It is mentioned by Java_author in section 5.3.1,
... many producer-consumer designs can be expressed using the Executor task execution framework, which itself uses the producer-consumer pattern.
... The producer-consumer pattern offers a thread-friendly means of decomposing the problem into simpler components(if possible).
Does Executor framework implementation internally follow producer-consumer pattern?
If yes, How the idea of producer-consumer pattern helps in implementation of Executor framework?
Check implementation of ThreadPoolExecutor
public void execute(Runnable command) {
int c = ctl.get();
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
if (! isRunning(recheck) && remove(command))
reject(command);
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
else if (!addWorker(command, false))
reject(command);
}
Now check
private boolean addWorker(Runnable firstTask, boolean core) {
// After some checks, it creates Worker and start the thread
Worker w = new Worker(firstTask);
Thread t = w.thread;
// After some checks, thread has been started
t.start();
}
Implementation of Worker:
/**
* Class Worker mainly maintains interrupt control state for
* threads running tasks, along with other minor bookkeeping.
* This class opportunistically extends AbstractQueuedSynchronizer
* to simplify acquiring and releasing a lock surrounding each
* task execution. This protects against interrupts that are
* intended to wake up a worker thread waiting for a task from
* instead interrupting a task being run. We implement a simple
* non-reentrant mutual exclusion lock rather than use ReentrantLock
* because we do not want worker tasks to be able to reacquire the
* lock when they invoke pool control methods like setCorePoolSize.
*/
private final class Worker
extends AbstractQueuedSynchronizer
implements Runnable
{
/** Delegates main run loop to outer runWorker */
public void run() {
runWorker(this);
}
final void runWorker(Worker w) {
Runnable task = w.firstTask;
w.firstTask = null;
boolean completedAbruptly = true;
try {
while (task != null || (task = getTask()) != null) {
w.lock();
clearInterruptsForTaskRun();
try {
beforeExecute(w.thread, task);
Throwable thrown = null;
try {
task.run();
} catch (RuntimeException x) {
thrown = x; throw x;
} catch (Error x) {
thrown = x; throw x;
} catch (Throwable x) {
thrown = x; throw new Error(x);
} finally {
afterExecute(task, thrown);
}
} finally {
task = null;
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
processWorkerExit(w, completedAbruptly);
}
Which Runnable to execute is dependent on below logic.
/**
* Performs blocking or timed wait for a task, depending on
* current configuration settings, or returns null if this worker
* must exit because of any of:
* 1. There are more than maximumPoolSize workers (due to
* a call to setMaximumPoolSize).
* 2. The pool is stopped.
* 3. The pool is shutdown and the queue is empty.
* 4. This worker timed out waiting for a task, and timed-out
* workers are subject to termination (that is,
* {#code allowCoreThreadTimeOut || workerCount > corePoolSize})
* both before and after the timed wait.
*
* #return task, or null if the worker must exit, in which case
* workerCount is decremented
*/
private Runnable getTask() {
// After some checks, below code returns Runnable
try {
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
In Summary:
Producer adds Runnable or Callable in execute API with workQueue.offer(command)
execute() method creates Worker thread if needed
This Worker thread runs in infinite loop. It gets task (e.g. Runnable) from getTask()
getTask() pools on BlockingQueue<Runnable> workQueue) and take Runnable. It is consumer of BlockingQueue.
Does Executor framework implementation internally follow producer-consumer pattern?
Yes as explained above.
If yes, How the idea of producer-consumer pattern helps in implementation of Executor framework?
BlockingQueue implementation like ArrayBlockingQueue and ExecutorService implementationThreadPoolExecutor are thread safe. Overhead on programmer on explicit implementation of synchronized, wait and notify calls to implement the same has been reduced.
Executor framework uses producer-consumer pattern.
From Wikipedia,
In computing, the producer–consumer problem (also known as the
bounded-buffer problem) is a classic example of a multi-process
synchronization problem. The problem describes two processes, the
producer and the consumer, who share a common, fixed-size buffer used
as a queue. The producer's job is to generate data, put it into the
buffer, and start again. At the same time, the consumer is consuming
the data (i.e., removing it from the buffer), one piece at a time. The
problem is to make sure that the producer won't try to add data into
the buffer if it's full and that the consumer won't try to remove data
from an empty buffer.
If we have a look on different ExecutorService framework implementations, more specifically ThreadPoolExecutor class, it basically has the following:
A queue, where the jobs are submitted and held
Number of threads which consumes the tasks submitted to the queue.
Based on the type of the executor service, these parameters changes
For example,
Fixed thread pool uses a LinkedBlockingQueue and user configured no of threads
Cached Thread pool uses a SynchronousQueue and no of threads between 0 to Integer.MAX_VALUE based on the number of submitted tasks
Related
I want to write a program that runs every 30 minutes. I am using java scheduled threadpool executor to process the tasks that i submit to the executor.
