FIFO queue synchronization - algorithm

Should FIFO queue be synchronized if there is only one reader and one writer?

What do you mean by "synchronized"? If your reader & writer are in separate threads, you want the FIFO to handle the concurrency "correctly", including such details as:
proper use of FIFO API should never cause data structures to be corrupted
proper use of FIFO API should not cause deadlock (although there should be a mechanism for a reader to wait until there is something to read)
the objects read from the FIFO should be the same objects, in the same order, written to the FIFO (there shouldn't be missing objects or rearranged order)
there should be a bounded time (one would hope!) between when the writer puts something into the FIFO, and when it is available to the reader.
In the Java world there's a good book on this, Java Concurrency In Practice. There are multiple ways to implement a FIFO that handles concurrency correctly. The simplest implementations are blocking, more complex ones use non-blocking algorithms based on compare-and-swap instructions found on most processors these days.

Yes, if the reader and writer interact with the FIFO queue from different threads.

Depending on implementation, but most likely. You don't want reader to read partially written data.

Yes, unless its documentation explicitly says otherwise.
(It is possible to implement a specialized FIFO that doesn't need synchronization if there is only one reader and one writer thread, e.g. on Windows using InterlockedXXX functions.)

Try this code for concurrent fifo usage:
public class MyObjectQueue {
private static final ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
private static final ReadLock readLock;
private static final WriteLock writeLock;
private static final LinkedList<MyObject> objects;
static {
readLock = lock.readLock();
writeLock = lock.writeLock();
objects = new LinkedList<MyObject>();
}
public static boolean put(MyObject p) {
writeLock.lock();
try {
objects.push(p);
return objects.contains(p);
} finally {
writeLock.unlock();
}
}
public static boolean remove(MyObject p) {
writeLock.lock();
try {
return objects.remove(p);
} finally {
writeLock.unlock();
}
}
public static boolean contains(MyObject p) {
readLock.lock();
try {
return objects.contains(p);
} finally {
readLock.unlock();
}
}
public MyObject get() {
MyObject o = null;
writeLock.lock();
try {
o = objects.getLast();
} catch (NoSuchElementException nse) {
//list is empty
} finally {
writeLock.unlock();
}
return o;
}
}

Related

java 8 parallel stream with ForkJoinPool and ThreadLocal

We are using java 8 parallel stream to process a task, and we are submitting the task through ForkJoinPool#submit. We are not using jvm wide ForkJoinPool.commonPool, instead we are creating our own custom pool to specify the parallelism and storing it as static variable.
We have validation framework, where we subject a list of tables to a List of Validators, and we submit this job through the custom ForkJoinPool as follows:
static ForkJoinPool forkJoinPool = new ForkJoinPool(4);
List<Table> tables = tableDAO.findAll();
ModelValidator<Table, ValidationResult> validator = ValidatorFactory
.getInstance().getTableValidator();
List<ValidationResult> result = forkJoinPool.submit(
() -> tables.stream()
.parallel()
.map(validator)
.filter(result -> result.getValidationMessages().size() > 0)
.collect(Collectors.toList())).get();
The problem we are having is, in the downstream components, the individual validators which run on separate threads from our static ForkJoinPool rely on tenant_id, which is different for every request and is stored in an InheritableThreadLocal variable. Since we are creating a static ForkJoinPool, the threads pooled by the ForkJoinPool will only inherit the value of the parent thread, when it is created first time. But these pooled threads will not know the new tenant_id for the current request. So for subsequent execution these pooled threads are using old tenant_id.
I tried creating a custom ForkJoinPool and specifying ForkJoinWorkerThreadFactory in the constructor and overriding the onStart method to feed the new tenant_id. But that doesnt work, since the onStart method is called only once at creation time and not during individual execution time.
Seems like we need something like the ThreadPoolExecutor#beforeExecute which is not available in case of ForkJoinPool. So what alternative do we have if we want to pass the current thread local value to the statically pooled threads?
One workaround would be to create the ForkJoinPool for each request, rather than make it static but we wouldn't want to do it, to avoid the expensive nature of thread creation.
What alternatives do we have?
I found the following solution that works without changing any underlying code. Basically, the map method takes a functional interface which I am representing as a lambda expression. This expression adds a preExecution hook to set the new tenantId in the current ThreadLocal and cleaning it up in postExecution.
forkJoinPool.submit(tables.stream()
.parallel()
.map((item) -> {
preExecution(tenantId);
try {
return validator.apply(item);
} finally {
postExecution();
}
}
)
.filter(validationResult ->
validationResult.getValidationMessages()
.size() > 0)
.collect(Collectors.toList())).get();
The best option in my view would be to get rid of the thread local and pass it as an argument instead. I understand that this could be a massive undertaking though. Another option would be to use a wrapper.
Assuming that your validator has a validate method you could do something like:
public class WrappingModelValidator implements ModelValidator<Table. ValidationResult> {
private final ModelValidator<Table. ValidationResult> v;
private final String tenantId;
public WrappingModelValidator(ModelValidator<Table. ValidationResult> v, String tenantId) {
this.v = v;
this.tenantId = tenantId;
}
public ValidationResult validate(Table t) {
String oldValue = YourThreadLocal.get();
YourThreadLocal.set(tenantId);
try {
return v.validate(t);
} finally {
YourThreadLocal.set(oldValue);
}
}
}
Then you simply wrap your old validator and it will set the thread local on entry and restore it when done.

