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Thread executes too many times and causes race condition even though I'm using locks

I'm working on a multithread application for an exercise used to simulate a warehouse (similar to the producer consumer problem) however I'm running into some trouble with the program where increasing the number of consumer threads makes the program behave in unexpected ways.
The code:
I'm creating a producer thread called buyer which has as a goal to order precisely 10 orders from the warehouse each. To do this they have a shared object called warehouse on which a buyer can place an order, the order is then stored in a buffer in the shared object. After this the buyer sleeps for some time until it either tries again or all packs have been bought. The code to do this looks like this:
public void run() {
//Run until the thread has bought 10 packages, this ensures the thread
//will eventually stop execution automatically.
while(this.packsBought < 10) {
try {
//Sleep for a random amount of time between 1 and 50
//milliseconds.
Thread.sleep(this.rand.nextInt(49) + 1);
//Catch any interruptExceptions.
} catch (InterruptedException ex) {
//There is no problem if this exception is thrown, the thread
//will just make an order earlier than planned. that being said
//there should be no manner in which this exception is thrown.
}
//Create a new order.
Order order = new Order(this.rand.nextInt(3)+ 1,
this,
this.isPrime);
//Set the time at which the order was placed as now.
order.setOrderTime(System.currentTimeMillis());
//place the newly created order in the warehouse.
this.warehouse.placeOrder(order);
}
//Notify the thread has finished execution.
System.out.println("Thread: " + super.getName() + " has finished.");
}
As you can see the function placeOrder(Order order); is used to place an order at the warehouse. this function is responsible for placing the order in the queue based on some logic related to prime status. The function looks like this:
public void placeOrder(Order order) {
try{
//halt untill there are enough packs to handle an order.
this.notFullBuffer.acquire();
//Lock to signify the start of the critical section.
this.mutexBuffer.lock();
//Insert the order in the buffer depending on prime status.
if (order.isPrime()) {
//prime order, insert behind all prime orders in buffer.
//Enumerate all non prime orders in the list.
for (int i = inPrime; i < sizeOrderList - 1; i++) {
//Move the non prime order back 1 position in the list.
buffer[i + 1] = buffer[i];
}
// Insert the prime order.
buffer[inPrime++] = order;
} else {
//No prime order, insert behind all orders in buffer.
buffer[inPrime + inNormal++] = order;
}
//Notify the DispatchWorkers that a new order has been placed.
this.notEmptyBuffer.release();
//Catch any InterruptException that might occure.
} catch(InterruptedException e){
//Even though this isn't expected behavior, there is no reason to
//notify the user of this event or to preform any other action as
//the thread will just return to the queue before placing another
//error if it is still required to do so.
} finally {
//Unlock and finalize the critical section.
mutexBuffer.unlock();
}
}
The orders are consumed by workers which act as the consumer thread. The thread itself contains very simple code looping until all orders have been processed. In this loop a different function handleOrder(); is called on the same warehouse object which handles a single order from the buffer. It does so with the following code:
public void handleOrder(){
//Create a variable to store the order being handled.
Order toHandle = null;
try{
//wait until there is an order to handle.
this.notEmptyBuffer.acquire();
//Lock to signify the start of the critical section.
this.mutexBuffer.lock();
//obtain the first order to handle as the first element of the buffer
toHandle = buffer[0];
//move all buffer elementst back by 1 position.
for(int i = 1; i < sizeOrderList; i++){
buffer[i - 1] = buffer[i];
}
//set the last element in the buffer to null
buffer[sizeOrderList - 1] = null;
//We have obtained an order from the buffer and now we can handle it.
if(toHandle != null) {
int nPacks = toHandle.getnPacks();
//wait until the appropriate resources are available.
this.hasBoxes.acquire(nPacks);
this.hasTape.acquire(nPacks * 50);
//Now we can handle the order (Simulated by sleeping. Although
//in real live Amazon workers also have about 5ms of time per
//package).
Thread.sleep(5 * nPacks);
//Calculate the total time this order took.
long time = System.currentTimeMillis() -
toHandle.getOrderTime();
//Update the total waiting time for the buyer.
toHandle.getBuyer().setWaitingTime(time +
