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• Creating a podcast feed for iTunes & BlackBerry users using WCF Syndication

  - by brian_ritchie

   In my previous post, I showed how to create a RSS feed using WCF Syndication.  Next, I'll show how to add the additional tags needed to turn a RSS feed into an iTunes podcast.   A podcast is merely a RSS feed with some special characteristics: iTunes RSS tags.  These are additional tags beyond the standard RSS spec.  Apple has a good page on the requirements. Audio file enclosure.  This is a link to the audio file (such as mp3) hosted by your site.  Apple doesn't host the audio, they just read the meta-data from the RSS feed into their system. The SyndicationFeed class supports both AttributeExtensions & ElementExtensions to add custom tags to the RSS feeds. A couple of points of interest in the code below: The imageUrl below provides the album cover for iTunes (170px × 170px) Each SyndicationItem corresponds to an audio episode in your podcast So, here's the code: .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: Consolas, "Courier New", Courier, Monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } 1: XNamespace itunesNS = "http://www.itunes.com/dtds/podcast-1.0.dtd"; 2: string prefix = "itunes"; 3:   4: var feed = new SyndicationFeed(title, description, new Uri(link)); 5: feed.Categories.Add(new SyndicationCategory(category)); 6: feed.AttributeExtensions.Add(new XmlQualifiedName(prefix, 7: "http://www.w3.org/2000/xmlns/"), itunesNS.NamespaceName); 8: feed.Copyright = new TextSyndicationContent(copyright); 9: feed.Language = "en-us"; 10: feed.Copyright = new TextSyndicationContent(DateTime.Now.Year + " " + ownerName); 11: feed.ImageUrl = new Uri(imageUrl); 12: feed.LastUpdatedTime = DateTime.Now; 13: feed.Authors.Add(new SyndicationPerson() {Name=ownerName, Email=ownerEmail }); 14: var extensions = feed.ElementExtensions; 15: extensions.Add(new XElement(itunesNS + "subtitle", subTitle).CreateReader()); 16: extensions.Add(new XElement(itunesNS + "image", 17: new XAttribute("href", imageUrl)).CreateReader()); 18: extensions.Add(new XElement(itunesNS + "author", ownerName).CreateReader()); 19: extensions.Add(new XElement(itunesNS + "summary", description).CreateReader()); 20: extensions.Add(new XElement(itunesNS + "category", 21: new XAttribute("text", category), 22: new XElement(itunesNS + "category", 23: new XAttribute("text", subCategory))).CreateReader()); 24: extensions.Add(new XElement(itunesNS + "explicit", "no").CreateReader()); 25: extensions.Add(new XDocument( 26: new XElement(itunesNS + "owner", 27: new XElement(itunesNS + "name", ownerName), 28: new XElement(itunesNS + "email", ownerEmail))).CreateReader()); 29:   30: var feedItems = new List<SyndicationItem>(); 31: foreach (var i in Items) 32: { 33: var item = new SyndicationItem(i.title, null, new Uri(link)); 34: item.Summary = new TextSyndicationContent(i.summary); 35: item.Id = i.id; 36: if (i.publishedDate != null) 37: item.PublishDate = (DateTimeOffset)i.publishedDate; 38: item.Links.Add(new SyndicationLink() { 39: Title = i.title, Uri = new Uri(link), 40: Length = i.size, MediaType = i.mediaType }); 41: var itemExt = item.ElementExtensions; 42: itemExt.Add(new XElement(itunesNS + "subtitle", i.subTitle).CreateReader()); 43: itemExt.Add(new XElement(itunesNS + "summary", i.summary).CreateReader()); 44: itemExt.Add(new XElement(itunesNS + "duration", 45: string.Format("{0}:{1:00}:{2:00}", 46: i.duration.Hours, i.duration.Minutes, i.duration.Seconds) 47: ).CreateReader()); 48: itemExt.Add(new XElement(itunesNS + "keywords", i.keywords).CreateReader()); 49: itemExt.Add(new XElement(itunesNS + "explicit", "no").CreateReader()); 50: itemExt.Add(new XElement("enclosure", new XAttribute("url", i.url), 51: new XAttribute("length", i.size), new XAttribute("type", i.mediaType))); 52: feedItems.Add(item); 53: } 54:   55: feed.Items = feedItems; If you're hosting your podcast feed within a MVC project, you can use the code from my previous post to stream it. Once you have created your feed, you can use the Feed Validator tool to make sure it is up to spec.  Or you can use iTunes: Launch iTunes. In the Advanced menu, select Subscribe to Podcast. Enter your feed URL in the text box and click OK. After you've verified your feed is solid & good to go, you can submit it to iTunes.  Launch iTunes. In the left navigation column, click on iTunes Store to open the store. Once the store loads, click on Podcasts along the top navigation bar to go to the Podcasts page. In the right column of the Podcasts page, click on the Submit a Podcast link. Follow the instructions on the Submit a Podcast page. Here are the full instructions.  Once they have approved your podcast, it will be available within iTunes. RIM has also gotten into the podcasting business...which is great for BlackBerry users.  They accept the same enhanced-RSS feed that iTunes uses, so just create an account with them & submit the feed's URL.  It goes through a similar approval process to iTunes.  BlackBerry users must be on BlackBerry 6 OS or download the Podcast App from App World. In my next post, I'll show how to build the podcast feed dynamically from the ID3 tags within the MP3 files.

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• C# Performance Pitfall – Interop Scenarios Change the Rules

  - by Reed

  C# and .NET, overall, really do have fantastic performance in my opinion.  That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar.  However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#. I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be… The rules in C# when optimizing code are very different than C or C++.  Often, they’re exactly backwards.  For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages.  If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine. This can be a tricky bottleneck to track down, even with a profiler.  Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph.  For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to: for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i]); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data.  As such, any performance optimization we could achieve would be greatly appreciated by our users. After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing: // Allocate some data required DataStructure* data = new DataStructure(num); // Call into a subroutine that passed around and manipulated this data highly CallSubroutine(data); // Read and use some values from here double values = data->Foo; // Cleanup delete data; // ... return bar; Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack.  However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem.  In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating.  At the highest level, my code effectively changed to something like: DataStructure* data = new DataStructure(numberToProcess); for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i], data); } delete data; Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit.  In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes.  This was such a significant improvement, I felt it was worth the reduction in maintainability. In C and C++, it’s generally a good idea (for performance) to: Reduce the number of memory allocations as much as possible, Use fewer, larger memory allocations instead of many smaller ones, and Allocate as high up the call stack as possible, and reuse memory I’ve seen many people try to make similar optimizations in C# code.  For good or bad, this is typically not a good idea.  The garbage collector in .NET completely changes the rules here. In C#, reallocating memory in a loop is not always a bad idea.  In this scenario, for example, I may have been much better off leaving the original code alone.  The reason for this is the garbage collector.  The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages.  First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code.  This is something that should be avoided unless there is a significant reason.  Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast.  Finally, and most critically, there is a large advantage to having short lived objects.  If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2).  This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later. As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops.  This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole. In C#, I tend to: Keep variable declarations in the tightest scope possible Declare and allocate objects at usage While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1.  It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean.  It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time. Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true.  That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary.  In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules. In this case, I was dealing with an API that, internally, used some COM objects.  In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph.  Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.  After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration.  This required going back to a “native programming” mindset for optimization.  Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement. Overall, the lessons here are: Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be Remember to check memory allocations when profiling, not just CPU cycles Interop scenarios often cause managed code to act very differently than “normal” managed code. Native code can be hidden very cleverly inside of managed wrappers

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• Inheritance Mapping Strategies with Entity Framework Code First CTP5: Part 2 – Table per Type (TPT)

