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Thread-safe FIFO queue with unique items and thread pool

I have to manage scheduled file replications in a system. The file replications are scheduled by users and I need to restrict the amount of system resources used during replication. The amount of time that each replication may take is not defined (i.e. a replication may be scheduled to run every 15 minutes and the previous run may still be running when the next run is due) and a replication should not be queued if it's already queued or running.
I have a scheduler that periodically checks for due file replications and, for each one, (1) add it to a blocking queue if it is not queued nor running or (2) drop it otherwise.
private final Object scheduledReplicationsLock = new Object();
private final BlockingQueue<Replication> replicationQueue = new LinkedBlockingQueue<>();
private final Set<Long> queuedReplicationIds = new HashSet<>();
private final Set<Long> runningReplicationIds = new HashSet<>();
public boolean add(Replication replication) {
synchronized (scheduledReplicationsLock) {
// If the replication job is either still executing or is already queued, do not add it.
if (queuedReplicationIds.contains(replication.id) || runningReplicationIds.contains(replication.id)) {
return false;
}
replicationQueue.add(replication)
queuedReplicationIds.add(replication.id);
}
I also have a pool of threads that waits until there is a replication in the queue and executes it. Below is the main method of each thread in the thread pool:
public void run() {
while (True) {
Replication replication = null;
synchronized (scheduledReplicationsLock) {
// This will block until a replication job is ready to be run or the current thread is interrupted.
replication = replicationQueue.take();
// Move the ID value out of the queued set and into the active set
Long replicationId = replication.getId();
queuedReplicationIds.remove(replicationId);
runningReplicationIds.add(replicationId);
}
executeReplication(replication)
}
}
This code gets into a deadlock because the first thread in the thread poll will get scheduledLock and prevent the scheduler to add replications to the queue. Moving replicationQueue.take() out of the synchronized block will eliminate the deadlock, but then it's possible that a element is removed from the queue and the hash sets are not atomically updated with it, which could cause a replication to be incorrectly dropped.
Should I use BlockingQueue.poll() and release the lock + sleep if the queue is empty instead of using BlockingQueue.take() ?
Fixes to the current solution or other solutions that meet the requirements are welcome.

wait / notify
Keeping your same control flow, instead of blocking on the BlockingQueue instance while holding the mutex lock, you can wait on notifications for the scheduledReplicationsLock forcing the worker thread to release the lock and return to the waiting pool.
Here down a reduced sample of your producer:
private final List<Replication> replicationQueue = new LinkedList<>();
private final Set<Long> runningReplicationIds = new HashSet<>();
public boolean add(Replication replication) {
synchronized (replicationQueue) {
// If the replication job is either still executing or is already queued, do not add it.
if (replicationQueue.contains(replication) || runningReplicationIds.contains(replication.id)) {
return false;
} else {
replicationQueue.add(replication);
replicationQueue.notifyAll();
}
}
}
The worker Runnable would then be updated as follows:
public void run() {
synchronized (replicationQueue) {
while (true) {
if (replicationQueue.isEmpty()) {
scheduledReplicationsLock.wait();
}
if (!replicationQueue.isEmpty()) {
Replication replication = replicationQueue.poll();
runningReplicationIds.add(replication.getId())
executeReplication(replication);
}
}
}
}
BlockingQueue
Generally you are better off using the BlockingQueue to coordinate your producer and replicating worker pool.
The BlockingQueue is, as the name implies, blocking by nature and will cause the calling thread to block only if items cannot be pulled / pushed from / to the queue.
Meanwhile, note that you will have to update your running / enqueued state management as you will only synchronizing on the BlockingQueue items dropping any constraints. This then will depend on the context, whether this would be acceptable or not.
This way, you would drop all other used mutex(es) and use on the BlockingQueue as your synchronization state:
private final BlockingQueue<Replication> replicationQueue = new LinkedBlockingQueue<>();
public boolean add(Replication replication) {
// not sure if this is the proper invariant to check as at some point the replication would be neither queued nor running while still have been processed
if (replicationQueue.contains(replication)) {
return false;
}
// use `put` instead of `add` as this will block waiting for free space
replicationQueue.put(replication);
return true;
}
The workers will then take indefinitely from the BlockingQueue:
public void run() {
while (true) {
Replication replication = replicationQueue.take();
executeReplication(replication);
}
}