toHandle.getBuyer().getWaitingTime());
//Check if the order to handle is prime or not.
if(toHandle.isPrime()) {
//Decrement the position of which prime orders are
//inserted into the buffer.
inPrime--;
} else {
//Decrement the position of which normal orders are
//inserted into the buffer.
inNormal--;
}
//Print a message informing the user a new order was completed.
System.out.println("An order has been completed for: "
+ toHandle.getBuyer().getName());
//Notify the buyer he has sucsessfully ordered a new package.
toHandle.getBuyer().setPacksBought(
toHandle.getBuyer().getPacksBought() + 1);
}else {
//Notify the user there was a critical error obtaining the
//error to handle. (There shouldn't exist a case where this
//should happen but you never know.)
System.err.println("Something went wrong obtaining an order.");
}
//Notify the buyers that a new spot has been opened in the buffer.
this.notFullBuffer.release();
//Catch any interrupt exceptions.
} catch(InterruptedException e){
//This is expected behavior as it allows us to force the thread to
//revaluate it's main running loop when notifying it to finish
//execution.
} finally {
//Check if the current thread is locking the buffer lock. This is
//done as in the case of an interrupt we don't want to execute this
//code if the thread interrupted doesn't hold the lock as that
//would result in an exception we don't want.
if (mutexBuffer.isHeldByCurrentThread())
//Unlock the buffer lock.
mutexBuffer.unlock();
}
}
The problem:
To verify the functionallity of the program I use the output from the statement:
System.out.println("An order has been completed for: "
+ toHandle.getBuyer().getName());
from the handleOrder(); function. I place the whole output in a text file, remove all the lines which aren't added by this println(); statement and count the number of lines to know how many orders have been handled. I expect this value to be equal to the amount of threads times 10, however this is often not the case. Running tests I've noticed sometimes it does work and there are no problems but sometimes one or more buyer threads take more orders than they should. with 5 buyer threads there should be 50 outputs but I get anywhere from 50 to 60 lines (orders places).
Turning the amount of threads up to 30 increases the problem and now I can expect an increase of up to 50% more orders with some threads placing up to 30 orders.
Doing some research this is called a data-race and is caused by 2 threads accessing the same data at the same time while 1 of them writes to the data. This basically changes the data such that the other thread isn't working with the same data it expects to be working with.
My attempt:
I firmly believe ReentrantLocks are designed to handle situations like this as they should stop any thread from entering a section of code if another thread hasn't left it. Both the placeOrder(Order order); and handleOrder(); function make use of this mechanic. I'm therefor assuming I didn't implement this correctly. Here is a version of the project which is compileable and executable from a single file called Test.java. Would anyone be able to take a look at that or the code explained above and tell me what I'm doing wrong?
EDIT
I noticed there was a way a buyer could place more than 10 orders so I changed the code to:
/*
* The run method which is ran once the thread is started.
*/
public void run() {
//Run until the thread has bought 10 packages, this ensures the thread
//will eventually stop execution automatically.
for(packsBought = 0; packsBought < 10; packsBought++)
{
try {
//Sleep for a random amount of time between 1 and 50
//milliseconds.
Thread.sleep(this.rand.nextInt(49) + 1);
//Catch any interruptExceptions.
} catch (InterruptedException ex) {
//There is no problem if this exception is thrown, the thread
//will just make an order earlier than planned. that being said
//there should be no manner in which this exception is thrown.
}
//Create a new order.
Order order = new Order(this.rand.nextInt(3)+ 1,
this,
this.isPrime);
//Set the time at which the order was placed as now.
order.setOrderTime(System.currentTimeMillis());
//place the newly created order in the warehouse.
this.warehouse.placeOrder(order);
}
//Notify the thread has finished execution.
System.out.println("Thread: " + super.getName() + " has finished.");
}
in the buyers run(); function yet I'm still getting some threads which place over 10 orders. I also removed the update of the amount of packs bought in the handleOrder(); function as that is now unnecessary. here is an updated version of Test.java (where all classes are together for easy execution) There seems to be a different problem here.