  - by mortezam

  In the previous blog post you saw that there are three different approaches to representing an inheritance hierarchy and I explained Table per Hierarchy (TPH) as the default mapping strategy in EF Code First. We argued that the disadvantages of TPH may be too serious for our design since it results in denormalized schemas that can become a major burden in the long run. In today’s blog post we are going to learn about Table per Type (TPT) as another inheritance mapping strategy and we'll see that TPT doesn’t expose us to this problem. Table per Type (TPT)Table per Type is about representing inheritance relationships as relational foreign key associations. Every class/subclass that declares persistent properties—including abstract classes—has its own table. The table for subclasses contains columns only for each noninherited property (each property declared by the subclass itself) along with a primary key that is also a foreign key of the base class table. This approach is shown in the following figure: For example, if an instance of the CreditCard subclass is made persistent, the values of properties declared by the BillingDetail base class are persisted to a new row of the BillingDetails table. Only the values of properties declared by the subclass (i.e. CreditCard) are persisted to a new row of the CreditCards table. The two rows are linked together by their shared primary key value. Later, the subclass instance may be retrieved from the database by joining the subclass table with the base class table. TPT Advantages The primary advantage of this strategy is that the SQL schema is normalized. In addition, schema evolution is straightforward (modifying the base class or adding a new subclass is just a matter of modify/add one table). Integrity constraint definition are also straightforward (note how CardType in CreditCards table is now a non-nullable column). Another much more important advantage is the ability to handle polymorphic associations (a polymorphic association is an association to a base class, hence to all classes in the hierarchy with dynamic resolution of the concrete class at runtime). A polymorphic association to a particular subclass may be represented as a foreign key referencing the table of that particular subclass. Implement TPT in EF Code First We can create a TPT mapping simply by placing Table attribute on the subclasses to specify the mapped table name (Table attribute is a new data annotation and has been added to System.ComponentModel.DataAnnotations namespace in CTP5): public abstract class BillingDetail {     public int BillingDetailId { get; set; }     public string Owner { get; set; }     public string Number { get; set; } } [Table("BankAccounts")] public class BankAccount : BillingDetail {     public string BankName { get; set; }     public string Swift { get; set; } } [Table("CreditCards")] public class CreditCard : BillingDetail {     public int CardType { get; set; }     public string ExpiryMonth { get; set; }     public string ExpiryYear { get; set; } } public class InheritanceMappingContext : DbContext {     public DbSet<BillingDetail> BillingDetails { get; set; } } If you prefer fluent API, then you can create a TPT mapping by using ToTable() method: protected override void OnModelCreating(ModelBuilder modelBuilder) {     modelBuilder.Entity<BankAccount>().ToTable("BankAccounts");     modelBuilder.Entity<CreditCard>().ToTable("CreditCards"); } Generated SQL For QueriesLet’s take an example of a simple non-polymorphic query that returns a list of all the BankAccounts: var query = from b in context.BillingDetails.OfType<BankAccount>() select b; Executing this query (by invoking ToList() method) results in the following SQL statements being sent to the database (on the bottom, you can also see the result of executing the generated query in SQL Server Management Studio): Now, let’s take an example of a very simple polymorphic query that requests all the BillingDetails which includes both BankAccount and CreditCard types: projects some properties out of the base class BillingDetail, without querying for anything from any of the subclasses: var query = from b in context.BillingDetails             select new { b.BillingDetailId, b.Number, b.Owner }; -- var query = from b in context.BillingDetails select b; This LINQ query seems even more simple than the previous one but the resulting SQL query is not as simple as you might expect: -- As you can see, EF Code First relies on an INNER JOIN to detect the existence (or absence) of rows in the subclass tables CreditCards and BankAccounts so it can determine the concrete subclass for a particular row of the BillingDetails table. Also the SQL CASE statements that you see in the beginning of the query is just to ensure columns that are irrelevant for a particular row have NULL values in the returning flattened table. (e.g. BankName for a row that represents a CreditCard type) TPT ConsiderationsEven though this mapping strategy is deceptively simple, the experience shows that performance can be unacceptable for complex class hierarchies because queries always require a join across many tables. In addition, this mapping strategy is more difficult to implement by hand— even ad-hoc reporting is more complex. This is an important consideration if you plan to use handwritten SQL in your application (For ad hoc reporting, database views provide a way to offset the complexity of the TPT strategy. A view may be used to transform the table-per-type model into the much simpler table-per-hierarchy model.) SummaryIn this post we learned about Table per Type as the second inheritance mapping in our series. So far, the strategies we’ve discussed require extra consideration with regard to the SQL schema (e.g. in TPT, foreign keys are needed). This situation changes with the Table per Concrete Type (TPC) that we will discuss in the next post. References ADO.NET team blog Java Persistence with Hibernate book a { text-decoration: none; } a:visited { color: Blue; } .title { padding-bottom: 5px; font-family: Segoe UI; font-size: 11pt; font-weight: bold; padding-top: 15px; } .code, .typeName { font-family: consolas; } .typeName { color: #2b91af; } .padTop5 { padding-top: 5px; } .padTop10 { padding-top: 10px; } p.MsoNormal { margin-top: 0in; margin-right: 0in; margin-bottom: 10.0pt; margin-left: 0in; line-height: 115%; font-size: 11.0pt; font-family: "Calibri" , "sans-serif"; }

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• Parallelism in .NET – Part 3, Imperative Data Parallelism: Early Termination

  - by Reed

  Although simple data parallelism allows us to easily parallelize many of our iteration statements, there are cases that it does not handle well.  In my previous discussion, I focused on data parallelism with no shared state, and where every element is being processed exactly the same. Unfortunately, there are many common cases where this does not happen.  If we are dealing with a loop that requires early termination, extra care is required when parallelizing. Often, while processing in a loop, once a certain condition is met, it is no longer necessary to continue processing.  This may be a matter of finding a specific element within the collection, or reaching some error case.  The important distinction here is that, it is often impossible to know until runtime, what set of elements needs to be processed. In my initial discussion of data parallelism, I mentioned that this technique is a candidate when you can decompose the problem based on the data involved, and you wish to apply a single operation concurrently on all of the elements of a collection.  This covers many of the potential cases, but sometimes, after processing some of the elements, we need to stop processing. As an example, lets go back to our previous Parallel.ForEach example with contacting a customer.  However, this time, we’ll change the requirements slightly.  In this case, we’ll add an extra condition – if the store is unable to email the customer, we will exit gracefully.  The thinking here, of course, is that if the store is currently unable to email, the next time this operation runs, it will handle the same situation, so we can just skip our processing entirely.  The original, serial case, with this extra condition, might look something like the following: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) break; customer.LastEmailContact = DateTime.Now; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re processing our loop, but at any point, if we fail to send our email successfully, we just abandon this process, and assume that it will get handled correctly the next time our routine is run.  If we try to parallelize this using Parallel.ForEach, as we did previously, we’ll run into an error almost immediately: the break statement we’re using is only valid when enclosed within an iteration statement, such as foreach.  When we switch to Parallel.ForEach, we’re no longer within an iteration statement – we’re a delegate running in a method. This needs to be handled slightly differently when parallelized.  Instead of using the break statement, we need to utilize a new class in the Task Parallel Library: ParallelLoopState.  The ParallelLoopState class is intended to allow concurrently running loop bodies a way to interact with each other, and provides us with a way to break out of a loop.  In order to use this, we will use a different overload of Parallel.ForEach which takes an IEnumerable<T> and an Action<T, ParallelLoopState> instead of an Action<T>.  Using this, we can parallelize the above operation by doing: Parallel.ForEach(customers, (customer, parallelLoopState) => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) parallelLoopState.Break(); else customer.LastEmailContact = DateTime.Now; } }); There are a couple of important points here.  First, we didn’t actually instantiate the ParallelLoopState instance.  It was provided directly to us via the Parallel class.  All we needed to do was change our lambda expression to reflect that we want to use the loop state, and the Parallel class creates an instance for our use.  We also needed to change our logic slightly when we call Break().  Since Break() doesn’t stop the program flow within our block, we needed to add an else case to only set the property in customer when we succeeded.  This same technique can be used to break out of a Parallel.For loop. That being said, there is a huge difference between using ParallelLoopState to cause early termination and to use break in a standard iteration statement.  When dealing with a loop serially, break will immediately terminate the processing within the closest enclosing loop statement.  Calling ParallelLoopState.Break(), however, has a very different behavior. The issue is that, now, we’re no longer processing one element at a time.  If we break in one of our threads, there are other threads that will likely still be executing.  This leads to an important observation about termination of parallel code: Early termination in parallel routines is not immediate.  Code will continue to run after you request a termination. This may seem problematic at first, but it is something you just need to keep in mind while designing your routine.  ParallelLoopState.Break() should be thought of as a request.  We are telling the runtime that no elements that were in the collection past the element we’re currently processing need to be processed, and leaving it up to the runtime to decide how to handle this as gracefully as possible.  Although this may seem problematic at first, it is a good thing.  If the runtime tried to immediately stop processing, many of our elements would be partially processed.  It would be like putting a return statement in a random location throughout our loop body – which could have horrific consequences to our code’s maintainability. In order to understand and effectively write parallel routines, we, as developers, need a subtle, but profound shift in our thinking.  We can no longer think in terms of sequential processes, but rather need to think in terms of requests to the system that may be handled differently than we’d first expect.  This is more natural to developers who have dealt with asynchronous models previously, but is an important distinction when moving to concurrent programming models. As an example, I’ll discuss the Break() method.  ParallelLoopState.Break() functions in a way that may be unexpected at first.  When you call Break() from a loop body, the runtime will continue to process all elements of the collection that were found prior to the element that was being processed when the Break() method was called.  This is done to keep the behavior of the Break() method as close to the behavior of the break statement as possible. We can see the behavior in this simple code: var collection = Enumerable.Range(0, 20); var pResult = Parallel.ForEach(collection, (element, state) => { if (element > 10) { Console.WriteLine("Breaking on {0}", element); state.Break(); } Console.WriteLine(element); }); If we run this, we get a result that may seem unexpected at first: 0 2 1 5 6 3 4 10 Breaking on 11 11 Breaking on 12 12 9 Breaking on 13 13 7 8 Breaking on 15 15 What is occurring here is that we loop until we find the first element where the element is greater than 10.  In this case, this was found, the first time, when one of our threads reached element 11.  It requested that the loop stop by calling Break() at this point.  However, the loop continued processing until all of the elements less than 11 were completed, then terminated.  This means that it will guarantee that elements 9, 7, and 8 are completed before it stops processing.  You can see our other threads that were running each tried to break as well, but since Break() was called on the element with a value of 11, it decides which elements (0-10) must be processed. If this behavior is not desirable, there is another option.  Instead of calling ParallelLoopState.Break(), you can call ParallelLoopState.Stop().  The Stop() method requests that the runtime terminate as soon as possible , without guaranteeing that any other elements are processed.  Stop() will not stop the processing within an element, so elements already being processed will continue to be processed.  It will prevent new elements, even ones found earlier in the collection, from being processed.  Also, when Stop() is called, the ParallelLoopState’s IsStopped property will return true.  This lets longer running processes poll for this value, and return after performing any necessary cleanup. The basic rule of thumb for choosing between Break() and Stop() is the following. Use ParallelLoopState.Stop() when possible, since it terminates more quickly.  This is particularly useful in situations where you are searching for an element or a condition in the collection.  Once you’ve found it, you do not need to do any other processing, so Stop() is more appropriate. Use ParallelLoopState.Break() if you need to more closely match the behavior of the C# break statement. Both methods behave differently than our C# break statement.  Unfortunately, when parallelizing a routine, more thought and care needs to be put into every aspect of your routine than you may otherwise expect.  This is due to my second observation: Parallelizing a routine will almost always change its behavior. This sounds crazy at first, but it’s a concept that’s so simple its easy to forget.  We’re purposely telling the system to process more than one thing at the same time, which means that the sequence in which things get processed is no longer deterministic.  It is easy to change the behavior of your routine in very subtle ways by introducing parallelism.  Often, the changes are not avoidable, even if they don’t have any adverse side effects.  This leads to my final observation for this post: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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• Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects

  - by Reed

  In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations.  Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects.  By introducing Parallelism to our declarative model, we add some extra complexity.  This, in turn, adds some extra requirements that must be addressed. In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ . LINQ to Objects is mainly built upon a single class: Enumerable.  The Enumerable class is a static class that defines a large set of extension methods, nearly all of which work upon an IEnumerable<T>.  Many of these methods return a new IEnumerable<T>, allowing the methods to be chained together into a fluent style interface.  This is what allows us to write statements that chain together, and lead to the nice declarative programming model of LINQ: double min = collection .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Other LINQ variants work in a similar fashion.  For example, most data-oriented LINQ providers are built upon an implementation of IQueryable<T>, which allows the database provider to turn a LINQ statement into an underlying SQL query, to be performed directly on the remote database. PLINQ is similar, but instead of being built upon the Enumerable class, most of PLINQ is built upon a new static class: ParallelEnumerable.  When using PLINQ, you typically begin with any collection which implements IEnumerable<T>, and convert it to a new type using an extension method defined on ParallelEnumerable: AsParallel().  This method takes any IEnumerable<T>, and converts it into a ParallelQuery<T>, the core class for PLINQ.  There is a similar ParallelQuery class for working with non-generic IEnumerable implementations. This brings us to our first subtle, but important difference between PLINQ and LINQ – PLINQ always works upon specific types, which must be explicitly created. Typically, the type you’ll use with PLINQ is ParallelQuery<T>, but it can sometimes be a ParallelQuery or an OrderedParallelQuery<T>.  Instead of dealing with an interface, implemented by an unknown class, we’re dealing with a specific class type.  This works seamlessly from a usage standpoint – ParallelQuery<T> implements IEnumerable<T>, so you can always “switch back” to an IEnumerable<T>.  The difference only arises at the beginning of our parallelization.  When we’re using LINQ, and we want to process a normal collection via PLINQ, we need to explicitly convert the collection into a ParallelQuery<T> by calling AsParallel().  There is an important consideration here – AsParallel() does not need to be called on your specific collection, but rather any IEnumerable<T>.  This allows you to place it anywhere in the chain of methods involved in a LINQ statement, not just at the beginning.  This can be useful if you have an operation which will not parallelize well or is not thread safe.  For example, the following is perfectly valid, and similar to our previous examples: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); However, if SomeOperation() is not thread safe, we could just as easily do: double min = collection .Select(item => item.SomeOperation()) .AsParallel() .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); In this case, we’re using standard LINQ to Objects for the Select(…) method, then converting the results of that map routine to a ParallelQuery<T>, and processing our filter (the Where method) and our aggregation (the Min method) in parallel. PLINQ also provides us with a way to convert a ParallelQuery<T> back into a standard IEnumerable<T>, forcing sequential processing via standard LINQ to Objects.  If SomeOperation() was thread-safe, but PerformComputation() was not thread-safe, we would need to handle this by using the AsEnumerable() method: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .AsEnumerable() .Min(item => item.PerformComputation()); Here, we’re converting our collection into a ParallelQuery<T>, doing our map operation (the Select(…) method) and our filtering in parallel, then converting the collection back into a standard IEnumerable<T>, which causes our aggregation via Min() to be performed sequentially. This could also be written as two statements, as well, which would allow us to use the language integrated syntax for the first portion: var tempCollection = from item in collection.AsParallel() let e = item.SomeOperation() where (e.SomeProperty > 6 && e.SomeProperty < 24) select e; double min = tempCollection.AsEnumerable().Min(item => item.PerformComputation()); This allows us to use the standard LINQ style language integrated query syntax, but control whether it’s performed in parallel or serial by adding AsParallel() and AsEnumerable() appropriately. The second important difference between PLINQ and LINQ deals with order preservation.  PLINQ, by default, does not preserve the order of of source collection. This is by design.  In order to process a collection in parallel, the system needs to naturally deal with multiple elements at the same time.  Maintaining the original ordering of the sequence adds overhead, which is, in many cases, unnecessary.  Therefore, by default, the system is allowed to completely change the order of your sequence during processing.  If you are doing a standard query operation, this is usually not an issue.  However, there are times when keeping a specific ordering in place is important.  If this is required, you can explicitly request the ordering be preserved throughout all operations done on a ParallelQuery<T> by using the AsOrdered() extension method.  This will cause our sequence ordering to be preserved. For example, suppose we wanted to take a collection, perform an expensive operation which converts it to a new type, and display the first 100 elements.  In LINQ to Objects, our code might look something like: // Using IEnumerable<SourceClass> collection IEnumerable<ResultClass> results = collection .Select(e => e.CreateResult()) .Take(100); If we just converted this to a parallel query naively, like so: IEnumerable<ResultClass> results = collection .AsParallel() .Select(e => e.CreateResult()) .Take(100); We could very easily get a very different, and non-reproducable, set of results, since the ordering of elements in the input collection is not preserved.  To get the same results as our original query, we need to use: IEnumerable<ResultClass> results = collection .AsParallel() .AsOrdered() .Select(e => e.CreateResult()) .Take(100); This requests that PLINQ process our sequence in a way that verifies that our resulting collection is ordered as if it were processed serially.  This will cause our query to run slower, since there is overhead involved in maintaining the ordering.  However, in this case, it is required, since the ordering is required for correctness. PLINQ is incredibly useful.  It allows us to easily take nearly any LINQ to Objects query and run it in parallel, using the same methods and syntax we’ve used previously.  There are some important differences in operation that must be considered, however – it is not a free pass to parallelize everything.  When using PLINQ in order to parallelize your routines declaratively, the same guideline I mentioned before still applies: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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• Parallelism in .NET – Part 9, Configuration in PLINQ and TPL

