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How to synchronize code based on boolean value?

I have this code:
private volatile boolean immortal;
private Object lock = new Object();
public void set(boolean immortal) {
this.immortal = immortal;
}
public void kill() {
// .... contains some other code.
synchronized(lock) {
if (!immortal) {
for (int i = 0; i < numThreads; i++) {
runnableList.add(POISON_PILL);
}
}
}
}
My use case is that I would like the if statement in the kill method to run to completion before immortal value is changed. Is there a better way of doing this without locking on an object?
I mean what is the best way to synchronize a block only if the value of a boolean variable is false and not allow the boolean value to be changed till it runs to completion? Can I achieve this using AtomicBoolean?

A neat way to do this could be to declare your runnableList as a synchronized list:
// where T is whatever type it needs to be
List<T> runnableList = Collections.synchronizedList(new ArrayList<>());
Then you could add to it without explicit synchronization:
if (!immortal) {
runnableList.addAll(Collections.nCopies(numThreads, POISON_PILL));
}
This works because a single call to addAll is atomic.
This isn't doing it without synchronization, though, it's just internal to the list.
With this said, it's hard to recommend a "better" solution because it's not clear what the requirements are. Synchronization (etc) is used to preserve the invariants of your object when operated on by multiple threads.
For example, why do you need immortal to remain unchanged while you add things to runnableList? How else do you access immortal and runnableList? etc

Use two locks:
private boolean immortal;
private final Object killMonitor = new Object();
private final Object flagMonitor = new Object();
public void set(boolean immortal) {
synchronized (flagMonitor) {
this.immortal = immortal;
}
}
public void kill() {
// ...
synchronized (flagMonitor) {
if (!immortal) {
synchronized (killMonitor) {
runnableList.addAll(Collections.nCopies(numThreads, POISON_PILL));
}
}
}
}

Java - Synchronized but allow one method to be accessed by different threads

In the example below:
public class MsLunch {
private long c1 = 0;
private long c2 = 0;
private Object lock1 = new Object();
private Object lock2 = new Object();
public void inc1() {
synchronized(lock1) {
c1++;
}
}
public void inc2() {
synchronized(lock2) {
c2++;
}
}
}
inc1 and inc2 can be accessed at the same time, but neither can be accessed by multiple threads at the same time.
How would it be possible to allow only inc1 or inc2 to be accessed whilst the other is like regular syncing however allowing the one that is being accessed to be done so by as many threads as possible.