I have been looking at what the official docs say https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/ThreadPoolExecutor.html
and i have run into a dilemma.
Lets say i have submitted 5 tasks to the queue and i have defined 10 threads in the thread pool.
Is there a likelyhood that one of the tasks shall be performed twice
Does the threadpool executor make sure that a task is removed when it has been processed by one of the threads or must i remove the task myself once it has been processed.
Having the task removed is desirable since i wouldn't like old tasks to still be in the queue 30 minutes later.
Executors.newFixedThreadPool() creates a new ThreadPoolExecutor using a LinkedBlockingQueue.
From Executors.newFixedThreadPool():
public static ExecutorService newFixedThreadPool(int nThreads) {
return new ThreadPoolExecutor(nThreads, nThreads,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>());
}
When tasks are submitted for executions, the ThreadPoolExecutor will add them to this queue. Further, the worker threads will take tasks from this queue and execute them.
From ThreadPoolExecutor.getTask():
private Runnable getTask() {
// ...
try {
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
}
As per BlockingQueue.take() contract, taking an element from the queue implies removing it as well.
/**
* Retrieves and removes the head of this queue, waiting if necessary
* until an element becomes available.
*
* #return the head of this queue
* #throws InterruptedException if interrupted while waiting
*/
E take() throws InterruptedException;
It will be executed only once, the executor will remove it automatically.
This is not explicitly documented, while the doc implys it:
Executes the given task sometime in the future.
I want to ask for a little more detail to the same question posted by Zeller over a year ago...
The javadoc says that the service returned by Executors.newCachedThreadPool reuses threads. How is this possible?
I get how the queue structure is setup internally, what I don't see is how it reuses threads in the queue.
All examples I've seen have the developer create an instance of their thread and pass it in through the "execute" method.
For example...
ExecutorService executor = Executors.newCachedThreadPool();
for (int i = 0; i < 10; i++) {
Runnable worker = new WorkerThread(i); //will create 10 instances
executor.execute(worker);
}
I understand that a thread pool can easily manage the life cycle of each thread, but again, I see no methods nor the ability to access or restart any of the threads in the pool.
In the above example, I would then expect that each thread would be started, run, terminated and disposed of by the thread pool, but never reused.
A messaging system would be an example of where you'd need this. Say you have an onMessage handler and you'd like to reuse one of the threads in the pool to handle it, so I'd expect methods like...
worker = executor.getIdleThread;
worker.setData(message);
executor.resubmit(worker);
or maybe have the ExecutorService acting as a factory class and have it return an instance of your threads, where internally it decides to create a new one or reuse an old one.
ExecutorService executor = Executors.newCachedThreadPool(WorkerThread);
Runnable worker = executor.getThread;
worker.setData(message);
So I'm missing something. It's probably something simple but I've spent the afternoon reading tutorials and examples and still haven't figured it out. Can someone shed some light on the subject?
I was curious too how this was possible since Threads can't be restarted, so I analyzed the code of ThreadPoolExecutor which is the implementation of all the ThreadPool ExecutorService you get through the static constructor.
First of all as stated in the other answer you don't use Threads, but Runnables in ThreadPools, because that would defeat the purpose. So here is a detailed explaination how an ExecutorService reuses Threads:
You usually add a Runnable through submit() which internally calls the execute() method. Basically this adds the runnable to a queue and adds a Worker if none is working ATM
public void execute(Runnable command) {
...
int c = ctl.get();
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
if (! isRunning(recheck) && remove(command))
reject(command);
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
else if (!addWorker(command, false))
reje
ct(command);
}
The executor maintains a bunch of Worker (inner Class of ThreadPoolExecutor). It has your submitted runnable and a Thread that will be created through the ThreadFactory you maybe set or else just a default one; also the Worker itself is a Runnable, which is used to create the Thread from the factory
private final class Worker
extends AbstractQueuedSynchronizer
implements Runnable
{
...
Worker(Runnable firstTask) {
this.firstTask = firstTask;
this.thread = getThreadFactory().newThread(this);
}
public void run() {
runWorker(this);
}
...
}
When adding a Worker it gets kickstarted
private boolean addWorker(Runnable firstTask, boolean core) {
...
Worker w = new Worker(firstTask);
Thread t = w.thread;
...
t.start();
...
return true;
}
the runWorker() method runs in a loop and get with getTask() the runnables you submitted which are queued in the workingQueue and will wait at getTask unitl a timeout happens.
final void runWorker(Worker w) {
Runnable task = w.firstTask;
w.firstTask = null;
boolean completedAbruptly = true;
try {
while (task != null || (task = getTask()) != null) {
w.lock();
clearInterruptsForTaskRun();
try {
beforeExecute(w.thread, task);
Throwable thrown = null;
try {
task.run();
} catch (RuntimeException x) {
...
