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  • Is NFS capable of preserving order of operations?

    - by JustJeff
    I have a diskless host 'A', that has a directory NFS mounted on server 'B'. A process on A writes to two files F1 and F2 in that directory, and a process on B monitors these files for changes. Assume that B polls for changes faster than A is expected to make them. Process A seeks the head of the files, writes data, and flushes. Process B seeks the head of the files and does reads. Are there any guarantees about how the order of the changes performed by A will be detected at B? Specifically, if A alternately writes to one file, and then the other, is it reasonable to expect that B will notice alternating changes to F1 and F2? Or could B conceivably detect a series of changes on F1 and then a series on F2? I know there are a lot of assumptions embedded in the question. For instance, I am virtually certain that, even operating on just one file, if A performs 100 operations on the file, B may see a smaller number of changes that give the same result, due to NFS caching some of the actions on A before they are communicated to B. And of course there would be issues with concurrent file access even if NFS weren't involved and both the reading and the writing process were running on the same real file system. The reason I'm even putting the question up here is that it seems like most of the time, the setup described above does detect the changes at B in the same order they are made at A, but that occasionally some events come through in transposed order. So, is it worth trying to make this work? Is there some way to tune NFS to make it work, perhaps cache settings or something? Or is fine-grained behavior like this just too much expect from NFS?

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  • Is this technically thread safe despite being mutable?

    - by Finbarr
    Yes, the private member variable bar should be final right? But actually, in this instance, it is an atomic operation to simply read the value of an int. So is this technically thread safe? class foo { private int bar; public foo(int bar) { this.bar = bar; } public int getBar() { return bar; } } // assume infinite number of threads repeatedly calling getBar on the same instance of foo.

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  • Find messages from certain key till certain key while being able to remove stale keys.

    - by Alfred
    My problem Let's say I add messages to some sort of datastructure: 1. "dude" 2. "where" 3. "is" 4. "my" 5. "car" Asking for messages from index[4,5] should return: "my","car". Next let's assume that after a while I would like to purge old messages because they aren't useful anymore and I want to save memory. Let's say at time x messages[1-3] became stale. I assume that it would be most efficient to just do the deletion once every x seconds. Next my datastructure should contain: 4. "my" 5. "car" My solution? I was thinking of using a concurrentskiplistset or concurrentskiplist map. Also I was thinking of deleting the old messages from inside a newSingleThreadScheduledExecutor. I would like to know how you would implement(efficiently/thread-safe) this or maybe use a library?

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  • Why wait should always be in synchronized block

    - by diy
    Hi gents, We all know that in order to invoke Object.wait() , this call must be placed in synchronized block,otherwise,IllegalMonitorStateException is thrown.But what's the reason for making this restriction?I know that wait() releases the monitor, but why do we need to explicitly acquire the monitor by making particular block synchronized and then release the monitor by calling wait() ? What is the potential damage if it was possible to invoke wait() outside synch block, retaining it's semantics - suspending the caller thread ? Thanks in advance

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  • re: adding more threads to forkjoinpool

    - by paintcan
    Word up y'all I recently successfully experimented with Scala futures, got future { my shiznit } all over da place. I'm pleased as punch w/ the gains I'm seeing from the parallelism and whatnot, but I'm only seeing 4 worker threads. Wanna see some more. I've been looking all over for how I can crank up the number of threads to 11, but no luck. Help me out doods

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  • Test plans and how best to write them

    - by Karim
    We're trying to figure out the best way to write tests in our test plan. Specifically, when writing a test that is meant to be used by anyone including QA staff, should the steps in the test be very specific or more broad giving the tester more leeway in how the task can be accomplished. As a very simple example, if you're testing opening a document in word processing document, should the test read: Using the mouse, open the file menu Choose "Open File..." in the file menu In the open file dialog that appears, navigate to x and double-click the document called y OR Bring up the file open dialog Open the file y Now I realize one answer is probably going to be "it depends on what you're trying to test" but I'm trying to answer a broader question here: If the test steps are too specific do we risk a) making the testing process to laborious and tedious and more importantly b) do we risk missing something because we wrote down too specific a path to achieve a goal. Alternatively, if we make it broad do we depend too much on the whims of the tester at the time and lose crucial testing of paths that are more common to customers/clients?

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  • Spring.Net how does WebApplicationContext.GetObject handle concurrent requests?