  - by Reed

  Parallel LINQ and the Task Parallel Library contain many options for configuration.  Although the default configuration options are often ideal, there are times when customizing the behavior is desirable.  Both frameworks provide full configuration support. When working with Data Parallelism, there is one primary configuration option we often need to control – the number of threads we want the system to use when parallelizing our routine.  By default, PLINQ and the TPL both use the ThreadPool to schedule tasks.  Given the major improvements in the ThreadPool in CLR 4, this default behavior is often ideal.  However, there are times that the default behavior is not appropriate.  For example, if you are working on multiple threads simultaneously, and want to schedule parallel operations from within both threads, you might want to consider restricting each parallel operation to using a subset of the processing cores of the system.  Not doing this might over-parallelize your routine, which leads to inefficiencies from having too many context switches. In the Task Parallel Library, configuration is handled via the ParallelOptions class.  All of the methods of the Parallel class have an overload which accepts a ParallelOptions argument. We configure the Parallel class by setting the ParallelOptions.MaxDegreeOfParallelism property.  For example, let’s revisit one of the simple data parallel examples from Part 2: Parallel.For(0, pixelData.GetUpperBound(0), row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re looping through an image, and calling a method on each pixel in the image.  If this was being done on a separate thread, and we knew another thread within our system was going to be doing a similar operation, we likely would want to restrict this to using half of the cores on the system.  This could be accomplished easily by doing: var options = new ParallelOptions(); options.MaxDegreeOfParallelism = Math.Max(Environment.ProcessorCount / 2, 1); Parallel.For(0, pixelData.GetUpperBound(0), options, row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Now, we’re restricting this routine to using no more than half the cores in our system.  Note that I included a check to prevent a single core system from supplying zero; without this check, we’d potentially cause an exception.  I also did not hard code a specific value for the MaxDegreeOfParallelism property.  One of our goals when parallelizing a routine is allowing it to scale on better hardware.  Specifying a hard-coded value would contradict that goal. Parallel LINQ also supports configuration, and in fact, has quite a few more options for configuring the system.  The main configuration option we most often need is the same as our TPL option: we need to supply the maximum number of processing threads.  In PLINQ, this is done via a new extension method on ParallelQuery<T>: ParallelEnumerable.WithDegreeOfParallelism. Let’s revisit our declarative data parallelism sample from Part 6: double min = collection.AsParallel().Min(item => item.PerformComputation()); Here, we’re performing a computation on each element in the collection, and saving the minimum value of this operation.  If we wanted to restrict this to a limited number of threads, we would add our new extension method: int maxThreads = Math.Max(Environment.ProcessorCount / 2, 1); double min = collection .AsParallel() .WithDegreeOfParallelism(maxThreads) .Min(item => item.PerformComputation()); This automatically restricts the PLINQ query to half of the threads on the system. PLINQ provides some additional configuration options.  By default, PLINQ will occasionally revert to processing a query in parallel.  This occurs because many queries, if parallelized, typically actually cause an overall slowdown compared to a serial processing equivalent.  By analyzing the “shape” of the query, PLINQ often decides to run a query serially instead of in parallel.  This can occur for (taken from MSDN): Queries that contain a Select, indexed Where, indexed SelectMany, or ElementAt clause after an ordering or filtering operator that has removed or rearranged original indices. Queries that contain a Take, TakeWhile, Skip, SkipWhile operator and where indices in the source sequence are not in the original order. Queries that contain Zip or SequenceEquals, unless one of the data sources has an originally ordered index and the other data source is indexable (i.e. an array or IList(T)). Queries that contain Concat, unless it is applied to indexable data sources. Queries that contain Reverse, unless applied to an indexable data source. If the specific query follows these rules, PLINQ will run the query on a single thread.  However, none of these rules look at the specific work being done in the delegates, only at the “shape” of the query.  There are cases where running in parallel may still be beneficial, even if the shape is one where it typically parallelizes poorly.  In these cases, you can override the default behavior by using the WithExecutionMode extension method.  This would be done like so: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .Select(i => i.PerformComputation()) .Reverse(); Here, the default behavior would be to not parallelize the query unless collection implemented IList<T>.  We can force this to run in parallel by adding the WithExecutionMode extension method in the method chain. Finally, PLINQ has the ability to configure how results are returned.  When a query is filtering or selecting an input collection, the results will need to be streamed back into a single IEnumerable<T> result.  For example, the method above returns a new, reversed collection.  In this case, the processing of the collection will be done in parallel, but the results need to be streamed back to the caller serially, so they can be enumerated on a single thread. This streaming introduces overhead.  IEnumerable<T> isn’t designed with thread safety in mind, so the system needs to handle merging the parallel processes back into a single stream, which introduces synchronization issues.  There are two extremes of how this could be accomplished, but both extremes have disadvantages. The system could watch each thread, and whenever a thread produces a result, take that result and send it back to the caller.  This would mean that the calling thread would have access to the data as soon as data is available, which is the benefit of this approach.  However, it also means that every item is introducing synchronization overhead, since each item needs to be merged individually. On the other extreme, the system could wait until all of the results from all of the threads were ready, then push all of the results back to the calling thread in one shot.  The advantage here is that the least amount of synchronization is added to the system, which means the query will, on a whole, run the fastest.  However, the calling thread will have to wait for all elements to be processed, so this could introduce a long delay between when a parallel query begins and when results are returned. The default behavior in PLINQ is actually between these two extremes.  By default, PLINQ maintains an internal buffer, and chooses an optimal buffer size to maintain.  Query results are accumulated into the buffer, then returned in the IEnumerable<T> result in chunks.  This provides reasonably fast access to the results, as well as good overall throughput, in most scenarios. However, if we know the nature of our algorithm, we may decide we would prefer one of the other extremes.  This can be done by using the WithMergeOptions extension method.  For example, if we know that our PerformComputation() routine is very slow, but also variable in runtime, we may want to retrieve results as they are available, with no bufferring.  This can be done by changing our above routine to: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.NotBuffered) .Select(i => i.PerformComputation()) .Reverse(); On the other hand, if are already on a background thread, and we want to allow the system to maximize its speed, we might want to allow the system to fully buffer the results: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.FullyBuffered) .Select(i => i.PerformComputation()) .Reverse(); Notice, also, that you can specify multiple configuration options in a parallel query.  By chaining these extension methods together, we generate a query that will always run in parallel, and will always complete before making the results available in our IEnumerable<T>.

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• Parallelism in .NET – Part 2, Simple Imperative Data Parallelism

  - by Reed

  In my discussion of Decomposition of the problem space, I mentioned that Data Decomposition is often the simplest abstraction to use when trying to parallelize a routine.  If a problem can be decomposed based off the data, we will often want to use what MSDN refers to as Data Parallelism as our strategy for implementing our routine.  The Task Parallel Library in .NET 4 makes implementing Data Parallelism, for most cases, very simple. Data Parallelism is the main technique we use to parallelize a routine which can be decomposed based off data.  Data Parallelism refers to taking a single collection of data, and having a single operation be performed concurrently on elements in the collection.  One side note here: Data Parallelism is also sometimes referred to as the Loop Parallelism Pattern or Loop-level Parallelism.  In general, for this series, I will try to use the terminology used in the MSDN Documentation for the Task Parallel Library.  This should make it easier to investigate these topics in more detail. Once we’ve determined we have a problem that, potentially, can be decomposed based on data, implementation using Data Parallelism in the TPL is quite simple.  Let’s take our example from the Data Decomposition discussion – a simple contrast stretching filter.  Here, we have a collection of data (pixels), and we need to run a simple operation on each element of the pixel.  Once we know the minimum and maximum values, we most likely would have some simple code like the following: for (int row=0; row < pixelData.GetUpperBound(0); ++row) { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This simple routine loops through a two dimensional array of pixelData, and calls the AdjustContrast routine on each pixel. As I mentioned, when you’re decomposing a problem space, most iteration statements are potentially candidates for data decomposition.  Here, we’re using two for loops – one looping through rows in the image, and a second nested loop iterating through the columns.  We then perform one, independent operation on each element based on those loop positions. This is a prime candidate – we have no shared data, no dependencies on anything but the pixel which we want to change.  Since we’re using a for loop, we can easily parallelize this using the Parallel.For method in the TPL: Parallel.For(0, pixelData.GetUpperBound(0), row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Here, by simply changing our first for loop to a call to Parallel.For, we can parallelize this portion of our routine.  Parallel.For works, as do many methods in the TPL, by creating a delegate and using it as an argument to a method.  In this case, our for loop iteration block becomes a delegate creating via a lambda expression.  This lets you write code that, superficially, looks similar to the familiar for loop, but functions quite differently at runtime. We could easily do this to our second for loop as well, but that may not be a good idea.  There is a balance to be struck when writing parallel code.  We want to have enough work items to keep all of our processors busy, but the more we partition our data, the more overhead we introduce.  In this case, we have an image of data – most likely hundreds of pixels in both dimensions.  By just parallelizing our first loop, each row of pixels can be run as a single task.  With hundreds of rows of data, we are providing fine enough granularity to keep all of our processors busy. If we parallelize both loops, we’re potentially creating millions of independent tasks.  This introduces extra overhead with no extra gain, and will actually reduce our overall performance.  This leads to my first guideline when writing parallel code: Partition your problem into enough tasks to keep each processor busy throughout the operation, but not more than necessary to keep each processor busy. Also note that I parallelized the outer loop.  I could have just as easily partitioned the inner loop.  However, partitioning the inner loop would have led to many more discrete work items, each with a smaller amount of work (operate on one pixel instead of one row of pixels).  My second guideline when writing parallel code reflects this: Partition your problem in a way to place the most work possible into each task. This typically means, in practice, that you will want to parallelize the routine at the “highest” point possible in the routine, typically the outermost loop.  If you’re looking at parallelizing methods which call other methods, you’ll want to try to partition your work high up in the stack – as you get into lower level methods, the performance impact of parallelizing your routines may not overcome the overhead introduced. Parallel.For works great for situations where we know the number of elements we’re going to process in advance.  If we’re iterating through an IList<T> or an array, this is a typical approach.  However, there are other iteration statements common in C#.  In many situations, we’ll use foreach instead of a for loop.  This can be more understandable and easier to read, but also has the advantage of working with collections which only implement IEnumerable<T>, where we do not know the number of elements involved in advance. As an example, lets take the following situation.  Say we have a collection of Customers, and we want to iterate through each customer, check some information about the customer, and if a certain case is met, send an email to the customer and update our instance to reflect this change.  Normally, this might look something like: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { theStore.EmailCustomer(customer); customer.LastEmailContact = DateTime.Now; } } Here, we’re doing a fair amount of work for each customer in our collection, but we don’t know how many customers exist.  If we assume that theStore.GetLastContact(customer) and theStore.EmailCustomer(customer) are both side-effect free, thread safe operations, we could parallelize this using Parallel.ForEach: Parallel.ForEach(customers, customer => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { theStore.EmailCustomer(customer); customer.LastEmailContact = DateTime.Now; } }); Just like Parallel.For, we rework our loop into a method call accepting a delegate created via a lambda expression.  This keeps our new code very similar to our original iteration statement, however, this will now execute in parallel.  The same guidelines apply with Parallel.ForEach as with Parallel.For. The other iteration statements, do and while, do not have direct equivalents in the Task Parallel Library.  These, however, are very easy to implement using Parallel.ForEach and the yield keyword. Most applications can benefit from implementing some form of Data Parallelism.  Iterating through collections and performing “work” is a very common pattern in nearly every application.  When the problem can be decomposed by data, we often can parallelize the workload by merely changing foreach statements to Parallel.ForEach method calls, and for loops to Parallel.For method calls.  Any time your program operates on a collection, and does a set of work on each item in the collection where that work is not dependent on other information, you very likely have an opportunity to parallelize your routine.

