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  • How to Profile R Code that Includes SNOW Cluster

    - by James
    Hi, I have a nested loop that I'm using foreach, DoSNOW, and a SNOW socket cluster to solve for. How should I go about profiling the code to make sure I'm not doing something grossly inefficient. Also is there anyway to measure the data flows going between the master and nodes in a Snow cluster? Thanks, James

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  • Using an object in an if statement... (Android)

    - by James Rattray
    I have an object variable Object test = Spinner.getSelectedItem(); -It gets the selected item from the Spinner (called spinner) and names the item 'test' I want to do an if statement related to that object e.g: 'if (test = "hello") { //do something }' But it appears not to work.... Can someone give me some help? -Do I have to use a different if? or convert the object to string etc.? Thanks alot... James

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  • PHP cron script with twitter (problem with oauth)

    - by James Lin
    Hi guys, I am trying to write an php twitter script which will be run by crontab, what the script does is to get the tweets from a dedicated twitter account. I have looked at some of the php twitter oauth libraries, all of them seem to use redirect to a twitter page to get a token, then goes back to a callback link. In my case I don't want to have any user interaction at all. Could anyone please tell me what I should do? Regards James

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  • Why is System.arraycopy native in Java?

    - by James B
    I was surprised to see in the Java source that System.arraycopy is a native method. Of course the reason is because it's faster. But what native tricks is the code able to employ that make it faster? Why not just loop over the original array and copy each pointer to the new array - surely this isn't that slow and cumbersome? Thanks, -James

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  • C#/.NET Little Wonders: The Concurrent Collections (1 of 3)