You no need to use any additional synchronization block if you using BlockingQueue
Quote from docs (https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/BlockingQueue.html)
BlockingQueue implementations are thread-safe. All queuing methods achieve their effects atomically using internal locks or other forms of concurrency control.
just use something like this
public void run() {
try {
while (replicationQueue.take()) { //Thread will be wait for the next element in the queue
Long replicationId = replication.getId();
queuedReplicationIds.remove(replicationId);
runningReplicationIds.add(replicationId);
executeReplication(replication);
}
} catch (InterruptedException ex) {
//if interrupted while waiting next element
}
}
}
look in javadoc https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/LinkedBlockingQueue.html#take()
Or you can use BlockinQueue.pool() with timeout settings
UPD: After discussion, I extend LinkedBlockingQueue with two ConcurrentHashSets and add method afterTake() to remove processed Replicas. You do not need an additional synchronizations outside the queue. Just put replica in the first thread and take it in another, and call afterTake() when replication finished. You need to override other method if you want to use it.
package ru.everytag;
import io.vertx.core.impl.ConcurrentHashSet;
import java.util.concurrent.LinkedBlockingQueue;
public class TwoPhaseBlockingQueue<E> extends LinkedBlockingQueue<E> {
private ConcurrentHashSet<E> items = new ConcurrentHashSet<>();
private ConcurrentHashSet<E> taken = new ConcurrentHashSet<>();
#Override
public void put(E e) throws InterruptedException {
if (!items.contains(e)) {
items.add(e);
super.put(e);
}
}
public E take() {
E item = take();
taken.add(item);
items.remove(item);
return item;
}
public void afterTake(E e) {
if (taken.contains(e)) {
taken.remove(e);
} else if (items.contains(e)) {
throw new IllegalArgumentException("Element still in the queue");
}
}
}

Java timeout function is not working for my below codes

I tried to set a 1-second time limit for my SQL query in Java, using the methods:
How to timeout a thread
public class App {
public static void main(String[] args) throws Exception {
ExecutorService executor = Executors.newSingleThreadExecutor();
Future<String> future = executor.submit(new Task());
try {
System.out.println("Started..");
System.out.println(future.get(1, TimeUnit.SECONDS));
System.out.println("Finished!");
} catch (TimeoutException e) {
future.cancel(true);
System.out.println("Terminated!");
}
executor.shutdownNow();
}
}
class Task implements Callable<String> {
#Override
public String call() throws Exception {
try {
// some codes to do query via SQL Server JDBC, assuming it takes 10 seconds.
ResultSet result = statement.executeQuery();
// some codes to print the query result
return "Done";
}
catch (Exception e) {
System.out.println();
e.printStackTrace();
}
}
}
However, I found that though it prints 'Terminated' after 1 second, the program keeps running and prints the query result after 10 seconds. What's the reason why it doesn't work and how to fix it?

shutdownNow doesn't actually stop a thread, it merely sends a signal (an interrupt) that the Thread can act upon. Stopping a Thread in Java is tricky because while you can just kil the thread (with Thread.stop), you really shouldn't because you have no idea what state the Thread is in and what it will leave behind.
You can find more information in the documentation.

Calling cancel on a future does not guarantee that the job will be cancelled. It depends on the method checking periodically for interrupts, and then aborting if an interrupt is detected. Statement.execute() does not do that.
In your case, given you are executing a SQL statement, there is a method in the Statement class (setQueryTimeout) which achieves what you appear to be after without over-engineering timeouts by other means.

Another way you can approach this is by using the thread.sleep() method. I often use it when I want my program to simply pause for a short or long period of time. In the parameters, you put values in thousands that correspond to seconds. For example:
public static void main(String[] args) throws InterruptedException // Required for thread.sleep()
{
System.out.println("Hi there.");
Thread.sleep(2000); // Wait two seconds before running the next line of code
System.out.println("Goodbye.");
}
This is quite basic, but can be used for more than just strings. Hope this helps.