I think a useful analogy is traffic passing through an intersection, where you can have multiple cars sharing one road, as long as they're driving in parallel. The challenge is finding a coordination strategy for intersecting traffic.
The solution proposed by #Greg works if traffic is intermittent and we can wait for one stream to stop before allowing the intersecting stream to proceed. But I suspect that's not very realistic. If there's steady traffic on one road, the rest of the cars will wait forever, a.k.a. thread starvation.
An alternative strategy is to allow cars to cross on a first come, first served basis, like at a stop sign. We can implement that using a dedicated semaphore for each "road", or segment, where each user takes a permit, after first making sure none of the other segments have permits in use:
public class StopSign {
private final Semaphore[] locks;
private volatile int current = 0;
public StopSign(int segments) {
// create and populate lock array, leaving
// all segments drained besides the first
locks = new Semaphore[segments];
Arrays.setAll(locks, i -> new Semaphore(i == 0 ? Integer.MAX_VALUE : 0, true));
}
public void enter(int segment) {
// synchronization is necessary to guard `current`,
// with the added benefit of holding up new threads
// in the active segment while we're gathering permits
synchronized (locks) {
if (segment == current) {
// if our segment is active, acquire a permit
locks[segment].acquireUninterruptibly();
} else {
// otherwise, gather all permits from the active segment
// as they become available and then reclaim our own permits
locks[current].acquireUninterruptibly(Integer.MAX_VALUE);
current = segment;
locks[segment].release(Integer.MAX_VALUE - 1);
}
}
}
public void exit(int segment) {
if (segment != current) {
// we don't own the lock!
throw new IllegalMonitorStateException();
}
locks[segment].release();
}
}
To use the class, we simply call enter(i) and exit(i), where i identifies the road/segment/method we want to use. Here's a demo using 3 segments:
public static void main(String args[]) {
int segments = 3;
StopSign lock = new StopSign(segments);
IntStream.range(0, segments).parallel().forEach(i -> {
for (int j = 0; j < 10; j++) {
lock.enter(i);
System.out.print(i);
lock.exit(i);
sleepUninterruptibly(20, TimeUnit.MILLISECONDS);
}
});
}
A test run on my machine produces this alternating pattern:
120201210012012210102120021021
This strategy could make sense if traffic is relatively light, but in heavy traffic the overhead of coordinating each crossing can significantly restrict throughput. For busy intersections, you'll usually want a traffic light, or a third party that can transfer control at a reasonable frequency. Here's an implementation of a such a concept, using a background thread that manages a set of read/write locks, making sure only one segment has a write lock available at a time:
public class TrafficLight {
private final ReadWriteLock[] locks;
private final Thread changer;
public TrafficLight(int segments, long changeFrequency, TimeUnit unit) {
// create and populate lock array
locks = new ReadWriteLock[segments];
Arrays.setAll(locks, i -> new ReentrantReadWriteLock(true));
CountDownLatch initialized = new CountDownLatch(1);
changer = new Thread(() -> {
// lock every segment besides the first
for (int i = 1; i < locks.length; i++) {
locks[i].writeLock().lock();
}
initialized.countDown();
int current = 0;
try {
while (true) {
unit.sleep(changeFrequency);
// lock the current segment and cycle to the next
locks[current].writeLock().lock();
current = (current + 1) % locks.length;
locks[current].writeLock().unlock();
}
} catch (InterruptedException e) {}
});
changer.setDaemon(true);
changer.start();
// wait for the locks to be initialized
awaitUninterruptibly(initialized);
}
public void enter(int segment) {
locks[segment].readLock().lock();
}
public void exit(int segment) {
locks[segment].readLock().unlock();
}
public void shutdown() {
changer.interrupt();
}
}
Now let's tweak the test code:
TrafficLight lock = new TrafficLight(segments, 100, TimeUnit.MILLISECONDS);
The result is an orderly pattern:
000111112222200000111112222200
Notes:
awaitUninterruptibly() and sleepUninterruptibly() are Guava helper methods to avoid handling InterruptedException. Feel free to copy the implementation if you don't want to import the library.
TrafficLight could be implemented by delegating state management to visiting threads, instead of relying on a background thread. This implementation is simpler (I think), but it does have some extra overhead and it requires a shutdown() to be garbage collected.
The test code uses parallel streams for convenience, but depending on your environment, it may not interleave very well. You can always use proper threads instead.

You could keep track of what mode you're in, and how many operations of that type are in progress, then only flip the mode when all of those operations are complete, eg:
public class MsLunch {
private enum LockMode {IDLE, C1_ACTIVE, C2_ACTIVE};
private LockMode lockMode = IDLE:
private int activeThreads = 0;
private long c1 = 0;
private long c2 = 0;
public void inc1() {
try {
enterMode(C1_ACTIVE);
c1++
} finally {
exitMode();
}
}
public void inc2() {
try {
enterMode(C2_ACTIVE);
c2++
} finally {
exitMode();
}
}
private synchronized void enterMode(LockMode newMode){
while(mode != IDLE && mode != newMode) {
try {
this.wait(); // don't continue while threads are busy in the other mode
} catch(InterruptedException e) {}
}
mode = newMode;
activeThreads++;
}
private synchronized void exitMode(){
activeThreads--;
if (activeThreads == 0) {
mode = IDLE;
this.notifyAll(); // no more threads in this mode, wake up anything waiting
}
}
}

Both sequential and parallel processing

I have one producer and many consumers.
the producer is fast and generating a lot of results
tokens with the same value need to be processed sequentially
tokens with different values must be processed in parallel
creating new Runnables would be very expensive and also the production code could work with 100k of Tokens(in order to create a Runnable I have to pass to the constructor some complex to build objects)
Can I achieve the same results with a simpler algorithm? Nesting a syncronization block with a reentrant lock seems a bit unnatural.
Are there any race conditions you might notice?
Update: a second solution I found was working with 3 collections. One to cache the producer results, second a blocking queue and 3rd using a list to track in the tasks in progress. Again a bit to complicated.
My version of code
import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.locks.ReentrantLock;
public class Main1 {
static class Token {
private int order;
private String value;
Token() {
}
Token(int o, String v) {
order = o;
value = v;
}
int getOrder() {
return order;
}
String getValue() {
return value;
}
}
private final static BlockingQueue<Token> queue = new ArrayBlockingQueue<Token>(10);
private final static ConcurrentMap<String, Object> locks = new ConcurrentHashMap<String, Object>();
private final static ReentrantLock reentrantLock = new ReentrantLock();
private final static Token STOP_TOKEN = new Token();
private final static List<String> lockList = Collections.synchronizedList(new ArrayList<String>());
public static void main(String[] args) {
ExecutorService producerExecutor = Executors.newSingleThreadExecutor();
producerExecutor.submit(new Runnable() {
public void run() {
Random random = new Random();
try {
for (int i = 1; i <= 100; i++) {
Token token = new Token(i, String.valueOf(random.nextInt(1)));
queue.put(token);
}
queue.put(STOP_TOKEN);
}catch(InterruptedException e){
e.printStackTrace();
}
}
});
ExecutorService consumerExecutor = Executors.newFixedThreadPool(10);
for(int i=1; i<=10;i++) {
// creating to many runnable would be inefficient because of this complex not thread safe object
final Object dependecy = new Object(); //new ComplexDependecy()
consumerExecutor.submit(new Runnable() {
public void run() {
while(true) {
try {
//not in order
Token token = queue.take();
if (token == STOP_TOKEN) {
queue.add(STOP_TOKEN);
return;
}
System.out.println("Task start" + Thread.currentThread().getId() + " order " + token.getOrder());
Random random = new Random();
Thread.sleep(random.nextInt(200)); //doLongRunningTask(dependecy)
lockList.remove(token.getValue());
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}});
}
}}