    - by James Michael Hare
    Once again we consider some of the lesser known classes and keywords of C#.  In the next few weeks, we will discuss the concurrent collections and how they have changed the face of concurrent programming. This week’s post will begin with a general introduction and discuss the ConcurrentStack<T> and ConcurrentQueue<T>.  Then in the following post we’ll discuss the ConcurrentDictionary<T> and ConcurrentBag<T>.  Finally, we shall close on the third post with a discussion of the BlockingCollection<T>. For more of the "Little Wonders" posts, see the index here. A brief history of collections In the beginning was the .NET 1.0 Framework.  And out of this framework emerged the System.Collections namespace, and it was good.  It contained all the basic things a growing programming language needs like the ArrayList and Hashtable collections.  The main problem, of course, with these original collections is that they held items of type object which means you had to be disciplined enough to use them correctly or you could end up with runtime errors if you got an object of a type you weren't expecting. Then came .NET 2.0 and generics and our world changed forever!  With generics the C# language finally got an equivalent of the very powerful C++ templates.  As such, the System.Collections.Generic was born and we got type-safe versions of all are favorite collections.  The List<T> succeeded the ArrayList and the Dictionary<TKey,TValue> succeeded the Hashtable and so on.  The new versions of the library were not only safer because they checked types at compile-time, in many cases they were more performant as well.  So much so that it's Microsoft's recommendation that the System.Collections original collections only be used for backwards compatibility. So we as developers came to know and love the generic collections and took them into our hearts and embraced them.  The problem is, thread safety in both the original collections and the generic collections can be problematic, for very different reasons. Now, if you are only doing single-threaded development you may not care – after all, no locking is required.  Even if you do have multiple threads, if a collection is “load-once, read-many” you don’t need to do anything to protect that container from multi-threaded access, as illustrated below: 1: public static class OrderTypeTranslator 2: { 3: // because this dictionary is loaded once before it is ever accessed, we don't need to synchronize 4: // multi-threaded read access 5: private static readonly Dictionary<string, char> _translator = new Dictionary<string, char> 6: { 7: {"New", 'N'}, 8: {"Update", 'U'}, 9: {"Cancel", 'X'} 10: }; 11:  12: // the only public interface into the dictionary is for reading, so inherently thread-safe 13: public static char? Translate(string orderType) 14: { 15: char charValue; 16: if (_translator.TryGetValue(orderType, out charValue)) 17: { 18: return charValue; 19: } 20:  21: return null; 22: } 23: } Unfortunately, most of our computer science problems cannot get by with just single-threaded applications or with multi-threading in a load-once manner.  Looking at  today's trends, it's clear to see that computers are not so much getting faster because of faster processor speeds -- we've nearly reached the limits we can push through with today's technologies -- but more because we're adding more cores to the boxes.  With this new hardware paradigm, it is even more important to use multi-threaded applications to take full advantage of parallel processing to achieve higher application speeds. So let's look at how to use collections in a thread-safe manner. Using historical collections in a concurrent fashion The early .NET collections (System.Collections) had a Synchronized() static method that could be used to wrap the early collections to make them completely thread-safe.  This paradigm was dropped in the generic collections (System.Collections.Generic) because having a synchronized wrapper resulted in atomic locks for all operations, which could prove overkill in many multithreading situations.  Thus the paradigm shifted to having the user of the collection specify their own locking, usually with an external object: 1: public class OrderAggregator 2: { 3: private static readonly Dictionary<string, List<Order>> _orders = new Dictionary<string, List<Order>>(); 4: private static readonly _orderLock = new object(); 5:  6: public void Add(string accountNumber, Order newOrder) 7: { 8: List<Order> ordersForAccount; 9:  10: // a complex operation like this should all be protected 11: lock (_orderLock) 12: { 13: if (!_orders.TryGetValue(accountNumber, out ordersForAccount)) 14: { 15: _orders.Add(accountNumber, ordersForAccount = new List<Order>()); 16: } 17:  18: ordersForAccount.Add(newOrder); 19: } 20: } 21: } Notice how we’re performing several operations on the dictionary under one lock.  With the Synchronized() static methods of the early collections, you wouldn’t be able to specify this level of locking (a more macro-level).  So in the generic collections, it was decided that if a user needed synchronization, they could implement their own locking scheme instead so that they could provide synchronization as needed. The need for better concurrent access to collections Here’s the problem: it’s relatively easy to write a collection that locks itself down completely for access, but anything more complex than that can be difficult and error-prone to write, and much less to make it perform efficiently!  For example, what if you have a Dictionary that has frequent reads but in-frequent updates?  Do you want to lock down the entire Dictionary for every access?  This would be overkill and would prevent concurrent reads.  In such cases you could use something like a ReaderWriterLockSlim which allows for multiple readers in a lock, and then once a writer grabs the lock it blocks all further readers until the writer is done (in a nutshell).  This is all very complex stuff to consider. Fortunately, this is where the Concurrent Collections come in.  The Parallel Computing Platform team at Microsoft went through great pains to determine how to make a set of concurrent collections that would have the best performance characteristics for general case multi-threaded use. Now, as in all things involving threading, you should always make sure you evaluate all your container options based on the particular usage scenario and the degree of parallelism you wish to acheive. This article should not be taken to understand that these collections are always supperior to the generic collections. Each fills a particular need for a particular situation. Understanding what each container is optimized for is key to the success of your application whether it be single-threaded or multi-threaded. General points to consider with the concurrent collections The MSDN points out that the concurrent collections all support the ICollection interface. However, since the collections are already synchronized, the IsSynchronized property always returns false, and SyncRoot always returns null.  Thus you should not attempt to use these properties for synchronization purposes. Note that since the concurrent collections also may have different operations than the traditional data structures you may be used to.  Now you may ask why they did this, but it was done out of necessity to keep operations safe and atomic.  For example, in order to do a Pop() on a stack you have to know the stack is non-empty, but between the time you check the stack’s IsEmpty property and then do the Pop() another thread may have come in and made the stack empty!  This is why some of the traditional operations have been changed to make them safe for concurrent use. In addition, some properties and methods in the concurrent collections achieve concurrency by creating a snapshot of the collection, which means that some operations that were traditionally O(1) may now be O(n) in the concurrent models.  I’ll try to point these out as we talk about each collection so you can be aware of any potential performance impacts.  Finally, all the concurrent containers are safe for enumeration even while being modified, but some of the containers support this in different ways (snapshot vs. dirty iteration).  Once again I’ll highlight how thread-safe enumeration works for each collection. ConcurrentStack<T>: The thread-safe LIFO container The ConcurrentStack<T> is the thread-safe counterpart to the System.Collections.Generic.Stack<T>, which as you may remember is your standard last-in-first-out container.  If you think of algorithms that favor stack usage (for example, depth-first searches of graphs and trees) then you can see how using a thread-safe stack would be of benefit. The ConcurrentStack<T> achieves thread-safe access by using System.Threading.Interlocked operations.  