} finally {
afterExecute(task, thrown);
}
} finally {
task = null;
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
processWorkerExit(w, completedAbruptly);
}
}
Here is the getTask() method
private Runnable getTask() {
...
try {
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
..
} catch (InterruptedException retry) {
...
}
}
}
tl;dr So basically the Threadpool maintains worker threads that run in loops and execute runnables given by a blocking queue. The worker will be created and destroyed due to demand (no more tasks, a worker will end; if no free worker and < maxPoolSize then create new worker). Also I wouldn't call it "reuse" more the thread will be used as a looper to execute all runnables.
I understand that a thread pool can easily manage the life cycle of
each thread, but again, I see no methods nor the ability to access or
restart any of the threads in the pool.
The management of threads is done internally. The ExecutorService interface only provides the externally visible methods.
The javadoc of newCachedThreadPool simply states
Creates a thread pool that creates new threads as needed, but will
reuse previously constructed threads when they are available. [...] Calls to execute will reuse
previously constructed threads if available. If no existing thread is
available, a new thread will be created and added to the pool. Threads
that have not been used for sixty seconds are terminated and removed
from the cache. Thus, a pool that remains idle for long enough will
not consume any resources. [...]
So that is the guarantee you get. If you want to know how it is implemented, you can look at the source code, in particular, the code of ThreadPoolExecutor. Basically, idle threads will terminate after not executing a task after some time.
I've been frustrated for some time with the default behavior of ThreadPoolExecutor which backs the ExecutorService thread-pools that so many of us use. To quote from the Javadocs:
If there are more than corePoolSize but less than maximumPoolSize threads running, a new thread will be created only if the queue is full.
What this means is that if you define a thread pool with the following code, it will never start the 2nd thread because the LinkedBlockingQueue is unbounded.
ExecutorService threadPool =
new ThreadPoolExecutor(1 /*core*/, 50 /*max*/, 60 /*timeout*/,
TimeUnit.SECONDS, new LinkedBlockingQueue<Runnable>(/* unlimited queue*/));
Only if you have a bounded queue and the queue is full are any threads above the core number started. I suspect a large number of junior Java multithreaded programmers are unaware of this behavior of the ThreadPoolExecutor.
Now I have specific use case where this is not-optimal. I'm looking for ways, without writing my own TPE class, to work around it.
My requirements are for a web service that is making call-backs to a possibly unreliable 3rd party.
I don't want to make the call-back synchronously with the web-request, so I want to use a thread-pool.
I typically get a couple of these a minute so I don't want to have a newFixedThreadPool(...) with a large number of threads that mostly are dormant.
Every so often I get a burst of this traffic and I want to scale up the number of threads to some max value (let's say 50).
I need to make a best attempt to do all callbacks so I want to queue up any additional ones above 50. I don't want to overwhelm the rest of my web-server by using a newCachedThreadPool().
How can I work around this limitation in ThreadPoolExecutor where the queue needs to be bounded and full before more threads will be started? How can I get it to start more threads before queuing tasks?
Edit:
#Flavio makes a good point about using the ThreadPoolExecutor.allowCoreThreadTimeOut(true) to have the core threads timeout and exit. I considered that but I still wanted the core-threads feature. I did not want the number of threads in the pool to drop below the core-size if possible.
How can I work around this limitation in ThreadPoolExecutor where the queue needs to be bounded and full before more threads will be started.
I believe I have finally found a somewhat elegant (maybe a little hacky) solution to this limitation with ThreadPoolExecutor. It involves extending LinkedBlockingQueue to have it return false for queue.offer(...) when there are already some tasks queued. If the current threads are not keeping up with the queued tasks, the TPE will add additional threads. If the pool is already at max threads, then the RejectedExecutionHandler will be called which does the put(...) into the queue.
It certainly is strange to write a queue where offer(...) can return false and put() never blocks so that's the hack part. But this works well with TPE's usage of the queue so I don't see any problem with doing this.