There are some concurrency issues with the code, but the main bug is not related to them: it's in the block starting in line 512 on placeOrder
//Enumerate all non prime orders in the list.
for (int i = inPrime; i < sizeOrderList - 1; i++) {
//Move the non prime order back 1 position in the list.
buffer[i + 1] = buffer[i];
}
when there is only one normal order in the buffer, then inPrime value is 0, inNormal is 1, buffer[0] is the normal order and the rest of the buffer is null.
The code to move non primer orders, starts in index 0, and then does:
buffer[1] = buffer[0] //normal order in 0 get copied to 1
buffer[2] = buffer[1] //now its in 1, so it gets copied to 2
buffer[3] = buffer[2] //now its in 2 too, so it gets copied to 3
....
so it moves the normal order to buffer[1] but then it copies the contents filling all the buffer with that order.
To solve it you should copy the array in reverse order:
//Enumerate all non prime orders in the list.
for (int i = (sizeOrderList-1); i > inPrime; i--) {
//Move the non prime order back 1 position in the list.
buffer[i] = buffer[i-1];
}
As for the concurrency issues:
If you check a field on a thread, updated by another thread you should declare it as volatile. Thats the case of the run field in DispatcherWorker and ResourceSupplier. See: https://stackoverflow.com/a/8063587/11751648
You start interrupting the dispatcher threads (line 183) while they are still processing packages. So if they are stopped at 573, 574 or 579, they will throw an InterruptedException and not finish the processing (hence in the last code not always all packages are delivered). You could avoid this by checking that the buffer is empty before start interrupting dispatcher threads, calling warehouse.notFullBuffer.acquire(warehouse.sizeOrderList); on 175
When catching InterruptedException you should always call Thread.currentThread().interrupt(); the preserve the interrupted status of the Thread. See: https://stackoverflow.com/a/3976377/11751648

I believe you may be chasing ghosts. I'm not entirely sure why you're seeing more outputs than you're expecting, but the number of orders placed appears to be in order. Allow me to clarify:
I've added a Map<String,Integer> to the Warehouse class to map how many orders each thread places:
private Map<String,Integer> ordersPlaced = new TreeMap<>();
// Code omitted for brevity
public void placeOrder(Order order)
{
try
{
//halt untill there are enough packs to handle an order.
this.notFullBuffer.acquire();
//Lock to signify the start of the critical section.
this.mutexBuffer.lock();
ordersPlaced.merge(Thread.currentThread().getName(), 1, Integer::sum);
// Rest of method
}
I then added a for-loop to the main method to execute the code 100 times, and added the following code to the end of each iteration:
warehouse.ordersPlaced.forEach((thread, orders) -> System.out.printf(" %s - %d%n", thread, orders));
I placed a breakpoint inside the lambda expression, with condition orders != 10. This condition never triggered in the 100+ runs I executed. As far as I can tell, your code is working as intended. I've increased both nWorkers and nBuyers to 100 just to be sure.
I believe you're using ReentrantLock correctly, and I agree that it is probably the best choice for your use case.

referring at your code on pastebin
THE GENERIC PROBLEM:
In the function public void handleOrder() he sleep (line 582) Thread.sleep(5 * nPacks); is inside the lock(): unlock(): block.
With this position of sleep, it has no sense to have many DispatchWorker because n-1 will wait at line 559 this.mutexBuffer.lock() while one is sleeping at line 582.
THE BUG:
The bug is in line 173. You should remove it.
In your main() you join all buyers and this is correct. Then you try to stop the workers. The workers at this time are already running to complete orders that will be completed seconds after. You should only set worker.runThread(false); and then join the thead (possibly in two separate loops). This solution really waits for workers to complete orders. Interrupting the thread that is sleeping at line 582 will raise an InterruptedException and the following lines are skipped, in particular line 596 or 600 that update inPrime and in Normal counters generating unpredictable behaviours.
moving line 582 after line 633 and removing line 173 will solve the problem
HOW TO TEST:
My suggestion is to introduce a counter of all Packs boxes generated by supplier and a counter of all boxes ordered and finally check if generated boxes are equals at ordered plus that left in the whorehouse.

