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  • Multiple vulnerabilities in libexif

    - by Umang_D
    CVE DescriptionCVSSv2 Base ScoreComponentProduct and Resolution CVE-2012-2812 Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability 6.4 libexif Solaris 11 11/11 SRU 12.4 CVE-2012-2813 Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability 6.4 CVE-2012-2814 Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability 7.5 CVE-2012-2836 Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability 6.4 CVE-2012-2837 Numeric Errors vulnerability 5.0 CVE-2012-2840 Numeric Errors vulnerability 7.5 CVE-2012-2841 Numeric Errors vulnerability 7.5 CVE-2012-2845 Numeric Errors vulnerability 6.4 This notification describes vulnerabilities fixed in third-party components that are included in Oracle's product distributions.Information about vulnerabilities affecting Oracle products can be found on Oracle Critical Patch Updates and Security Alerts page.

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  • Maximizing Throughput with TVPs

    TVPs offer several performance optimization possibilities that other bulk operations do not allow, and these operations may allow for TVP performance to exceed other bulk operations by an order of magnitude, especially for a pattern where subsets of the data are frequently updated. Want to work faster with SQL Server?If you want to work faster try out the SQL Toolbelt. "The SQL Toolbelt provides tools that database developers as well as DBAs should not live without." William Van Orden. Download the SQL Toolbelt here.

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  • How to make write operation idempotent?

    - by Morgan Cheng
    I'm reading article about recently release Gizzard sharding framework by twitter(http://engineering.twitter.com/2010/04/introducing-gizzard-framework-for.html). It mentions that all write operations must be idempotent to make sure high reliability. According to wikipedia, "Idempotent operations are operations that can be applied multiple times without changing the result." But, IMHO, in Gazzard case, idempotent write operation should be operations that sequence doesn't matter. Now, my question is: How to make write operation idempotent? The only thing I can image is to have a version number attached to each write. For example, in blog system. Each blog must have a $blog_id and $content. In application level, we always write a blog content like this write($blog_id, $content, $version). The $version is determined to be unique in application level. So, if application first try to set one blog to "Hello world" and second want it to be "Goodbye", the write is idempotent. We have such two write operations: write($blog_id, "Hello world", 1); write($blog_id, "Goodbye", 2); These two operations are supposed to changed two different records in DB. So, no matter how many times and what sequence these two operations executed, the results are same. This is just my understanding. Please correct me if I'm wrong.

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  • Old operational master still thinks it is the "one"

    - by Doug
    Hi there, I have a domain with 3 AD servers for now i'll just call them: AD01 (Win 2008 GC, Operations master) AD02 (Win 2008 GC) AD03 (Win 2003 GC) A couple of months there was some hardware issues with AD01 so the operations master, PDC and Infrastructure Master was moved to AD02. All machines where on while this was happening. AD01 (Win 2008 GC) AD02 (Win 2008 GC, Operations master) AD03 (Win 2003 GC) AD01 was then shutdown for a month. Upon starting this machine up with replaced hardware (NIC and RAID card) i now have a weird problem. AD01 Thinks it is operations master still in AD on the local box AD02 & AD03 Thinks AD02 is operations master in AD on both boxes When running DCDIAG on AD01 i get a number of issues (listed below) When running "dcdiag /test:advertising" on AD01: Doing primary tests Testing server: Default-First-Site-Name\AD01 Starting test: Advertising Warning: DsGetDcName returned information for \\ad02.domain.local, when we were trying to reach AD01. SERVER IS NOT RESPONDING or IS NOT CONSIDERED SUITABLE. ......................... AD01 failed test Advertising Running partition tests on : ForestDnsZones Running partition tests on : DomainDnsZones Running partition tests on : Schema Running partition tests on : Configuration Running partition tests on : domain Running enterprise tests on : domain.local When running "dcdiag" on AD01 i get the following errors (excerpt of the Final output): Testing server: Default-First-Site-Name\AD01 Starting test: Advertising Warning: DsGetDcName returned information for \\ad02.domain.local, when we were trying to reach AD01. SERVER IS NOT RESPONDING or IS NOT CONSIDERED SUITABLE. ......................... AD01 failed test Advertising Starting test: FrsEvent There are warning or error events within the last 24 hours after the SYSVOL has been shared. Failing SYSVOL replication problems may cause Group Policy problems. Starting test: NCSecDesc Error NT AUTHORITY\ENTERPRISE DOMAIN CONTROLLERS doesn't have Replicating Directory Changes In Filtered Set access rights for the naming context: DC=ForestDnsZones,DC=domain,DC=local Error NT AUTHORITY\ENTERPRISE DOMAIN CONTROLLERS doesn't have Replicating Directory Changes In Filtered Set access rights for the naming context: DC=DomainDnsZones,DC=domain,DC=local Starting test: Replications [Replications Check,Replications Check] Inbound replication is disabled. To correct, run "repadmin /options AD01 -DISABLE_INBOUND_REPL" [Replications Check,AD01] Outbound replication is disabled. To correct, run "repadmin /options AD01 -DISABLE_OUTBOUND_REPL" So the problem appeasr to be that when i moved the operations master, AD01 never got the memo, and now that it's started up, all the other AD servers don't think its the boss anymore when it trys to replicate etc. So i really need to manually update AD01 so that it knows who the operations master, instrastructure and PDC is - but i'm not having any luck I've been googling for nearly a day and all solutions lead to "the cake is a lie" Your ninja skills will be greatly appreciated

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  • C#/.NET Little Wonders: The Useful But Overlooked Sets