Here's the code:
// extend LinkedBlockingQueue to force offer() to return false conditionally
BlockingQueue<Runnable> queue = new LinkedBlockingQueue<Runnable>() {
private static final long serialVersionUID = -6903933921423432194L;
#Override
public boolean offer(Runnable e) {
// Offer it to the queue if there is 0 items already queued, else
// return false so the TPE will add another thread. If we return false
// and max threads have been reached then the RejectedExecutionHandler
// will be called which will do the put into the queue.
if (size() == 0) {
return super.offer(e);
} else {
return false;
}
}
};
ThreadPoolExecutor threadPool = new ThreadPoolExecutor(1 /*core*/, 50 /*max*/,
60 /*secs*/, TimeUnit.SECONDS, queue);
threadPool.setRejectedExecutionHandler(new RejectedExecutionHandler() {
#Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
try {
// This does the actual put into the queue. Once the max threads
// have been reached, the tasks will then queue up.
executor.getQueue().put(r);
// we do this after the put() to stop race conditions
if (executor.isShutdown()) {
throw new RejectedExecutionException(
"Task " + r + " rejected from " + e);
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
return;
}
}
});
With this mechanism, when I submit tasks to the queue, the ThreadPoolExecutor will:
Scale the number of threads up to the core size initially (here 1).
Offer it to the queue. If the queue is empty it will be queued to be handled by the existing threads.
If the queue has 1 or more elements already, the offer(...) will return false.
If false is returned, scale up the number of threads in the pool until they reach the max number (here 50).
If at the max then it calls the RejectedExecutionHandler
The RejectedExecutionHandler then puts the task into the queue to be processed by the first available thread in FIFO order.
Although in my example code above, the queue is unbounded, you could also define it as a bounded queue. For example, if you add a capacity of 1000 to the LinkedBlockingQueue then it will:
scale the threads up to max
then queue up until it is full with 1000 tasks
then block the caller until space becomes available to the queue.
Also, if you needed to use offer(...) in the
RejectedExecutionHandler then you could use the offer(E, long, TimeUnit) method instead with Long.MAX_VALUE as the timeout.
Warning:
If you expect tasks to be added to the executor after it has been shutdown, then you may want to be smarter about throwing RejectedExecutionException out of our custom RejectedExecutionHandler when the executor-service has been shutdown. Thanks to #RaduToader for pointing this out.
Edit:
Another tweak to this answer could be to ask the TPE if there are idle threads and only enqueue the item if there is so. You would have to make a true class for this and add ourQueue.setThreadPoolExecutor(tpe); method on it.
Then your offer(...) method might look something like:
Check to see if the tpe.getPoolSize() == tpe.getMaximumPoolSize() in which case just call super.offer(...).
Else if tpe.getPoolSize() > tpe.getActiveCount() then call super.offer(...) since there seem to be idle threads.
Otherwise return false to fork another thread.
Maybe this:
int poolSize = tpe.getPoolSize();
int maximumPoolSize = tpe.getMaximumPoolSize();
if (poolSize >= maximumPoolSize || poolSize > tpe.getActiveCount()) {
return super.offer(e);
} else {
return false;
}
Note that the get methods on TPE are expensive since they access volatile fields or (in the case of getActiveCount()) lock the TPE and walk the thread-list. Also, there are race conditions here that may cause a task to be enqueued improperly or another thread forked when there was an idle thread.
Set core size and max size to the same value, and allow core threads to be removed from the pool with allowCoreThreadTimeOut(true).
I've already got two other answers on this question, but I suspect this one is the best.
It's based on the technique of the currently accepted answer, namely:
Override the queue's offer() method to (sometimes) return false,
which causes the ThreadPoolExecutor to either spawn a new thread or reject the task, and
set the RejectedExecutionHandler to actually queue the task on rejection.
The problem is when offer() should return false. The currently accepted answer returns false when the queue has a couple of tasks on it, but as I've pointed out in my comment there, this causes undesirable effects. Alternately, if you always return false, you'll keep spawning new threads even when you have threads waiting on the queue.
The solution is to use Java 7 LinkedTransferQueue and have offer() call tryTransfer(). When there is a waiting consumer thread the task will just get passed to that thread. Otherwise, offer() will return false and the ThreadPoolExecutor will spawn a new thread.
BlockingQueue<Runnable> queue = new LinkedTransferQueue<Runnable>() {
#Override
public boolean offer(Runnable e) {
return tryTransfer(e);
}
};
ThreadPoolExecutor threadPool = new ThreadPoolExecutor(1, 50, 60, TimeUnit.SECONDS, queue);
threadPool.setRejectedExecutionHandler(new RejectedExecutionHandler() {
#Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
try {
executor.getQueue().put(r);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
}
});
Note: I now prefer and recommend my other answer.
Here's a version which feels to me much more straightforward: Increase the corePoolSize (up to the limit of maximumPoolSize) whenever a new task is executed, then decrease the corePoolSize (down to the limit of the user specified "core pool size") whenever a task completes.
