with Futures and still having a tough time wrapping my head around many concepts.
Thanks a bunch!
Just remove the outer Future {...} wrapper. It's not necessary. There's no good reason for the require call to go inside the Future. It's actually better outside since then it will report immediately (in the same thread) to the caller that the argument is invalid.
By the way, the original code is wrong too. The Future.failed(...) is created but not returned. So essentially it didn't do anything.
I have a fairly large collection that I would like to iterate and find out if the collection contains more than one instance of a particular number. Since the collection is large, i'd like to exit early, i.e not traverse the complete list.
I have a dirty looking piece of code that does this in a non-functional programming way. However, i'm unable to find a functional programming way of doing this (In Groovy or Scala), since I need to do 2 things at the same time.
Accumulate state
Exit Early
The "accumulate state" can be done using the "inject" or "fold" methods in Groovy/Scala but there's no way of exiting early from those methods. Original groovy code is below. Any thoughts?
def collection = [1,2,3,2,4,6,0,65,... 1 million more numbers]
def n = 2
boolean foundMoreThanOnce(List<Integer> collection, Integer n) {
def foundCount = 0
for(Integer i : collection) {
if(i == n) {
foundCount = foundCount + 1
}
if(foundCount > 1) {
return true
}
}
return false
}
print foundMoreThanOnce(collection, n)
One of many possible Scala solutions.
def foundMoreThanOnce[A](collection: Seq[A], target: A): Boolean =
collection.dropWhile(_ != target).indexOf(target,1) > 0
Or a slight variation...
collection.dropWhile(target.!=).drop(1).contains(target)
Scans the collection only until the 2nd target element is found.
Not sure about groovy, but if possible for you to use Java 8 then there is a possibility
collection.stream().filter(z -> {return z ==2;} ).limit(2)
the limit will stop the stream processing as soon as it get 2nd occurrence of 2.
You can use it as below, to ensure there are exact two occurrences
Long occ = collection.stream().filter(z -> {return z ==2;} ).limit(2).count();
if(occ == 2)
return true;
I have a recursive function that needs to compare the results of the current call to the previous call to figure out whether it has reached a convergence. My function does not contain any action - it only contains map, flatMap, and reduceByKey. Since Spark does not evaluate transformations (until an action is called), my next iteration does not get the proper values to compare for convergence.
Here is a skeleton of the function -
def func1(sc: SparkContext, nodes:RDD[List[Long]], didConverge: Boolean, changeCount: Int) RDD[(Long] = {
if (didConverge)
nodes
else {
val currChangeCount = sc.accumulator(0, "xyz")
val newNodes = performSomeOps(nodes, currChangeCount) // does a few map/flatMap/reduceByKey operations
if (currChangeCount.value == changeCount) {
func1(sc, newNodes, true, currChangeCount.value)
} else {
func1(sc, newNode, false, currChangeCount.value)
}
}
}
performSomeOps only contains map, flatMap, and reduceByKey transformations. Since it does not have any action, the code in performSomeOps does not execute. So my currChangeCount does not get the actual count. What that implies, the condition to check for the convergence (currChangeCount.value == changeCount) is going to be invalid. One way to overcome is to force an action within each iteration by calling a count but that is an unnecessary overhead.
I am wondering what I can do to force an action w/o much overhead or is there another way to address this problem?
I believe there is a very important thing you're missing here:
For accumulator updates performed inside actions only, Spark guarantees that each task’s update to the accumulator will only be applied once, i.e. restarted tasks will not update the value. In transformations, users should be aware of that each task’s update may be applied more than once if tasks or job stages are re-executed.
Because of that accumulators cannot be reliably used for managing control flow and are better suited for job monitoring.
Moreover executing an action is not an unnecessary overhead. If you want to know what is the result of the computation you have to perform it. Unless of course the result is trivial. The cheapest action possible is:
rdd.foreach { case _ => }
but it won't address the problem you have here.
In general iterative computations in Spark can be structured as follows:
def func1(chcekpoinInterval: Int)(sc: SparkContext, nodes:RDD[List[Long]],
didConverge: Boolean, changeCount: Int, iteration: Int) RDD[(Long] = {
if (didConverge) nodes
else {
// Compute and cache new nodes
val newNodes = performSomeOps(nodes, currChangeCount).cache
// Periodically checkpoint to avoid stack overflow
if (iteration % checkpointInterval == 0) newNodes.checkpoint
/* Call a function which computes values
that determines control flow. This execute an action on newNodes.