Is this a safe way to generate new threads and terminate them?

I have an application that receives alerts from other applications, usually once a minute or so but I need to be able to handle higher volume per minute. The interface I am using, and the Alert framework in general, requires that alerts may be processed asynchronously and can be stopped if they are being processed asynchronously. The stop method specifically is documented as stopping a thread. I wrote the code below to create an AlertRunner thread and then stop the thread. However, is this a proper way to handle terminating a thread? And will this code be able to scale easily (not to a ridiculous volume, but maybe an alert a second or multiple alerts at the same time)?
private AlertRunner alertRunner;
#Override
public void receive(Alert a) {
assert a != null;
alertRunner = new alertRunner(a.getName());
a.start();
}
#Override
public void stop(boolean synchronous) {
if(!synchronous) {
if(alertRunner != null) {
Thread.currentThread().interrupt();
}
}
}
class AlertRunner extends Thread {
private final String alertName;
public AlertRunner(String alertName) {
this.alertName = alertName;
}
#Override
public void run() {
try {
TimeUnit.SECONDS.sleep(5);
log.info("New alert received: " + alertName);
} catch (InterruptedException e) {
log.error("Thread interrupted: " + e.getMessage());
}
}
}

This code will not scale easily because Thread is quite 'heavy' object. It's expensive to create and it's expensive to start. It's much better to use ExecutorService for your task. It will contain a limited number of threads that are ready to process your requests:
int threadPoolSize = 5;
ExecutorService executor = Executors.newFixedThreadPool(threadPoolSize);
public void receive(Alert a) {
assert a != null;
executor.submit(() -> {
// Do your work here
});
}
Here executor.submit() will handle your request in a separate thread. If all threads are busy now, the request will wait in a queue, preventing resource exhausting. It also returns an instance of Future that you can use to wait for the completion of the handling, setting the timeout, receiving the result, for cancelling execution and many other useful things.

Future.get() does not return

I have the following piece of code:
public class Test {
List<Future> future = new ArrayList<Future>();
public static void main(String args[]) throws Exception {
Adapter b1 = new Adapter();
final ExecutorService threadPool = Executors.newCachedThreadPool();
for(//iterate for number of files) {
while(data exists in file) {
//Call a function to process and update values in db
future.add(threadPool.submit(new Xyz(b1)));
//read next set of data in file;
}
}
try {
for(Future f: future) {
f.get();
}
}
catch(Exception e) {
throw e;
}
}
}
class Xyz implements Runnable {
private Adapter a1;
public Xyz(Adapter al) {
this.a1=a1;
}
#Override
public void run() {
try {
a1.abc();
} catch (Exception e) {
throw new RuntimeException(e);
}
}
}
When the number of files is 1 (for loop runs for 1 time), the code runs fine.
But, when the number of files increases, the code never returns back from future.get() method.

just out of curiosity.. do i need to shutdown the executor somewhere ??
Yes, and this is likely the problem. Each Future.get() will block until the corresponding task is complete, then once all the tasks are complete your main thread will exit. But your java process will not exit because the thread pool threads are still active in the background. You should shut down the executor once you have finished with it, most likely as the last thing in your main method.
I also note that you're submitting many tasks that wrap the same Adapter instance and all call its abc() method - check that there's nothing in there that will deadlock when called simultaneously in more than one thread.

Your Callable::call / Runable::run does not return. Otherwise the corresponding future would not block.
Additional executor.shutdown or future.cancel will thow an InterruptedException to stop the thread processing the object you submitted but it is up to you if to catch it or not. Your are responsible for making the jobs you submitted stop.
When you submit thousands Callables/Runnables to a CachedExecutor that it might spawn so many threads that your machine gets so slow that you think it takes forever. But you would have noticed that.
When dealing with an undefined number of parallelizable tasks i suggest to use a FixedThreadPool with not much more threads that there are cpu cores.
Edit: Therefore when you set a breakpoints at a1.abc(); and step forward you will probably find out that it never returns.