    - by James Michael Hare
    Once again we consider some of the lesser known classes and keywords of C#.  Today we will be looking at two set implementations in the System.Collections.Generic namespace: HashSet<T> and SortedSet<T>.  Even though most people think of sets as mathematical constructs, they are actually very useful classes that can be used to help make your application more performant if used appropriately. A Background From Math In mathematical terms, a set is an unordered collection of unique items.  In other words, the set {2,3,5} is identical to the set {3,5,2}.  In addition, the set {2, 2, 4, 1} would be invalid because it would have a duplicate item (2).  In addition, you can perform set arithmetic on sets such as: Intersections: The intersection of two sets is the collection of elements common to both.  Example: The intersection of {1,2,5} and {2,4,9} is the set {2}. Unions: The union of two sets is the collection of unique items present in either or both set.  Example: The union of {1,2,5} and {2,4,9} is {1,2,4,5,9}. Differences: The difference of two sets is the removal of all items from the first set that are common between the sets.  Example: The difference of {1,2,5} and {2,4,9} is {1,5}. Supersets: One set is a superset of a second set if it contains all elements that are in the second set. Example: The set {1,2,5} is a superset of {1,5}. Subsets: One set is a subset of a second set if all the elements of that set are contained in the first set. Example: The set {1,5} is a subset of {1,2,5}. If We’re Not Doing Math, Why Do We Care? Now, you may be thinking: why bother with the set classes in C# if you have no need for mathematical set manipulation?  The answer is simple: they are extremely efficient ways to determine ownership in a collection. For example, let’s say you are designing an order system that tracks the price of a particular equity, and once it reaches a certain point will trigger an order.  Now, since there’s tens of thousands of equities on the markets, you don’t want to track market data for every ticker as that would be a waste of time and processing power for symbols you don’t have orders for.  Thus, we just want to subscribe to the stock symbol for an equity order only if it is a symbol we are not already subscribed to. Every time a new order comes in, we will check the list of subscriptions to see if the new order’s stock symbol is in that list.  If it is, great, we already have that market data feed!  If not, then and only then should we subscribe to the feed for that symbol. So far so good, we have a collection of symbols and we want to see if a symbol is present in that collection and if not, add it.  This really is the essence of set processing, but for the sake of comparison, let’s say you do a list instead: 1: // class that handles are order processing service 2: public sealed class OrderProcessor 3: { 4: // contains list of all symbols we are currently subscribed to 5: private readonly List<string> _subscriptions = new List<string>(); 6:  7: ... 8: } Now whenever you are adding a new order, it would look something like: 1: public PlaceOrderResponse PlaceOrder(Order newOrder) 2: { 3: // do some validation, of course... 4:  5: // check to see if already subscribed, if not add a subscription 6: if (!_subscriptions.Contains(newOrder.Symbol)) 7: { 8: // add the symbol to the list 9: _subscriptions.Add(newOrder.Symbol); 10: 11: // do whatever magic is needed to start a subscription for the symbol 12: } 13:  14: // place the order logic! 15: } What’s wrong with this?  In short: performance!  Finding an item inside a List<T> is a linear - O(n) – operation, which is not a very performant way to find if an item exists in a collection. (I used to teach algorithms and data structures in my spare time at a local university, and when you began talking about big-O notation you could immediately begin to see eyes glossing over as if it was pure, useless theory that would not apply in the real world, but I did and still do believe it is something worth understanding well to make the best choices in computer science). Let’s think about this: a linear operation means that as the number of items increases, the time that it takes to perform the operation tends to increase in a linear fashion.  Put crudely, this means if you double the collection size, you might expect the operation to take something like the order of twice as long.  Linear operations tend to be bad for performance because they mean that to perform some operation on a collection, you must potentially “visit” every item in the collection.  Consider finding an item in a List<T>: if you want to see if the list has an item, you must potentially check every item in the list before you find it or determine it’s not found. Now, we could of course sort our list and then perform a binary search on it, but sorting is typically a linear-logarithmic complexity – O(n * log n) - and could involve temporary storage.  So performing a sort after each add would probably add more time.  As an alternative, we could use a SortedList<TKey, TValue> which sorts the list on every Add(), but this has a similar level of complexity to move the items and also requires a key and value, and in our case the key is the value. This is why sets tend to be the best choice for this type of processing: they don’t rely on separate keys and values for ordering – so they save space – and they typically don’t care about ordering – so they tend to be extremely performant.  The .NET BCL (Base Class Library) has had the HashSet<T> since .NET 3.5, but at that time it did not implement the ISet<T> interface.  As of .NET 4.0, HashSet<T> implements ISet<T> and a new set, the SortedSet<T> was added that gives you a set with ordering. HashSet<T> – For Unordered Storage of Sets When used right, HashSet<T> is a beautiful collection, you can think of it as a simplified Dictionary<T,T>.  That is, a Dictionary where the TKey and TValue refer to the same object.  This is really an oversimplification, but logically it makes sense.  I’ve actually seen people code a Dictionary<T,T> where they store the same thing in the key and the value, and that’s just inefficient because of the extra storage to hold both the key and the value. As it’s name implies, the HashSet<T> uses a hashing algorithm to find the items in the set, which means it does take up some additional space, but it has lightning fast lookups!  Compare the times below between HashSet<T> and List<T>: Operation HashSet<T> List<T> Add() O(1) O(1) at end O(n) in middle Remove() O(1) O(n) Contains() O(1) O(n)   Now, these times are amortized and represent the typical case.  In the very worst case, the operations could be linear if they involve a resizing of the collection – but this is true for both the List and HashSet so that’s a less of an issue when comparing the two. The key thing to note is that in the general case, HashSet is constant time for adds, removes, and contains!  This means that no matter how large the collection is, it takes roughly the exact same amount of time to find an item or determine if it’s not in the collection.  Compare this to the List where almost any add or remove must rearrange potentially all the elements!  And to find an item in the list (if unsorted) you must search every item in the List. So as you can see, if you want to create an unordered collection and have very fast lookup and manipulation, the HashSet is a great collection. And since HashSet<T> implements ICollection<T> and IEnumerable<T>, it supports nearly all the same basic operations as the List<T> and can use the System.Linq extension methods as well. All we have to do to switch from a List<T> to a HashSet<T>  is change our declaration.  Since List and HashSet support many of the same members, chances are we won’t need to change much else. 1: public sealed class OrderProcessor 2: { 3: private readonly HashSet<string> _subscriptions = new HashSet<string>(); 4:  5: // ... 6:  7: public PlaceOrderResponse PlaceOrder(Order newOrder) 8: { 9: // do some validation, of course... 10: 11: // check to see if already subscribed, if not add a subscription 12: if (!_subscriptions.Contains(newOrder.Symbol)) 13: { 14: // add the symbol to the list 15: _subscriptions.Add(newOrder.Symbol); 16: 17: // do whatever magic is needed to start a subscription for the symbol 18: } 19: 20: // place the order logic! 21: } 22:  23: // ... 24: } 25: Notice, we didn’t change any code other than the declaration for _subscriptions to be a HashSet<T>.  Thus, we can pick up the performance improvements in this case with minimal code changes. SortedSet<T> – Ordered Storage of Sets Just like HashSet<T> is logically similar to Dictionary<T,T>, the SortedSet<T> is logically similar to the SortedDictionary<T,T>. The SortedSet can be used when you want to do set operations on a collection, but you want to maintain that collection in sorted order.  Now, this is not necessarily mathematically relevant, but if your collection needs do include order, this is the set to use. So the SortedSet seems to be implemented as a binary tree (possibly a red-black tree) internally.  Since binary trees are dynamic structures and non-contiguous (unlike List and SortedList) this means that inserts and deletes do not involve rearranging elements, or changing the linking of the nodes.  There is some overhead in keeping the nodes in order, but it is much smaller than a contiguous storage collection like a List<T>.  Let’s compare the three: Operation HashSet<T> SortedSet<T> List<T> Add() O(1) O(log n) O(1) at end O(n) in middle Remove() O(1) O(log n) O(n) Contains() O(1) O(log n) O(n)   The MSDN documentation seems to indicate that operations on SortedSet are O(1), but this seems to be inconsistent with its implementation and seems to be a documentation error.  There’s actually a separate MSDN document (here) on SortedSet that indicates that it is, in fact, logarithmic in complexity.  Let’s put it in layman’s terms: logarithmic means you can double the collection size and typically you only add a single extra “visit” to an item in the collection.  Take that in contrast to List<T>’s linear operation where if you double the size of the collection you double the “visits” to items in the collection.  This is very good performance!  It’s still not as performant as HashSet<T> where it always just visits one item (amortized), but for the addition of sorting this is a good thing. Consider the following table, now this is just illustrative data of the relative complexities, but it’s enough to get the point: Collection Size O(1) Visits O(log n) Visits O(n) Visits 1 1 1 1 10 1 4 10 100 1 7 100 1000 1 10 1000   Notice that the logarithmic – O(log n) – visit count goes up very slowly compare to the linear – O(n) – visit count.  This is because since the list is sorted, it can do one check in the middle of the list, determine which half of the collection the data is in, and discard the other half (binary search).  So, if you need your set to be sorted, you can use the SortedSet<T> just like the HashSet<T> and gain sorting for a small performance hit, but it’s still faster than a List<T>. Unique Set Operations Now, if you do want to perform more set-like operations, both implementations of ISet<T> support the following, which play back towards the mathematical set operations described before: IntersectWith() – Performs the set intersection of two sets.  Modifies the current set so that it only contains elements also in the second set. UnionWith() – Performs a set union of two sets.  Modifies the current set so it contains all elements present both in the current set and the second set. ExceptWith() – Performs a set difference of two sets.  Modifies the current set so that it removes all elements present in the second set. IsSupersetOf() – Checks if the current set is a superset of the second set. IsSubsetOf() – Checks if the current set is a subset of the second set. For more information on the set operations themselves, see the MSDN description of ISet<T> (here). What Sets Don’t Do Don’t get me wrong, sets are not silver bullets.  You don’t really want to use a set when you want separate key to value lookups, that’s what the IDictionary implementations are best for. Also sets don’t store temporal add-order.  That is, if you are adding items to the end of a list all the time, your list is ordered in terms of when items were added to it.  This is something the sets don’t do naturally (though you could use a SortedSet with an IComparer with a DateTime but that’s overkill) but List<T> can. Also, List<T> allows indexing which is a blazingly fast way to iterate through items in the collection.  Iterating over all the items in a List<T> is generally much, much faster than iterating over a set. Summary Sets are an excellent tool for maintaining a lookup table where the item is both the key and the value.  In addition, if you have need for the mathematical set operations, the C# sets support those as well.  The HashSet<T> is the set of choice if you want the fastest possible lookups but don’t care about order.  In contrast the SortedSet<T> will give you a sorted collection at a slight reduction in performance.   Technorati Tags: C#,.Net,Little Wonders,BlackRabbitCoder,ISet,HashSet,SortedSet

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  • CRUD operations; do you notify whether the insert,update etc. went well ?

    - by danielovich
    Hi guys. I have a simple question for you (i hope) :) I have pretty much always used void as a "return" type when doing CRUD operations on data. Eg. Consider this code: public void Insert(IAuctionItem item) { if (item == null) { AuctionLogger.LogException(new ArgumentNullException("item is null")); } _dataStore.DataContext.AuctionItems.InsertOnSubmit((AuctionItem)item); _dataStore.DataContext.SubmitChanges(); } and then considen this code: public bool Insert(IAuctionItem item) { if (item == null) { AuctionLogger.LogException(new ArgumentNullException("item is null")); } _dataStore.DataContext.AuctionItems.InsertOnSubmit((AuctionItem)item); _dataStore.DataContext.SubmitChanges(); return true; } It actually just comes down to whether you should notify that something was inserted (and went well) or not ?

