Reputation: 11952

Scala system process hangs

I have an actor that uses ProcessBuilder to execute an external process:

  def act {
    while (true) {
      receive {
        case param: String => {
           val filePaths = Seq("/tmp/file1","/tmp/file2")
           val fileList = new ByteArrayInputStream(filePaths.mkString("\n").getBytes())
           val output = s"myExecutable.sh ${param}" #< fileList !!<

           doSomethingWith(output)
        }
      }
    }
  }

I run hundreds this actors running in parallel. Sometimes, for an unknown reason, the execution of the process (!!) never returns. It hangs forever. This specific actor cannot handle new messages. Is there any way to setup a timeout for this process to return, and if it exceeds retry?

What could be the reason for these executions to hold forever? Because these commands are not supposed to last more than a few milliseconds.

Edit 1: Two important facts that I observed:

This problem does not occur on Max OS X, only in Linux
When I don't use ByteArrayInputStream as input for the execution, the program does not hang

Upvotes: 3

Answers (4)

James_pic

Reputation: 3307

I don't have enough info to reproduce the issue, so I can't diagnose it exactly, but here's how I'd go about diagnosing it if I were in your shoes. The basic approach is a differential diagnosis - identify possible causes, and tests that would prove or rule them out.

The first thing I'd do is to validate that the myExecutable.sh process spawned by the application is actually terminating.

If the process isn't terminating, then this is part of the problem, so we need to understand why. One thing we could do is to run something other than myExecutable.sh. You suggested that ByteArrayInputStream may be part of the problem, which suggests that myExecutable.sh is getting bad input on stdin. If that's the case, then you could instead run a script that simply logs its input to a file, which would show this. If the input is invalid, then ByteArrayInputStream is providing bad data for some reason - thread safety and unicode are the obvious culprits, but looking at the actual bad data should give you a clue. If the input is valid, then it's a bug in myExecutable.sh.

If the process is terminating, then the problem is somewhere else. My first guesses would be that it's either related to actor scheduling (actor libraries typically use ForkJoin for execution, which is great, but doesn't deal well with blocking code), or a bug in the scala.sys.process library (wouldn't be unprecedented - I had to drop scala.sys.process from a project I was working on because of a memory leak).

Looking at the stack trace for a hung thread should give you some clues (VisualVM is your friend), as you should be able to see what's waiting. You can then find the relevant code in the OpenJDK or Scala standard library source code. Where you go from there depends on what you find.

Upvotes: 1

Glen Best

Reputation: 23115

I have an actor that uses ProcessBuilder to execute an external process: ... I run hundreds this actors running in parallel ...

That's some very heavy processing happening in parallel just to achieve a few millisecs of work in each case. Concurrent processing mechanisms rank as follows (from worst to best in terms of resource-usage, scalability and performance):

process = heavy-weight
thread = medium-weight (dozens of threads can execute within a single process space)
actor = light-weight (dozens of actors can execute by leveraging a single shared thread or multiple shared threads)

Concurrently spawning many processes takes significant operating system resources - for process creation and termination. In extreme cases, the O/S overhead to start & end processes could consume hundreds or thousands more CPU and memory resources than the actual job execution. That's why the thread-model was created (and the more efficient actor model). Think of your current processing as doing 'CGI-like' non-scalable O/S-stressing-processing from within your extremely-scalable actors - that's an anti-pattern. It doesn't take much to stress some operating systems to the point of breakage: this could be happening.

Also, if the files being read are very large in size, it would be best for scalability and reliability to limit the number of processes that concurrently read files on the same disk. It might be OK for up to 10 processes to read concurrently, I doubt it would be OK for 100.

How should an Actor invoke an external program?

Of course, if you converted your logic in myExecutable.sh into Scala, you would not need to create processes at all. Achieving scalability, performance and reliability would be more straightforward.

Assuming this is not possible/desirable, you should limit the total number of processes created and you should reuse them across different Actors / requests over time.

First solution option: (1) create a pool of processes that are reused (say size 10) (2) create actors (say 100) that communicate to/from the processes via ProcessIO (3) if all processes are busy with processing, then it is OK/appropriate that Actors block until one becomes available. The issue with this option: complexity; the 100 actors must do work to interact with the process pool and the actors themselves add little value when the processes are the bottle-neck.

Better solution option: (1) create a limited number of actors (say 10) (2) have each actor create 1 private long-running process (i.e. no pool as such) (3) have each actor communicate to/from via ProcessIO, blocking if the process is busy. Issue: still not as simple as possible; actors interact poorly with blocking processes.

Best solution option: (1) no actors, a simple for-loop from your main thread will achieve the same benefits as actors (2) create a limited number of processes (10) (3) via for-loop, sequentially interact each process using ProcessIO (if busy - block or skip to next iteration)

Is there any way to setup a timeout for this process to return, and if it exceeds retry?

Indeed there is. One of the most powerful features of actors is the ability for some actors to spawn other actors and to act as supervisor of them (receiving failure or timeout messages, from which they can recover/restart). With 'native scala actors' this is done via rudimentary programming, generating your own checks and timeout messages. But I won't cover that because the Akka approaches are more powerful and simpler. Plus the next major Scala release (2.11) will use Akka as the supported actor model, with 'native scala actors' deprecated.

Here's an example Akka supervising actor with programmatic timeout/restart (not compiled/tested). Of course, this is not useful if you go with the 3rd solution option):

import scala.concurrent.duration._
import scala.collection.immutable.Set

class Supervisor extends Actor {
  override val supervisorStrategy =
    OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1 minute) {
      case _: ArithmeticException => Resume     // resumes (reuses) all child actors
      case _: NullPointerException => Restart   // restarts all child actors
      case _: IllegalArgumentException => Stop  // terminates this actor & all children
      case _: Exception => Escalate             // supervisor to receive exception
    }

  val worker = context.actorOf(Props[Worker])  // creates a supervised child actor
  var pendingRequests = Set.empty[WorkerRequest]

  def receive = {
    case req: WorkRequest(sender, jobReq) => 
      pendingRequests = pendingRequests + req
      worker ! req
      system.scheduler.scheduleOnce(10 seconds, self, WorkTimeout(req))
    case resp: WorkResponse(req @ WorkRequest(sender, jobReq), jobResp) => 
      pendingRequests = pendingRequests - req
      sender ! resp
    case timeout: WorkTimeout(req) =>
      if (pendingRequests get req != None) {
        // restart the unresponsive worker
        worker restart
        // resend all pending requests
        pendingRequests foreach{ worker ! _ }
      }
  }
}

A word of caution: this approach to actor supervision will not overcome poor architecture & design. If you start with suitable process/thread/actor design to meet your requirements, then supervision will promote reliability. But if you start with poor design, then there's a risk that using 'brute-force' recovery from O/S-level failures could exacerbate your problems - making process reliability worse or even causing the machine to crash.

Upvotes: 3