How to make ThreadPoolExecutor's submit() method block if it is saturated?

I want to create a ThreadPoolExecutor such that when it has reached its maximum size and the queue is full, the submit() method blocks when trying to add new tasks. Do I need to implement a custom RejectedExecutionHandler for that or is there an existing way to do this using a standard Java library?

One of the possible solutions I've just found:
public class BoundedExecutor {
private final Executor exec;
private final Semaphore semaphore;
public BoundedExecutor(Executor exec, int bound) {
this.exec = exec;
this.semaphore = new Semaphore(bound);
}
public void submitTask(final Runnable command)
throws InterruptedException, RejectedExecutionException {
semaphore.acquire();
try {
exec.execute(new Runnable() {
public void run() {
try {
command.run();
} finally {
semaphore.release();
}
}
});
} catch (RejectedExecutionException e) {
semaphore.release();
throw e;
}
}
}
Are there any other solutions? I'd prefer something based on RejectedExecutionHandler since it seems like a standard way to handle such situations.

You can use ThreadPoolExecutor and a blockingQueue:
public class ImageManager {
BlockingQueue<Runnable> blockingQueue = new ArrayBlockingQueue<Runnable>(blockQueueSize);
RejectedExecutionHandler rejectedExecutionHandler = new ThreadPoolExecutor.CallerRunsPolicy();
private ExecutorService executorService = new ThreadPoolExecutor(numOfThread, numOfThread,
0L, TimeUnit.MILLISECONDS, blockingQueue, rejectedExecutionHandler);
private int downloadThumbnail(String fileListPath){
executorService.submit(new yourRunnable());
}
}

You should use the CallerRunsPolicy, which executes the rejected task in the calling thread. This way, it can't submit any new tasks to the executor until that task is done, at which point there will be some free pool threads or the process will repeat.
http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/ThreadPoolExecutor.CallerRunsPolicy.html
From the docs:
Rejected tasks
New tasks submitted in method execute(java.lang.Runnable) will be
rejected when the Executor has been
shut down, and also when the Executor
uses finite bounds for both maximum
threads and work queue capacity, and
is saturated. In either case, the
execute method invokes the
RejectedExecutionHandler.rejectedExecution(java.lang.Runnable,
java.util.concurrent.ThreadPoolExecutor)
method of its
RejectedExecutionHandler. Four
predefined handler policies are
provided:
In the default ThreadPoolExecutor.AbortPolicy, the
handler throws a runtime
RejectedExecutionException upon
rejection.
In ThreadPoolExecutor.CallerRunsPolicy,
the thread that invokes execute itself
runs the task. This provides a simple
feedback control mechanism that will
slow down the rate that new tasks are
submitted.
In ThreadPoolExecutor.DiscardPolicy, a
task that cannot be executed is simply
dropped.
In ThreadPoolExecutor.DiscardOldestPolicy,
if the executor is not shut down, the
task at the head of the work queue is
dropped, and then execution is retried
(which can fail again, causing this to
be repeated.)
Also, make sure to use a bounded queue, such as ArrayBlockingQueue, when calling the ThreadPoolExecutor constructor. Otherwise, nothing will get rejected.
Edit: in response to your comment, set the size of the ArrayBlockingQueue to be equal to the max size of the thread pool and use the AbortPolicy.
Edit 2: Ok, I see what you're getting at. What about this: override the beforeExecute() method to check that getActiveCount() doesn't exceed getMaximumPoolSize(), and if it does, sleep and try again?

I know, it is a hack, but in my opinion most clean hack between those offered here ;-)
Because ThreadPoolExecutor uses blocking queue "offer" instead of "put", lets override behaviour of "offer" of the blocking queue:
class BlockingQueueHack<T> extends ArrayBlockingQueue<T> {
BlockingQueueHack(int size) {
super(size);
}
public boolean offer(T task) {
try {
this.put(task);
} catch (InterruptedException e) {
throw new RuntimeException(e);
}
return true;
}
}
ThreadPoolExecutor tp = new ThreadPoolExecutor(1, 2, 1, TimeUnit.MINUTES, new BlockingQueueHack(5));
I tested it and it seems to work.
Implementing some timeout policy is left as a reader's exercise.

