Note: if you are limiting this to “3” just so you don’t overwhelm the machine running your app, I’d make sure this is a problem first. The threadpool is supposed to manage this for you. On the other hand, if you don’t want to overwhelm some other resource, then read on! You can’t manage the … Read more
Yes, you are right, the user delegates are not synchronized by ConcurrentDictionary. If you need those synchronized it is your responsibility. The MSDN itself says: Also, although all methods of ConcurrentDictionary are thread-safe, not all methods are atomic, specifically GetOrAdd and AddOrUpdate. The user delegate that is passed to these methods is invoked outside of … Read more
You can do that using the below code, Hope it helps! final Handler handler = new Handler(); Runnable runnable = new Runnable() { @Override public void run() { try{ //do your code here } catch (Exception e) { // TODO: handle exception } finally{ //also call the same runnable to call it at regular interval … Read more
If you are using Spring’s Java-configuration, your config class needs to implements AsyncConfigurer: @Configuration @EnableAsync public class AppConfig implements AsyncConfigurer { […] @Override public Executor getAsyncExecutor() { ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor(); executor.setCorePoolSize(2); executor.setMaxPoolSize(5); executor.setQueueCapacity(50); executor.setThreadNamePrefix(“MyExecutor-“); executor.initialize(); return executor; } } See @EnableAsync documentation for more details : http://docs.spring.io/spring/docs/3.1.x/javadoc-api/org/springframework/scheduling/annotation/EnableAsync.html
A few easy examples of CPU-intensive tasks: searching for prime numbers (involves lots of BigInteger divisions) calculating large factorials e.g. 2000! ((involves lots of BigInteger multiplications) many Math.tan() calculations (this is interesting because Math.tan is native, so you’re using two call stacks: one for Java calls, the other for C calls.)
I’ve discovered that TaskCompletionSource.SetResult(); invokes the code awaiting the task before returning. In my case that result in a deadlock. Yes, I have a blog post documenting this (AFAIK it’s not documented on MSDN). The deadlock happens because of two things: There’s a mixture of async and blocking code (i.e., an async method is calling … Read more
When you use new Thread you’re really creating a new thread every time you execute that. AsyncTask however, uses a static pool of max 128 threads and will reuse an old thread whenever it exists. So, running AsyncTask 10 times in serial will only create one thread that runs the task 10 times instead of … Read more
The solution is to write to the file in one thread only. import Queue # or queue in Python 3 import threading class PrintThread(threading.Thread): def __init__(self, queue): threading.Thread.__init__(self) self.queue = queue def printfiles(self, p): for path, dirs, files in os.walk(p): for f in files: print(f, file=output) def run(self): while True: result = self.queue.get() self.printfiles(result) self.queue.task_done() … Read more
You better mark it volatile though: The volatile keyword indicates that a field might be modified by multiple concurrently executing threads. Fields that are declared volatile are not subject to compiler optimizations that assume access by a single thread. This ensures that the most up-to-date value is present in the field at all times. But … Read more
The Linux scheduler (on recent Linux kernels, e.g. 3.0 at least) is scheduling schedulable tasks or simply tasks. A task may be : a single-threaded process (e.g. created by fork without any thread library) any thread inside a multi-threaded process (including its main thread), in particular Posix threads (pthreads) kernel tasks, which are started internally … Read more