You can pre-create set of Runnables which will pick incoming tasks (tokens) and place them in queues according to their order value.
As pointed out in comments, it's not guaranteed that tokens with different values will always execute in parallel (all in all, you are bounded, at least, by nr of physical cores in your box). However, it is guaranteed that tokens with same order will be executed in the order of arrival.
Sample code:
/**
* Executor which ensures incoming tasks are executed in queues according to provided key (see {#link Task#getOrder()}).
*/
public class TasksOrderingExecutor {
public interface Task extends Runnable {
/**
* #return ordering value which will be used to sequence tasks with the same value.<br>
* Tasks with different ordering values <i>may</i> be executed in parallel, but not guaranteed to.
*/
String getOrder();
}
private static class Worker implements Runnable {
private final LinkedBlockingQueue<Task> tasks = new LinkedBlockingQueue<>();
private volatile boolean stopped;
void schedule(Task task) {
tasks.add(task);
}
void stop() {
stopped = true;
}
#Override
public void run() {
while (!stopped) {
try {
Task task = tasks.take();
task.run();
} catch (InterruptedException ie) {
// perhaps, handle somehow
}
}
}
}
private final Worker[] workers;
private final ExecutorService executorService;
/**
* #param queuesNr nr of concurrent task queues
*/
public TasksOrderingExecutor(int queuesNr) {
Preconditions.checkArgument(queuesNr >= 1, "queuesNr >= 1");
executorService = new ThreadPoolExecutor(queuesNr, queuesNr, 0, TimeUnit.SECONDS, new SynchronousQueue<>());
workers = new Worker[queuesNr];
for (int i = 0; i < queuesNr; i++) {
Worker worker = new Worker();
executorService.submit(worker);
workers[i] = worker;
}
}
public void submit(Task task) {
Worker worker = getWorker(task);
worker.schedule(task);
}
public void stop() {
for (Worker w : workers) w.stop();
executorService.shutdown();
}
private Worker getWorker(Task task) {
return workers[task.getOrder().hashCode() % workers.length];
}
}