Hibernate has a BlockPolicy that is simple and may do what you want:
See: Executors.java
/**
* A handler for rejected tasks that will have the caller block until
* space is available.
*/
public static class BlockPolicy implements RejectedExecutionHandler {
/**
* Creates a <tt>BlockPolicy</tt>.
*/
public BlockPolicy() { }
/**
* Puts the Runnable to the blocking queue, effectively blocking
* the delegating thread until space is available.
* #param r the runnable task requested to be executed
* #param e the executor attempting to execute this task
*/
public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
try {
e.getQueue().put( r );
}
catch (InterruptedException e1) {
log.error( "Work discarded, thread was interrupted while waiting for space to schedule: {}", r );
}
}
}

The BoundedExecutor answer quoted above from Java Concurrency in Practice only works correctly if you use an unbounded queue for the Executor, or the semaphore bound is no greater than the queue size. The semaphore is state shared between the submitting thread and the threads in the pool, making it possible to saturate the executor even if queue size < bound <= (queue size + pool size).
Using CallerRunsPolicy is only valid if your tasks don't run forever, in which case your submitting thread will remain in rejectedExecution forever, and a bad idea if your tasks take a long time to run, because the submitting thread can't submit any new tasks or do anything else if it's running a task itself.
If that's not acceptable then I suggest checking the size of the executor's bounded queue before submitting a task. If the queue is full, then wait a short time before trying to submit again. The throughput will suffer, but I suggest it's a simpler solution than many of the other proposed solutions and you're guaranteed no tasks will get rejected.

The following class wraps around a ThreadPoolExecutor and uses a Semaphore to block then the work queue is full:
public final class BlockingExecutor {
private final Executor executor;
private final Semaphore semaphore;
public BlockingExecutor(int queueSize, int corePoolSize, int maxPoolSize, int keepAliveTime, TimeUnit unit, ThreadFactory factory) {
BlockingQueue<Runnable> queue = new LinkedBlockingQueue<Runnable>();
this.executor = new ThreadPoolExecutor(corePoolSize, maxPoolSize, keepAliveTime, unit, queue, factory);
this.semaphore = new Semaphore(queueSize + maxPoolSize);
}
private void execImpl (final Runnable command) throws InterruptedException {
semaphore.acquire();
try {
executor.execute(new Runnable() {
#Override
public void run() {
try {
command.run();
} finally {
semaphore.release();
}
}
});
} catch (RejectedExecutionException e) {
// will never be thrown with an unbounded buffer (LinkedBlockingQueue)
semaphore.release();
throw e;
}
}
public void execute (Runnable command) throws InterruptedException {
execImpl(command);
}
}
This wrapper class is based on a solution given in the book Java Concurrency in Practice by Brian Goetz. The solution in the book only takes two constructor parameters: an Executor and a bound used for the semaphore. This is shown in the answer given by Fixpoint. There is a problem with that approach: it can get in a state where the pool threads are busy, the queue is full, but the semaphore has just released a permit. (semaphore.release() in the finally block). In this state, a new task can grab the just released permit, but is rejected because the task queue is full. Of course this is not something you want; you want to block in this case.
To solve this, we must use an unbounded queue, as JCiP clearly mentions. The semaphore acts as a guard, giving the effect of a virtual queue size. This has the side effect that it is possible that the unit can contain maxPoolSize + virtualQueueSize + maxPoolSize tasks. Why is that? Because of the
semaphore.release() in the finally block. If all pool threads call this statement at the same time, then maxPoolSize permits are released, allowing the same number of tasks to enter the unit. If we were using a bounded queue, it would still be full, resulting in a rejected task. Now, because we know that this only occurs when a pool thread is almost done, this is not a problem. We know that the pool thread will not block, so a task will soon be taken from the queue.
You are able to use a bounded queue though. Just make sure that its size equals virtualQueueSize + maxPoolSize. Greater sizes are useless, the semaphore will prevent to let more items in. Smaller sizes will result in rejected tasks. The chance of tasks getting rejected increases as the size decreases. For example, say you want a bounded executor with maxPoolSize=2 and virtualQueueSize=5. Then take a semaphore with 5+2=7 permits and an actual queue size of 5+2=7. The real number of tasks that can be in the unit is then 2+5+2=9. When the executor is full (5 tasks in queue, 2 in thread pool, so 0 permits available) and ALL pool threads release their permits, then exactly 2 permits can be taken by tasks coming in.
Now the solution from JCiP is somewhat cumbersome to use as it doesn't enforce all these constraints (unbounded queue, or bounded with those math restrictions, etc.). I think that this only serves as a good example to demonstrate how you can build new thread safe classes based on the parts that are already available, but not as a full-grown, reusable class. I don't think that the latter was the author's intention.