*/
val changeCount = computeChangeCount(newNodes)
// Unpersist old nodes
nodes.unpersist
func1(checkpointInterval)(
sc, newNodes, currChangeCount.value == changeCount,
currChangeCount.value, iteration + 1
)
}
}
I see that these map/flatMap/reduceByKey transformations are updating an accumulator. Therefore the only way to perform all updates is to execute all these functions and count is the easiest way to achieve that and gives the lowest overhead compared to other ways (cache + count, first or collect).
Previous answers put me on the right track to solve a similar convergence detection problem.
foreach is presented in the docs as:
foreach(func) : Run a function func on each element of the dataset. This is usually done for side effects such as updating an Accumulator or interacting with external storage systems.
It seems like instead of using rdd.foreach() as a cheap action to trigger accumulator increments placed in various transformations, it should be used to do the incrementing itself.
I'm unable to produce a scala example, but here's a basic java version, if it can still help:
// Convergence is reached when two iterations
// return the same number of results
long previousCount = -1;
long currentCount = 0;
while (previousCount != currentCount){
rdd = doSomethingThatUpdatesRdd(rdd);
// Count entries in new rdd with foreach + accumulator
rdd.foreach(tuple -> accumulator.add(1));
// Update helper values
previousCount = currentCount;
currentCount = accumulator.sum();
accumulator.reset();
}
// Convergence is reached
for (String stock : allStocks) {
Quote quote = getQuote(...);
if (null == quoteLast) {
continue;
}
Price price = quote.getPrice();
if (null == price) {
continue;
}
}
I don't necessarily need a line by line translation, but I'm looking for the "Scala way" to handle this type of problem.
You don't need continue or breakable or anything like that in cases like this: Options and for comprehensions do the trick very nicely,
val stocksWithPrices =
for {
stock <- allStocks
quote <- Option(getQuote(...))
price <- Option(quote.getPrice())
} yield (stock, quote, price);
Generally you try to avoid those situations to begin with by filtering before you even start:
val goodStocks = allStocks.view.
map(stock => (stock, stock.getQuote)).filter(_._2 != null).
map { case (stock, quote) => (stock,quote, quote.getPrice) }.filter(_._3 != null)
(this example showing how you'd carry along partial results if you need them). I've used a view so that results will be computed as-needed, instead of creating a bunch of new collections at each step.
Actually, you'd probably have the quotes and such return options--look around on StackOverflow for examples of how to use those instead of null return values.
But, anyway, if that sort of thing doesn't work so well (e.g. because you are generating too many intermediate results that you need to keep, or you are relying on updating mutable variables and you want to keep the evaluation pattern simple so you know what's happening when) and you can't conceive of the problem in a different, possibly more robust way, then you can
import scala.util.control.Breaks._
for (stock <- allStocks) {
breakable {
val quote = getQuote(...)
if (quoteLast eq null) break;
...
}
}
The breakable construct specifies where breaks should take you to. If you put breakable outside a for loop, it works like a standard Java-style break. If you put it inside, it acts like continue.
Of course, if you have a very small number of conditions, you don't need the continue at all; just use the else of the if-statement.
Your control structure here can be mapped very idiomatically into the following for loop, and your code demonstrates the kind of filtering that Scala's for loop was designed for.
for {stock <- allStocks.view
quote = getQuote(...)
if quoteLast != null
price = quote.getPrice
if null != price
}{
// whatever comes after all of the null tests
}
By the way, Scala will automatically desugar this into the code from Rex Kerr's solution
val goodStocks = allStocks.view.
map(stock => (stock, stock.getQuote)).filter(_._2 != null).
map { case (stock, quote) => (stock,quote, quote.getPrice) }.filter(_._3 != null)
This solution probably doesn't work in general for all different kinds of more complex flows that might use continue, but it does address a lot of common ones.
If the focus is really on the continue and not on the null handling, just define an inner method (the null handling part is a different idiom in scala):
def handleStock(stock: String): Unit {
val quote = getQuote(...)
if (null == quoteLast) {
return
}
val price = quote.getPrice();
if (null == price) {
return
}
}
for (stock <- allStocks) {
handleStock(stock)
}
The simplest way is to embed the skipped-over code in an if with reversed-sense to what you have.
See http://www.scala-lang.org/node/257