To put it another way, keep track of the number of running or enqueued tasks, and ensure that the corePoolSize is equal to the number of tasks as long as it is between the user specified "core pool size" and the maximumPoolSize.
public class GrowBeforeQueueThreadPoolExecutor extends ThreadPoolExecutor {
private int userSpecifiedCorePoolSize;
private int taskCount;
public GrowBeforeQueueThreadPoolExecutor(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit, BlockingQueue<Runnable> workQueue) {
super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue);
userSpecifiedCorePoolSize = corePoolSize;
}
#Override
public void execute(Runnable runnable) {
synchronized (this) {
taskCount++;
setCorePoolSizeToTaskCountWithinBounds();
}
super.execute(runnable);
}
#Override
protected void afterExecute(Runnable runnable, Throwable throwable) {
super.afterExecute(runnable, throwable);
synchronized (this) {
taskCount--;
setCorePoolSizeToTaskCountWithinBounds();
}
}
private void setCorePoolSizeToTaskCountWithinBounds() {
int threads = taskCount;
if (threads < userSpecifiedCorePoolSize) threads = userSpecifiedCorePoolSize;
if (threads > getMaximumPoolSize()) threads = getMaximumPoolSize();
setCorePoolSize(threads);
}
}
As written the class doesn't support changing the user specified corePoolSize or maximumPoolSize after construction, and doesn't support manipulating the work queue directly or via remove() or purge().
We have a subclass of ThreadPoolExecutor that takes an additional creationThreshold and overrides execute.
public void execute(Runnable command) {
super.execute(command);
final int poolSize = getPoolSize();
if (poolSize < getMaximumPoolSize()) {
if (getQueue().size() > creationThreshold) {
synchronized (this) {
setCorePoolSize(poolSize + 1);
setCorePoolSize(poolSize);
}
}
}
}
maybe that helps too, but yours looks more artsy of course…
The recommended answer resolves only one (1) of the issue with the JDK thread pool:
JDK thread pools are biased towards queuing. So instead of spawning a new thread, they will queue the task. Only if the queue reaches its limit will the thread pool spawn a new thread.
Thread retirement does not happen when load lightens. For example if we have a burst of jobs hitting the pool that causes the pool to go to max, followed by light load of max 2 tasks at a time, the pool will use all threads to service the light load preventing thread retirement. (only 2 threads would be needed…)
Unhappy with the behavior above, I went ahead and implemented a pool to overcome the deficiencies above.
To resolve 2) Using Lifo scheduling resolves the issue. This idea was presented by Ben Maurer at ACM applicative 2015 conference:
Systems # Facebook scale
So a new implementation was born:
LifoThreadPoolExecutorSQP
So far this implementation improves async execution perfomance for ZEL.
The implementation is spin capable to reduce context switch overhead, yielding superior performance for certain use cases.
Hope it helps...
PS: JDK Fork Join Pool implement ExecutorService and works as a "normal" thread pool, Implementation is performant, It uses LIFO Thread scheduling, however there is no control over internal queue size, retirement timeout..., and most importantly tasks cannot be interrupted when canceling them
Note: I now prefer and recommend my other answer.
I have another proposal, following to the original idea of changing the queue to return false. In this one all tasks can enter the queue, but whenever a task is enqueued after execute(), we follow it with a sentinel no-op task which the queue rejects, causing a new thread to spawn, which will execute the no-op immediately followed by something from the queue.
Because worker threads may be polling the LinkedBlockingQueue for a new task, it's possible for a task to get enqueued even when there's an available thread. To avoid spawning new threads even when there are threads available, we need to keep track of how many threads are waiting for new tasks on the queue, and only spawn a new thread when there are more tasks on the queue than waiting threads.
final Runnable SENTINEL_NO_OP = new Runnable() { public void run() { } };
final AtomicInteger waitingThreads = new AtomicInteger(0);
BlockingQueue<Runnable> queue = new LinkedBlockingQueue<Runnable>() {
#Override
public boolean offer(Runnable e) {
// offer returning false will cause the executor to spawn a new thread
if (e == SENTINEL_NO_OP) return size() <= waitingThreads.get();
else return super.offer(e);
}
#Override
public Runnable poll(long timeout, TimeUnit unit) throws InterruptedException {
try {
waitingThreads.incrementAndGet();
return super.poll(timeout, unit);
} finally {
waitingThreads.decrementAndGet();
}
}
#Override
public Runnable take() throws InterruptedException {
try {
waitingThreads.incrementAndGet();
return super.take();
} finally {
waitingThreads.decrementAndGet();
}
}
};
ThreadPoolExecutor threadPool = new ThreadPoolExecutor(1, 50, 60, TimeUnit.SECONDS, queue) {
#Override
public void execute(Runnable command) {
super.execute(command);
if (getQueue().size() > waitingThreads.get()) super.execute(SENTINEL_NO_OP);
}
};
threadPool.setRejectedExecutionHandler(new RejectedExecutionHandler() {
#Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
if (r == SENTINEL_NO_OP) return;
else throw new RejectedExecutionException();
}
});
The best solution that I can think of is to extend.