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• Parallelism in .NET – Part 4, Imperative Data Parallelism: Aggregation

  - by Reed

  In the article on simple data parallelism, I described how to perform an operation on an entire collection of elements in parallel.  Often, this is not adequate, as the parallel operation is going to be performing some form of aggregation. Simple examples of this might include taking the sum of the results of processing a function on each element in the collection, or finding the minimum of the collection given some criteria.  This can be done using the techniques described in simple data parallelism, however, special care needs to be taken into account to synchronize the shared data appropriately.  The Task Parallel Library has tools to assist in this synchronization. The main issue with aggregation when parallelizing a routine is that you need to handle synchronization of data.  Since multiple threads will need to write to a shared portion of data.  Suppose, for example, that we wanted to parallelize a simple loop that looked for the minimum value within a dataset: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This seems like a good candidate for parallelization, but there is a problem here.  If we just wrap this into a call to Parallel.ForEach, we’ll introduce a critical race condition, and get the wrong answer.  Let’s look at what happens here: // Buggy code! Do not use! double min = double.MaxValue; Parallel.ForEach(collection, item => { double value = item.PerformComputation(); min = System.Math.Min(min, value); }); This code has a fatal flaw: min will be checked, then set, by multiple threads simultaneously.  Two threads may perform the check at the same time, and set the wrong value for min.  Say we get a value of 1 in thread 1, and a value of 2 in thread 2, and these two elements are the first two to run.  If both hit the min check line at the same time, both will determine that min should change, to 1 and 2 respectively.  If element 1 happens to set the variable first, then element 2 sets the min variable, we’ll detect a min value of 2 instead of 1.  This can lead to wrong answers. Unfortunately, fixing this, with the Parallel.ForEach call we’re using, would require adding locking.  We would need to rewrite this like: // Safe, but slow double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach(collection, item => { double value = item.PerformComputation(); lock(syncObject) min = System.Math.Min(min, value); }); This will potentially add a huge amount of overhead to our calculation.  Since we can potentially block while waiting on the lock for every single iteration, we will most likely slow this down to where it is actually quite a bit slower than our serial implementation.  The problem is the lock statement – any time you use lock(object), you’re almost assuring reduced performance in a parallel situation.  This leads to two observations I’ll make: When parallelizing a routine, try to avoid locks. That being said: Always add any and all required synchronization to avoid race conditions. These two observations tend to be opposing forces – we often need to synchronize our algorithms, but we also want to avoid the synchronization when possible.  Looking at our routine, there is no way to directly avoid this lock, since each element is potentially being run on a separate thread, and this lock is necessary in order for our routine to function correctly every time. However, this isn’t the only way to design this routine to implement this algorithm.  Realize that, although our collection may have thousands or even millions of elements, we have a limited number of Processing Elements (PE).  Processing Element is the standard term for a hardware element which can process and execute instructions.  This typically is a core in your processor, but many modern systems have multiple hardware execution threads per core.  The Task Parallel Library will not execute the work for each item in the collection as a separate work item. Instead, when Parallel.ForEach executes, it will partition the collection into larger “chunks” which get processed on different threads via the ThreadPool.  This helps reduce the threading overhead, and help the overall speed.  In general, the Parallel class will only use one thread per PE in the system. Given the fact that there are typically fewer threads than work items, we can rethink our algorithm design.  We can parallelize our algorithm more effectively by approaching it differently.  Because the basic aggregation we are doing here (Min) is communitive, we do not need to perform this in a given order.  We knew this to be true already – otherwise, we wouldn’t have been able to parallelize this routine in the first place.  With this in mind, we can treat each thread’s work independently, allowing each thread to serially process many elements with no locking, then, after all the threads are complete, “merge” together the results. This can be accomplished via a different set of overloads in the Parallel class: Parallel.ForEach<TSource,TLocal>.  The idea behind these overloads is to allow each thread to begin by initializing some local state (TLocal).  The thread will then process an entire set of items in the source collection, providing that state to the delegate which processes an individual item.  Finally, at the end, a separate delegate is run which allows you to handle merging that local state into your final results. To rewriting our routine using Parallel.ForEach<TSource,TLocal>, we need to provide three delegates instead of one.  The most basic version of this function is declared as: public static ParallelLoopResult ForEach<TSource, TLocal>( IEnumerable<TSource> source, Func<TLocal> localInit, Func<TSource, ParallelLoopState, TLocal, TLocal> body, Action<TLocal> localFinally ) The first delegate (the localInit argument) is defined as Func<TLocal>.  This delegate initializes our local state.  It should return some object we can use to track the results of a single thread’s operations. The second delegate (the body argument) is where our main processing occurs, although now, instead of being an Action<T>, we actually provide a Func<TSource, ParallelLoopState, TLocal, TLocal> delegate.  This delegate will receive three arguments: our original element from the collection (TSource), a ParallelLoopState which we can use for early termination, and the instance of our local state we created (TLocal).  It should do whatever processing you wish to occur per element, then return the value of the local state after processing is completed. The third delegate (the localFinally argument) is defined as Action<TLocal>.  This delegate is passed our local state after it’s been processed by all of the elements this thread will handle.  This is where you can merge your final results together.  This may require synchronization, but now, instead of synchronizing once per element (potentially millions of times), you’ll only have to synchronize once per thread, which is an ideal situation. Now that I’ve explained how this works, lets look at the code: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Although this is a bit more complicated than the previous version, it is now both thread-safe, and has minimal locking.  This same approach can be used by Parallel.For, although now, it’s Parallel.For<TLocal>.  When working with Parallel.For<TLocal>, you use the same triplet of delegates, with the same purpose and results. Also, many times, you can completely avoid locking by using a method of the Interlocked class to perform the final aggregation in an atomic operation.  The MSDN example demonstrating this same technique using Parallel.For uses the Interlocked class instead of a lock, since they are doing a sum operation on a long variable, which is possible via Interlocked.Add. By taking advantage of local state, we can use the Parallel class methods to parallelize algorithms such as aggregation, which, at first, may seem like poor candidates for parallelization.  Doing so requires careful consideration, and often requires a slight redesign of the algorithm, but the performance gains can be significant if handled in a way to avoid excessive synchronization.

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• Parallelism in .NET – Part 11, Divide and Conquer via Parallel.Invoke

  - by Reed

  Many algorithms are easily written to work via recursion.  For example, most data-oriented tasks where a tree of data must be processed are much more easily handled by starting at the root, and recursively “walking” the tree.  Some algorithms work this way on flat data structures, such as arrays, as well.  This is a form of divide and conquer: an algorithm design which is based around breaking up a set of work recursively, “dividing” the total work in each recursive step, and “conquering” the work when the remaining work is small enough to be solved easily. Recursive algorithms, especially ones based on a form of divide and conquer, are often a very good candidate for parallelization. This is apparent from a common sense standpoint.  Since we’re dividing up the total work in the algorithm, we have an obvious, built-in partitioning scheme.  Once partitioned, the data can be worked upon independently, so there is good, clean isolation of data. Implementing this type of algorithm is fairly simple.  The Parallel class in .NET 4 includes a method suited for this type of operation: Parallel.Invoke.  This method works by taking any number of delegates defined as an Action, and operating them all in parallel.  The method returns when every delegate has completed: Parallel.Invoke( () => { Console.WriteLine("Action 1 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 2 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 3 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); } ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Running this simple example demonstrates the ease of using this method.  For example, on my system, I get three separate thread IDs when running the above code.  By allowing any number of delegates to be executed directly, concurrently, the Parallel.Invoke method provides us an easy way to parallelize any algorithm based on divide and conquer.  We can divide our work in each step, and execute each task in parallel, recursively. For example, suppose we wanted to implement our own quicksort routine.  The quicksort algorithm can be designed based on divide and conquer.  In each iteration, we pick a pivot point, and use that to partition the total array.  We swap the elements around the pivot, then recursively sort the lists on each side of the pivot.  For example, let’s look at this simple, sequential implementation of quicksort: public static void QuickSort<T>(T[] array) where T : IComparable<T> { QuickSortInternal(array, 0, array.Length - 1); } private static void QuickSortInternal<T>(T[] array, int left, int right) where T : IComparable<T> { if (left >= right) { return; } SwapElements(array, left, (left + right) / 2); int last = left; for (int current = left + 1; current <= right; ++current) { if (array[current].CompareTo(array[left]) < 0) { ++last; SwapElements(array, last, current); } } SwapElements(array, left, last); QuickSortInternal(array, left, last - 1); QuickSortInternal(array, last + 1, right); } static void SwapElements<T>(T[] array, int i, int j) { T temp = array[i]; array[i] = array[j]; array[j] = temp; } Here, we implement the quicksort algorithm in a very common, divide and conquer approach.  Running this against the built-in Array.Sort routine shows that we get the exact same answers (although the framework’s sort routine is slightly faster).  On my system, for example, I can use framework’s sort to sort ten million random doubles in about 7.3s, and this implementation takes about 9.3s on average. Looking at this routine, though, there is a clear opportunity to parallelize.  At the end of QuickSortInternal, we recursively call into QuickSortInternal with each partition of the array after the pivot is chosen.  This can be rewritten to use Parallel.Invoke by simply changing it to: // Code above is unchanged... SwapElements(array, left, last); Parallel.Invoke( () => QuickSortInternal(array, left, last - 1), () => QuickSortInternal(array, last + 1, right) ); } This routine will now run in parallel.  When executing, we now see the CPU usage across all cores spike while it executes.  However, there is a significant problem here – by parallelizing this routine, we took it from an execution time of 9.3s to an execution time of approximately 14 seconds!  We’re using more resources as seen in the CPU usage, but the overall result is a dramatic slowdown in overall processing time. This occurs because parallelization adds overhead.  Each time we split this array, we spawn two new tasks to parallelize this algorithm!  This is far, far too many tasks for our cores to operate upon at a single time.  In effect, we’re “over-parallelizing” this routine.  This is a common problem when working with divide and conquer algorithms, and leads to an important observation: When parallelizing a recursive routine, take special care not to add more tasks than necessary to fully utilize your system. This can be done with a few different approaches, in this case.  Typically, the way to handle this is to stop parallelizing the routine at a certain point, and revert back to the serial approach.  Since the first few recursions will all still be parallelized, our “deeper” recursive tasks will be running in parallel, and can take full advantage of the machine.  This also dramatically reduces the overhead added by parallelizing, since we’re only adding overhead for the first few recursive calls.  There are two basic approaches we can take here.  The first approach would be to look at the total work size, and if it’s smaller than a specific threshold, revert to our serial implementation.  In this case, we could just check right-left, and if it’s under a threshold, call the methods directly instead of using Parallel.Invoke. The second approach is to track how “deep” in the “tree” we are currently at, and if we are below some number of levels, stop parallelizing.  This approach is a more general-purpose approach, since it works on routines which parse trees as well as routines working off of a single array, but may not work as well if a poor partitioning strategy is chosen or the tree is not balanced evenly. This can be written very easily.  If we pass a maxDepth parameter into our internal routine, we can restrict the amount of times we parallelize by changing the recursive call to: // Code above is unchanged... SwapElements(array, left, last); if (maxDepth < 1) { QuickSortInternal(array, left, last - 1, maxDepth); QuickSortInternal(array, last + 1, right, maxDepth); } else { --maxDepth; Parallel.Invoke( () => QuickSortInternal(array, left, last - 1, maxDepth), () => QuickSortInternal(array, last + 1, right, maxDepth)); } We no longer allow this to parallelize indefinitely – only to a specific depth, at which time we revert to a serial implementation.  By starting the routine with a maxDepth equal to Environment.ProcessorCount, we can restrict the total amount of parallel operations significantly, but still provide adequate work for each processing core. With this final change, my timings are much better.  On average, I get the following timings: Framework via Array.Sort: 7.3 seconds Serial Quicksort Implementation: 9.3 seconds Naive Parallel Implementation: 14 seconds Parallel Implementation Restricting Depth: 4.7 seconds Finally, we are now faster than the framework’s Array.Sort implementation.