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  • Transactional Messaging in the Windows Azure Service Bus

    - by Alan Smith
    Introduction I’m currently working on broadening the content in the Windows Azure Service Bus Developer Guide. One of the features I have been looking at over the past week is the support for transactional messaging. When using the direct programming model and the WCF interface some, but not all, messaging operations can participate in transactions. This allows developers to improve the reliability of messaging systems. There are some limitations in the transactional model, transactions can only include one top level messaging entity (such as a queue or topic, subscriptions are no top level entities), and transactions cannot include other systems, such as databases. As the transaction model is currently not well documented I have had to figure out how things work through experimentation, with some help from the development team to confirm any questions I had. Hopefully I’ve got the content mostly correct, I will update the content in the e-book if I find any errors or improvements that can be made (any feedback would be very welcome). I’ve not had a chance to look into the code for transactions and asynchronous operations, maybe that would make a nice challenge lab for my Windows Azure Service Bus course. Transactional Messaging Messaging entities in the Windows Azure Service Bus provide support for participation in transactions. This allows developers to perform several messaging operations within a transactional scope, and ensure that all the actions are committed or, if there is a failure, none of the actions are committed. There are a number of scenarios where the use of transactions can increase the reliability of messaging systems. Using TransactionScope In .NET the TransactionScope class can be used to perform a series of actions in a transaction. The using declaration is typically used de define the scope of the transaction. Any transactional operations that are contained within the scope can be committed by calling the Complete method. If the Complete method is not called, any transactional methods in the scope will not commit.   // Create a transactional scope. using (TransactionScope scope = new TransactionScope()) {     // Do something.       // Do something else.       // Commit the transaction.     scope.Complete(); }     In order for methods to participate in the transaction, they must provide support for transactional operations. Database and message queue operations typically provide support for transactions. Transactions in Brokered Messaging Transaction support in Service Bus Brokered Messaging allows message operations to be performed within a transactional scope; however there are some limitations around what operations can be performed within the transaction. In the current release, only one top level messaging entity, such as a queue or topic can participate in a transaction, and the transaction cannot include any other transaction resource managers, making transactions spanning a messaging entity and a database not possible. When sending messages, the send operations can participate in a transaction allowing multiple messages to be sent within a transactional scope. This allows for “all or nothing” delivery of a series of messages to a single queue or topic. When receiving messages, messages that are received in the peek-lock receive mode can be completed, deadlettered or deferred within a transactional scope. In the current release the Abandon method will not participate in a transaction. The same restrictions of only one top level messaging entity applies here, so the Complete method can be called transitionally on messages received from the same queue, or messages received from one or more subscriptions in the same topic. Sending Multiple Messages in a Transaction A transactional scope can be used to send multiple messages to a queue or topic. This will ensure that all the messages will be enqueued or, if the transaction fails to commit, no messages will be enqueued.     An example of the code used to send 10 messages to a queue as a single transaction from a console application is shown below.   QueueClient queueClient = messagingFactory.CreateQueueClient(Queue1);   Console.Write("Sending");   // Create a transaction scope. using (TransactionScope scope = new TransactionScope()) {     for (int i = 0; i < 10; i++)     {         // Send a message         BrokeredMessage msg = new BrokeredMessage("Message: " + i);         queueClient.Send(msg);         Console.Write(".");     }     Console.WriteLine("Done!");     Console.WriteLine();       // Should we commit the transaction?     Console.WriteLine("Commit send 10 messages? (yes or no)");     string reply = Console.ReadLine();     if (reply.ToLower().Equals("yes"))     {         // Commit the transaction.         scope.Complete();     } } Console.WriteLine(); messagingFactory.Close();     The transaction scope is used to wrap the sending of 10 messages. Once the messages have been sent the user has the option to either commit the transaction or abandon the transaction. If the user enters “yes”, the Complete method is called on the scope, which will commit the transaction and result in the messages being enqueued. If the user enters anything other than “yes”, the transaction will not commit, and the messages will not be enqueued. Receiving Multiple Messages in a Transaction The receiving of multiple messages is another scenario where the use of transactions can improve reliability. When receiving a group of messages that are related together, maybe in the same message session, it is possible to receive the messages in the peek-lock receive mode, and then complete, defer, or deadletter the messages in one transaction. (In the current version of Service Bus, abandon is not transactional.)   The following code shows how this can be achieved. using (TransactionScope scope = new TransactionScope()) {       while (true)     {         // Receive a message.         BrokeredMessage msg = q1Client.Receive(TimeSpan.FromSeconds(1));         if (msg != null)         {             // Wrote message body and complete message.             string text = msg.GetBody<string>();             Console.WriteLine("Received: " + text);             msg.Complete();         }         else         {             break;         }     }     Console.WriteLine();       // Should we commit?     Console.WriteLine("Commit receive? (yes or no)");     string reply = Console.ReadLine();     if (reply.ToLower().Equals("yes"))     {         // Commit the transaction.         scope.Complete();     }     Console.WriteLine(); }     Note that if there are a large number of messages to be received, there will be a chance that the transaction may time out before it can be committed. It is possible to specify a longer timeout when the transaction is created, but It may be better to receive and commit smaller amounts of messages within the transaction. It is also possible to complete, defer, or deadletter messages received from more than one subscription, as long as all the subscriptions are contained in the same topic. As subscriptions are not top level messaging entities this scenarios will work. The following code shows how this can be achieved. try {     using (TransactionScope scope = new TransactionScope())     {         // Receive one message from each subscription.         BrokeredMessage msg1 = subscriptionClient1.Receive();         BrokeredMessage msg2 = subscriptionClient2.Receive();           // Complete the message receives.         msg1.Complete();         msg2.Complete();           Console.WriteLine("Msg1: " + msg1.GetBody<string>());         Console.WriteLine("Msg2: " + msg2.GetBody<string>());           // Commit the transaction.         scope.Complete();     } } catch (Exception ex) {     Console.WriteLine(ex.Message); }     Unsupported Scenarios The restriction of only one top level messaging entity being able to participate in a transaction makes some useful scenarios unsupported. As the Windows Azure Service Bus is under continuous development and new releases are expected to be frequent it is possible that this restriction may not be present in future releases. The first is the scenario where messages are to be routed to two different systems. The following code attempts to do this.   try {     // Create a transaction scope.     using (TransactionScope scope = new TransactionScope())     {         BrokeredMessage msg1 = new BrokeredMessage("Message1");         BrokeredMessage msg2 = new BrokeredMessage("Message2");           // Send a message to Queue1         Console.WriteLine("Sending Message1");         queue1Client.Send(msg1);           // Send a message to Queue2         Console.WriteLine("Sending Message2");         queue2Client.Send(msg2);           // Commit the transaction.         Console.WriteLine("Committing transaction...");         scope.Complete();     } } catch (Exception ex) {     Console.WriteLine(ex.Message); }     The results of running the code are shown below. When attempting to send a message to the second queue the following exception is thrown: No active Transaction was found for ID '35ad2495-ee8a-4956-bbad-eb4fedf4a96e:1'. The Transaction may have timed out or attempted to span multiple top-level entities such as Queue or Topic. The server Transaction timeout is: 00:01:00..TrackingId:947b8c4b-7754-4044-b91b-4a959c3f9192_3_3,TimeStamp:3/29/2012 7:47:32 AM.   Another scenario where transactional support could be useful is when forwarding messages from one queue to another queue. This would also involve more than one top level messaging entity, and is therefore not supported.   Another scenario that developers may wish to implement is performing transactions across messaging entities and other transactional systems, such as an on-premise database. In the current release this is not supported.   Workarounds for Unsupported Scenarios There are some techniques that developers can use to work around the one top level entity limitation of transactions. When sending two messages to two systems, topics and subscriptions can be used. If the same message is to be sent to two destinations then the subscriptions would have the default subscriptions, and the client would only send one message. If two different messages are to be sent, then filters on the subscriptions can route the messages to the appropriate destination. The client can then send the two messages to the topic in the same transaction.   In scenarios where a message needs to be received and then forwarded to another system within the same transaction topics and subscriptions can also be used. A message can be received from a subscription, and then sent to a topic within the same transaction. As a topic is a top level messaging entity, and a subscription is not, this scenario will work.

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  • Agile Development Requires Agile Support

    - by Matt Watson
    Agile developmentAgile development has become the standard methodology for application development. The days of long term planning with giant Gantt waterfall charts and detailed requirements is fading away. For years the product planning process frustrated product owners and businesses because no matter the plan, nothing ever went to plan. Agile development throws the detailed planning out the window and instead focuses on giving developers some basic requirements and pointing them in the right direction. Constant collaboration via quick iterations with the end users, product owners, and the development team helps ensure the project is done correctly.  The various agile development methodologies have helped greatly with creating products faster, but not without causing new problems. Complicated application deployments now occur weekly or monthly. Most of the products are web-based and deployed as a software service model. System performance and availability of these apps becomes mission critical. This is all much different from the old process of mailing new releases of client-server apps on CD once per quarter or year.The steady stream of new products and product enhancements puts a lot of pressure on IT operations to keep up with the software deployments and adding infrastructure capacity. The problem is most operations teams still move slowly thanks to change orders, documentation, procedures, testing and other processes. Operations can slow the process down and push back on the development team in some organizations. The DevOps movement is trying to solve some of these problems by integrating the development and operations teams more together. Rapid change introduces new problemsThe rapid product change ultimately creates some application problems along the way. Higher rates of change increase the likelihood of new application defects. Delivering applications as a software service also means that scalability of applications is critical. Development teams struggle to keep up with application defects and scalability concerns in their applications. Fixing application problems is a never ending job for agile development teams. Fixing problems before your customers do and fixing them quickly is critical. Most companies really struggle with this due to the divide between the development and operations groups. Fixing application problems typically requires querying databases, looking at log files, reviewing config files, reviewing error logs and other similar tasks. It becomes difficult to work on new features when your lead developers are working on defects from the last product version. Developers need more visibilityThe problem is most developers are not given access to see server and application information in the production environments. The operations team doesn’t trust giving all the developers the keys to the kingdom to log in to production and poke around the servers. The challenge is either give them no access, or potentially too much access. Those with access can still waste time figuring out the location of the application and how to connect to it over VPN. In addition, reproducing problems in test environments takes too much time and isn't always possible. System administrators spend a lot of time helping developers track down server information. Most companies give key developers access to all of the production resources so they can help resolve application defects. The problem is only those key people have access and they become a bottleneck. They end up spending 25-50% of their time on a daily basis trying to solve application issues because they are the only ones with access. These key employees’ time is best spent on strategic new projects, not addressing application defects. This job should fall to entry level developers, provided they have access to all the information they need to troubleshoot the problems.The solution to agile application support is giving all the developers limited access to the production environment and all the server information they need to see. Some companies create their own solutions internally to collect log files, centralize errors or other things to address the problem. Some developers even have access to server monitoring or other tools. But they key is giving them access to everything they need so they can see the full picture and giving access to the whole team. Giving access to everyone scales up the application support team and creates collaboration around providing improved application support.Stackify enables agile application supportStackify has created a solution that can give all developers a secure and read only view of the entire production server environment without console or remote desktop access.They provide a web application that provides real time visibility to the important information that developers need to see. An application centric view enables them to see all of their apps across multiple datacenters and environments. They don’t need to know where the application is deployed, just the name of the application to find it and dig in to see more. All your developers can see server health, application health, log files, config files, windows event viewer, deployment history, application notes, and much more. They can receive email and text alerts when problems arise and even safely query your production databases.Stackify enables companies that do agile development to scale up their application support team by getting more team members involved. The lead developers can spend more time on new projects. Application issues can be fixed quicker than ever. Operations can spend less time helping developers collect server information. Agile application support starts with Stackify. Visit Stackify.com to learn more.