This means that the multi-threaded access to the stack requires no traditional locking and is very, very fast! For the most part, the ConcurrentStack<T> behaves like it’s Stack<T> counterpart with a few differences: Pop() was removed in favor of TryPop() Returns true if an item existed and was popped and false if empty. PushRange() and TryPopRange() were added Allows you to push multiple items and pop multiple items atomically. Count takes a snapshot of the stack and then counts the items. This means it is a O(n) operation, if you just want to check for an empty stack, call IsEmpty instead which is O(1). ToArray() and GetEnumerator() both also take snapshots. This means that iteration over a stack will give you a static view at the time of the call and will not reflect updates. Pushing on a ConcurrentStack<T> works just like you’d expect except for the aforementioned PushRange() method that was added to allow you to push a range of items concurrently. 1: var stack = new ConcurrentStack<string>(); 2:  3: // adding to stack is much the same as before 4: stack.Push("First"); 5:  6: // but you can also push multiple items in one atomic operation (no interleaves) 7: stack.PushRange(new [] { "Second", "Third", "Fourth" }); For looking at the top item of the stack (without removing it) the Peek() method has been removed in favor of a TryPeek().  This is because in order to do a peek the stack must be non-empty, but between the time you check for empty and the time you execute the peek the stack contents may have changed.  Thus the TryPeek() was created to be an atomic check for empty, and then peek if not empty: 1: // to look at top item of stack without removing it, can use TryPeek. 2: // Note that there is no Peek(), this is because you need to check for empty first. TryPeek does. 3: string item; 4: if (stack.TryPeek(out item)) 5: { 6: Console.WriteLine("Top item was " + item); 7: } 8: else 9: { 10: Console.WriteLine("Stack was empty."); 11: } Finally, to remove items from the stack, we have the TryPop() for single, and TryPopRange() for multiple items.  Just like the TryPeek(), these operations replace Pop() since we need to ensure atomically that the stack is non-empty before we pop from it: 1: // to remove items, use TryPop or TryPopRange to get multiple items atomically (no interleaves) 2: if (stack.TryPop(out item)) 3: { 4: Console.WriteLine("Popped " + item); 5: } 6:  7: // TryPopRange will only pop up to the number of spaces in the array, the actual number popped is returned. 8: var poppedItems = new string[2]; 9: int numPopped = stack.TryPopRange(poppedItems); 10:  11: foreach (var theItem in poppedItems.Take(numPopped)) 12: { 13: Console.WriteLine("Popped " + theItem); 14: } Finally, note that as stated before, GetEnumerator() and ToArray() gets a snapshot of the data at the time of the call.  That means if you are enumerating the stack you will get a snapshot of the stack at the time of the call.  This is illustrated below: 1: var stack = new ConcurrentStack<string>(); 2:  3: // adding to stack is much the same as before 4: stack.Push("First"); 5:  6: var results = stack.GetEnumerator(); 7:  8: // but you can also push multiple items in one atomic operation (no interleaves) 9: stack.PushRange(new [] { "Second", "Third", "Fourth" }); 10:  11: while(results.MoveNext()) 12: { 13: Console.WriteLine("Stack only has: " + results.Current); 14: } The only item that will be printed out in the above code is "First" because the snapshot was taken before the other items were added. This may sound like an issue, but it’s really for safety and is more correct.  You don’t want to enumerate a stack and have half a view of the stack before an update and half a view of the stack after an update, after all.  In addition, note that this is still thread-safe, whereas iterating through a non-concurrent collection while updating it in the old collections would cause an exception. ConcurrentQueue<T>: The thread-safe FIFO container The ConcurrentQueue<T> is the thread-safe counterpart of the System.Collections.Generic.Queue<T> class.  The concurrent queue uses an underlying list of small arrays and lock-free System.Threading.Interlocked operations on the head and tail arrays.  Once again, this allows us to do thread-safe operations without the need for heavy locks! The ConcurrentQueue<T> (like the ConcurrentStack<T>) has some departures from the non-concurrent counterpart.  Most notably: Dequeue() was removed in favor of TryDequeue(). Returns true if an item existed and was dequeued and false if empty. Count does not take a snapshot It subtracts the head and tail index to get the count.  This results overall in a O(1) complexity which is quite good.  It’s still recommended, however, that for empty checks you call IsEmpty instead of comparing Count to zero. ToArray() and GetEnumerator() both take snapshots. This means that iteration over a queue will give you a static view at the time of the call and will not reflect updates. The Enqueue() method on the ConcurrentQueue<T> works much the same as the generic Queue<T>: 1: var queue = new ConcurrentQueue<string>(); 2:  3: // adding to queue is much the same as before 4: queue.Enqueue("First"); 5: queue.Enqueue("Second"); 6: queue.Enqueue("Third"); For front item access, the TryPeek() method must be used to attempt to see the first item if the queue.  There is no Peek() method since, as you’ll remember, we can only peek on a non-empty queue, so we must have an atomic TryPeek() that checks for empty and then returns the first item if the queue is non-empty. 1: // to look at first item in queue without removing it, can use TryPeek. 2: // Note that there is no Peek(), this is because you need to check for empty first. TryPeek does. 3: string item; 4: if (queue.TryPeek(out item)) 5: { 6: Console.WriteLine("First item was " + item); 7: } 8: else 9: { 10: Console.WriteLine("Queue was empty."); 11: } Then, to remove items you use TryDequeue().  Once again this is for the same reason we have TryPeek() and not Peek(): 1: // to remove items, use TryDequeue. If queue is empty returns false. 2: if (queue.TryDequeue(out item)) 3: { 4: Console.WriteLine("Dequeued first item " + item); 5: } Just like the concurrent stack, the ConcurrentQueue<T> takes a snapshot when you call ToArray() or GetEnumerator() which means that subsequent updates to the queue will not be seen when you iterate over the results.  Thus once again the code below will only show the first item, since the other items were added after the snapshot. 1: var queue = new ConcurrentQueue<string>(); 2:  3: // adding to queue is much the same as before 4: queue.Enqueue("First"); 5:  6: var iterator = queue.GetEnumerator(); 7:  8: queue.Enqueue("Second"); 9: queue.Enqueue("Third"); 10:  11: // only shows First 12: while (iterator.MoveNext()) 13: { 14: Console.WriteLine("Dequeued item " + iterator.Current); 15: } Using collections concurrently You’ll notice in the examples above I stuck to using single-threaded examples so as to make them deterministic and the results obvious.  Of course, if we used these collections in a truly multi-threaded way the results would be less deterministic, but would still be thread-safe and with no locking on your part required! For example, say you have an order processor that takes an IEnumerable<Order> and handles each other in a multi-threaded fashion, then groups the responses together in a concurrent collection for aggregation.  This can be done easily with the TPL’s Parallel.ForEach(): 1: public static IEnumerable<OrderResult> ProcessOrders(IEnumerable<Order> orderList) 2: { 3: var proxy = new OrderProxy(); 4: var results = new ConcurrentQueue<OrderResult>(); 5:  6: // notice that we can process all these in parallel and put the results 7: // into our concurrent collection without needing any external locking! 8: Parallel.ForEach(orderList, 9: order => 10: { 11: var result = proxy.PlaceOrder(order); 12:  13: results.Enqueue(result); 14: }); 15:  16: return results; 17: } Summary Obviously, if you do not need multi-threaded safety, you don’t need to use these collections, but when you do need multi-threaded collections these are just the ticket! The plethora of features (I always think of the movie The Three Amigos when I say plethora) built into these containers and the amazing way they acheive thread-safe access in an efficient manner is wonderful to behold. Stay tuned next week where we’ll continue our discussion with the ConcurrentBag<T> and the ConcurrentDictionary<TKey,TValue>. For some excellent information on the performance of the concurrent collections and how they perform compared to a traditional brute-force locking strategy, see this wonderful whitepaper by the Microsoft Parallel Computing Platform team here.   Tweet Technorati Tags: C#,.NET,Concurrent Collections,Collections,Multi-Threading,Little Wonders,BlackRabbitCoder,James Michael Hare