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• Building and Deploying Windows Azure Web Sites using Git and GitHub for Windows

  - by shiju

  Microsoft Windows Azure team has released a new version of Windows Azure which is providing many excellent features. The new Windows Azure provides Web Sites which allows you to deploy up to 10 web sites  for free in a multitenant shared environment and you can easily upgrade this web site to a private, dedicated virtual server when the traffic is grows. The Meet Windows Azure Fact Sheet provides the following information about a Windows Azure Web Site: Windows Azure Web Sites enable developers to easily build and deploy websites with support for multiple frameworks and popular open source applications, including ASP.NET, PHP and Node.js. With just a few clicks, developers can take advantage of Windows Azure’s global scale without having to worry about operations, servers or infrastructure. It is easy to deploy existing sites, if they run on Internet Information Services (IIS) 7, or to build new sites, with a free offer of 10 websites upon signup, with the ability to scale up as needed with reserved instances. Windows Azure Web Sites includes support for the following: Multiple frameworks including ASP.NET, PHP and Node.js Popular open source software apps including WordPress, Joomla!, Drupal, Umbraco and DotNetNuke Windows Azure SQL Database and MySQL databases Multiple types of developer tools and protocols including Visual Studio, Git, FTP, Visual Studio Team Foundation Services and Microsoft WebMatrix Signup to Windows and Enable Azure Web Sites You can signup for a 90 days free trial account in Windows Azure from here. After creating an account in Windows Azure, go to https://account.windowsazure.com/ , and select to preview features to view the available previews. In the Web Sites section of the preview features, click “try it now” which will enables the web sites feature Create Web Site in Windows Azure To create a web sites, login to the Windows Azure portal, and select Web Sites from and click New icon from the left corner  Click WEB SITE, QUICK CREATE and put values for URL and REGION dropdown. You can see the all web sites from the dashboard of the Windows Azure portal Set up Git Publishing Select your web site from the dashboard, and select Set up Git publishing To enable Git publishing , you must give user name and password which will initialize a Git repository Clone Git Repository We can use GitHub for Windows to publish apps to non-GitHub repositories which is well explained by Phil Haack on his blog post. Here we are going to deploy the web site using GitHub for Windows. Let’s clone a Git repository using the Git Url which will be getting from the Windows Azure portal. Let’s copy the Git url and execute the “git clone” with the git url. You can use the Git Shell provided by GitHub for Windows. To get it, right on the GitHub for Windows, and select open shell here as shown in the below picture. When executing the Git Clone command, it will ask for a password where you have to give password which specified in the Windows Azure portal. After cloning the GIT repository, you can drag and drop the local Git repository folder to GitHub for Windows GUI. This will automatically add the Windows Azure Web Site repository onto GitHub for Windows where you can commit your changes and publish your web sites to Windows Azure. Publish the Web Site using GitHub for Windows We can add multiple framework level files including ASP.NET, PHP and Node.js, to the local repository folder can easily publish to Windows Azure from GitHub for Windows GUI. For this demo, let me just add a simple Node.js file named Server.js which handles few request handlers. 1: var http = require('http'); 2: var port=process.env.PORT; 3: var querystring = require('querystring'); 4: var utils = require('util'); 5: var url = require("url"); 6:   7: var server = http.createServer(function(req, res) { 8: switch (req.url) { //checking the request url 9: case '/': 10: homePageHandler (req, res); //handler for home page 11: break; 12: case '/register': 13: registerFormHandler (req, res);//hamdler for register 14: break; 15: default: 16: nofoundHandler (req, res);// handler for 404 not found 17: break; 18: } 19: }); 20: server.listen(port); 21: //function to display the html form 22: function homePageHandler (req, res) { 23: console.log('Request handler home was called.'); 24: res.writeHead(200, {'Content-Type': 'text/html'}); 25: var body = '<html>'+ 26: '<head>'+ 27: '<meta http-equiv="Content-Type" content="text/html; '+ 28: 'charset=UTF-8" />'+ 29: '</head>'+ 30: '<body>'+ 31: '<form action="/register" method="post">'+ 32: 'Name:<input type=text value="" name="name" size=15></br>'+ 33: 'Email:<input type=text value="" name="email" size=15></br>'+ 34: '<input type="submit" value="Submit" />'+ 35: '</form>'+ 36: '</body>'+ 37: '</html>'; 38: //response content 39: res.end(body); 40: } 41: //handler for Post request 42: function registerFormHandler (req, res) { 43: console.log('Request handler register was called.'); 44: var pathname = url.parse(req.url).pathname; 45: console.log("Request for " + pathname + " received."); 46: var postData = ""; 47: req.on('data', function(chunk) { 48: // append the current chunk of data to the postData variable 49: postData += chunk.toString(); 50: }); 51: req.on('end', function() { 52: // doing something with the posted data 53: res.writeHead(200, "OK", {'Content-Type': 'text/html'}); 54: // parse the posted data 55: var decodedBody = querystring.parse(postData); 56: // output the decoded data to the HTTP response 57: res.write('<html><head><title>Post data</title></head><body><pre>'); 58: res.write(utils.inspect(decodedBody)); 59: res.write('</pre></body></html>'); 60: res.end(); 61: }); 62: } 63: //Error handler for 404 no found 64: function nofoundHandler(req, res) { 65: console.log('Request handler nofound was called.'); 66: res.writeHead(404, {'Content-Type': 'text/plain'}); 67: res.end('404 Error - Request handler not found'); 68: } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } If there is any change in the local repository folder, GitHub for Windows will automatically detect the changes. In the above step, we have just added a Server.js file so that GitHub for Windows will detect the changes. Let’s commit the changes to the local repository before publishing the web site to Windows Azure. After committed the all changes, you can click publish button which will publish the all changes to Windows Azure repository. The following screen shot shows deployment history from the Windows Azure portal.   GitHub for Windows is providing a sync button which can use for synchronizing between local repository and Windows Azure repository after making any commit on the local repository after any changes. Our web site is running after the deployment using Git Summary Windows Azure Web Sites lets the developers to easily build and deploy websites with support for multiple framework including ASP.NET, PHP and Node.js and can easily deploy the Web Sites using Visual Studio, Git, FTP, Visual Studio Team Foundation Services and Microsoft WebMatrix. In this demo, we have deployed a Node.js Web Site to Windows Azure using Git. We can use GitHub for Windows to publish apps to non-GitHub repositories and can use to publish Web SItes to Windows Azure.