you can use a custom RejectedExecutionHandler like this
ThreadPoolExecutor tp= new ThreadPoolExecutor(core_size, // core size
max_handlers, // max size
timeout_in_seconds, // idle timeout
TimeUnit.SECONDS, queue, new RejectedExecutionHandler() {
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
// This will block if the queue is full
try {
executor.getQueue().put(r);
} catch (InterruptedException e) {
System.err.println(e.getMessage());
}
}
});

I don't always like the CallerRunsPolicy, especially since it allows the rejected task to 'skip the queue' and get executed before tasks that were submitted earlier. Moreover, executing the task on the calling thread might take much longer than waiting for the first slot to become available.
I solved this problem using a custom RejectedExecutionHandler, which simply blocks the calling thread for a little while and then tries to submit the task again:
public class BlockWhenQueueFull implements RejectedExecutionHandler {
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
// The pool is full. Wait, then try again.
try {
long waitMs = 250;
Thread.sleep(waitMs);
} catch (InterruptedException interruptedException) {}
executor.execute(r);
}
}
This class can just be used in the thread-pool executor as a RejectedExecutinHandler like any other, for example:
executorPool = new ThreadPoolExecutor(1, 1, 10,
TimeUnit.SECONDS, new SynchronousQueue<Runnable>(),
new BlockWhenQueueFull());
The only downside I see is that the calling thread might get locked slightly longer than strictly necessary (up to 250ms). Moreover, since this executor is effectively being called recursively, very long waits for a thread to become available (hours) might result in a stack overflow.
Nevertheless, I personally like this method. It's compact, easy to understand, and works well.

Create your own blocking queue to be used by the Executor, with the blocking behavior you are looking for, while always returning available remaining capacity (ensuring the executor will not try to create more threads than its core pool, or trigger the rejection handler).
I believe this will get you the blocking behavior you are looking for. A rejection handler will never fit the bill, since that indicates the executor can not perform the task. What I could envision there is that you get some form of 'busy waiting' in the handler. That is not what you want, you want a queue for the executor that blocks the caller...

To avoid issues with #FixPoint solution. One could use ListeningExecutorService and release the semaphore onSuccess and onFailure inside FutureCallback.