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  • C#/.NET Little Wonders: The ConcurrentDictionary

    - by James Michael Hare
    Once again we consider some of the lesser known classes and keywords of C#.  In this series of posts, we will discuss how the concurrent collections have been developed to help alleviate these multi-threading concerns.  Last week’s post began with a general introduction and discussed the ConcurrentStack<T> and ConcurrentQueue<T>.  Today's post discusses the ConcurrentDictionary<T> (originally I had intended to discuss ConcurrentBag this week as well, but ConcurrentDictionary had enough information to create a very full post on its own!).  Finally next week, we shall close with a discussion of the ConcurrentBag<T> and BlockingCollection<T>. For more of the "Little Wonders" posts, see the index here. Recap As you'll recall from the previous post, the original collections were object-based containers that accomplished synchronization through a Synchronized member.  While these were convenient because you didn't have to worry about writing your own synchronization logic, they were a bit too finely grained and if you needed to perform multiple operations under one lock, the automatic synchronization didn't buy much. With the advent of .NET 2.0, the original collections were succeeded by the generic collections which are fully type-safe, but eschew automatic synchronization.  This cuts both ways in that you have a lot more control as a developer over when and how fine-grained you want to synchronize, but on the other hand if you just want simple synchronization it creates more work. With .NET 4.0, we get the best of both worlds in generic collections.  A new breed of collections was born called the concurrent collections in the System.Collections.Concurrent namespace.  These amazing collections are fine-tuned to have best overall performance for situations requiring concurrent access.  They are not meant to replace the generic collections, but to simply be an alternative to creating your own locking mechanisms. Among those concurrent collections were the ConcurrentStack<T> and ConcurrentQueue<T> which provide classic LIFO and FIFO collections with a concurrent twist.  As we saw, some of the traditional methods that required calls to be made in a certain order (like checking for not IsEmpty before calling Pop()) were replaced in favor of an umbrella operation that combined both under one lock (like TryPop()). Now, let's take a look at the next in our series of concurrent collections!For some excellent information on the performance of the concurrent collections and how they perform compared to a traditional brute-force locking strategy, see this wonderful whitepaper by the Microsoft Parallel Computing Platform team here. ConcurrentDictionary – the fully thread-safe dictionary The ConcurrentDictionary<TKey,TValue> is the thread-safe counterpart to the generic Dictionary<TKey, TValue> collection.  Obviously, both are designed for quick – O(1) – lookups of data based on a key.  If you think of algorithms where you need lightning fast lookups of data and don’t care whether the data is maintained in any particular ordering or not, the unsorted dictionaries are generally the best way to go. Note: as a side note, there are sorted implementations of IDictionary, namely SortedDictionary and SortedList which are stored as an ordered tree and a ordered list respectively.  While these are not as fast as the non-sorted dictionaries – they are O(log2 n) – they are a great combination of both speed and ordering -- and still greatly outperform a linear search. Now, once again keep in mind that if all you need to do is load a collection once and then allow multi-threaded reading you do not need any locking.  Examples of this tend to be situations where you load a lookup or translation table once at program start, then keep it in memory for read-only reference.  In such cases locking is completely non-productive. However, most of the time when we need a concurrent dictionary we are interleaving both reads and updates.  This is where the ConcurrentDictionary really shines!  It achieves its thread-safety with no common lock to improve efficiency.  It actually uses a series of locks to provide concurrent updates, and has lockless reads!  This means that the ConcurrentDictionary gets even more efficient the higher the ratio of reads-to-writes you have. ConcurrentDictionary and Dictionary differences For the most part, the ConcurrentDictionary<TKey,TValue> behaves like it’s Dictionary<TKey,TValue> counterpart with a few differences.  Some notable examples of which are: Add() does not exist in the concurrent dictionary. This means you must use TryAdd(), AddOrUpdate(), or GetOrAdd().  It also means that you can’t use a collection initializer with the concurrent dictionary. TryAdd() replaced Add() to attempt atomic, safe adds. Because Add() only succeeds if the item doesn’t already exist, we need an atomic operation to check if the item exists, and if not add it while still under an atomic lock. TryUpdate() was added to attempt atomic, safe updates. If we want to update an item, we must make sure it exists first and that the original value is what we expected it to be.  If all these are true, we can update the item under one atomic step. TryRemove() was added to attempt atomic, safe removes. To safely attempt to remove a value we need to see if the key exists first, this checks for existence and removes under an atomic lock. AddOrUpdate() was added to attempt an thread-safe “upsert”. There are many times where you want to insert into a dictionary if the key doesn’t exist, or update the value if it does.  This allows you to make a thread-safe add-or-update. GetOrAdd() was added to attempt an thread-safe query/insert. Sometimes, you want to query for whether an item exists in the cache, and if it doesn’t insert a starting value for it.  This allows you to get the value if it exists and insert if not. Count, Keys, Values properties take a snapshot of the dictionary. Accessing these properties may interfere with add and update performance and should be used with caution. ToArray() returns a static snapshot of the dictionary. That is, the dictionary is locked, and then copied to an array as a O(n) operation.  GetEnumerator() is thread-safe and efficient, but allows dirty reads. Because reads require no locking, you can safely iterate over the contents of the dictionary.  The only downside is that, depending on timing, you may get dirty reads. Dirty reads during iteration The last point on GetEnumerator() bears some explanation.  Picture a scenario in which you call GetEnumerator() (or iterate using a foreach, etc.) and then, during that iteration the dictionary gets updated.  This may not sound like a big deal, but it can lead to inconsistent results if used incorrectly.  The problem is that items you already iterated over that are updated a split second after don’t show the update, but items that you iterate over that were updated a split second before do show the update.  Thus you may get a combination of items that are “stale” because you iterated before the update, and “fresh” because they were updated after GetEnumerator() but before the iteration reached them. Let’s illustrate with an example, let’s say you load up a concurrent dictionary like this: 1: // load up a dictionary. 2: var dictionary = new ConcurrentDictionary<string, int>(); 3:  4: dictionary["A"] = 1; 5: dictionary["B"] = 2; 6: dictionary["C"] = 3; 7: dictionary["D"] = 4; 8: dictionary["E"] = 5; 9: dictionary["F"] = 6; Then you have one task (using the wonderful TPL!) to iterate using dirty reads: 1: // attempt iteration in a separate thread 2: var iterationTask = new Task(() => 3: { 4: // iterates using a dirty read 5: foreach (var pair in dictionary) 6: { 7: Console.WriteLine(pair.Key + ":" + pair.Value); 8: } 9: }); And one task to attempt updates in a separate thread (probably): 1: // attempt updates in a separate thread 2: var updateTask = new Task(() => 3: { 4: // iterates, and updates the value by one 5: foreach (var pair in dictionary) 6: { 7: dictionary[pair.Key] = pair.Value + 1; 8: } 9: }); Now that we’ve done this, we can fire up both tasks and wait for them to complete: 1: // start both tasks 2: updateTask.Start(); 3: iterationTask.Start(); 4:  5: // wait for both to complete. 6: Task.WaitAll(updateTask, iterationTask); Now, if I you didn’t know about the dirty reads, you may have expected to see the iteration before the updates (such as A:1, B:2, C:3, D:4, E:5, F:6).  However, because the reads are dirty, we will quite possibly get a combination of some updated, some original.  My own run netted this result: 1: F:6 2: E:6 3: D:5 4: C:4 5: B:3 6: A:2 Note that, of course, iteration is not in order because ConcurrentDictionary, like Dictionary, is unordered.  Also note that both E and F show the value 6.  