ThreadPoolExecutor offers a few hook methods: beforeExecute and afterExecute. In your extension you could maintain use a bounded queue to feed in tasks and a second unbounded queue to handle overflow. When someone calls submit, you could attempt to place the request into the bounded queue. If you're met with an exception, you just stick the task in your overflow queue. You could then utilize the afterExecute hook to see if there is anything in the overflow queue after finishing a task. This way, the executor will take care of the stuff in it's bounded queue first, and automatically pull from this unbounded queue as time permits.
It seems like more work than your solution, but at least it doesn't involve giving queues unexpected behaviors. I also imagine that there's a better way to check the status of the queue and threads rather than relying on exceptions, which are fairly slow to throw.
Note: For JDK ThreadPoolExecutor when you have a bounded queue, you are only creating new threads when offer is returning false. You might obtain something usefull with CallerRunsPolicy which creates a bit of BackPressure and directly calls run in caller thread.
I need tasks to be executed from threads created by the pool and have an ubounded queue for scheduling, while the number of threads within the pool may grow or shrink between corePoolSize and maximumPoolSize so...
I ended up doing a full copy paste from ThreadPoolExecutor and change a bit the execute method because
unfortunately this could not be done by extension(it calls private methods).
I didn't wanted to spawn new threads just immediately when new request arrive and all threads are busy(because I have in general short lived tasks). I've added a threshold but feel free to change it to your needs ( maybe for mostly IO is better to remove this threshold)
private final AtomicInteger activeWorkers = new AtomicInteger(0);
private volatile double threshold = 0.7d;
protected void beforeExecute(Thread t, Runnable r) {
activeWorkers.incrementAndGet();
}
protected void afterExecute(Runnable r, Throwable t) {
activeWorkers.decrementAndGet();
}
public void execute(Runnable command) {
if (command == null)
throw new NullPointerException();
int c = ctl.get();
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
if (isRunning(c) && this.workQueue.offer(command)) {
int recheck = this.ctl.get();
if (!isRunning(recheck) && this.remove(command)) {
this.reject(command);
} else if (workerCountOf(recheck) == 0) {
this.addWorker((Runnable) null, false);
}
//>>change start
else if (workerCountOf(recheck) < maximumPoolSize //
&& (activeWorkers.get() > workerCountOf(recheck) * threshold
|| workQueue.size() > workerCountOf(recheck) * threshold)) {
this.addWorker((Runnable) null, false);
}
//<<change end
} else if (!this.addWorker(command, false)) {
this.reject(command);
}
}
Below is a solution using two Threadpools both with core and max pool size as same. The second pool is used when the 1st pool is busy.
import java.util.concurrent.Future;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
public class MyExecutor {
ThreadPoolExecutor tex1, tex2;
public MyExecutor() {
tex1 = new ThreadPoolExecutor(15, 15, 5, TimeUnit.SECONDS, new LinkedBlockingQueue<>());
tex1.allowCoreThreadTimeOut(true);
tex2 = new ThreadPoolExecutor(45, 45, 100, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>());
tex2.allowCoreThreadTimeOut(true);
}
public Future<?> submit(Runnable task) {
ThreadPoolExecutor ex = tex1;
int excessTasks1 = tex1.getQueue().size() + tex1.getActiveCount() - tex1.getCorePoolSize();
if (excessTasks1 >= 0) {
int excessTasks2 = tex2.getQueue().size() + tex2.getActiveCount() - tex2.getCorePoolSize();;
if (excessTasks2 <= 0 || excessTasks2 / (double) tex2.getCorePoolSize() < excessTasks1 / (double) tex1.getCorePoolSize()) {
ex = tex2;
}
}
return ex.submit(task);
}
}
I need to ask about how thread pooling is implemented for having constant number of thread executing each time when there is task submission happened . (In Executor to avoid each time thread creation and deletion overhead)
executor.submit(Runnable)
Lets say we create some threads in the start and when task come we assign task to them(Thread) using any Queue impl . But after completing it s task how could a thread return to its pool again when as per the lifecycle of thread says that
"After execution of its run method it goes into TERMINATED state and can't be used again"
I am not understood how thread pool works for having constant number of threads for execution of any task to its queue .
It would be great if anyone could provide me an example of thread reuse after its completion of task .
!!Thanks in advance .!!
"After execution of its run method it goes into TERMINATED state and can't be used again"
It doesn't finish its run() Instead it has a loop which runs the run() of the tasks you provide it.