    - by Alfamale
    Apologies if I have missed something obvious here but having gone through the documentation, forums and googled for a number of hours, I just can't find a definitive answer to the following questions: How does the WebApplicationContext.GetObject() method handle concurrent requests? Are the requests serialized or executed in parallel? Is there any performance data available to demonstrate how it behaves under load? Thanks in advance for your help, Andrew

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  • "FOR UPDATE" v/s "LOCK IN SHARE MODE" : Allow concurrent threads to read updated "state" value of locked row

    - by shadesco
    I have the following scenario: User X logs in to the application from location lc1: call it Ulc1 User X (has been hacked, or some friend of his knows his login credential, or he just logs in from a different browser on his machine,etc.. u got the point) logs in at the same time from location lc2: call it Ulc2 I am using a main servlet which : - gets a connection from database pooling - sets autocommit to false - executes a command that goes through app layers: if all successful, set autocommit to true in a "finally" statement, and closes connection. Else if an exception happens, rollback(). In my database (mysql/innoDb) i have a "history" table, with row columns: id(primary key) |username | date | topic | locked The column "locked" has by default value "false" and it serves as a flag that marks if a specific row is locked or not. Each row is specific to a user (as u can see from the username column) So back to the scenario: --Ulc1 sends the command to update his history from the db for date "D" and topic "T". --Ulc2 sends the same command to update history from the db for the same date "D" and same topic "T" at the exact same time. I want to implement an mysql/innoDB locking system that will enable whichever thread arriving to do the following check: Is column "locked" for this row true or not? if true, return a message to the user that " he is already updating the same data from another location" if not true (ie not locked) : flag it as locked and update then reset locked to false once finished. Which of these two mysql locking techniques, will actually allow the 2nd arriving thread from reading the "updated" value of the locked column to decide wt action to take?Should i use "FOR UPDATE" or "LOCK IN SHARE MODE"? This scenario explains what i want to accomplish: - Ulc1 thread arrives first: column "locked" is false, set it to true and continue updating process - Ulc2 thread arrives while Ulc1's transaction is still in process, and even though the row is locked through innoDb functionalities, it doesn't have to wait but in fact reads the "new" value of column locked which is "true", and so doesn't in fact have to wait till Ulc1 transaction commits to read the value of the "locked" column(anyway by that time the value of this column will already have been reset to false). I am not very experienced with the 2 types of locking mechanisms, what i understand so far is that LOCK IN SHARE MODE allow other transaction to read the locked row while FOR UPDATE doesn't even allow reading. But does this read gets on the updated value? or the 2nd arriving thread has to wait the first thread to commit to then read the value? Any recommendations about which locking mechanism to use for this scenario is appreciated. Also if there's a better way to "check" if the row has been locked (other than using a true/false column flag) please let me know about it. thank you SOLUTION (Jdbc pseudocode example based on @Darhazer's answer) Table : [ id(primary key) |username | date | topic | locked ] connection.setautocommit(false); //transaction-1 PreparedStatement ps1 = "Select locked from tableName for update where id="key" and locked=false); ps1.executeQuery(); //transaction 2 PreparedStatement ps2 = "Update tableName set locked=true where id="key"; ps2.executeUpdate(); connection.setautocommit(true);// here we allow other transactions threads to see the new value connection.setautocommit(false); //transaction 3 PreparedStatement ps3 = "Update tableName set aField="Sthg" where id="key" And date="D" and topic="T"; ps3.executeUpdate(); // reset locked to false PreparedStatement ps4 = "Update tableName set locked=false where id="key"; ps4.executeUpdate(); //commit connection.setautocommit(true);

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  • Grails Services / Transactions / RuntimeException / Testing

    - by Rob
    I'm testing come code in a service with transactional set to true , which talks to a customer supplied web service the main part of which looks like class BarcodeService { .. /// some stuff ... try{ cancelBarCodeResponse = cancelBarCode(cancelBarcodeRequest) } catch(myCommsException e) { throw new RuntimeException(e) } ... where myCommsException extends Exception .. I have a test which looks like // As no connection from my machine, it should fail .. shouldFailWithCause(RuntimeException){ barcodeServices.cancelBarcodeDetails() } The test fails cause it's catching a myCommsException rather than the RuntimeException i thought i'd converted it to .. Anyone care to point out what i'm doing wrong ? Also will the fact that it's not a RuntimeException mean any transaction related info done before my try/catch actually be written out rather than thrown away ?? Thanks

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  • FileInputStream and FileOutputStream to the same file: Is a read() guaranteed to see all write()s that "happened before"?

    - by user946850
    I am using a file as a cache for big data. One thread writes to it sequentially, another thread reads it sequentially. Can I be sure that all data that has been written (by write()) in one thread can be read() from another thread, assuming a proper "happens-before" relationship in terms of the Java memory model? Is this behavior documented? EDIT: In my JDK, FileOutputSream does not override flush(), and OutputStream.flush() is empty. That's why I'm wondering... EDIT^2: The streams in question are owned exclusively by a class that I have full control of. Each stream is guaranteed to be accesses by one thread only. My tests show that it works as expected, but I'm still wondering if this is guaranteed and documented. See also this related discussion: http://chat.stackoverflow.com/rooms/17598/discussion-between-hussain-al-mutawa-and-user946850

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  • How do I ensure data consistency in this concurrent situation?