Recently I found this question having the same problem. The OP does not say so explicitly, but we do not want to use the RejectedExecutionHandler which executes a task on the submitter's thread, because this will under-utilize the worker threads if this task is a long running one.
Reading all the answers and comments, in particular the flawed solution with the semaphore or using afterExecute I had a closer look at the code of the ThreadPoolExecutor to see if there is some way out. I was amazed to see that there are more than 2000 lines of (commented) code, some of which make me feel dizzy. Given the rather simple requirement I actually have --- one producer, several consumers, let the producer block when no consumers can take work --- I decided to roll my own solution. It is not an ExecutorService but just an Executor. And it does not adapt the number of threads to the work load, but holds a fixed number of threads only, which also fits my requirements. Here is the code. Feel free to rant about it :-)
package x;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.Executor;
import java.util.concurrent.RejectedExecutionException;
import java.util.concurrent.SynchronousQueue;
/**
* distributes {#code Runnable}s to a fixed number of threads. To keep the
* code lean, this is not an {#code ExecutorService}. In particular there is
* only very simple support to shut this executor down.
*/
public class ParallelExecutor implements Executor {
// other bounded queues work as well and are useful to buffer peak loads
private final BlockingQueue<Runnable> workQueue =
new SynchronousQueue<Runnable>();
private final Thread[] threads;
/*+**********************************************************************/
/**
* creates the requested number of threads and starts them to wait for
* incoming work
*/
public ParallelExecutor(int numThreads) {
this.threads = new Thread[numThreads];
for(int i=0; i<numThreads; i++) {
// could reuse the same Runner all over, but keep it simple
Thread t = new Thread(new Runner());
this.threads[i] = t;
t.start();
}
}
/*+**********************************************************************/
/**
* returns immediately without waiting for the task to be finished, but may
* block if all worker threads are busy.
*
* #throws RejectedExecutionException if we got interrupted while waiting
* for a free worker
*/
#Override
public void execute(Runnable task) {
try {
workQueue.put(task);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RejectedExecutionException("interrupt while waiting for a free "
+ "worker.", e);
}
}
/*+**********************************************************************/
/**
* Interrupts all workers and joins them. Tasks susceptible to an interrupt
* will preempt their work. Blocks until the last thread surrendered.
*/
public void interruptAndJoinAll() throws InterruptedException {
for(Thread t : threads) {
t.interrupt();
}
for(Thread t : threads) {
t.join();
}
}
/*+**********************************************************************/
private final class Runner implements Runnable {
#Override
public void run() {
while (!Thread.currentThread().isInterrupted()) {
Runnable task;
try {
task = workQueue.take();
} catch (InterruptedException e) {
// canonical handling despite exiting right away
Thread.currentThread().interrupt();
return;
}
try {
task.run();
} catch (RuntimeException e) {
// production code to use a logging framework
e.printStackTrace();
}
}
}
}
}

I believe there is quite elegant way to solve this problem by using java.util.concurrent.Semaphore and delegating behavior of Executor.newFixedThreadPool.
The new executor service will only execute new task when there is a thread to do so. Blocking is managed by Semaphore with number of permits equal to number of threads. When a task is finished it returns a permit.
public class FixedThreadBlockingExecutorService extends AbstractExecutorService {
private final ExecutorService executor;
private final Semaphore blockExecution;
public FixedThreadBlockingExecutorService(int nTreads) {
this.executor = Executors.newFixedThreadPool(nTreads);
blockExecution = new Semaphore(nTreads);
}
#Override
public void shutdown() {
executor.shutdown();
}
#Override
public List<Runnable> shutdownNow() {
return executor.shutdownNow();
}
#Override
public boolean isShutdown() {
return executor.isShutdown();
}
#Override
public boolean isTerminated() {
return executor.isTerminated();
}
#Override
public boolean awaitTermination(long timeout, TimeUnit unit) throws InterruptedException {
return executor.awaitTermination(timeout, unit);
}
#Override
public void execute(Runnable command) {
blockExecution.acquireUninterruptibly();
executor.execute(() -> {
try {
command.run();
} finally {
blockExecution.release();
}
});
}

I had the same need in the past: a kind of blocking queue with a fixed size for each client backed by a shared thread pool. I ended up writing my own kind of ThreadPoolExecutor:
UserThreadPoolExecutor
(blocking queue (per client) + threadpool (shared amongst all clients))
See: https://github.com/d4rxh4wx/UserThreadPoolExecutor
Each UserThreadPoolExecutor is given a maximum number of threads from a shared ThreadPoolExecutor
Each UserThreadPoolExecutor can:
submit a task to the shared thread pool executor if its quota is not reached. If its quota is reached, the job is queued (non-consumptive blocking waiting for CPU). Once one of its submitted task is completed, the quota is decremented, allowing another task waiting to be submitted to the ThreadPoolExecutor
wait for the remaining tasks to complete