This is because the output task reached F before the update, but the updates for the rest of the items occurred before their output (probably because console output is very slow, comparatively). If we want to always guarantee that we will get a consistent snapshot to iterate over (that is, at the point we ask for it we see precisely what is in the dictionary and no subsequent updates during iteration), we should iterate over a call to ToArray() instead: 1: // attempt iteration in a separate thread 2: var iterationTask = new Task(() => 3: { 4: // iterates using a dirty read 5: foreach (var pair in dictionary.ToArray()) 6: { 7: Console.WriteLine(pair.Key + ":" + pair.Value); 8: } 9: }); The atomic Try…() methods As you can imagine TryAdd() and TryRemove() have few surprises.  Both first check the existence of the item to determine if it can be added or removed based on whether or not the key currently exists in the dictionary: 1: // try add attempts an add and returns false if it already exists 2: if (dictionary.TryAdd("G", 7)) 3: Console.WriteLine("G did not exist, now inserted with 7"); 4: else 5: Console.WriteLine("G already existed, insert failed."); TryRemove() also has the virtue of returning the value portion of the removed entry matching the given key: 1: // attempt to remove the value, if it exists it is removed and the original is returned 2: int removedValue; 3: if (dictionary.TryRemove("C", out removedValue)) 4: Console.WriteLine("Removed C and its value was " + removedValue); 5: else 6: Console.WriteLine("C did not exist, remove failed."); Now TryUpdate() is an interesting creature.  You might think from it’s name that TryUpdate() first checks for an item’s existence, and then updates if the item exists, otherwise it returns false.  Well, note quite... It turns out when you call TryUpdate() on a concurrent dictionary, you pass it not only the new value you want it to have, but also the value you expected it to have before the update.  If the item exists in the dictionary, and it has the value you expected, it will update it to the new value atomically and return true.  If the item is not in the dictionary or does not have the value you expected, it is not modified and false is returned. 1: // attempt to update the value, if it exists and if it has the expected original value 2: if (dictionary.TryUpdate("G", 42, 7)) 3: Console.WriteLine("G existed and was 7, now it's 42."); 4: else 5: Console.WriteLine("G either didn't exist, or wasn't 7."); The composite Add methods The ConcurrentDictionary also has composite add methods that can be used to perform updates and gets, with an add if the item is not existing at the time of the update or get. The first of these, AddOrUpdate(), allows you to add a new item to the dictionary if it doesn’t exist, or update the existing item if it does.  For example, let’s say you are creating a dictionary of counts of stock ticker symbols you’ve subscribed to from a market data feed: 1: public sealed class SubscriptionManager 2: { 3: private readonly ConcurrentDictionary<string, int> _subscriptions = new ConcurrentDictionary<string, int>(); 4:  5: // adds a new subscription, or increments the count of the existing one. 6: public void AddSubscription(string tickerKey) 7: { 8: // add a new subscription with count of 1, or update existing count by 1 if exists 9: var resultCount = _subscriptions.AddOrUpdate(tickerKey, 1, (symbol, count) => count + 1); 10:  11: // now check the result to see if we just incremented the count, or inserted first count 12: if (resultCount == 1) 13: { 14: // subscribe to symbol... 15: } 16: } 17: } Notice the update value factory Func delegate.  If the key does not exist in the dictionary, the add value is used (in this case 1 representing the first subscription for this symbol), but if the key already exists, it passes the key and current value to the update delegate which computes the new value to be stored in the dictionary.  The return result of this operation is the value used (in our case: 1 if added, existing value + 1 if updated). Likewise, the GetOrAdd() allows you to attempt to retrieve a value from the dictionary, and if the value does not currently exist in the dictionary it will insert a value.  This can be handy in cases where perhaps you wish to cache data, and thus you would query the cache to see if the item exists, and if it doesn’t you would put the item into the cache for the first time: 1: public sealed class PriceCache 2: { 3: private readonly ConcurrentDictionary<string, double> _cache = new ConcurrentDictionary<string, double>(); 4:  5: // adds a new subscription, or increments the count of the existing one. 6: public double QueryPrice(string tickerKey) 7: { 8: // check for the price in the cache, if it doesn't exist it will call the delegate to create value. 9: return _cache.GetOrAdd(tickerKey, symbol => GetCurrentPrice(symbol)); 10: } 11:  12: private double GetCurrentPrice(string tickerKey) 13: { 14: // do code to calculate actual true price. 15: } 16: } There are other variations of these two methods which vary whether a value is provided or a factory delegate, but otherwise they work much the same. Oddities with the composite Add methods The AddOrUpdate() and GetOrAdd() methods are totally thread-safe, on this you may rely, but they are not atomic.  It is important to note that the methods that use delegates execute those delegates outside of the lock.  This was done intentionally so that a user delegate (of which the ConcurrentDictionary has no control of course) does not take too long and lock out other threads. This is not necessarily an issue, per se, but it is something you must consider in your design.  The main thing to consider is that your delegate may get called to generate an item, but that item may not be the one returned!  Consider this scenario: A calls GetOrAdd and sees that the key does not currently exist, so it calls the delegate.  Now thread B also calls GetOrAdd and also sees that the key does not currently exist, and for whatever reason in this race condition it’s delegate completes first and it adds its new value to the dictionary.  Now A is done and goes to get the lock, and now sees that the item now exists.  In this case even though it called the delegate to create the item, it will pitch it because an item arrived between the time it attempted to create one and it attempted to add it. Let’s illustrate, assume this totally contrived example program which has a dictionary of char to int.  And in this dictionary we want to store a char and it’s ordinal (that is, A = 1, B = 2, etc).  So for our value generator, we will simply increment the previous value in a thread-safe way (perhaps using Interlocked): 1: public static class Program 2: { 3: private static int _nextNumber = 0; 4:  5: // the holder of the char to ordinal 6: private static ConcurrentDictionary<char, int> _dictionary 7: = new ConcurrentDictionary<char, int>(); 8:  9: // get the next id value 10: public static int NextId 11: { 12: get { return Interlocked.Increment(ref _nextNumber); } 13: } Then, we add a method that will perform our insert: 1: public static void Inserter() 2: { 3: for (int i = 0; i < 26; i++) 4: { 5: _dictionary.GetOrAdd((char)('A' + i), key => NextId); 6: } 7: } Finally, we run our test by starting two tasks to do this work and get the results… 1: public static void Main() 2: { 3: // 3 tasks attempting to get/insert 4: var tasks = new List<Task> 5: { 6: new Task(Inserter), 7: new Task(Inserter) 8: }; 9:  10: tasks.ForEach(t => t.Start()); 11: Task.WaitAll(tasks.ToArray()); 12:  13: foreach (var pair in _dictionary.OrderBy(p => p.Key)) 14: { 15: Console.WriteLine(pair.Key + ":" + pair.Value); 16: } 17: } If you run this with only one task, you get the expected A:1, B:2, ..., Z:26.  But running this in parallel you will get something a bit more complex.  My run netted these results: 1: A:1 2: B:3 3: C:4 4: D:5 5: E:6 6: F:7 7: G:8 8: H:9 9: I:10 10: J:11 11: K:12 12: L:13 13: M:14 14: N:15 15: O:16 16: P:17 17: Q:18 18: R:19 19: S:20 20: T:21 21: U:22 22: V:23 23: W:24 24: X:25 25: Y:26 26: Z:27 Notice that B is 3?  This is most likely because both threads attempted to call GetOrAdd() at roughly the same time and both saw that B did not exist, thus they both called the generator and one thread got back 2 and the other got back 3.  However, only one of those threads can get the lock at a time for the actual insert, and thus the one that generated the 3 won and the 3 was inserted and the 2 got discarded.  This is why on these methods your factory delegates should be careful not to have any logic that would be unsafe if the value they generate will be pitched in favor of another item generated at roughly the same time.  As such, it is probably a good idea to keep those generators as stateless as possible. Summary The ConcurrentDictionary is a very efficient and thread-safe version of the Dictionary generic collection.  It has all the benefits of type-safety that it’s generic collection counterpart does, and in addition is extremely efficient especially when there are more reads than writes concurrently. Tweet Technorati Tags: C#, .NET, Concurrent Collections, Collections, Little Wonders, Black Rabbit Coder,James Michael Hare