Simplifying the thread pool pattern dramatically you have code which looks like this.
final BlockingQueue<Runnable> tasks = new LinkedBlockingQueue<Runnable>();
public void submit(Runnable runs) {
tasks.add(runs);
}
volatile boolean running = true;
// running in each thread in the pool
class RunsRunnable implement Runnable {
public void run() {
while(running) {
Runnable runs = tasks.take();
try {
runs.run();
} catch(Throwable t) {
// handles t
}
}
}
}
In this example, you can see that while the run() of each task completes, the run() of the thread itself does not until the pool is shutdown.
Usually what happens when we use thread pool , Its inside Run method it is forced to run iteratively. Until there are tasks available in the Queue.
in the below example pool.removeFromQueue() will run iteratively.
public class MyThread<V> extends Thread {
private MyThreadPool<V> pool;
private boolean active = true;
public boolean isActive() {
return active;
}
public void setPool(MyThreadPool<V> p) {
pool = p;
}
/**
* Checks if there are any unfinished tasks left. if there are , then runs
* the task and call back with output on resultListner Waits if there are no
* tasks available to run If shutDown is called on MyThreadPool, all waiting
* threads will exit and all running threads will exit after finishing the
* task
*/
#Override
public void run() {
ResultListener<V> result = pool.getResultListener();
Callable<V> task;
while (true) {
task = pool.removeFromQueue();
if (task != null) {
try {
V output = task.call();
result.finish(output);
} catch (Exception e) {
result.error(e);
}
} else {
if (!isActive())
break;
else {
synchronized (pool.getWaitLock()) {
try {
pool.getWaitLock().wait();
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}
}
}
}
void shutdown() {
active = false;
}
Need to design your thread pool
public MyThreadPool(int size, ResultListener<V> myResultListener) {
tasks = new LinkedList<Callable<V>>();
threads = new LinkedList<MyThread<V>>();
shutDown = false;
resultListener = myResultListener;
for (int i = 0; i < size; i++) {
MyThread<V> myThread = new MyThread<V>();
myThread.setPool(this);
threads.add(myThread);
myThread.start();
}
}
You can take a look here: http://www.ibm.com/developerworks/library/j-jtp0730/index.html for more details and an implementation example. The threads in the pool will wait if the queue is empty and will each start consome messages once they are notified that the queue has some elements.
ExecutorService executor = Executors.newFixedThreadPool(2);
- The above statement created a ThreadPool with fixed size of 2.
executor.execute(new Worker());
- The above statement takes an instance of the class Worker which has implemented Runnable Interface.
- Now here the Executors is an intermediate object, executing the task. Which manages the Thread Objects.
- By executing the above statement the run() method will be executed, and once the run() method completes, the thread doesNot go into dead state but moves back into the pool, waiting to have another work assigned to it, so it can once again move into Runnable state and then to running, all this is handled by Executors .
executor.shutdown();
- The above statement will shutdown the Executors itself, gracefully handling the shutdown of all the threads managed by it..shutdown() on that central object, which in turn could terminate each of the registered executors.
////////// Edited Part//////////////////////
- First of all Runnable has a run() method which canNot return anything, and run() method canNot throw a checked exception, So Callable was introduced in Java 5, which is of Parametric type , and has a method called call(), and it is capable of returning , and throwing Checked exceptions.
Now see this Example:
Thread t = new Thread(new Worker());
t.run();
t.start();
- t.run() is just a simple call to run() method, this won't span a thread of execution.
- t.start() whereas prepares for the things important for the initialization of the thread of execution, and then calls the run() method of the Runnable, and then assign the Task to the newly formed thread of execution, and returns quickly....
Threads in Java becomes a necessity when using Swing and AWT. Mainly the GUI component.
I am totally agree with Peter but want add steps related to ExecutorService execution flow, for clear understanding.
If you create pool (fixed size pool) of threads it does not means that threads were created.
If you submit and/or execute new Task (Runnuble or Callable) new thread will be created JUTS if count of created threads < size of pool
Created threads not returning to pool, threads can wait for new value in blocking queue, this point we can call RETURNING TO POOL
All threads from pool execs like Peter described above.
I'm trying to find a less clunky solution to a Java concurrency problem.
The gist of the problem is that I need a shutdown call to block while there are still worker threads active, but the crucial aspect is that the worker tasks are each spawned and completed asynchronously so the hold and release must be done by different threads. I need them to somehow send a signal to the shutdown thread once their work has completed. Just to make things more interesting, the worker threads cannot block each other so I'm unsure about the application of a Semaphore in this particular instance.
I have a solution which I think safely does the job, but my unfamiliarity with the Java concurrency utils leads me to think that there might be a much easier or more elegant pattern. Any help in this regard would be greatly appreciated.