By the nature of your code, the only way to guarantee that the tokens with the
same value are processed in serial manner is to wait for STOP_TOKEN to arrive.
You'll need single producer-single consumer setup, with consumer collecting and sorting
the tokens by their value (into the Multimap, let say).
Only then you know which tokens can be process serially and which may be processed in parallel.
Anyway, I advise you to look at LMAX Disruptor, which offers very effective way for sharing data between threads.
It doesn't suffer from synchronization overhead as Executors as it is lock free (which may give you nice performance benefits, depending on the way how you process the data).
The solution using two Disruptors
// single thread for processing as there will be only on consumer
Disruptor<InEvent> inboundDisruptor = new Disruptor<>(InEvent::new, 32, Executors.newSingleThreadExecutor());
// outbound disruptor that uses 3 threads for event processing
Disruptor<OutEvent> outboundDisruptor = new Disruptor<>(OutEvent::new, 32, Executors.newFixedThreadPool(3));
inboundDisruptor.handleEventsWith(new InEventHandler(outboundDisruptor));
// setup 3 event handlers, doing round robin consuming, effectively processing OutEvents in 3 threads
outboundDisruptor.handleEventsWith(new OutEventHandler(0, 3, new Object()));
outboundDisruptor.handleEventsWith(new OutEventHandler(1, 3, new Object()));
outboundDisruptor.handleEventsWith(new OutEventHandler(2, 3, new Object()));
inboundDisruptor.start();
outboundDisruptor.start();
// publisher code
for (int i = 0; i < 10; i++) {
inboundDisruptor.publishEvent(InEventTranslator.INSTANCE, new Token());
}
The event handler on the inbound disruptor just collects incoming tokens. When STOP token is received, it publishes the series of tokens to outbound disruptor for further processing:
public class InEventHandler implements EventHandler<InEvent> {
private ListMultimap<String, Token> tokensByValue = ArrayListMultimap.create();
private Disruptor<OutEvent> outboundDisruptor;
public InEventHandler(Disruptor<OutEvent> outboundDisruptor) {
this.outboundDisruptor = outboundDisruptor;
}
#Override
public void onEvent(InEvent event, long sequence, boolean endOfBatch) throws Exception {
if (event.token == STOP_TOKEN) {
// publish indexed tokens to outbound disruptor for parallel processing
tokensByValue.asMap().entrySet().stream().forEach(entry -> outboundDisruptor.publishEvent(OutEventTranslator.INSTANCE, entry.getValue()));
} else {
tokensByValue.put(event.token.value, event.token);
}
}
}
Outbound event handler processes tokens of the same value sequentially:
public class OutEventHandler implements EventHandler<OutEvent> {
private final long order;
private final long allHandlersCount;
private Object yourComplexDependency;
public OutEventHandler(long order, long allHandlersCount, Object yourComplexDependency) {
this.order = order;
this.allHandlersCount = allHandlersCount;
this.yourComplexDependency = yourComplexDependency;
}
#Override
public void onEvent(OutEvent event, long sequence, boolean endOfBatch) throws Exception {
if (sequence % allHandlersCount != order ) {
// round robin, do not consume every event to allow parallel processing
return;
}
for (Token token : event.tokensToProcessSerially) {
// do procesing of the token using your complex class
}
}
}
The rest of the required infrastructure (purpose described in the Disruptor docs):
public class InEventTranslator implements EventTranslatorOneArg<InEvent, Token> {
public static final InEventTranslator INSTANCE = new InEventTranslator();
#Override
public void translateTo(InEvent event, long sequence, Token arg0) {
event.token = arg0;
}
}
public class OutEventTranslator implements EventTranslatorOneArg<OutEvent, Collection<Token>> {
public static final OutEventTranslator INSTANCE = new OutEventTranslator();
#Override
public void translateTo(OutEvent event, long sequence, Collection<Token> tokens) {
event.tokensToProcessSerially = tokens;
}
}
public class InEvent {
// Note that no synchronization is used here,
// even though the field is used among multiple threads.
// Memory barrier used by Disruptor guarantee changes are visible.
public Token token;
}
public class OutEvent {
// ... again, no locks.
public Collection<Token> tokensToProcessSerially;
}
public class Token {
String value;
}

If you have lots of different tokens, then the simplest solution is to create some number of single-thread executors (about 2x your number of cores), and then distribute each task to an executor determined by the hash of its token.
That way all tasks with the same token will go to the same executor and execute sequentially, because each executor only has one thread.
If you have some unstated requirements about scheduling fairness, then it is easy enough to avoid any significant imbalances by having the producer thread queue up its requests (or block) before distributing them, until there are, say, less than 10 requests per executor outstanding.