Can we get parallel processing of each of Multi pipeline steps?

Lets say we have:
a list of URLs, that is a source for our Multi
as a first step we grab HTML of this page using HTTP client call
then we try to find some specific tag and grab its content
then we store things we found into database
Now we have a 3 steps here. Is there a way how these steps can be run in parallel? I mean after some time it should: grab HTML and simultaneously processing html + getting tags content and also simultaneously saving data into database from item that was processed already.(hopefully its obvious what I mean here) This way we can have parallel processing. As default, what I can see, mutiny does it in serial manner.
Here is an example:
#Test
public void test3() {
Multi<String> source = Multi.createFrom().items("a", "b", "c");
source
.onItem().transform(i -> trans(i, "-step1"))
.onItem().transform(i -> trans(i, "-step2"))
.onItem().transform(i -> trans(i, "-step3"))
.subscribe().with(item -> System.out.println("Subscriber received " + item));
}
private String trans(String s, String add) {
int t = new Random().nextInt(4) * 1000;
try {
print("Sleeping for '" + s + "' miliseconds: " + t);
Thread.sleep(t);
} catch (InterruptedException e) {
e.printStackTrace();
}
return s + add;
}
Now this reports following console output:
Sleeping for 'a' miliseconds: 2000
Sleeping for 'a-step1' miliseconds: 3000
Sleeping for 'a-step1-step2' miliseconds: 3000
Subscriber received a-step1-step2-step3
Sleeping for 'b' miliseconds: 0
Sleeping for 'b-step1' miliseconds: 0
Sleeping for 'b-step1-step2' miliseconds: 0
Subscriber received b-step1-step2-step3
Sleeping for 'c' miliseconds: 1000
Sleeping for 'c-step1' miliseconds: 3000
Sleeping for 'c-step1-step2' miliseconds: 3000
Subscriber received c-step1-step2-step3
One can see that its not running in parallel. What did I miss here?

As #jponge mentioned, you can collect your items in some List<Uni<String>>
and then call
Uni.combine().all().unis(listOfUnis).onitem().subscribe().with()
List<Uni<String>> listOfUnis = new ArrayList<>();
Multi<String> source = Multi.createFrom().items("a", "b", "c");
source
.onItem().invoke(i -> listOfUnis.add(trans(i, "-step1")))
.onItem().invoke(i -> listOfUnis.add(trans(i, "-step2")))
.onItem().invoke(i -> listOfUnis.add(trans(i, "-step3")))
// do not subscribe on Multis here
one more note here - if you are going to make HTTP calls, better add
.emitOn(someBlockingPoolExecutor)
since you don't want to block Netty threads waiting for http calls to complete

This is expected, Multi processes items as a stream.
If you want to make parallel operations (say, launch 10 HTTP requests) you should combine Uni, see https://smallrye.io/smallrye-mutiny/guides/combining-items

Project reactor: subscribeOn() after Flux.create() doesn't consistently use the scheduler thread