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  • Interesting interview question. .Net

    - by rahul
    Coding Problem NumTrans There is an integer K. You are allowed to add to K any of its divisors not equal to 1and K. The same operation can be applied to the resulting number and so on. Notice that starting from the number 4, we can reach any composite number by applying several such operations. For example, the number 24 can be reached starting from 4 using 5 operations: 468121824 You will solve a more general problem. Given integers n and m, return the minimal number of the described operations necessary to transform n into m. Return -1 if m can't be obtained from n. Definition Method signature: int GetLeastCount (int n, int m) Constraints N will be between 4 and 100000, inclusive. M will be between N and 100000, inclusive. Examples 1) 4 576 Returns: 14 The shortest order of operations is: 468121827365481108162243324432576 2) 8748 83462 Returns: 10 The shortest order of operations is: 874813122196832624439366590497873283106834488346083462 3) 4 99991 Returns: -1 The number 99991 can't be obtained because it’s prime!

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  • How to achieve this site structure?

    - by Sushant
    Hi, I need to develop a website that looks like this. In central administration however, in the operations tab, It shows Central Administration-- Operations. But I checked, operations is not a subsite. Then what is it. In my application, I always get Home-- Operations. To add to trouble,it changes the name at the top as Operations. I need to keep it central administration only. Please help me sort this out. Thanks.

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  • Asynchrony in C# 5 (Part I)

    - by javarg
    I’ve been playing around with the new Async CTP preview available for download from Microsoft. It’s amazing how language trends are influencing the evolution of Microsoft’s developing platform. Much effort is being done at language level today than previous versions of .NET. In these post series I’ll review some major features contained in this release: Asynchronous functions TPL Dataflow Task based asynchronous Pattern Part I: Asynchronous Functions This is a mean of expressing asynchronous operations. This kind of functions must return void or Task/Task<> (functions returning void let us implement Fire & Forget asynchronous operations). The two new keywords introduced are async and await. async: marks a function as asynchronous, indicating that some part of its execution may take place some time later (after the method call has returned). Thus, all async functions must include some kind of asynchronous operations. This keyword on its own does not make a function asynchronous thought, its nature depends on its implementation. await: allows us to define operations inside a function that will be awaited for continuation (more on this later). Async function sample: Async/Await Sample async void ShowDateTimeAsync() {     while (true)     {         var client = new ServiceReference1.Service1Client();         var dt = await client.GetDateTimeTaskAsync();         Console.WriteLine("Current DateTime is: {0}", dt);         await TaskEx.Delay(1000);     } } The previous sample is a typical usage scenario for these new features. Suppose we query some external Web Service to get data (in this case the current DateTime) and we do so at regular intervals in order to refresh user’s UI. Note the async and await functions working together. The ShowDateTimeAsync method indicate its asynchronous nature to the caller using the keyword async (that it may complete after returning control to its caller). The await keyword indicates the flow control of the method will continue executing asynchronously after client.GetDateTimeTaskAsync returns. The latter is the most important thing to understand about the behavior of this method and how this actually works. The flow control of the method will be reconstructed after any asynchronous operation completes (specified with the keyword await). This reconstruction of flow control is the real magic behind the scene and it is done by C#/VB compilers. Note how we didn’t use any of the regular existing async patterns and we’ve defined the method very much like a synchronous one. Now, compare the following code snippet  in contrast to the previuous async/await: Traditional UI Async void ComplicatedShowDateTime() {     var client = new ServiceReference1.Service1Client();     client.GetDateTimeCompleted += (s, e) =>     {         Console.WriteLine("Current DateTime is: {0}", e.Result);         client.GetDateTimeAsync();     };     client.GetDateTimeAsync(); } The previous implementation is somehow similar to the first shown, but more complicated. Note how the while loop is implemented as a chained callback to the same method (client.GetDateTimeAsync) inside the event handler (please, do not do this in your own application, this is just an example).  How it works? Using an state workflow (or jump table actually), the compiler expands our code and create the necessary steps to execute it, resuming pending operations after any asynchronous one. The intention of the new Async/Await pattern is to let us think and code as we normally do when designing and algorithm. It also allows us to preserve the logical flow control of the program (without using any tricky coding patterns to accomplish this). The compiler will then create the necessary workflow to execute operations as the happen in time.

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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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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  • Questions re: Eclipse Jobs API

    - by BenCole
    Similar to http://stackoverflow.com/questions/8738160/eclipse-jobs-api-for-a-stand-alone-swing-project This question mentions the Jobs API from the Eclipse IDE: ...The disadvantage of the pre-3.0 approach was that the user had to wait until an operation completed before the UI became responsive again. The UI still provided the user the ability to cancel the currently running operation but no other work could be done until the operation completed. Some operations were performed in the background (resource decoration and JDT file indexing are two such examples) but these operations were restricted in the sense that they could not modify the workspace. If a background operation did try to modify the workspace, the UI thread would be blocked if the user explicitly performed an operation that modified the workspace and, even worse, the user would not be able to cancel the operation. A further complication with concurrency was that the interaction between the independent locking mechanisms of different plug-ins often resulted in deadlock situations. Because of the independent nature of the locks, there was no way for Eclipse to recover from the deadlock, which forced users to kill the application... ...The functionality provided by the workspace locking mechanism can be broken down into the following three aspects: Resource locking to ensure multiple operations did not concurrently modify the same resource Resource change batching to ensure UI stability during an operation Identification of an appropriate time to perform incremental building With the introduction of the Jobs API, these areas have been divided into separate mechanisms and a few additional facilities have been added. The following list summarizes the facilities added. Job class: support for performing operations or other work in the background. ISchedulingRule interface: support for determining which jobs can run concurrently. WorkspaceJob and two IWorkspace#run() methods: support for batching of delta change notifications. Background auto-build: running of incremental build at a time when no other running operations are affecting resources. ILock interface: support for deadlock detection and recovery. Job properties for configuring user feedback for jobs run in the background. The rest of this article provides examples of how to use the above-mentioned facilities... In regards to above API, is this an implementation of a particular design pattern? Which one?

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  • CodePlex Daily Summary for Thursday, January 06, 2011