    - by MalcomTucker
    The problem is this: I have multiple competing threads (100+) that need to access one database table Each thread will pass a String name - where that name exists in the table, the database should return the id for the row, where the name doesn't already exist, the name should be inserted and the id returned. There can only ever be one instance of name in the database - ie. name must be unique How do I ensure that thread one doesn't insert name1 at the same time as thread two also tries to insert name1? In other words, how do I guarantee the uniqueness of name in a concurrent environment? This also needs to be as efficient as possible - this has the potential to be a serious bottleneck. I am using MySQL and Java. Thanks

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  • Putting a thread to sleep until event X occurs

    - by tipu
    I'm writing to many files in a threaded app and I'm creating one handler per file. I have HandlerFactory class that manages the distribution of these handlers. What I'd like to do is that thread A requests and gets foo.txt's file handle from the HandlerFactory class thread B requests foo.txt's file handler handler class recognizes that this file handle has been checked out handler class puts thread A to sleep thread B closes file handle using a wrapper method from HandlerFactory HandlerFactory notifies sleeping threads thread B wakes and successfully gets foo.txt's file handle This is what I have so far, def get_handler(self, file_path, type): self.lock.acquire() if file_path not in self.handlers: self.handlers[file_path] = open(file_path, type) elif not self.handlers[file_path].closed: time.sleep(1) self.lock.release() return self.handlers[file_path][type] I believe this covers the sleeping and handler retrieval successfully, but I am unsure how to wake up all threads, or even better wake up a specific thread.

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  • Multithreaded java cache for objects that are heavy to create ?

    - by krosenvold
    I need a cache some objects with fairly heavy creation times, and I need exactly-once creation semantics. It should be possible to create objects for different CacheKeys concurrently. I think I need something that (under the hood) does something like this: ConcurrentHashMap<CacheKey, Future<HeavyObject>> Are there any existing open-source implementations of this that I can re-use ?

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  • Best way to reuse a Runnable

    - by Gandalf
    I have a class that implements Runnable and am currently using an Executor as my thread pool to run tasks (indexing documents into Lucene). executor.execute(new LuceneDocIndexer(doc, writer)); My issue is that my Runnable class creates many Lucene Field objects and I would rather reuse them then create new ones every call. What's the best way to reuse these objects (Field objects are not thread safe so I cannot simple make them static) - should I create my own ThreadFactory? I notice that after a while the program starts to degrade drastically and the only thing I can think of is it's GC overhead. I am currently trying to profile the project to be sure this is even an issue - but for now lets just assume it is.

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  • Synchronize write to two collections

    - by glaz666
    I need to put some value to maps if it is not there yet. The key-value (if set) should always be in two collections (that is put should happen in two maps atomically). I have tried to implement this as follows: private final ConcurrentMap<String, Object> map1 = new ConcurrentHashMap<String, Object>(); private final ConcurrentMap<String, Object> map2 = new ConcurrentHashMap<String, Object>(); public Object putIfAbsent(String key) { Object retval = map1.get(key); if (retval == null) { synchronized (map1) { retval = map1.get(key); if (retval == null) { Object value = new Object(); //or get it somewhere synchronized (map2) { map1.put(key, value); map2.put(key, new Object()); } retval = value; } } } return retval; } public void doSomething(String key) { Object obj1 = map1.get(key); Object obj2 = map2.get(key); //do smth } Will that work fine in all cases? Thanks

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  • How to avoid concurrent execution of a time-consuming task without blocking?

    - by Diego V
    I want to efficiently avoid concurrent execution of a time-consuming task in a heavily multi-threaded environment without making threads wait for a lock when another thread is already running the task. Instead, in that scenario, I want them to gracefully fail (i.e. skip its attempt to execute the task) as fast as possible. To illustrate the idea considerer this unsafe (has race condition!) code: private static boolean running = false; public void launchExpensiveTask() { if (running) return; // Do nothing running = true; try { runExpensiveTask(); } finally { running = false; } } I though about using a variation of Double-Checked Locking (consider that running is a primitive 32-bit field, hence atomic, it could work fine even for Java below 5 without the need of volatile). It could look like this: private static boolean running = false; public void launchExpensiveTask() { if (running) return; // Do nothing synchronized (ThisClass.class) { if (running) return; running = true; try { runExpensiveTask(); } finally { running = false; } } } Maybe I should also use a local copy of the field as well (not sure now, please tell me). But then I realized that anyway I will end with an inner synchronization block, that still could hold a thread with the right timing at monitor entrance until the original executor leaves the critical section (I know the odds usually are minimal but in this case we are thinking in several threads competing for this long-running resource). So, could you think in a better approach?