Reactor Flux conditional emit

Is it possible to allow emitting values from a Flux conditionally based on a global boolean variable?
I'm working with Flux delayUntil(...) but not able to fully grasp the functionality or my assumptions are wrong.
I have a global AtomicBoolean that represents the availability of a downstream connection and only want the upstream Flux to emit if the downstream is ready to process.
To represent the scenario, created a (not working) test sample
//Randomly generates a boolean value every 5 seconds
private Flux<Boolean> signalGenerator() {
return Flux.range(1, Integer.MAX_VALUE)
.delayElements(Duration.ofMillis(5000))
.map(integer -> new Random().nextBoolean());
}
and
Flux.range(1, Integer.MAX_VALUE)
.delayElements(Duration.ofMillis(1000))
.delayUntil(evt -> signalGenerator()) // ?? Only proceed when signalGenerator returns true
.subscribe(System.out::println);
I have another scenario where a downstream process can accept only x messages a second. In the current non-reactive implementation we have a Semaphore of x permits and the thread is blocked if no more permits are available, with Semaphore permits resetting every second.
In both scenarios I want upstream Flux to emit only when there is a demand from the downstream process, and I do not want to Buffer.
You might consider using Mono.fromRunnable() as an input to delayUntil() like below;
Helper class;
public class FluxCondition {
CountDownLatch latch = new CountDownLatch(10); // it depends, might be managed somehow
Runnable r = () -> { latch.await(); }
public void lock() { Mono.fromRunnable(r) };
public void release() { latch.countDown(); }
}
Usage;
FluxCondition delayCondition = new FluxCondition();
Flux.range(1, 10).delayUntil(o -> delayCondition.lock()).subscribe();
.....
delayCondition.release(); // shall call this for each element
I guess there might be a better solution by using sink.emitNext but this might also require a condition variable for controlling Flux flow.
According my understanding, in reactive programming, your data should be considered in every operator step. So it might be better for you to design your consumer as a reactive processor. In my case I had no chance and followed the way as I described above

Downlolad and save file from ClientRequest using ExchangeFunction in Project Reactor

I have problem with correctly saving a file after its download is complete in Project Reactor.
class HttpImageClientDownloader implements ImageClientDownloader {
private final ExchangeFunction exchangeFunction;
HttpImageClientDownloader() {
this.exchangeFunction = ExchangeFunctions.create(new ReactorClientHttpConnector());
}
#Override
public Mono<File> downloadImage(String url, Path destination) {
ClientRequest clientRequest = ClientRequest.create(HttpMethod.GET, URI.create(url)).build();
return exchangeFunction.exchange(clientRequest)
.map(clientResponse -> clientResponse.body(BodyExtractors.toDataBuffers()))
//.flatMapMany(clientResponse -> clientResponse.body(BodyExtractors.toDataBuffers()))
.flatMap(dataBuffer -> {
AsynchronousFileChannel fileChannel = createFile(destination);
return DataBufferUtils
.write(dataBuffer, fileChannel, 0)
.publishOn(Schedulers.elastic())
.doOnNext(DataBufferUtils::release)
.then(Mono.just(destination.toFile()));
});
}
private AsynchronousFileChannel createFile(Path path) {
try {
return AsynchronousFileChannel.open(path, StandardOpenOption.CREATE);
} catch (Exception e) {
throw new ImageDownloadException("Error while creating file: " + path, e);
}
}
}
So my question is:
Is DataBufferUtils.write(dataBuffer, fileChannel, 0) blocking?
What about when the disk is slow?
And second question about what happens when ImageDownloadException occurs ,
In doOnNext I want to release the given data buffer, is that a good place for this kind operation?
I think also this line:
.map(clientResponse -> clientResponse.body(BodyExtractors.toDataBuffers()))
could be blocking...
Here's another (shorter) way to achieve that:
Flux<DataBuffer> data = this.webClient.get()
.uri("/greeting")
.retrieve()
.bodyToFlux(DataBuffer.class);
Path file = Files.createTempFile("spring", null);
WritableByteChannel channel = Files.newByteChannel(file, StandardOpenOption.WRITE);
Mono<File> result = DataBufferUtils.write(data, channel)
.map(DataBufferUtils::release)
.then(Mono.just(file));
Now DataBufferUtils::write operations are not blocking because they use non-blocking IO with channels. Writing to such channels means it'll write whatever it can to the output buffer (i.e. may write all the DataBuffer or just part of it).
Using Flux::map or Flux::doOnNext is the right place to do that. But you're right, if an error occurs, you're still responsible for releasing the current buffer (and all the remaining ones). There might be something we can improve here in Spring Framework, please keep an eye on SPR-16782.
I don't see how your last sample shows anything blocking: all methods return reactive types and none are doing blocking I/O.