    CodePlex Daily Summary for Thursday, January 06, 2011Popular ReleasesStyleCop for ReSharper: StyleCop for ReSharper 5.1.14980.000: A considerable amount of work has gone into this release: Huge focus on performance around the violation scanning subsystem: - caching added to reduce IO operations around reading and merging of settings files - caching added to reduce creation of expensive objects Users should notice condsiderable perf boost and a decrease in memory usage. Bug Fixes: - StyleCop's new ObjectBasedEnvironment object does not resolve the StyleCop installation path, thus it does not return the correct path ...VivoSocial: VivoSocial 7.4.1: New release with bug fixes and updates for performance.SSH.NET Library: 2011.1.6: Fixes CommandTimeout default value is fixed to infinite. Port Forwarding feature improvements Memory leaks fixes New Features Add ErrorOccurred event to handle errors that occurred on different thread New and improve SFTP features SftpFile now has more attributes and some operations Most standard operations now available Allow specify encoding for command execution KeyboardInteractiveConnectionInfo class added for "keyboard-interactive" authentication. Add ability to specify bo...UltimateJB: Ultimate JB 2.03 PL3 KAKAROTO: Voici une version attendu avec impatience pour beaucoup : - La version PL3 KAKAROTO intégre ses dernières modification et intégre maintenant le firmware 2.43 !!! Conclusion : - ultimateJB DEFAULT => Pas de spoof mais disponible pour les PS3 suivantes : 3.41_kiosk 3.41 3.40 3.30 3.21 3.15 3.10 3.01 2.76 2.70 2.60 2.53 2.43.NET Extensions - Extension Methods Library for C# and VB.NET: Release 2011.03: Added lot's of new extensions and new projects for MVC and Entity Framework. object.FindTypeByRecursion Int32.InRange String.RemoveAllSpecialCharacters String.IsEmptyOrWhiteSpace String.IsNotEmptyOrWhiteSpace String.IfEmptyOrWhiteSpace String.ToUpperFirstLetter String.GetBytes String.ToTitleCase String.ToPlural DateTime.GetDaysInYear DateTime.GetPeriodOfDay IEnumberable.RemoveAll IEnumberable.Distinct ICollection.RemoveAll IList.Join IList.Match IList.Cast Array.IsNullOrEmpty Array.W...VidCoder: 0.8.0: Added x64 version. Made the audio output preview more detailed and accurate. If the chosen encoder or mixdown is incompatible with the source, the fallback that will be used is displayed. Added "Auto" to the audio mixdown choices. Reworked non-anamorphic size calculation to work better with non-standard pixel aspect ratios and cropping. Reworked Custom anamorphic to be more intuitive and allow display width to be set automatically (Thanks, Statick). Allowing higher bitrates for 6-ch....NET Voice Recorder: Auto-Tune Release: This is the source code and binaries to accompany the article on the Coding 4 Fun website. It is the Auto Tuner release of the .NET Voice Recorder application.BloodSim: BloodSim - 1.3.2.0: - Simulation Log is now automatically disabled and hidden when running 10 or more iterations - Hit and Expertise are now entered by Rating, and include option for a Racial Expertise bonus - Added option for boss to use a periodic magic ability (Dragon Breath) - Added option for boss to periodically Enrage, gaining a Damage/Attack Speed buffASP.NET MVC CMS ( Using CommonLibrary.NET ): CommonLibrary.NET CMS 0.9.5 Alpha: CommonLibrary CMSA simple yet powerful CMS system in ASP.NET MVC 2 using C# 4.0. ActiveRecord based components for Blogs, Widgets, Pages, Parts, Events, Feedback, BlogRolls, Links Includes several widgets ( tag cloud, archives, recent, user cloud, links twitter, blog roll and more ) Built using the http://commonlibrarynet.codeplex.com framework. ( Uses TDD, DDD, Models/Entities, Code Generation ) Can run w/ In-Memory Repositories or Sql Server Database See Documentation tab for Ins...EnhSim: EnhSim 2.2.9 BETA: 2.2.9 BETAThis release supports WoW patch 4.03a at level 85 To use this release, you must have the Microsoft Visual C++ 2010 Redistributable Package installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=A7B7A05E-6DE6-4D3A-A423-37BF0912DB84 To use the GUI you must have the .NET 4.0 Framework installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=9cfb2d51-5ff4-4491-b0e5-b386f32c0992 - Added in the Gobl...xUnit.net - Unit Testing for .NET: xUnit.net 1.7 Beta: xUnit.net release 1.7 betaBuild #1533 Important notes for Resharper users: Resharper support has been moved to the xUnit.net Contrib project. Important note for TestDriven.net users: If you are having issues running xUnit.net tests in TestDriven.net, especially on 64-bit Windows, we strongly recommend you upgrade to TD.NET version 3.0 or later. This release adds the following new features: Added support for ASP.NET MVC 3 Added Assert.Equal(double expected, double actual, int precision)...Json.NET: Json.NET 4.0 Release 1: New feature - Added Windows Phone 7 project New feature - Added dynamic support to LINQ to JSON New feature - Added dynamic support to serializer New feature - Added INotifyCollectionChanged to JContainer in .NET 4 build New feature - Added ReadAsDateTimeOffset to JsonReader New feature - Added ReadAsDecimal to JsonReader New feature - Added covariance to IJEnumerable type parameter New feature - Added XmlSerializer style Specified property support New feature - Added ...DbDocument: DbDoc Initial Version: DbDoc Initial versionASP .NET MVC CMS (Content Management System): Atomic CMS 2.1.2: Atomic CMS 2.1.2 release notes Atomic CMS installation guide N2 CMS: 2.1: N2 is a lightweight CMS framework for ASP.NET. It helps you build great web sites that anyone can update. Major Changes Support for auto-implemented properties ({get;set;}, based on contribution by And Poulsen) All-round improvements and bugfixes File manager improvements (multiple file upload, resize images to fit) New image gallery Infinite scroll paging on news Content templates First time with N2? Try the demo site Download one of the template packs (above) and open the proj...Wii Backup Fusion: Wii Backup Fusion 1.0: - Norwegian translation - French translation - German translation - WBFS dump for analysis - Scalable full HQ cover - Support for log file - Load game images improved - Support for image splitting - Diff for images after transfer - Support for scrubbing modes - Search functionality for log - Recurse depth for Files/Load - Show progress while downloading game cover - Supports more databases for cover download - Game cover loading routines improvedAutoLoL: AutoLoL v1.5.1: Fix: Fixed a bug where pressing Save As would not select the Mastery Directory by default Unexpected errors are now always reported to the user before closing AutoLoL down.* Extracted champion data to Data directory** Added disclaimer to notify users this application has nothing to do with Riot Games Inc. Updated Codeplex image * An error report will be shown to the user which can help the developers to find out what caused the error, this should improve support ** We are working on ...TortoiseHg: TortoiseHg 1.1.8: TortoiseHg 1.1.8 is a minor bug fix release, with minor improvementsBlogEngine.NET: BlogEngine.NET 2.0: Get DotNetBlogEngine for 3 Months Free! Click Here for More Info 3 Months FREE – BlogEngine.NET Hosting – Click Here! If you want to set up and start using BlogEngine.NET right away, you should download the Web project. If you want to extend or modify BlogEngine.NET, you should download the source code. If you are upgrading from a previous version of BlogEngine.NET, please take a look at the Upgrading to BlogEngine.NET 2.0 instructions. To get started, be sure to check out our installatio...Free Silverlight & WPF Chart Control - Visifire: Visifire SL and WPF Charts v3.6.6 Released: Hi, Today we are releasing final version of Visifire, v3.6.6 with the following new feature: * TextDecorations property is implemented in Title for Chart. * TitleTextDecorations property is implemented in Axis. * MinPointHeight property is now applicable for Column and Bar Charts. Also this release includes few bug fixes: * ToolTipText property of DataSeries was not getting applied from Style. * Chart threw exception if IndicatorEnabled property was set to true and Too...New Projects.NET Framework Extensions Packages: Lightweight NuGet packages with reusable source code extending core .NET functionality, typically in self-contained source files added to your projects as internal classes that can be easily kept up-to-date with NuGet..NET Random Mock Extensions: .NET Random Mock Extensions allow to generate by 1 line of code object implementing any interface or class and fill its properties with random values. This can be usefull for generating test data objects for View or unit testing while you have no real domain object model.ancc: anccASP.NET Social Controls: ASP.NET Social Controls is a small collection of server controls designed to make integrating social sharing utilities such as ShareThis, AddThis and AddToAny easier, more manageable, and X/HTML-compliant, with configuration files and per-instance settings.Autofac for WindowsPhone7: This project hosts the releases for Autofac built for WindowsPhone7AutoSensitivity: AutoSensitivity allows you to define different mouse sensitivities (speeds) for your tocuhpad and mouse and automatically switch between them (based on mouse connect / disconnect).BaseCode: basecodeCaliburn Micro Silverlight Navigation: Caliburn Micro Silverlight Navigation adds navigation to Caliburn Micro UI Framework by applying the ViewModel-First principle. Debian 5 Agent for System Center Operations Manager 2007 R2: Debian 5 System Center Operations Manager 2007 R2 Agent. Debian 5 Management Pack For System Center Operations Manager 2007 R2: Debian 5 Management Pack for SCOM 2007 R2. It will be useless without the Agent (in another project).Eventbrite Helper for WebMatrix: The Eventbrite Helper for WebMatrix makes it simple to promote your Eventbrite events in your WebMatrix site. With a few lines of code you will be able to display your events on your web site with integration with Windows Live Calendar and Google Calendar.Eye Check: EyeCheck is an eye health testing project. It contains a set of tests to examine eye health. It's developed in C# using the Silverlight technology.Hooly Search: This ASP.NET project lets you browse through and search text within holy booksIssueVision.ST: A Silverlight LOB sample using Self-tracking Entities, WCF Services, WIF, MVVM Light toolkit, MEF, and T4 Templates.Lawyer Officer: Projeto desenvolvido como meu trabalho de conclusão de curso para formação em bacharelado em sistemas da informação da FATEF-São VicenteLINQtoROOT: Translates LINQ queries from the .NET world in to CERN's ROOT language (C++) and then runs them (locally or on a PROOF server).OA: ??????????Open Manuscript System: Open Manuscript Systems (OMS) is a research journal management and publishing system with manuscript tracking that has been developed in this project to expand and improve access to research.ProjectCNPM_Vinhlt_Teacher: Ðây là b?n CNPM demo c?a nhóm 6,K52a3 HUS VN. b?n demo này cung là project dâu ti?n tri?n khai phát tri?n th? nghi?m trên mô hình m?ng - Nhi?u member cùng phát tri?n cùng lúc QuanLyNhanKhau: WPF test.RazorPad: RazorPad is a quick and simple stand-alone editing environment that allows anyone (even non-developers) to author Razor templates. It is developed in WPF using C# and relies on the System.WebPages.Razor libraries (included in the project download). Rovio Tour Guide: More details to follow soon....long story short building a robotic tour guide using the Rovio roving webcam platform for proof of concept.ScrumPilot: ScrumPilot is a viewer of events coming from Team Foundation Server The main goal of this project is to help team to follow in real time the Checkins and WorkItems changing. Team can do comments to each event and they can preview some TFS artifacts.S-DMS: S-DMS?????????(Document Manage System)Sharepoint Documentation Generator: New MOSS feature to automatically generate documentation/tables for fields, content types, lists, users, etc...ShengjieGao's projects: ?????Stylish DOS Box: Since the introduction of Windows 3.11 I am trying to avoid the DOS box and use any applet provided with GUI in Windows system. Yet, I realize that there is no week passed by without me opening the DOS box! This project will give the DOS Box a new look.Table2DTO: Auto generate code to build objects (DTOs, Models, etc) from a data table.Techweb: Alon's and Simon's 236607 homework assignments.TLC5940 Driver for Netduino: An Netduino Library for the TI TLC5940 16-Channel PWM Chip. Tratando Exceptions da Send Port na Orchestration: Quando a Send Port é do tipo Request-Response manipular o exception é intuitivo, já que basta colocar um escopo e adicionar um exception do tipo System.Exception. Mas quando a porta é one-way a coisa complica um pouco.UAC Runner: UAC Runner is a small application which allows the running of applications as an administrator from the command line using Windows UAC.Ubuntu 10 Agent for System Center Operations Manager 2007 R2: Ubuntu 10 System Center Operations Manager 2007 R2 Agent.Ubuntu 10 Management Pack For System Center Operations Manager 2007 R2: Ubuntu 10 Management Pack for SCOM 2007 R2. It will be useless without the Agent (in another project). It is based on Red Hat 5 Management Pack. See the Download section to download the MPs and the source files (XML) Whe Online Storage: Whe Online Storage, is an 3. party online storage system and tools for free source. C#, .NET 4.0, SilverlightWindows Phone MVP: An MVP implementation for Windows Phone.