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  • Help with split

    - by Andeeh
    I have something that splits each line of a file. here is a sample of a line it might split "James","Project5","15/05/2010","3" I have this code Private Sub Command1_Click() Open jobs For Input As #1 Do While Not EOF(1) Line Input #1, tmpstring splititems = Split(tmpstring, ",") Form1.Print splititems(0) Form1.Print splititems(1); Form1.Print splititems(2); Form1.Print splititems(3) Loop Close #1 End Sub I would like it to instead of outputting a name each time there is a name, just put the project under the name that is already there. e.g. if there was another line in the file with the name james and he had been working on project 2 in that line I would like it to just put project 2 under the "James" that had already been put on the form. Any help would be fantastic

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  • Getting mysql row that doesn't conflict with another row

    - by user939951
    I have two tables that link together through an id one is "submit_moderate" and one is "submit_post" The "submit_moderate" table looks like this id moderated_by post 1 James 60 2 Alice 32 3 Tim 18 4 Michael 60 Im using a simple query to get data from the "submit_post" table according to the "submit_moderate" table. $get_posts = mysql_query("SELECT * FROM submit_moderate WHERE moderated_by!='$user'"); $user is the person who is signed in. Now my problem is when I run this query, with the user 'Michael' it will retrieve this 1 James 60 2 Alice 32 3 Tim 18 Now technically this is correct however I don't want to retrieve the first row because 60 is associated with Michael as well as James. Basically I don't want to retrieve that value '60'. I know why this is happening however I can't figure out how to do this. I appreciate any hints or advice I can get.

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  • Cannot import SQLite with Python 2.6

    - by David McLaughlin
    I'm running Python 2.6 on Unix and when I run the interactive prompt (SQLite is supposed to be preinstalled) I get: [root@idev htdocs]# python Python 2.6 (r26:66714, Oct 23 2008, 16:25:34) [GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-5)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import sqlite Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: No module named sqlite >>> import sqlite Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: No module named sqlite >>> How do I resolve this?