The following solution will only use a single Map that is used by the producer and consumers to process orders in sequential order for each order number while processing different order numbers in parallel. Here is the code:
public class Main {
private static final int NUMBER_OF_CONSUMER_THREADS = 10;
private static volatile int sync = 0;
public static void main(String[] args) {
final ConcurrentHashMap<String,Controller> queues = new ConcurrentHashMap<String, Controller>();
final CountDownLatch latch = new CountDownLatch(NUMBER_OF_CONSUMER_THREADS);
final AtomicBoolean done = new AtomicBoolean(false);
// Create a Producer
new Thread() {
{
this.setDaemon(true);
this.setName("Producer");
this.start();
}
public void run() {
Random rand = new Random();
for(int i =0 ; i < 1000 ; i++) {
int order = rand.nextInt(20);
String key = String.valueOf(order);
String value = String.valueOf(rand.nextInt());
Controller controller = queues.get(key);
if (controller == null) {
controller = new Controller();
queues.put(key, controller);
}
controller.add(new Token(order, value));
Main.sync++;
}
done.set(true);
}
};
while (queues.size() < 10) {
try {
// Allow the producer to generate several entries that need to
// be processed.
Thread.sleep(5000);
} catch (InterruptedException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
}
// System.out.println(queues);
// Create the Consumers
ExecutorService consumers = Executors.newFixedThreadPool(NUMBER_OF_CONSUMER_THREADS);
for(int i = 0 ; i < NUMBER_OF_CONSUMER_THREADS ; i++) {
consumers.submit(new Runnable() {
private Random rand = new Random();
public void run() {
String name = Thread.currentThread().getName();
try {
boolean one_last_time = false;
while (true) {
for (Map.Entry<String, Controller> entry : queues.entrySet()) {
Controller controller = entry.getValue();
if (controller.lock(this)) {
ConcurrentLinkedQueue<Token> list = controller.getList();
Token token;
while ((token = list.poll()) != null) {
try {
System.out.println(name + " processing order: " + token.getOrder()
+ " value: " + token.getValue());
Thread.sleep(rand.nextInt(200));
} catch (InterruptedException e) {
}
}
int last = Main.sync;
queues.remove(entry.getKey());
while(done.get() == false && last == Main.sync) {
// yield until the producer has added at least another entry
Thread.yield();
}
// Purge any new entries added
while ((token = list.poll()) != null) {
try {
System.out.println(name + " processing order: " + token.getOrder()
+ " value: " + token.getValue());
Thread.sleep(200);
} catch (InterruptedException e) {
}
}
controller.unlock(this);
}
}
if (one_last_time) {
return;
}
if (done.get()) {
one_last_time = true;
}
}
} finally {
latch.countDown();
}
}
});
}
try {
latch.await();
} catch (InterruptedException e) {
e.printStackTrace();
}
consumers.shutdown();
System.out.println("Exiting.. remaining number of entries: " + queues.size());
}
}
Note that the Main class contains a queues instance that is a Map. The map key is the order id that you want to process sequentially by the consumers. The value is a Controller class that will contain all of the orders associated with that order id.
The producer will generate the orders and add the order, (Token), to its associated Controller. The consumers will iterator over the queues map values and call the Controller lock method to determine if it can process orders for that particular order id. If the lock returns false it will check the next Controller instance. If the lock returns true, it will process all orders and then check the next Controller.
updated Added the sync integer that is used to guarantee that when an instance of the Controller is removed from the queues map. All of its entries will be consumed. There was an logic error in the consumer code where the unlock method was called to soon.
The Token class is similar to the one that you've posted here.
class Token {
private int order;
private String value;
Token(int order, String value) {
this.order = order;
this.value = value;
}
int getOrder() {
return order;
}
String getValue() {
return value;
}
#Override
public String toString() {
return "Token [order=" + order + ", value=" + value + "]\n";
}
}
The Controller class that follows is used to insure that only a single thread within the thread pool will be processing the orders. The lock/unlock methods are used to determine which of the threads will be allowed to process the orders.
class Controller {
private ConcurrentLinkedQueue<Token> tokens = new ConcurrentLinkedQueue<Token>();
private ReentrantLock lock = new ReentrantLock();
private Runnable current = null;
void add(Token token) {
tokens.add(token);
}
public ConcurrentLinkedQueue<Token> getList() {
return tokens;
}
public void unlock(Runnable runnable) {
lock.lock();
try {
if (current == runnable) {
current = null;
}
} finally {
lock.unlock();
}
}
public boolean lock(Runnable runnable) {
lock.lock();
try {
if (current == null) {
current = runnable;
}
} finally {
lock.unlock();
}
return current == runnable;
}
#Override
public String toString() {
return "Controller [tokens=" + tokens + "]";
}
}
Additional information about the implementation. It uses a CountDownLatch to insure that all produced orders will be processed prior to the process exiting. The done variable is just like your STOP_TOKEN variable.
The implementation does contain an issue that you would need to resolve. There is the issue that it does not purge the controller for an order id when all of the orders have been processed. This will cause instances where a thread in the thread pool gets assigned to a controller that contains no orders. Which will waste cpu cycles that could be used to perform other tasks.

Is all you need is to ensure that tokens with the same value are not being processed concurrently? Your code is too messy to understand what you mean (it does not compile, and has lots of unused variables, locks and maps, that are created but never used). It looks like you are greatly overthinking this. All you need is one queue, and one map.
Something like this I imagine:
class Consumer implements Runnable {
ConcurrentHashMap<String, Token> inProcess;
BlockingQueue<Token> queue;
public void run() {
Token token = null;
while ((token = queue.take()) != null) {
if(inProcess.putIfAbsent(token.getValue(), token) != null) {
queue.put(token);
continue;
}
processToken(token);
inProcess.remove(token.getValue());
}
}
}

tokens with the same value need to be processed sequentially
The way to insure that any two things happen in sequence is to do them in the same thread.
I'd have a collection of however many worker threads, and I'd have a Map. Any time I get a token that I've not seen before, I'll pick a thread at random, and enter the token and the thread into the map. From then on, I'll use that same thread to execute tasks associated with that token.
creating new Runnables would be very expensive
Runnable is an interface. Creating new objects that implement Runnable is not going to be significantly more expensive than creating any other kind of object.