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  • CUPS Web Admin Error 500 Unknown

    - by Floyd Resler
    I keep getting a 500 Unknown error whenever I navigate off the home page of my CUPS web admin. I'm sure I have something misconfigured but I'm not sure what. Here's my configuration: # # "$Id: cupsd.conf.in 8805 2009-08-31 16:34:06Z mike $" # # Sample configuration file for the CUPS scheduler. See "man cupsd.conf" for a # complete description of this file. # # Log general information in error_log - change "warn" to "debug" # for troubleshooting... LogLevel warn # Administrator user group... SystemGroup lpadmin sys root # Only listen for connections from the local machine. Listen 192.168.6.101:631 Listen /var/run/cups/cups.sock ServerName 192.168.6.101 # Show shared printers on the local network. Browsing On BrowseOrder allow,deny BrowseAllow all BrowseLocalProtocols CUPS BrowseAddress 192.168.6.255 # Default authentication type, when authentication is required... DefaultAuthType Basic # Restrict access to the server... Order allow,deny Allow From All Allow From 127.0.0.1 # Restrict access to the admin pages... AuthType Default Require user @SYSTEM Order allow,deny Allow From All Allow From 127.0.0.1 # Restrict access to configuration files... AuthType Default Require user @SYSTEM Order allow,deny Allow From All Allow From 127.0.0.1 # Set the default printer/job policies... # Job-related operations must be done by the owner or an administrator... Require user @OWNER @SYSTEM Order deny,allow # All administration operations require an administrator to authenticate... AuthType Default Require user @SYSTEM Order deny,allow # All printer operations require a printer operator to authenticate... AuthType Default Require user @SYSTEM Order deny,allow # Only the owner or an administrator can cancel or authenticate a job... Require user @OWNER @SYSTEM Order deny,allow Order deny,allow # Set the authenticated printer/job policies... # Job-related operations must be done by the owner or an administrator... AuthType Default Order deny,allow AuthType Default Require user @OWNER @SYSTEM Order deny,allow # All administration operations require an administrator to authenticate... AuthType Default Require user @SYSTEM Order deny,allow # All printer operations require a printer operator to authenticate... AuthType Default Require user @SYSTEM Order deny,allow # Only the owner or an administrator can cancel or authenticate a job... AuthType Default Require user @OWNER @SYSTEM Order deny,allow Order deny,allow # # End of "$Id: cupsd.conf.in 8805 2009-08-31 16:34:06Z mike $". #

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  • Tömörítés becslése - Compression Advisor

    - by lsarecz
    Az Oracle Database 11g verziójától már OLTP adatbázisok is hatékonyan tömöríthetok az Advanced Compression funkcióval. Nem csak a tárolandó adatok mennyisége csökken ezáltal felére, vagy akár negyedére, de az adatbázis teljesítménye is javulhat, amennyiben I/O korlátos a rendszer (és általában az). Hogy pontosan mekkora tömörítés várható az Advanced Compression bevezetésével, az kiválóan becsülheto a Compression Advisor eszközzel. Ez nem csak az OLTP tömörítés mértékét, de 11gR2 verziótól kezdve a HCC tömörítés arányát is becsülni tudja, amely Exadata Database Machine, Pillar Axiom illetve ZFS Storage alkalmazásával érheto el. A HCC tömörítés becsléséhez csak 11gR2 adatbázisra van szükség, nem kell hozzá a speciális célhardver (Exadata, Pillar, ZFS). A Compression Advisor valójában a DBMS_COMPRESSION package használatával érheto el. A package-hez tartozik 6 konstans, amellyel a kívánt tömörítési szintek választhatók ki: Constant Type Value Description COMP_NOCOMPRESS NUMBER 1 No compression COMP_FOR_OLTP NUMBER 2 OLTP compression COMP_FOR_QUERY_HIGH NUMBER 4 High compression level for query operations COMP_FOR_QUERY_LOW NUMBER 8 Low compression level for query operations COMP_FOR_ARCHIVE_HIGH NUMBER 16 High compression level for archive operations COMP_FOR_ARCHIVE_LOW NUMBER 32 Low compression level for archive operations A GET_COMPRESSION_RATIO tárolt eljárás elemzi a tömöríteni kívánt táblát. Mindig csak egy táblát, vagy opcionálisan annak egy partícióját tudja elemezni úgy, hogy a tábláról készít egy másolatot egy külön erre a célra kijelölt/létrehozott táblatérre. Amennyiben az elemzést egyszerre több tömörítési szintre futtatjuk, úgy a tábláról annyi másolatot készít. A jó közelítésu becslés (+-5%) feltétele, hogy táblánként/partíciónként minimum 1 millió sor legyen. 11gR1 esetében még a DBMS_COMP_ADVISOR csomag GET_RATIO eljárása volt használatos, de ez még nem támogatta a HCC becslést. Érdemes még megnézni és kipróbálni a Tyler Muth blogjában publikált formázó eszközt, amivel a compression advisor kimenete alakítható jól értelmezheto formátumúvá. Végül összegezném mit is tartalmaz az Advanced Compression opció, mivel gyakran nem világos a felhasználóknak miért kell fizetni: Data Guard Network Compression Data Pump Compression (COMPRESSION=METADATA_ONLY does not require the Advanced Compression option) Multiple RMAN Compression Levels (RMAN DEFAULT COMPRESS does not require the Advanced Compression option) OLTP Table Compression SecureFiles Compression and Deduplication Ez alapján RMAN esetében például a default compression (BZIP2) szint ingyen használható, viszont az új ZLIB Advanced Compression opciót igényel. A ZLIB hatékonyabban használja a CPU-t, azaz jóval gyorsabb, viszont kisebb tömörítési arány érheto el vele.