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• Inheritance Mapping Strategies with Entity Framework Code First CTP5 Part 1: Table per Hierarchy (TPH)

  - by mortezam

  A simple strategy for mapping classes to database tables might be “one table for every entity persistent class.” This approach sounds simple enough and, indeed, works well until we encounter inheritance. Inheritance is such a visible structural mismatch between the object-oriented and relational worlds because object-oriented systems model both “is a” and “has a” relationships. SQL-based models provide only "has a" relationships between entities; SQL database management systems don’t support type inheritance—and even when it’s available, it’s usually proprietary or incomplete. There are three different approaches to representing an inheritance hierarchy: Table per Hierarchy (TPH): Enable polymorphism by denormalizing the SQL schema, and utilize a type discriminator column that holds type information. Table per Type (TPT): Represent "is a" (inheritance) relationships as "has a" (foreign key) relationships. Table per Concrete class (TPC): Discard polymorphism and inheritance relationships completely from the SQL schema.I will explain each of these strategies in a series of posts and this one is dedicated to TPH. In this series we'll deeply dig into each of these strategies and will learn about "why" to choose them as well as "how" to implement them. Hopefully it will give you a better idea about which strategy to choose in a particular scenario. Inheritance Mapping with Entity Framework Code FirstAll of the inheritance mapping strategies that we discuss in this series will be implemented by EF Code First CTP5. The CTP5 build of the new EF Code First library has been released by ADO.NET team earlier this month. EF Code-First enables a pretty powerful code-centric development workflow for working with data. I’m a big fan of the EF Code First approach, and I’m pretty excited about a lot of productivity and power that it brings. When it comes to inheritance mapping, not only Code First fully supports all the strategies but also gives you ultimate flexibility to work with domain models that involves inheritance. The fluent API for inheritance mapping in CTP5 has been improved a lot and now it's more intuitive and concise in compare to CTP4. A Note For Those Who Follow Other Entity Framework ApproachesIf you are following EF's "Database First" or "Model First" approaches, I still recommend to read this series since although the implementation is Code First specific but the explanations around each of the strategies is perfectly applied to all approaches be it Code First or others. A Note For Those Who are New to Entity Framework and Code-FirstIf you choose to learn EF you've chosen well. If you choose to learn EF with Code First you've done even better. To get started, you can find a great walkthrough by Scott Guthrie here and another one by ADO.NET team here. In this post, I assume you already setup your machine to do Code First development and also that you are familiar with Code First fundamentals and basic concepts. You might also want to check out my other posts on EF Code First like Complex Types and Shared Primary Key Associations. A Top Down Development ScenarioThese posts take a top-down approach; it assumes that you’re starting with a domain model and trying to derive a new SQL schema. Therefore, we start with an existing domain model, implement it in C# and then let Code First create the database schema for us. However, the mapping strategies described are just as relevant if you’re working bottom up, starting with existing database tables. I’ll show some tricks along the way that help you dealing with nonperfect table layouts. Let’s start with the mapping of entity inheritance. -- The Domain ModelIn our domain model, we have a BillingDetail base class which is abstract (note the italic font on the UML class diagram below). We do allow various billing types and represent them as subclasses of BillingDetail class. As for now, we support CreditCard and BankAccount: Implement the Object Model with Code First As always, we start with the POCO classes. Note that in our DbContext, I only define one DbSet for the base class which is BillingDetail. Code First will find the other classes in the hierarchy based on Reachability Convention. public abstract class BillingDetail  {     public int BillingDetailId { get; set; }     public string Owner { get; set; }             public string Number { get; set; } } public class BankAccount : BillingDetail {     public string BankName { get; set; }     public string Swift { get; set; } } public class CreditCard : BillingDetail {     public int CardType { get; set; }                     public string ExpiryMonth { get; set; }     public string ExpiryYear { get; set; } } public class InheritanceMappingContext : DbContext {     public DbSet<BillingDetail> BillingDetails { get; set; } } This object model is all that is needed to enable inheritance with Code First. If you put this in your application you would be able to immediately start working with the database and do CRUD operations. Before going into details about how EF Code First maps this object model to the database, we need to learn about one of the core concepts of inheritance mapping: polymorphic and non-polymorphic queries. Polymorphic Queries LINQ to Entities and EntitySQL, as object-oriented query languages, both support polymorphic queries—that is, queries for instances of a class and all instances of its subclasses, respectively. For example, consider the following query: IQueryable<BillingDetail> linqQuery = from b in context.BillingDetails select b; List<BillingDetail> billingDetails = linqQuery.ToList(); Or the same query in EntitySQL: string eSqlQuery = @"SELECT VAlUE b FROM BillingDetails AS b"; ObjectQuery<BillingDetail> objectQuery = ((IObjectContextAdapter)context).ObjectContext                                                                          .CreateQuery<BillingDetail>(eSqlQuery); List<BillingDetail> billingDetails = objectQuery.ToList(); linqQuery and eSqlQuery are both polymorphic and return a list of objects of the type BillingDetail, which is an abstract class but the actual concrete objects in the list are of the subtypes of BillingDetail: CreditCard and BankAccount. Non-polymorphic QueriesAll LINQ to Entities and EntitySQL queries are polymorphic which return not only instances of the specific entity class to which it refers, but all subclasses of that class as well. On the other hand, Non-polymorphic queries are queries whose polymorphism is restricted and only returns instances of a particular subclass. In LINQ to Entities, this can be specified by using OfType<T>() Method. For example, the following query returns only instances of BankAccount: IQueryable<BankAccount> query = from b in context.BillingDetails.OfType<BankAccount>() select b; EntitySQL has OFTYPE operator that does the same thing: string eSqlQuery = @"SELECT VAlUE b FROM OFTYPE(BillingDetails, Model.BankAccount) AS b"; In fact, the above query with OFTYPE operator is a short form of the following query expression that uses TREAT and IS OF operators: string eSqlQuery = @"SELECT VAlUE TREAT(b as Model.BankAccount)                       FROM BillingDetails AS b                       WHERE b IS OF(Model.BankAccount)"; (Note that in the above query, Model.BankAccount is the fully qualified name for BankAccount class. You need to change "Model" with your own namespace name.) Table per Class Hierarchy (TPH)An entire class hierarchy can be mapped to a single table. This table includes columns for all properties of all classes in the hierarchy. The concrete subclass represented by a particular row is identified by the value of a type discriminator column. You don’t have to do anything special in Code First to enable TPH. It's the default inheritance mapping strategy: This mapping strategy is a winner in terms of both performance and simplicity. It’s the best-performing way to represent polymorphism—both polymorphic and nonpolymorphic queries perform well—and it’s even easy to implement by hand. Ad-hoc reporting is possible without complex joins or unions. Schema evolution is straightforward. Discriminator Column As you can see in the DB schema above, Code First has to add a special column to distinguish between persistent classes: the discriminator. This isn’t a property of the persistent class in our object model; it’s used internally by EF Code First. By default, the column name is "Discriminator", and its type is string. The values defaults to the persistent class names —in this case, “BankAccount” or “CreditCard”. EF Code First automatically sets and retrieves the discriminator values. TPH Requires Properties in SubClasses to be Nullable in the Database TPH has one major problem: Columns for properties declared by subclasses will be nullable in the database. For example, Code First created an (INT, NULL) column to map CardType property in CreditCard class. However, in a typical mapping scenario, Code First always creates an (INT, NOT NULL) column in the database for an int property in persistent class. But in this case, since BankAccount instance won’t have a CardType property, the CardType field must be NULL for that row so Code First creates an (INT, NULL) instead. If your subclasses each define several non-nullable properties, the loss of NOT NULL constraints may be a serious problem from the point of view of data integrity. TPH Violates the Third Normal FormAnother important issue is normalization. We’ve created functional dependencies between nonkey columns, violating the third normal form. Basically, the value of Discriminator column determines the corresponding values of the columns that belong to the subclasses (e.g. BankName) but Discriminator is not part of the primary key for the table. As always, denormalization for performance can be misleading, because it sacrifices long-term stability, maintainability, and the integrity of data for immediate gains that may be also achieved by proper optimization of the SQL execution plans (in other words, ask your DBA). Generated SQL QueryLet's take a look at the SQL statements that EF Code First sends to the database when we write queries in LINQ to Entities or EntitySQL. For example, the polymorphic query for BillingDetails that you saw, generates the following SQL statement: SELECT  [Extent1].[Discriminator] AS [Discriminator],  [Extent1].[BillingDetailId] AS [BillingDetailId],  [Extent1].[Owner] AS [Owner],  [Extent1].[Number] AS [Number],  [Extent1].[BankName] AS [BankName],  [Extent1].[Swift] AS [Swift],  [Extent1].[CardType] AS [CardType],  [Extent1].[ExpiryMonth] AS [ExpiryMonth],  [Extent1].[ExpiryYear] AS [ExpiryYear] FROM [dbo].[BillingDetails] AS [Extent1] WHERE [Extent1].[Discriminator] IN ('BankAccount','CreditCard') Or the non-polymorphic query for the BankAccount subclass generates this SQL statement: SELECT  [Extent1].[BillingDetailId] AS [BillingDetailId],  [Extent1].[Owner] AS [Owner],  [Extent1].[Number] AS [Number],  [Extent1].[BankName] AS [BankName],  [Extent1].[Swift] AS [Swift] FROM [dbo].[BillingDetails] AS [Extent1] WHERE [Extent1].[Discriminator] = 'BankAccount' Note how Code First adds a restriction on the discriminator column and also how it only selects those columns that belong to BankAccount entity. Change Discriminator Column Data Type and Values With Fluent API Sometimes, especially in legacy schemas, you need to override the conventions for the discriminator column so that Code First can work with the schema. The following fluent API code will change the discriminator column name to "BillingDetailType" and the values to "BA" and "CC" for BankAccount and CreditCard respectively: protected override void OnModelCreating(System.Data.Entity.ModelConfiguration.ModelBuilder modelBuilder) {     modelBuilder.Entity<BillingDetail>()                 .Map<BankAccount>(m => m.Requires("BillingDetailType").HasValue("BA"))                 .Map<CreditCard>(m => m.Requires("BillingDetailType").HasValue("CC")); } Also, changing the data type of discriminator column is interesting. In the above code, we passed strings to HasValue method but this method has been defined to accepts a type of object: public void HasValue(object value); Therefore, if for example we pass a value of type int to it then Code First not only use our desired values (i.e. 1 & 2) in the discriminator column but also changes the column type to be (INT, NOT NULL): modelBuilder.Entity<BillingDetail>()             .Map<BankAccount>(m => m.Requires("BillingDetailType").HasValue(1))             .Map<CreditCard>(m => m.Requires("BillingDetailType").HasValue(2)); SummaryIn this post we learned about Table per Hierarchy as the default mapping strategy in Code First. The disadvantages of the TPH strategy may be too serious for your design—after all, denormalized schemas can become a major burden in the long run. Your DBA may not like it at all. In the next post, we will learn about Table per Type (TPT) strategy that doesn’t expose you to this problem. References ADO.NET team blog Java Persistence with Hibernate book a { text-decoration: none; } a:visited { color: Blue; } .title { padding-bottom: 5px; font-family: Segoe UI; font-size: 11pt; font-weight: bold; padding-top: 15px; } .code, .typeName { font-family: consolas; } .typeName { color: #2b91af; } .padTop5 { padding-top: 5px; } .padTop10 { padding-top: 10px; } p.MsoNormal { margin-top: 0in; margin-right: 0in; margin-bottom: 10.0pt; margin-left: 0in; line-height: 115%; font-size: 11.0pt; font-family: "Calibri" , "sans-serif"; }