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  • SVNParentPath directory authorization

    - by James
    The question is a bit stupid but I can't get it sorted. I have a server with SVN that uses the SVNPath directive in httpd.conf and all works fine with path authorizations. Now I'm installing a second serer where I'm going to use SVNParentPath directive and I've got it all running except I can't get the authorization part quite right. From what I understand it's the same as when you use SVNPath but you need to specificy the repo name before the folder name.. My SVNParentPath is /srv/svn/ and I created a directory /srv/svn/testproj and then ran svnadmin create /srv/svn/testproj Now i'm configuring my authorization file: [/] * = svnadmin = rw adusgi = rw [testproj:/svn/testproj] demada = rw degari = rw scarja = rw Now if I try to commit /svn/testproj using user svnadmin or adusgi all is fine. If I try for example demada it doesn't work... (I've run the htpasswd2 commands for the user obviously. The directory is correct or atleast thats how I use the directory with the SVNPath server thats already running, the part I think I'm getting wrong is the repo name, I just used the directory name but what am I really supposed to put there?? Thank you, James

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  • How to install a desktop environment onto Ubuntu Server -- but without internet access or a CDROM?

    - by James
    I am playing around with a computer which has no CDROM drive or internet access and I have installed Ubuntu Server onto it. I have that all up and running nicely but now I'd like to install Xfce, GNOME or something similar so I can load up a desktop environment from the command line if I wish. Obviously with internet access or a CDROM, this would be a simple task of using apt-get and it finding & retrieving the packages for me, I assume, but I do not have either. I do however have a USB drive and I have used Unetbootin to make it into a bootable drive with the Ubuntu Server disk image files on there. I have mounted the USB drive to /media/usb0 and tried the command "sudo apt-cdrom add -d /media/usb0" to get apt to recognise the USb drive as an "Ubuntu CD" -- a source of package files but apt-get doesn't seem to be finding Xfce.. I try "sudo apt-get install xfce" and "sudo apt-get install xfce4" but neither find the package.. I would prefer to have Xfce but GNOME would be OK too.. My question is, am I doing something wrong? I figured that the Ubuntu Server disk (or rather, my Ubuntu Server USB drive) might not have any desktop environment packages on there so I tried the Xubuntu Desktop disk too (again, from my USB drive). I tried "sudo apt-get install xubuntu-desktop" but it couldn't find the package - even though it is listed under the /casper/ directory in some MANIFEST file. Anyone see where I'm going wrong? Maybe apt-get install is looking somewhere other than my USB drive? Maybe my commands are wrong? Maybe the disks don't even have the desktop environments on!? Thanks in advance guys, any input would be much appreciated. Cheers - James

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  • Stored proc running 30% slower through Java versus running directly on database

    - by James B
    Hi All, I'm using Java 1.6, JTDS 1.2.2 (also just tried 1.2.4 to no avail) and SQL Server 2005 to create a CallableStatement to run a stored procedure (with no parameters). I am seeing the Java wrapper running the same stored procedure 30% slower than using SQL Server Management Studio. I've run the MS SQL profiler and there is little difference in I/O between the two processes, so I don't think it's related to query plan caching. The stored proc takes no arguments and returns no data. It uses a server-side cursor to calculate the values that are needed to populate a table. I can't see how the calling a stored proc from Java should add a 30% overhead, surely it's just a pipe to the database that SQL is sent down and then the database executes it....Could the database be giving the Java app a different query plan?? I've posted to both the MSDN forums, and the sourceforge JTDS forums (topic: "stored proc slower in JTDS than direct in DB") I was wondering if anyone has any suggestions as to why this might be happening? Thanks in advance, -James (N.B. Fear not, I will collate any answers I get in other forums together here once I find the solution) Java code snippet: sLogger.info("Preparing call..."); stmt = mCon.prepareCall("SP_WB200_POPULATE_TABLE_limited_rows"); sLogger.info("Call prepared. Executing procedure..."); stmt.executeQuery(); sLogger.info("Procedure complete."); I have run sql profiler, and found the following: Java app : CPU: 466,514 Reads: 142,478,387 Writes: 284,078 Duration: 983,796 SSMS : CPU: 466,973 Reads: 142,440,401 Writes: 280,244 Duration: 769,851 (Both with DBCC DROPCLEANBUFFERS run prior to profiling, and both produce the correct number of rows) So my conclusion is that they both execute the same reads and writes, it's just that the way they are doing it is different, what do you guys think? It turns out that the query plans are significantly different for the different clients (the Java client is updating an index during an insert that isn't in the faster SQL client, also, the way it is executing joins is different (nested loops Vs. gather streams, nested loops Vs index scans, argh!)). Quite why this is, I don't know yet (I'll re-post when I do get to the bottom of it) Epilogue I couldn't get this to work properly. I tried homogenising the connection properties (arithabort, ansi_nulls etc) between the Java and Mgmt studio clients. It ended up the two different clients had very similar query/execution plans (but still with different actual plan_ids). I posted a summary of what I found to the MSDN SQL Server forums as I found differing performance not just between a JDBC client and management studio, but also between Microsoft's own command line client, SQLCMD, I also checked some more radical things like network traffic too, or wrapping the stored proc inside another stored proc, just for grins. I have a feeling the problem lies somewhere in the way the cursor was being executed, and it was somehow giving rise to the Java process being suspended, but why a different client should give rise to this different locking/waiting behaviour when nothing else is running and the same execution plan is in operation is a little beyond my skills (I'm no DBA!). As a result, I have decided that 4 days is enough of anyone's time to waste on something like this, so I will grudgingly code around it (if I'm honest, the stored procedure needed re-coding to be more incremental instead of re-calculating all data each week anyway), and chalk this one down to experience. I'll leave the question open, big thanks to everyone who put their hat in the ring, it was all useful, and if anyone comes up with anything further, I'd love to hear some more options...and if anyone finds this post as a result of seeing this behaviour in their own environments, then hopefully there's some pointers here that you can try yourself, and hope fully see further than we did. I'm ready for my weekend now! -James