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  • Problem measuring N times the execution time of a code block

    - by Nazgulled
    EDIT: I just found my problem after writing this long post explaining every little detail... If someone can give me a good answer on what I'm doing wrong and how can I get the execution time in seconds (using a float with 5 decimal places or so), I'll mark that as accepted. Hint: The problem was on how I interpreted the clock_getttime() man page. Hi, Let's say I have a function named myOperation that I need to measure the execution time of. To measure it, I'm using clock_gettime() as it was recommend here in one of the comments. My teacher recommends us to measure it N times so we can get an average, standard deviation and median for the final report. He also recommends us to execute myOperation M times instead of just one. If myOperation is a very fast operation, measuring it M times allow us to get a sense of the "real time" it takes; cause the clock being used might not have the required precision to measure such operation. So, execution myOperation only one time or M times really depends if the operation itself takes long enough for the clock precision we are using. I'm having trouble dealing with that M times execution. Increasing M decreases (a lot) the final average value. Which doesn't make sense to me. It's like this, on average you take 3 to 5 seconds to travel from point A to B. But then you go from A to B and back to A 5 times (which makes it 10 times, cause A to B is the same as B to A) and you measure that. Than you divide by 10, the average you get is supposed to be the same average you take traveling from point A to B, which is 3 to 5 seconds. This is what I want my code to do, but it's not working. If I keep increasing the number of times I go from A to B and back A, the average will be lower and lower each time, it makes no sense to me. Enough theory, here's my code: #include <stdio.h> #include <time.h> #define MEASUREMENTS 1 #define OPERATIONS 1 typedef struct timespec TimeClock; TimeClock diffTimeClock(TimeClock start, TimeClock end) { TimeClock aux; if((end.tv_nsec - start.tv_nsec) < 0) { aux.tv_sec = end.tv_sec - start.tv_sec - 1; aux.tv_nsec = 1E9 + end.tv_nsec - start.tv_nsec; } else { aux.tv_sec = end.tv_sec - start.tv_sec; aux.tv_nsec = end.tv_nsec - start.tv_nsec; } return aux; } int main(void) { TimeClock sTime, eTime, dTime; int i, j; for(i = 0; i < MEASUREMENTS; i++) { printf(" » MEASURE %02d\n", i+1); clock_gettime(CLOCK_REALTIME, &sTime); for(j = 0; j < OPERATIONS; j++) { myOperation(); } clock_gettime(CLOCK_REALTIME, &eTime); dTime = diffTimeClock(sTime, eTime); printf(" - NSEC (TOTAL): %ld\n", dTime.tv_nsec); printf(" - NSEC (OP): %ld\n\n", dTime.tv_nsec / OPERATIONS); } return 0; } Notes: The above diffTimeClock function is from this blog post. I replaced my real operation with myOperation() because it doesn't make any sense to post my real functions as I would have to post long blocks of code, you can easily code a myOperation() with whatever you like to compile the code if you wish. As you can see, OPERATIONS = 1 and the results are: » MEASURE 01 - NSEC (TOTAL): 27456580 - NSEC (OP): 27456580 For OPERATIONS = 100 the results are: » MEASURE 01 - NSEC (TOTAL): 218929736 - NSEC (OP): 2189297 For OPERATIONS = 1000 the results are: » MEASURE 01 - NSEC (TOTAL): 862834890 - NSEC (OP): 862834 For OPERATIONS = 10000 the results are: » MEASURE 01 - NSEC (TOTAL): 574133641 - NSEC (OP): 57413 Now, I'm not a math wiz, far from it actually, but this doesn't make any sense to me whatsoever. I've already talked about this with a friend that's on this project with me and he also can't understand the differences. I don't understand why the value is getting lower and lower when I increase OPERATIONS. The operation itself should take the same time (on average of course, not the exact same time), no matter how many times I execute it. You could tell me that that actually depends on the operation itself, the data being read and that some data could already be in the cache and bla bla, but I don't think that's the problem. In my case, myOperation is reading 5000 lines of text from an CSV file, separating the values by ; and inserting those values into a data structure. For each iteration, I'm destroying the data structure and initializing it again. Now that I think of it, I also that think that there's a problem measuring time with clock_gettime(), maybe I'm not using it right. I mean, look at the last example, where OPERATIONS = 10000. The total time it took was 574133641ns, which would be roughly 0,5s; that's impossible, it took a couple of minutes as I couldn't stand looking at the screen waiting and went to eat something.

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  • Oracle's Global Single Schema

    - by david.butler(at)oracle.com
    Maximizing business process efficiencies in a heterogeneous environment is very difficult. The difficulty stems from the fact that the various applications across the Information Technology (IT) landscape employ different integration standards, different message passing strategies, and different workflow engines. Vendors such as Oracle and others are delivering tools to help IT organizations manage the complexities introduced by these differences. But the one remaining intractable problem impacting efficient operations is the fact that these applications have different definitions for the same business data. Business data is your business information codified for computer programs to use. A good data model will represent the way your organization does business. The computer applications your organization deploys to improve operational efficiency are built to operate on the business data organized into this schema.  If the schema does not represent how you do business, the applications on that schema cannot provide the features you need to achieve the desired efficiencies. Business processes span these applications. Data problems break these processes rendering them far less efficient than they need to be to achieve organization goals. Thus, the expected return on the investment in these applications is never realized. The success of all business processes depends on the availability of accurate master data.  Clearly, the solution to this problem is to consolidate all the master data an organization uses to run its business. Then clean it up, augment it, govern it, and connect it back to the applications that need it. Until now, this obvious solution has been difficult to achieve because no one had defined a data model sufficiently broad, deep and flexible enough to support transaction processing on all key business entities and serve as a master superset to all other operational data models deployed in heterogeneous IT environments. Today, the situation has changed. Oracle has created an operational data model (aka schema) that can support accurate and consistent master data across heterogeneous IT systems. This is foundational for providing a way to consolidate and integrate master data without having to replace investments in existing applications. This Global Single Schema (GSS) represents a revolutionary breakthrough that allows for true master data consolidation. Oracle has deep knowledge of applications dating back to the early 1990s.  It developed applications in the areas of Supply Chain Management (SCM), Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Human Capital Management (HCM), Financials and Manufacturing. In addition, Oracle applications were delivered for key industries such as Communications, Financial Services, Retail, Public Sector, High Tech Manufacturing (HTM) and more. Expertise in all these areas drove requirements for GSS. The following figure illustrates Oracle's unique position that enabled the creation of the Global Single Schema. GSS Requirements Gathering GSS defines all the key business entities and attributes including Customers, Contacts, Suppliers, Accounts, Products, Services, Materials, Employees, Installed Base, Sites, Assets, and Inventory to name just a few. In addition, Oracle delivers GSS pre-integrated with a wide variety of operational applications.  Business Process Automation EBusiness is about maximizing operational efficiency. At the highest level, these 'operations' span all that you do as an organization.  The following figure illustrates some of these high-level business processes. Enterprise Business Processes Supplies are procured. Assets are maintained. Materials are stored. Inventory is accumulated. Products and Services are engineered, produced and sold. Customers are serviced. And across this entire spectrum, Employees do the procuring, supporting, engineering, producing, selling and servicing. Not shown, but not to be overlooked, are the accounting and the financial processes associated with all this procuring, manufacturing, and selling activity. Supporting all these applications is the master data. When this data is fragmented and inconsistent, the business processes fail and inefficiencies multiply. But imagine having all the data under these operational business processes in one place. ·            The same accurate and timely customer data will be provided to all your operational applications from the call center to the point of sale. ·            The same accurate and timely supplier data will be provided to all your operational applications from supply chain planning to procurement. ·            The same accurate and timely product information will be available to all your operational applications from demand chain planning to marketing. You would have a single version of the truth about your assets, financial information, customers, suppliers, employees, products and services to support your business automation processes as they flow across your business applications. All company and partner personnel will access the same exact data entity across all your channels and across all your lines of business. Oracle's Global Single Schema enables this vision of a single version of the truth across the heterogeneous operational applications supporting the entire enterprise. Global Single Schema Oracle's Global Single Schema organizes hundreds of thousands of attributes into 165 major schema objects supporting over 180 business application modules. It is designed for international operations, and extensibility.  The schema is delivered with a full set of public Application Programming Interfaces (APIs) and an Integration Repository with modern Service Oriented Architecture interfaces to make data available as a services (DaaS) to business processes and enable operations in heterogeneous IT environments. ·         Key tables can be extended with unlimited numbers of additional attributes and attribute groups for maximum flexibility.  o    This enables model extensions that reflect business entities unique to your organization's operations. ·         The schema is multi-organization enabled so data manipulation can be controlled along organizational boundaries. ·         It uses variable byte Unicode to support over 31 languages. ·         The schema encodes flexible date and flexible address formats for easy localizations. No matter how complex your business is, Oracle's Global Single Schema can hold your business objects and support your global operations. Oracle's Global Single Schema identifies and defines the business objects an enterprise needs within the context of its business operations. The interrelationships between the business objects are also contained within the GSS data model. Their presence expresses fundamental business rules for the interaction between business entities. The following figure illustrates some of these connections.   Interconnected Business Entities Interconnecte business processes require interconnected business data. No other MDM vendor has this capability. Everyone else has either one entity they can master or separate disconnected models for various business entities. Higher level integrations are made available, but that is a weak architectural alternative to data level integration in this critically important aspect of Master Data Management.    