When I run the code below, I expect to see both subscribers getting their own elastic thread. However, they do not consistently do so. For example, on my system if I uncomment the thread.Sleep(100) line, my print statements indicate that the consumers are receiving their data on the main thread. If leave it commented, I see the output I would expect: each consumer receives its data on its own elastic thread. Why do I see this nondeterministic behavior? How am I abusing the API?
List<FluxSink<String>> holder = new ArrayList<>();
ConnectableFlux<String> connect = Flux.
<String>create(holder::add).replay();
connect.connect();
Flux<String> flux = connect.subscribeOn(Schedulers.elastic());
FluxSink<String> sink = holder.get(0);
flux.subscribe(c -> {
System.out.println(Thread.currentThread().getName() + ", " +
"consumer 1 says " + c);
});
flux.subscribe(c -> {
System.out.println(Thread.currentThread().getName() + ", " +
"consumer 2 says " + c);
});
// When uncommented, subscribers receive on elastic threads; else,
// on the main thread, as if I had chosen schedulers.immediate()
//
// Thread.sleep(100);
sink.next("Hi!");
sink.complete();
Thread.sleep(1000);
//output with Thread.sleep(100):
// main, consumer 1 says Hi!
// main, consumer 2 says Hi!
//
//output without Thread.sleep(100):
// elastic-2, consumer 1 says Hi!
// elastic-3, consumer 2 says Hi!
What I'd like to achieve is a hot stream with multiple subscribers and each subscriber on its own thread.

Flux.generate with Consumer and parallel

I have simple Flux
Flux<Long> flux = Flux.generate(
AtomicLong::new,
(state, sink) -> {
long i = state.getAndIncrement();
sink.next(i);
if (i == 3) sink.complete();
return state;
}, (state) -> System.out.println("state: " + state));
Which works as expected in a single thread:
flux.subscribe(System.out::println);
The output is
0 1 2 3 state: 4
But when I switch to parallel:
flux.parallel().runOn(Schedulers.elastic()).subscribe(System.out::println);
The Consumer which should print state: Number isn't invoked. I just see:
0 3 2 1
Is it a bug or expected feature?

I'm not a reactive expert but after digging into the source code it seems that the behavior is by design; it seems that creating a ParallelFlux has the side effect of blocking the call of the State Consumer; if you want to go parallel and getting the State Consumer invoked you can use:
flux.publishOn(Schedulers.elastic()).subscribe(System.out::println);

RxJava: onBackpressureBlock() strange behavior

I am playing around with RxJava (RxKotlin to be precise). Here I have the following Observables:
fun metronome(ms: Int) = observable<Int> {
var i = 0;
while (true) {
if (ms > 0) {
Thread.sleep(ms.toLong())
}
if (it.isUnsubscribed()) {
break
}
it.onNext(++i)
}
}
And I'd like to have a few of them merged and running concurrently. They ignore backpressure so the backpressure operators have to be applied to them.
Then I create
val cores = Runtime.getRuntime().availableProcessors()
val threads = Executors.newFixedThreadPool(cores)
val scheduler = Schedulers.from(threads)
And then I merge the metronomes:
val o = Observable.merge(listOf(metronome(0),
metronome(1000).map { "---------" })
.map { it.onBackpressureBlock().subscribeOn(scheduler) })
.take(5000, TimeUnit.MILLISECONDS)
The first one is supposed to emit items incessantly.
If I do so in the last 3 seconds of the run I get the following output:
...
[RxComputationThreadPool-5]: 369255
[RxComputationThreadPool-5]: 369256
[RxComputationThreadPool-5]: 369257
[RxComputationThreadPool-5]: ---------
[RxComputationThreadPool-5]: ---------
[RxComputationThreadPool-5]: ---------
Seems that the Observables are subscribed on the same one thread, and the first observable is blocked for 3+ seconds.
But when I swap onBackpressureBlock() and subscribeOn(scheduler) calls the output becomes what I expected, the output gets merged during the whole execution.
It's obvious to me that calls order matters in RxJava, but I don't quite understand what happens in this particular situation.
So what happens when onBackpressureBlock operator is applied before subscribeOn and what if after?

The onBackpressureBlock operator is a failed experiment; it requires care where to apply. For example, subscribeOn().onBackpressureBlock() works but not the other way around.
RxJava has non-blocking periodic timer called interval so you don't need to roll your own.