I found this rejection policy in elastic search client. It blocks caller thread on blocking queue. Code below-
static class ForceQueuePolicy implements XRejectedExecutionHandler
{
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor)
{
try
{
executor.getQueue().put(r);
}
catch (InterruptedException e)
{
//should never happen since we never wait
throw new EsRejectedExecutionException(e);
}
}
#Override
public long rejected()
{
return 0;
}
}

I recently had a need to achieve something similar, but on a ScheduledExecutorService.
I had to also ensure that I handle the delay being passed on the method and ensure that either the task is submitted to execute at the time as the caller expects or just fails thus throwing a RejectedExecutionException.
Other methods from ScheduledThreadPoolExecutor to execute or submit a task internally call #schedule which will still in turn invoke the methods overridden.
import java.util.concurrent.*;
public class BlockingScheduler extends ScheduledThreadPoolExecutor {
private final Semaphore maxQueueSize;
public BlockingScheduler(int corePoolSize,
ThreadFactory threadFactory,
int maxQueueSize) {
super(corePoolSize, threadFactory, new AbortPolicy());
this.maxQueueSize = new Semaphore(maxQueueSize);
}
#Override
public ScheduledFuture<?> schedule(Runnable command,
long delay,
TimeUnit unit) {
final long newDelayInMs = beforeSchedule(command, unit.toMillis(delay));
return super.schedule(command, newDelayInMs, TimeUnit.MILLISECONDS);
}
#Override
public <V> ScheduledFuture<V> schedule(Callable<V> callable,
long delay,
TimeUnit unit) {
final long newDelayInMs = beforeSchedule(callable, unit.toMillis(delay));
return super.schedule(callable, newDelayInMs, TimeUnit.MILLISECONDS);
}
#Override
public ScheduledFuture<?> scheduleAtFixedRate(Runnable command,
long initialDelay,
long period,
TimeUnit unit) {
final long newDelayInMs = beforeSchedule(command, unit.toMillis(initialDelay));
return super.scheduleAtFixedRate(command, newDelayInMs, unit.toMillis(period), TimeUnit.MILLISECONDS);
}
#Override
public ScheduledFuture<?> scheduleWithFixedDelay(Runnable command,
long initialDelay,
long period,
TimeUnit unit) {
final long newDelayInMs = beforeSchedule(command, unit.toMillis(initialDelay));
return super.scheduleWithFixedDelay(command, newDelayInMs, unit.toMillis(period), TimeUnit.MILLISECONDS);
}
#Override
protected void afterExecute(Runnable runnable, Throwable t) {
super.afterExecute(runnable, t);
try {
if (t == null && runnable instanceof Future<?>) {
try {
((Future<?>) runnable).get();
} catch (CancellationException | ExecutionException e) {
t = e;
} catch (InterruptedException ie) {
Thread.currentThread().interrupt(); // ignore/reset
}
}
if (t != null) {
System.err.println(t);
}
} finally {
releaseQueueUsage();
}
}
private long beforeSchedule(Runnable runnable, long delay) {
try {
return getQueuePermitAndModifiedDelay(delay);
} catch (InterruptedException e) {
getRejectedExecutionHandler().rejectedExecution(runnable, this);
return 0;
}
}
private long beforeSchedule(Callable callable, long delay) {
try {
return getQueuePermitAndModifiedDelay(delay);
} catch (InterruptedException e) {
getRejectedExecutionHandler().rejectedExecution(new FutureTask(callable), this);
return 0;
}
}
private long getQueuePermitAndModifiedDelay(long delay) throws InterruptedException {
final long beforeAcquireTimeStamp = System.currentTimeMillis();
maxQueueSize.tryAcquire(delay, TimeUnit.MILLISECONDS);
final long afterAcquireTimeStamp = System.currentTimeMillis();
return afterAcquireTimeStamp - beforeAcquireTimeStamp;
}
private void releaseQueueUsage() {
maxQueueSize.release();
}
}
I have the code here, will appreciate any feedback.
https://github.com/AmitabhAwasthi/BlockingScheduler