Here's what I have so far, fairly sparse except for the comments:
final private ReentrantReadWriteLock shutdownLock = new ReentrantReadWriteLock();
volatile private int activeWorkerThreads;
private boolean isShutdown;
private void workerTask()
{
try
{
// Point A: Worker tasks mustn't block each other.
shutdownLock.readLock().lock();
// Point B: I only want worker tasks to continue if the shutdown signal
// hasn't already been received.
if (isShutdown)
return;
activeWorkerThreads ++;
// Point C: This async method call returns immediately, soon after which
// we release our lock. The shutdown thread may then acquire the write lock
// but we want it to continue blocking until all of the asynchronous tasks
// have completed.
executeAsynchronously(new Runnable()
{
#Override
final public void run()
{
try
{
// Do stuff.
}
finally
{
// Point D: Release of shutdown thread loop, if there are no other
// active worker tasks.
activeWorkerThreads --;
}
}
});
}
finally
{
shutdownLock.readLock().unlock();
}
}
final public void shutdown()
{
try
{
// Point E: Shutdown thread must block while any worker threads
// have breached Point A.
shutdownLock.writeLock().lock();
isShutdown = true;
// Point F: Is there a better way to wait for this signal?
while (activeWorkerThreads > 0)
;
// Do shutdown operation.
}
finally
{
shutdownLock.writeLock().unlock();
}
}
Thanks in advance for any help!
Russ
Declaring activeWorkerThreads as volatile doesn't allow you to do activeWorkerThreads++, as ++ is just shorthand for,
activeWorkerThreads = activeWorkerThreads + 1;
Which isn't atomic. Use AtomicInteger instead.
Does executeAsynchronously() send jobs to a ExecutorService? If so you can just use the awaitTermination method, so your shutdown hook will be,
executor.shutdown();
executor.awaitTermination(1, TimeUnit.Minutes);
You can use a semaphore in this scenario and not require a busy wait for the shutdown() call. The way to think of it is as a set of tickets that are handed out to workers to indicate that they are in-flight. If the shutdown() method can acquire all of the tickets then it knows that it has drained all workers and there is no activity. Because #acquire() is a blocking call the shutdown() won't spin. I've used this approach for a distributed master-worker library and its easy extend it to handle timeouts and retrials.
Executor executor = // ...
final int permits = // ...
final Semaphore semaphore = new Semaphore(permits);
void schedule(final Runnable task) {
semaphore.acquire();
try {
executor.execute(new Runnable() {
#Override public run() {
try {
task.run();
} finally {
semaphore.release();
}
}
});
} catch (RejectedExecutionException e) {
semaphore.release();
throw e;
}
}
void shutDown() {
semaphore.acquireUninterruptibly(permits);
// do stuff
}
ExecutorService should be a preferred solution as sbridges mentioned.
As an alternative, if the number of worker threads is fixed, then you can use CountDownLatch:
final CountDownLatch latch = new CountDownLatch(numberOfWorkers);
Pass the latch to every worker thread and call latch.countDown() when task is done.
Call latch.await() from the main thread to wait for all tasks to complete.
Whoa nelly. Never do this:
// Point F: Is there a better way to wait for this signal?
while (activeWorkerThreads > 0)
;
You're spinning and consuming CPU. Use a proper notification:
First: synchronize on an object, then check activeWorkerThreads, and wait() on the object if it's still > 0:
synchronized (mutexObject) {
while (activeWorkerThreads > 0) {
mutexObject.wait();
}
}
Second: Have the workers notify() the object after they decrement the activeWorkerThreads count. You must synchronize on the object before calling notify.
synchronized (mutexObject) {
activeWorkerThreads--;
mutexObject.notify();
}
Third: Seeing as you are (after implementing 1 & 2) synchronizing on an object whenever you touch activeWorkerThreads, use it as protection; there is no need for the variable to be volatile.
Then: the same object you use as a mutex for controlling access to activeWorkerThreads could also be used to control access to isShutdown. Example:
synchronized (mutexObject) {
if (isShutdown) {
return;
}
}
This won't cause workers to block each other except for immeasurably small amounts of time (which you likely do not avoid by using a read-write lock anyway).
This is more like a comment to sbridges answer, but it was a bit too long to submit as a comment.
Anyways, just 1 comment.
When you shutdown the executor, submitting new task to the executor will result in unchecked RejectedExecutionException if you use the default implementations (like Executors.newSingleThreadExecutor()). So in your case you probably want to use the following code.
code:
new ThreadPoolExecutor(1,
1,
1,
TimeUnit.HOURS,
new LinkedBlockingQueue<Runnable>(),
new ThreadPoolExecutor.DiscardPolicy());
This way, the tasks that were submitted to the executor after shutdown() was called, are simply ignored. The parameter above (1,1... etc) should produce an executor that basically is a single-thread executor, but doesn't throw the runtime exception.