Maybe I'm misunderstanding something. But it seems that it would be easier to filter the Tokens with same value from the ones with different values into two different queues initially.
And then use Stream with either map or foreach for the sequential. And simply use the parallel stream version for the rest.
If your Tokens in production environment are lazily generated and you only get one at a time you simply make some sort of filter which distributes them to the two different queues.
If you can implement it with Streams I suqqest doing that as they are simple, easy to use and FAST!
https://docs.oracle.com/javase/8/docs/api/java/util/stream/Stream.html
I made a brief example of what I mean. In this case the numbers Tokens are sort of artificially constructed but thats beside the point. Also the streams are both initiated on the main thread which would probably also not be ideal.
public static void main(String args[]) {
ArrayList<Token> sameValues = new ArrayList<Token>();
ArrayList<Token> distinctValues = new ArrayList<Token>();
Random random = new Random();
for (int i = 0; i < 100; i++) {
int next = random.nextInt(100);
Token n = new Token(i, String.valueOf(next));
if (next == i) {
sameValues.add(n);
} else {
distinctValues.add(n);
}
}
distinctValues.stream().parallel().forEach(token -> System.out.println("Distinct: " + token.value));
sameValues.stream().forEach(token -> System.out.println("Same: " + token.value));
}

I am not entirely sure I have understood the question but I'll take a stab at an algorithm.
The actors are:
A queue of tasks
A pool of free executors
A set of in-process tokens currently being processed
A controller
Then,
Initially all executors are available and the set is empty
controller picks an available executor and goes through the queue looking for a task with a token that is not in the in-process set and when it finds it
adds the token to the in-process set
assigns the executor to process the task and
goes back to the beginning of the queue
the executor removes the token from the set when it is done processing and adds itself back to the pool

One way of doing this is having one executor for sequence processing and one for parallel processing. We also need a single threaded manager service that will decide to which service token needs to be submitted for processing.
// Queue to be shared by both the threads. Contains the tokens produced by producer.
BlockingQueue tokenList = new ArrayBlockingQueue(10);
private void startProcess() {
ExecutorService producer = Executors.newSingleThreadExecutor();
final ExecutorService consumerForSequence = Executors
.newSingleThreadExecutor();
final ExecutorService consumerForParallel = Executors.newFixedThreadPool(10);
ExecutorService manager = Executors.newSingleThreadExecutor();
producer.submit(new Producer(tokenList));
manager.submit(new Runnable() {
public void run() {
try {
while (true) {
Token t = tokenList.take();
System.out.println("consumed- " + t.orderid
+ " element");
if (t.orderid % 7 == 0) { // any condition to check for sequence processing
consumerForSequence.submit(new ConsumerForSequenceProcess(t));
} else {
ConsumerForParallel.submit(new ConsumerForParallelProcess(t));
}
}
}
catch (InterruptedException e) { // TODO Auto-generated catch
// block
e.printStackTrace();
}
}
});
}

I think there is a more fundamental design issue hidden behind this task, but ok. I cant figure out from you problem description if you want in-order execution or if you just want operations on tasks described by single tokens to be atomic/transactional. What i propose below feels more like a "quick fix" to this issue than a real solution.
For the real "ordered execution" case I propose a solution which is based on queue proxies which order the output:
Define a implementation of Queue which provides a factory method generating proxy queues which are represented to the producer side by a this single queue object; the factory method should also register these proxy queue objects. adding an element to the input queue should add it directly to one of the output queues if it matches one of the elements in one of the output queues. Otherwise add it to any (the shortest) output queue. (implement the check for this efficiently). Alternatively (slightly better): don't do this when the element is added, but when any of the output queues runs empty.
Give each of your runnable consumers an field storing an individual Queue interface (instead of accessing a single object). Initialize this field by a the factory method defined above.
For the transaction case i think it's easier to span more threads than you have cores (use statistics to calculate this), and implement the blocking mechanism on an lower (object) level.