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  • ConcurentModificationException in Java HashMap

    - by Bear
    Suppose I have two methods in my classes, writeToMap() and processKey() and both methods are called by multiple threads. writeToMap is a method to write something in hashmap and processKey() is used to do sth based on the keySet of HashMap. Inside processKey, I first copy the originalMap before getting the key set. new HashMap<String, Map<String,String>(originalMap).get("xx").keySet(); But I am still getting ConcurrentModificationException even though I always copy the hashmap. Whats the problem?

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  • Controlling race condition at startup.

    - by Will Hartung
    I have some code that I want to have some one time initialisation performed. But this code doesn't have a definite lifecycle, so my logic can be potentially invoked by multiple threads before my initialisation is done. So, I want to basically ensure that my logic code "waits" until initialisation is done. This is my first cut. public class MyClass { private static final AtomicBoolean initialised = new AtomicBoolean(false); public void initialise() { synchronized(initialised) { initStuff(); initialised.getAndSet(true); initialised.notifyAll(); } } public void doStuff() { synchronized(initialised) { if (!initialised.get()) { try { initialised.wait(); } catch (InterruptedException ex) { throw new RuntimeException("Uh oh!", ex); } } } doOtherStuff(); } } I basically want to make sure this is going to do what I think it's going to do -- block doStuff until the initialised is true, and that I'm not missing a race condition where doStuff might get stuck on a Object.wait() that will never arrive. Edit: I have no control over the threads. And I want to be able to control when all of the initialisation is done, which is why doStuff() can't call initialise(). I used an AtomicBoolean as it was a combination of a value holder, and an object I could synchronize. I could have also simply had a "public static final Object lock = new Object();" and a simple boolean flag. AtomicBoolean conveniently gave me both. A Boolean can not be modified. The CountDownLatch is exactly what I was looking for. I also considered using a Sempahore with 0 permits. But the CountDownLatch is perfect for just this task.

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  • Are spinlocks a good choice for a memory allocator?

    - by dsimcha
    I've suggested to the maintainers of the D programming language runtime a few times that the memory allocator/garbage collector should use spinlocks instead of regular OS critical sections. This hasn't really caught on. Here are the reasons I think spinlocks would be better: At least in synthetic benchmarks that I did, it's several times faster than OS critical sections when there's contention for the memory allocator/GC lock. Edit: Empirically, using spinlocks didn't even have measurable overhead in a single-core environment, probably because locks need to be held for such a short period of time in a memory allocator. Memory allocations and similar operations usually take a small fraction of a timeslice, and even a small fraction of the time a context switch takes, making it silly to context switch in the case of contention. A garbage collection in the implementation in question stops the world anyhow. There won't be any spinning during a collection. Are there any good reasons not to use spinlocks in a memory allocator/garbage collector implementation?

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  • What is the absolute fastest way to implement a concurrent queue with ONLY one consumer and one producer?

    - by JohnPristine
    java.util.concurrent.ConcurrentLinkedQueue comes to mind, but is it really optimum for this two-thread scenario? I am looking for the minimum latency possible on both sides (producer and consumer). If the queue is empty you can immediately return null AND if the queue is full you can immediately discard the entry you are offering. Does ConcurrentLinkedQueue use super fast and light locks (AtomicBoolean) ? Has anyone benchmarked ConcurrentLinkedQueue or knows about the ultimate fastest way of doing that? Additional Details: I imagine the queue should be a fair one, meaning the consumer should not make the consumer wait any longer than it needs (by front-running it) and vice-versa.

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  • Which parallel sorting algorithm has the best average case performance?

    - by Craig P. Motlin
    Sorting takes O(n log n) in the serial case. If we have O(n) processors we would hope for a linear speedup. O(log n) parallel algorithms exist but they have a very high constant. They also aren't applicable on commodity hardware which doesn't have anywhere near O(n) processors. With p processors, reasonable algorithms should take O(n/p log n/p) time. In the serial case, quick sort has the best runtime complexity on average. A parallel quick sort algorithm is easy to implement (see here and here). However it doesn't perform well since the very first step is to partition the whole collection on a single core. I have found information on many parallel sort algorithms but so far I have not seen anything pointing to a clear winner. I'm looking to sort lists of 1 million to 100 million elements in a JVM language running on 8 to 32 cores.

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