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  • Back from Russia

    - by Stephen Walther
    Thanks everyone who came to my talks on ASP.NET Web Forms and MVC in Moscow last week!  Here are the slide decks and demo code for the two talks (You need Visual Studio 2010):   What’s New in ASP.NET MVC 2?   What’s New in ASP.NET 4 Web Forms?   I had a great time in Russia. On the second day, I had an opportunity to walk around Moscow. Here’s a picture of me standing in Red Square:   Here’s a picture of me eating Chicken Kiev with Microsoft evangelist James Senior. James has just started his worldwide Web Camp tour to promote ASP.NET 4. He is traveling non-stop country to country. After Russia, he is off to China and Australia. You can find out more about the Web Camps here: http://www.webcamps.ms/

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  • 2D Barcode Addendum

    - by Tim Dexter
    Having finally got my external drive back(long story) today from Oklahoma (thank you so much Sammy) Im back with a full compliment of Oracle and blogging tools at my disposal. I have missed JDeveloper this past week, which I have found, I immensely prefer over Eclipse (let the flaming commence :0) I use Zoundry Raven for writing articles and its not installed locally but on my external drove, so I have been soldiering on with the blog server's pain in the backside UI for writing. Now I have my favority editor back and things are calming down workwise, I will start to get the Excel template posts out. Today thou, a note about 2D barcode support or more specifically any barcode that needs some data manipulation before the barcode font is applied. I wrote about these fonts a long time back and laid out the java class you would need to write if you had an algorithm from the font manufacturer to use. I missed out a valuable point and James at Luminex fell into the trap. He was wanting to use the datamatrix font from IDAutomation but and had built the java class to be called from the RTF template but it was not encoding or at least did not appear to be. New debugging feature to the rescue. Kan over at the bipconsultng blog documented the feature a while back. Just adding <?xdo-debug-level:'STATEMENT'?> to my test template generated all the debug files in my c:\temp directory. No messing with files, just a simple command ... at last! Kan has documented the feature here. With the log in hand I spotted a java error stack referencing a missing code128a method, huh? Looking at James' class he had the following snippet: ENCODERS.put("code128a",mUtility.getClass().getMethod("code128a",clazz)); ENCODERS.put("code128b",mUtility.getClass().getMethod("code128b", clazz)); ENCODERS.put("code128c",mUtility.getClass().getMethod("code128c", clazz)); ENCODERS.put("pdf417",mUtility.getClass().getMethod("pdf417", clazz)); ENCODERS.put("datamatrix",mUtility.getClass().getMethod("datamatrix", clazz)); His class did not include the other code128 and pdf147 methods and BIP was expecting them. An easy fix, just comment them out, rebuild and deploy and the encoding started working. If you are hitting similar problems, check that class and ensure all of the referenced methods are available, if not, delete or get commenting. James now has purdy labels popping out that his hard ware can read, sweet!

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  • links for 2010-04-07

    - by Bob Rhubart
    James McGovern: Enterprise Architecture and Social CRM "With a few exceptions, the vast majority of enterprise architects I know spend an awful lot of time focused on internal issues whether it is rationalization, the cloud, storage governance, data center consolidation, creation of reference architectures, portfolio management and other considerations that aren’t even visible to customers. One should ask whether IT can be truly successful if we are busy listening to the business but otherwise are blissfully ignorant towards the customers they serve." -- James McGovern (tags: enterprisearchitecture crm socialcomputing) WRF Benchmark: X6275 Beats Power6 - BestPerf "Oracle's Sun Blade X6275 cluster is 28% faster than the IBM POWER6 cluster on Weather Research and Forecasting (WRF) continental United Status (CONUS) benchmark datasets. The Sun Blade X6275 cluster used a Quad Data Rate (QDR) InfiniBand connection along with Intel compilers and MPI." (tags: oracle sun x6275 benchmarks)

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  • Even EA's Have Bad Days - it's Time to Reset

    - by Pat Shepherd
    I saw this article and thought I'd share it because, even we EA's have bad days and the 7 points listed are a great way for you to hit the "reset" button. From Geoffrey James on INC.COM, here are 7 ways to change your view of things when, say, you are hitting a frustration point coordinating stakeholders to agree on an approach (never happens, right?) Positive Thinking: 7 Easy Ways to Improve a Bad Day http://www.inc.com/geoffrey-james/positive-thinking-7-easy-ways-to-improve-a-bad-day.html To paraphrase:          You can decide (in an instant) to change patterns of the past          Believe in (or even visualize) good things happening, and they will          Keep a healthy perspective on the work-life / life-life continuum (what things REALLY matter in the big scheme of things)                  Focus on the good (the laws of positive-attraction apply)

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  • OpenJDK DIO Project Now Live! Java SE Embedded API Accessing Peripherals

    - by hinkmond
    The DIO project on OpenJDK is now live! For those who grew up in the 1970's and 1980's, you might remember Ronnie James Dio, lead singer of Black Sabbath after Ozzy was fired, and lead singer of his own band, Dio. Well, this DIO is not that Dio. This DIO is the OpenJDK Device I/O project which provides a Java-level API for accessing generic device peripherals on embedded devices, like your Raspberry Pi running Java SE Embedded software. See: OpenJDK DIO Project Here's a quote: + General Purpose Input/Output (GPIO) + Inter-Integrated Circuit Bus (I2C) + Universal Asynchronous Receiver/Transmitter (UART) + Serial Peripheral Interface If you're familiar with Pi4J, then you're going to like DIO. And, if you liked Ozzy, you probably liked Ronnie James Dio. This will probably make Robert Savage happy too. The part about DIO being live now, not the part about Dio replacing Ozzy, because everyone likes Ozzy. Hinkmond

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