Two-way communication with a Java thread

In my application I'm performing somewhat heavy lookup operations. These operations must be done within a single thread (persistence framework limitation).
I want to cache the results. Thus, I have a class UMRCache, with an inner class Worker:
public class UMRCache {
private Worker worker;
private List<String> requests = Collections.synchronizedList<new ArrayList<String>>());
private Map<String, Object> cache = Collections.synchronizedMap(new HashMap<String, Object>());
public UMRCache(Repository repository) {
this.worker = new Worker(repository);
this.worker.start();
}
public Object get(String key) {
if (this.cache.containsKey(key)) {
// If the element is already cached, get value from cache
return this.cache.get(key);
}
synchronized (this.requests) {
// Add request to queue
this.requests.add(key);
// Notify the Worker thread that there's work to do
this.requests.notifyAll();
}
synchronized (this.cache) {
// Wait until Worker has updated the cache
this.cache.wait();
// Now, cache should contain a value for key
return this.cache.get(key);
}
}
private class Worker extends Thread {
public void run() {
boolean doRun = true;
while (doRun) {
synchronized (requests) {
while (requests.isEmpty() && doRun) {
requests.wait(); // Wait until there's work to do
}
synchronized (cache) {
Set<String> processed = new HashSet<String>();
for (String key : requests) {
// Do the lookup
Object result = respository.lookup(key);
// Save to cache
cache.put(key, result);
processed.add(key);
}
// Remove processed requests from queue
requests.removeAll(processed);
// Notify all threads waiting for their requests to be served
cache.notifyAll();
}
}
}
}
}
}
I have a testcase for this:
public class UMRCacheTest extends TestCase {
private UMRCache umrCache;
public void setUp() throws Exception {
super.setUp();
umrCache = new UMRCache(repository);
}
public void testGet() throws Exception {
for (int i = 0; i < 10000; i++) {
final List fetched = Collections.synchronizedList(new ArrayList());
final String[] keys = new String[]{"key1", "key2"};
final String[] expected = new String[]{"result1", "result2"}
final Random random = new Random();
Runnable run1 = new Runnable() {
public void run() {
for (int i = 0; i < keys.length; i++) {
final String key = keys[i];
final Object result = umrCache.get(key);
assertEquals(key, results[i]);
fetched.add(um);
try {
Thread.sleep(random.nextInt(3));
} catch (InterruptedException ignore) {
}
}
}
};
Runnable run2 = new Runnable() {
public void run() {
for (int i = keys.length - 1; i >= 0; i--) {
final String key = keys[i];
final String result = umrCache.get(key);
assertEquals(key, results[i]);
fetched.add(um);
try {
Thread.sleep(random.nextInt(3));
} catch (InterruptedException ignore) {
}
}
}
};
final Thread thread1 = new Thread(run1);
thread1.start();
final Thread thread2 = new Thread(run2);
thread2.start();
final Thread thread3 = new Thread(run1);
thread3.start();
thread1.join();
thread2.join();
thread3.join();
umrCache.dispose();
assertEquals(6, fetched.size());
}
}
}
The test fails randomly, at about 1 out of 10 runs. It will fail at the last assertion: assertEquals(6, fetched.size()), at assertEquals(key, results[i]), or sometimes the test runner will never finish.
So there's something buggy about my thread logic. Any tips?
EDIT:
I might have cracked it now, thanks to all who have helped.
The solution seems to be:
public Object get(String key) {
if (this.cache.containsKey(key)) {
// If the element is already cached, get value from cache
return this.cache.get(key);
}
synchronized (this.requests) {
// Add request to queue
this.requests.add(key);
// Notify the Worker thread that there's work to do
this.requests.notifyAll();
}
synchronized (this.cache) {
// Wait until Worker has updated the cache
while (!this.cache.containsKey(key)) {
this.cache.wait();
}
// Now, cache should contain a value for key
return this.cache.get(key);
}
}

get() method logic can miss result and get stuck
synchronized (this.requests) {
// Add request to queue
this.requests.add(key);
// Notify the Worker thread that there's work to do
this.requests.notifyAll();
}
// ----- MOMENT1. If at this moment Worker puts result into cache it
// will be missed since notification will be lost
synchronized (this.cache) {
// Wait until Worker has updated the cache
this.cache.wait();
// ----- MOMENT2. May be too late, since cache notifiation happened before at MOMENT1
// Now, cache should contain a value for key
return this.cache.get(key);
}

The variable fetched in your test is an ArrayList and is accessed and updated from your two anonymous Runnable instances.
ArrayList is not thread safe, from the documentation:
Note that this implementation is not
synchronized. If multiple threads
access an ArrayList instance
concurrently, and at least one of the
threads modifies the list
structurally, it must be synchronized
externally. (A structural modification
is any operation that adds or deletes
one or more elements, or explicitly
resizes the backing array; merely
setting the value of an element is not
a structural modification.) This is
typically accomplished by
synchronizing on some object that
naturally encapsulates the list. If no
such object exists, the list should be
"wrapped" using the
Collections.synchronizedList method.
This is best done at creation time, to
prevent accidental unsynchronized
access to the list:
Hence I think your test needs a little adjusting.