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  • Plan Caching and Query Memory Part I – When not to use stored procedure or other plan caching mechanisms like sp_executesql or prepared statement

    - by sqlworkshops
      The most common performance mistake SQL Server developers make: SQL Server estimates memory requirement for queries at compilation time. This mechanism is fine for dynamic queries that need memory, but not for queries that cache the plan. With dynamic queries the plan is not reused for different set of parameters values / predicates and hence different amount of memory can be estimated based on different set of parameter values / predicates. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union. This article covers Sort with examples. It is recommended to read Plan Caching and Query Memory Part II after this article which covers Hash Match operations.   When the plan is cached by using stored procedure or other plan caching mechanisms like sp_executesql or prepared statement, SQL Server estimates memory requirement based on first set of execution parameters. Later when the same stored procedure is called with different set of parameter values, the same amount of memory is used to execute the stored procedure. This might lead to underestimation / overestimation of memory on plan reuse, overestimation of memory might not be a noticeable issue for Sort operations, but underestimation of memory will lead to spill over tempdb resulting in poor performance.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   To read additional articles I wrote click here.   In most cases it is cheaper to pay for the compilation cost of dynamic queries than huge cost for spill over tempdb, unless memory requirement for a stored procedure does not change significantly based on predicates.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script. Most of these concepts are also covered in our webcasts: www.sqlworkshops.com/webcasts   Enough theory, let’s see an example where we sort initially 1 month of data and then use the stored procedure to sort 6 months of data.   Let’s create a stored procedure that sorts customers by name within certain date range.   --Example provided by www.sqlworkshops.com create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1)       end go Let’s execute the stored procedure initially with 1 month date range.   set statistics time on go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 48 ms to complete.     The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.       The estimated number of rows, 43199.9 is similar to actual number of rows 43200 and hence the memory estimation should be ok.       There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 679 ms to complete.      The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.      The estimated number of rows, 43199.9 is way different from the actual number of rows 259200 because the estimation is based on the first set of parameter value supplied to the stored procedure which is 1 month in our case. This underestimation will lead to sort spill over tempdb, resulting in poor performance.      There was Sort Warnings in SQL Profiler.    To monitor the amount of data written and read from tempdb, one can execute select num_of_bytes_written, num_of_bytes_read from sys.dm_io_virtual_file_stats(2, NULL) before and after the stored procedure execution, for additional information refer to the webcast: www.sqlworkshops.com/webcasts.     Let’s recompile the stored procedure and then let’s first execute the stored procedure with 6 month date range.  In a production instance it is not advisable to use sp_recompile instead one should use DBCC FREEPROCCACHE (plan_handle). This is due to locking issues involved with sp_recompile, refer to our webcasts for further details.   exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go Now the stored procedure took only 294 ms instead of 679 ms.    The stored procedure was granted 26832 KB of memory.      The estimated number of rows, 259200 is similar to actual number of rows of 259200. Better performance of this stored procedure is due to better estimation of memory and avoiding sort spill over tempdb.      There was no Sort Warnings in SQL Profiler.       Now let’s execute the stored procedure with 1 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 49 ms to complete, similar to our very first stored procedure execution.     This stored procedure was granted more memory (26832 KB) than necessary memory (6656 KB) based on 6 months of data estimation (259200 rows) instead of 1 month of data estimation (43199.9 rows). This is because the estimation is based on the first set of parameter value supplied to the stored procedure which is 6 months in this case. This overestimation did not affect performance, but it might affect performance of other concurrent queries requiring memory and hence overestimation is not recommended. This overestimation might affect performance Hash Match operations, refer to article Plan Caching and Query Memory Part II for further details.    Let’s recompile the stored procedure and then let’s first execute the stored procedure with 2 day date range. exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-02' go The stored procedure took 1 ms.      The stored procedure was granted 1024 KB based on 1440 rows being estimated.      There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go   The stored procedure took 955 ms to complete, way higher than 679 ms or 294ms we noticed before.      The stored procedure was granted 1024 KB based on 1440 rows being estimated. But we noticed in the past this stored procedure with 6 month date range needed 26832 KB of memory to execute optimally without spill over tempdb. This is clear underestimation of memory and the reason for the very poor performance.      There was Sort Warnings in SQL Profiler. Unlike before this was a Multiple pass sort instead of Single pass sort. This occurs when granted memory is too low.      Intermediate Summary: This issue can be avoided by not caching the plan for memory allocating queries. Other possibility is to use recompile hint or optimize for hint to allocate memory for predefined date range.   Let’s recreate the stored procedure with recompile hint. --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, recompile)       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.      The stored procedure with 1 month date range has good estimation like before.      The stored procedure with 6 month date range also has good estimation and memory grant like before because the query was recompiled with current set of parameter values.      The compilation time and compilation CPU of 1 ms is not expensive in this case compared to the performance benefit.     Let’s recreate the stored procedure with optimize for hint of 6 month date range.   --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, optimize for (@CreationDateFrom = '2001-01-01', @CreationDateTo ='2001-06-30'))       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.    The stored procedure with 1 month date range has overestimation of rows and memory. This is because we provided hint to optimize for 6 months of data.      The stored procedure with 6 month date range has good estimation and memory grant because we provided hint to optimize for 6 months of data.       Let’s execute the stored procedure with 12 month date range using the currently cashed plan for 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-12-31' go The stored procedure took 1138 ms to complete.      2592000 rows were estimated based on optimize for hint value for 6 month date range. Actual number of rows is 524160 due to 12 month date range.      The stored procedure was granted enough memory to sort 6 month date range and not 12 month date range, so there will be spill over tempdb.      There was Sort Warnings in SQL Profiler.      As we see above, optimize for hint cannot guarantee enough memory and optimal performance compared to recompile hint.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   Summary: Cached plan might lead to underestimation or overestimation of memory because the memory is estimated based on first set of execution parameters. It is recommended not to cache the plan if the amount of memory required to execute the stored procedure has a wide range of possibilities. One can mitigate this by using recompile hint, but that will lead to compilation overhead. However, in most cases it might be ok to pay for compilation rather than spilling sort over tempdb which could be very expensive compared to compilation cost. The other possibility is to use optimize for hint, but in case one sorts more data than hinted by optimize for hint, this will still lead to spill. On the other side there is also the possibility of overestimation leading to unnecessary memory issues for other concurrently executing queries. In case of Hash Match operations, this overestimation of memory might lead to poor performance. When the values used in optimize for hint are archived from the database, the estimation will be wrong leading to worst performance, so one has to exercise caution before using optimize for hint, recompile hint is better in this case. I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.     Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.     Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • ArvinMeritor Sees Business Improvement: Uses Oracle Demand Management, Supply Chain Planning and Tra

    - by [email protected]
    As manufacturers begin repositioning for the economic recovery, they are reevaluating their supply chain networks, extending lean into their supply chains and making logistics visibility a priority. ArvinMeritor leveraged Oracle's Demantra, ASCP and Transportation Management applications to: Optimize operations execution by building consensus-driven demand, sales and operations plans Slash transportation costs by rationalizing shippers, optimizing routes and improving delivery performance Demantra for demand management, forecasting, sales and operations planning and global trade management Advanced Supply Chain Planning for material and capacity planning across global distribution and manufacturing facilities based on consensus forecasts, sales orders, production status, purchase orders, and inventory policy recommendations Transportation Management for transportation planning, execution, freight payment, and business process automation on a single application across all modes of transportation, from full truckload to complex multileg air, ocean, and rail shipments Oracle hosted an 'open-house/showcase" on March 30th, 2010 atArvinMeritor Global Headquarters 2135 West Maple RoadTroy, MI 48084 

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  • Is WCF suitable for writing an application which is shared among applications?

    - by RPK
    I have developed and deployed few ASP.NET applications. Sometimes I want to stop the users from either inserting or updating a record when: Maintenance is going on. Stop operations due to payment delay. In one of my recent application I have implemented this feature to first check the database operations for locked status. If any of the above condition fulfils, database operations like insert and update are not carried out. I now need this feature in all the old applications and the future applications I build. I want to know whether WCF is suitable in this scenario as I want to share methods or an independent locking application among various other applications. Is WCF appropriate for this type of scenario?

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  • Eliminating Downtime During Database Upgrades: A Customer Case Study

    - by irem.radzik(at)oracle.com
    Planned outages, such as database, OS, hardware upgrades and migrations, are a fact of life. Even though they are "planned" and many of them are performed during "off business hours", they can still interrupt operations-- especially for global operations and online businesses. For this reason many IT organizations postpone these critical infrastructure improvement projects, which in turn result in delays in advancing business operations. This week, on Thursday January 13th, we will host a free webcast on this topic, and will feature Oracle GoldenGate's customer Atmos Energy. Atmos Energy implemented Oracle GoldenGate for eliminating downtime during their database upgrade from Oracle Database 8.1.7 to Oracle Database 11.1.0.7. Jos Francis, Lead DBA for Atmos, and Ronald Nedd, Sr. DBA for Atmos, will be presenting their database upgrade project and their solution architecture. Join us at this live webcast and hear from our customer and product management how to eliminate planned outages with Oracle GoldenGate's real-time, heterogeneous data replication capabilities.

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  • Architecture - 32-bit handling 64-bit instructions

    - by tkoomzaaskz
    tomasz@tomasz-lenovo-ideapad-Y530:~$ lscpu Architecture: i686 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 2 On-line CPU(s) list: 0,1 Thread(s) per core: 1 Core(s) per socket: 2 Socket(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 23 Stepping: 6 CPU MHz: 2000.000 BogoMIPS: 4000.12 Cache L1d: 32K Cache L1i: 32K Cache L2: 3072K I can see that my architecture is 32-bit (i686). But CPU op-mode(s) are 32-bit and 64-bit. The question is: how come? How is it handled that a 32-bit processor performs 64-bit operations? I guess it's a lot slower than native 32-bit operations. Is it built-in processor functionality (to emulate being 64-bit) or is it software dependent? When does it make sense for a 32-bit processor to run 64-bit operations?

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