Multiple c# SerialPorts seems to hang my application

I've an application in Windows forms that connects to 16 serialports. The structure I used for each one is:
private void Serial_CodeNip_15_DataReceived(object sender, System.IO.Ports.SerialDataReceivedEventArgs e)
{
string S = Serial_CodeNip_15.ReadExisting();
myProcess(S);
}
public delegate void del_myProcess(string stringa);
private void myProcess(string stringa)
{
if (this.InvokeRequired)
{
del_myProcess tmp = new del_myProcess(myProcess);
try
{
this.Invoke(tmp, stringa);
}
catch (Exception)
{
}
}
else
{
// my code here
}
}
Receiving data from barcode readers, works fine until more Readers (up 6 or 7) start reading at the same time. In this cases my application tends to hang and all readers denotes a difficult to catch data from serial input buffers. Is this the correct way to read async data from serialports or there’s another simple way to do that. I noticed that working with few serialports there’s no problem.
Thank you in advance for helping

Non-Blocking Endpoint: Returning an operation ID to the caller - Would like to get your opinion on my implementation?

Boot Pros,
I recently started to program in spring-boot and I stumbled upon a question where I would like to get your opinion on.
What I try to achieve:
I created a Controller that exposes a GET endpoint, named nonBlockingEndpoint. This nonBlockingEndpoint executes a pretty long operation that is resource heavy and can run between 20 and 40 seconds.(in the attached code, it is mocked by a Thread.sleep())
Whenever the nonBlockingEndpoint is called, the spring application should register that call and immediatelly return an Operation ID to the caller.
The caller can then use this ID to query on another endpoint queryOpStatus the status of this operation. At the beginning it will be started, and once the controller is done serving the reuqest it will be to a code such as SERVICE_OK. The caller then knows that his request was successfully completed on the server.
The solution that I found:
I have the following controller (note that it is explicitely not tagged with #Async)
It uses an APIOperationsManager to register that a new operation was started
I use the CompletableFuture java construct to supply the long running code as a new asynch process by using CompletableFuture.supplyAsync(() -> {}
I immdiatelly return a response to the caller, telling that the operation is in progress
Once the Async Task has finished, i use cf.thenRun() to update the Operation status via the API Operations Manager
Here is the code:
#GetMapping(path="/nonBlockingEndpoint")
public #ResponseBody ResponseOperation nonBlocking() {
// Register a new operation
APIOperationsManager apiOpsManager = APIOperationsManager.getInstance();
final int operationID = apiOpsManager.registerNewOperation(Constants.OpStatus.PROCESSING);
ResponseOperation response = new ResponseOperation();
response.setMessage("Triggered non-blocking call, use the operation id to check status");
response.setOperationID(operationID);
response.setOpRes(Constants.OpStatus.PROCESSING);
CompletableFuture<Boolean> cf = CompletableFuture.supplyAsync(() -> {
try {
// Here we will
Thread.sleep(10000L);
} catch (InterruptedException e) {}
// whatever the return value was
return true;
});
cf.thenRun(() ->{
// We are done with the super long process, so update our Operations Manager
APIOperationsManager a = APIOperationsManager.getInstance();
boolean asyncSuccess = false;
try {asyncSuccess = cf.get();}
catch (Exception e) {}
if(true == asyncSuccess) {
a.updateOperationStatus(operationID, Constants.OpStatus.OK);
a.updateOperationMessage(operationID, "success: The long running process has finished and this is your result: SOME RESULT" );
}
else {
a.updateOperationStatus(operationID, Constants.OpStatus.INTERNAL_ERROR);
a.updateOperationMessage(operationID, "error: The long running process has failed.");
}
});
return response;
}
Here is also the APIOperationsManager.java for completness:
public class APIOperationsManager {
private static APIOperationsManager instance = null;
private Vector<Operation> operations;
private int currentOperationId;
private static final Logger log = LoggerFactory.getLogger(Application.class);
protected APIOperationsManager() {}
public static APIOperationsManager getInstance() {
if(instance == null) {
synchronized(APIOperationsManager.class) {
if(instance == null) {
instance = new APIOperationsManager();
instance.operations = new Vector<Operation>();
instance.currentOperationId = 1;
}
}
}
return instance;
}
public synchronized int registerNewOperation(OpStatus status) {