I noticed your lookup in cache isn't atomic operation:
if (this.cache.containsKey(key)) {
// If the element is already cached, get value from cache
return this.cache.get(key);
}
Since you never delete from cache in your code, you always will get some value by this code. But if, in future, you plan to clean cache, lack of atomicity here will become a problem.

What is java's equivalent of ManualResetEvent? [duplicate]

This question already has answers here:
Java Equivalent of .NET's ManualResetEvent and WaitHandle
(4 answers)
Closed 4 years ago.
What is java's equivalent of ManualResetEvent?

class ManualResetEvent {
private final Object monitor = new Object();
private volatile boolean open = false;
public ManualResetEvent(boolean open) {
this.open = open;
}
public void waitOne() throws InterruptedException {
synchronized (monitor) {
while (open==false) {
monitor.wait();
}
}
}
public boolean waitOne(long milliseconds) throws InterruptedException {
synchronized (monitor) {
if (open)
return true;
monitor.wait(milliseconds);
return open;
}
}
public void set() {//open start
synchronized (monitor) {
open = true;
monitor.notifyAll();
}
}
public void reset() {//close stop
open = false;
}
}

The closest I know of is the Semaphore. Just use it with a "permit" count of 1, and aquire/release will be pretty much the same as what you know from the ManualResetEvent.
A semaphore initialized to one, and
which is used such that it only has at
most one permit available, can serve
as a mutual exclusion lock. This is
more commonly known as a binary
semaphore, because it only has two
states: one permit available, or zero
permits available. When used in this
way, the binary semaphore has the
property (unlike many Lock
implementations), that the "lock" can
be released by a thread other than the
owner (as semaphores have no notion of
ownership). This can be useful in some
specialized contexts, such as deadlock
recovery.

Try CountDownLatch with count of one.
CountDownLatch startSignal = new CountDownLatch(1);

Based on:
ManualResetEvent allows threads to communicate with each other by
signaling. Typically, this
communication concerns a task which
one thread must complete before other
threads can proceed.
from here:
http://msdn.microsoft.com/en-us/library/system.threading.manualresetevent.aspx
you possibly want to look at the Barriers in the Java concurrency package - specifically CyclicBarrier I believe:
http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/CyclicBarrier.html
It blocks a fixed number of threads until a particular event has occured. All the threads must come together at a barrier point.

I believe the crux of the .NET MRE is thread affinity and its ability to let all waiting threads go through when Set is called. I found the use of the Semaphore works well. However, if I get 10 or 15 threads waiting, then I run into another issue. Specifically, it occurs when Set is called. In .Net, all waiting threads are released. Using a semphore does not release all. So I wrapped it in a class. NOTE: I am very familiar with .NET threading. I am relatively new to Java threading and synchronization. Nevertheless, I am willing to jump in and get some real feedback. Here's my implementation with assumptions that a Java novice would make:
public class ManualEvent {
private final static int MAX_WAIT = 1000;
private final static String TAG = "ManualEvent";
private Semaphore semaphore = new Semaphore(MAX_WAIT, false);
private volatile boolean signaled = false;
public ManualEvent(boolean signaled) {
this.signaled = signaled;
if (!signaled) {
semaphore.drainPermits();
}
}
public boolean WaitOne() {
return WaitOne(Long.MAX_VALUE);
}
private volatile int count = 0;
public boolean WaitOne(long millis) {
boolean bRc = true;
if (signaled)
return true;
try {
++count;
if (count > MAX_WAIT) {
Log.w(TAG, "More requests than waits: " + String.valueOf(count));
}
Log.d(TAG, "ManualEvent WaitOne Entered");
bRc = semaphore.tryAcquire(millis, TimeUnit.MILLISECONDS);
Log.d(TAG, "ManualEvent WaitOne=" + String.valueOf(bRc));
}
catch (InterruptedException e) {
bRc = false;
}
finally {
--count;
}
Log.d(TAG, "ManualEvent WaitOne Exit");
return bRc;
}
public void Set() {
Log.d(TAG, "ManualEvent Set");
signaled = true;
semaphore.release(MAX_WAIT);
}
public void Reset() {
signaled = false;
//stop any new requests
int count = semaphore.drainPermits();
Log.d(TAG, "ManualEvent Reset: Permits drained=" + String.valueOf(count));
}
}
Also note that I am basically betting that there's no more than a 1000 requests waiting for a release at any given time. By releasing and aquiring in batches, I am attempting to release any waiting threads. Note the call to WaitOne is working 1 permit at a time.