cleanOperationsList();
currentOperationId = currentOperationId + 1;
Operation newOperation = new Operation(currentOperationId, status);
operations.add(newOperation);
log.info("Registered new Operation to watch: " + newOperation.toString());
return newOperation.getId();
}
public synchronized Operation getOperation(int id) {
for(Iterator<Operation> iterator = operations.iterator(); iterator.hasNext();) {
Operation op = iterator.next();
if(op.getId() == id) {
return op;
}
}
Operation notFound = new Operation(-1, OpStatus.INTERNAL_ERROR);
notFound.setCrated(null);
return notFound;
}
public synchronized void updateOperationStatus (int id, OpStatus newStatus) {
iteration : for(Iterator<Operation> iterator = operations.iterator(); iterator.hasNext();) {
Operation op = iterator.next();
if(op.getId() == id) {
op.setStatus(newStatus);
log.info("Updated Operation status: " + op.toString());
break iteration;
}
}
}
public synchronized void updateOperationMessage (int id, String message) {
iteration : for(Iterator<Operation> iterator = operations.iterator(); iterator.hasNext();) {
Operation op = iterator.next();
if(op.getId() == id) {
op.setMessage(message);
log.info("Updated Operation status: " + op.toString());
break iteration;
}
}
}
private synchronized void cleanOperationsList() {
Date now = new Date();
for(Iterator<Operation> iterator = operations.iterator(); iterator.hasNext();) {
Operation op = iterator.next();
if((now.getTime() - op.getCrated().getTime()) >= Constants.MIN_HOLD_DURATION_OPERATIONS ) {
log.info("Removed operation from watchlist: " + op.toString());
iterator.remove();
}
}
}
}
The questions that I have
Is that concept a valid one that also scales? What could be improved?
Will i run into concurrency issues / race conditions?
Is there a better way to achieve the same in boot spring, but I just didn't find that yet? (maybe with the #Async directive?)
I would be very happy to get your feedback.
Thank you so much,
Peter P
It is a valid pattern to submit a long running task with one request, returning an id that allows the client to ask for the result later.
But there are some things I would suggest to reconsider :
do not use an Integer as id, as it allows an attacker to guess ids and to get the results for those ids. Instead use a random UUID.
if you need to restart your application, all ids and their results will be lost. You should persist them to a database.
Your solution will not work in a cluster with many instances of your application, as each instance would only know its 'own' ids and results. This could also be solved by persisting them to a database or Reddis store.
The way you are using CompletableFuture gives you no control over the number of threads used for the asynchronous operation. It is possible to do this with standard Java, but I would suggest to use Spring to configure the thread pool
Annotating the controller method with #Async is not an option, this does not work no way. Instead put all asynchronous operations into a simple service and annotate this with #Async. This has some advantages :
You can use this service also synchronously, which makes testing a lot easier
You can configure the thread pool with Spring
The /nonBlockingEndpoint should not return the id, but a complete link to the queryOpStatus, including id. The client than can directly use this link without any additional information.
Additionally there are some low level implementation issues which you may also want to change :
Do not use Vector, it synchronizes on every operation. Use a List instead. Iterating over a List is also much easier, you can use for-loops or streams.
If you need to lookup a value, do not iterate over a Vector or List, use a Map instead.
APIOperationsManager is a singleton. That makes no sense in a Spring application. Make it a normal PoJo and create a bean of it, get it autowired into the controller. Spring beans by default are singletons.
You should avoid to do complicated operations in a controller method. Instead move anything into a service (which may be annotated with #Async). This makes testing easier, as you can test this service without a web context
Hope this helps.
Do I need to make database access transactional ?
As long as you write/update only one row, there is no need to make this transactional as this is indeed 'atomic'.
If you write/update many rows at once you should make it transactional to guarantee, that either all rows are updated or none.
However, if two operations (may be from two clients) update the same row, always the last one will win.

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