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• Inheritance Mapping Strategies with Entity Framework Code First CTP5: Part 3 – Table per Concrete Type (TPC) and Choosing Strategy Guidelines

  - by mortezam

  This is the third (and last) post in a series that explains different approaches to map an inheritance hierarchy with EF Code First. I've described these strategies in previous posts: Part 1 – Table per Hierarchy (TPH) Part 2 – Table per Type (TPT)In today’s blog post I am going to discuss Table per Concrete Type (TPC) which completes the inheritance mapping strategies supported by EF Code First. At the end of this post I will provide some guidelines to choose an inheritance strategy mainly based on what we've learned in this series. TPC and Entity Framework in the Past Table per Concrete type is somehow the simplest approach suggested, yet using TPC with EF is one of those concepts that has not been covered very well so far and I've seen in some resources that it was even discouraged. The reason for that is just because Entity Data Model Designer in VS2010 doesn't support TPC (even though the EF runtime does). That basically means if you are following EF's Database-First or Model-First approaches then configuring TPC requires manually writing XML in the EDMX file which is not considered to be a fun practice. Well, no more. You'll see that with Code First, creating TPC is perfectly possible with fluent API just like other strategies and you don't need to avoid TPC due to the lack of designer support as you would probably do in other EF approaches. Table per Concrete Type (TPC)In Table per Concrete type (aka Table per Concrete class) we use exactly one table for each (nonabstract) class. All properties of a class, including inherited properties, can be mapped to columns of this table, as shown in the following figure: As you can see, the SQL schema is not aware of the inheritance; effectively, we’ve mapped two unrelated tables to a more expressive class structure. If the base class was concrete, then an additional table would be needed to hold instances of that class. I have to emphasize that there is no relationship between the database tables, except for the fact that they share some similar columns. TPC Implementation in Code First Just like the TPT implementation, we need to specify a separate table for each of the subclasses. We also need to tell Code First that we want all of the inherited properties to be mapped as part of this table. In CTP5, there is a new helper method on EntityMappingConfiguration class called MapInheritedProperties that exactly does this for us. Here is the complete object model as well as the fluent API to create a TPC mapping: public abstract class BillingDetail {     public int BillingDetailId { get; set; }     public string Owner { get; set; }     public string Number { get; set; } }          public class BankAccount : BillingDetail {     public string BankName { get; set; }     public string Swift { get; set; } }          public class CreditCard : BillingDetail {     public int CardType { get; set; }     public string ExpiryMonth { get; set; }     public string ExpiryYear { get; set; } }      public class InheritanceMappingContext : DbContext {     public DbSet<BillingDetail> BillingDetails { get; set; }              protected override void OnModelCreating(ModelBuilder modelBuilder)     {         modelBuilder.Entity<BankAccount>().Map(m =>         {             m.MapInheritedProperties();             m.ToTable("BankAccounts");         });         modelBuilder.Entity<CreditCard>().Map(m =>         {             m.MapInheritedProperties();             m.ToTable("CreditCards");         });                 } } The Importance of EntityMappingConfiguration ClassAs a side note, it worth mentioning that EntityMappingConfiguration class turns out to be a key type for inheritance mapping in Code First. Here is an snapshot of this class: namespace System.Data.Entity.ModelConfiguration.Configuration.Mapping {     public class EntityMappingConfiguration<TEntityType> where TEntityType : class     {         public ValueConditionConfiguration Requires(string discriminator);         public void ToTable(string tableName);         public void MapInheritedProperties();     } } As you have seen so far, we used its Requires method to customize TPH. We also used its ToTable method to create a TPT and now we are using its MapInheritedProperties along with ToTable method to create our TPC mapping. TPC Configuration is Not Done Yet!We are not quite done with our TPC configuration and there is more into this story even though the fluent API we saw perfectly created a TPC mapping for us in the database. To see why, let's start working with our object model. For example, the following code creates two new objects of BankAccount and CreditCard types and tries to add them to the database: using (var context = new InheritanceMappingContext()) {     BankAccount bankAccount = new BankAccount();     CreditCard creditCard = new CreditCard() { CardType = 1 };                      context.BillingDetails.Add(bankAccount);     context.BillingDetails.Add(creditCard);     context.SaveChanges(); } Running this code throws an InvalidOperationException with this message: The changes to the database were committed successfully, but an error occurred while updating the object context. The ObjectContext might be in an inconsistent state. Inner exception message: AcceptChanges cannot continue because the object's key values conflict with another object in the ObjectStateManager. Make sure that the key values are unique before calling AcceptChanges. The reason we got this exception is because DbContext.SaveChanges() internally invokes SaveChanges method of its internal ObjectContext. ObjectContext's SaveChanges method on its turn by default calls AcceptAllChanges after it has performed the database modifications. AcceptAllChanges method merely iterates over all entries in ObjectStateManager and invokes AcceptChanges on each of them. Since the entities are in Added state, AcceptChanges method replaces their temporary EntityKey with a regular EntityKey based on the primary key values (i.e. BillingDetailId) that come back from the database and that's where the problem occurs since both the entities have been assigned the same value for their primary key by the database (i.e. on both BillingDetailId = 1) and the problem is that ObjectStateManager cannot track objects of the same type (i.e. BillingDetail) with the same EntityKey value hence it throws. If you take a closer look at the TPC's SQL schema above, you'll see why the database generated the same values for the primary keys: the BillingDetailId column in both BankAccounts and CreditCards table has been marked as identity. How to Solve The Identity Problem in TPC As you saw, using SQL Server’s int identity columns doesn't work very well together with TPC since there will be duplicate entity keys when inserting in subclasses tables with all having the same identity seed. Therefore, to solve this, either a spread seed (where each table has its own initial seed value) will be needed, or a mechanism other than SQL Server’s int identity should be used. Some other RDBMSes have other mechanisms allowing a sequence (identity) to be shared by multiple tables, and something similar can be achieved with GUID keys in SQL Server. While using GUID keys, or int identity keys with different starting seeds will solve the problem but yet another solution would be to completely switch off identity on the primary key property. As a result, we need to take the responsibility of providing unique keys when inserting records to the database. We will go with this solution since it works regardless of which database engine is used. Switching Off Identity in Code First We can switch off identity simply by placing DatabaseGenerated attribute on the primary key property and pass DatabaseGenerationOption.None to its constructor. DatabaseGenerated attribute is a new data annotation which has been added to System.ComponentModel.DataAnnotations namespace in CTP5: public abstract class BillingDetail {     [DatabaseGenerated(DatabaseGenerationOption.None)]     public int BillingDetailId { get; set; }     public string Owner { get; set; }     public string Number { get; set; } } As always, we can achieve the same result by using fluent API, if you prefer that: modelBuilder.Entity<BillingDetail>()             .Property(p => p.BillingDetailId)             .HasDatabaseGenerationOption(DatabaseGenerationOption.None); Working With The Object Model Our TPC mapping is ready and we can try adding new records to the database. But, like I said, now we need to take care of providing unique keys when creating new objects: using (var context = new InheritanceMappingContext()) {     BankAccount bankAccount = new BankAccount()      {          BillingDetailId = 1                          };     CreditCard creditCard = new CreditCard()      {          BillingDetailId = 2,         CardType = 1     };                      context.BillingDetails.Add(bankAccount);     context.BillingDetails.Add(creditCard);     context.SaveChanges(); } Polymorphic Associations with TPC is Problematic The main problem with this approach is that it doesn’t support Polymorphic Associations very well. After all, in the database, associations are represented as foreign key relationships and in TPC, the subclasses are all mapped to different tables so a polymorphic association to their base class (abstract BillingDetail in our example) cannot be represented as a simple foreign key relationship. For example, consider the the domain model we introduced here where User has a polymorphic association with BillingDetail. This would be problematic in our TPC Schema, because if User has a many-to-one relationship with BillingDetail, the Users table would need a single foreign key column, which would have to refer both concrete subclass tables. This isn’t possible with regular foreign key constraints. Schema Evolution with TPC is Complex A further conceptual problem with this mapping strategy is that several different columns, of different tables, share exactly the same semantics. This makes schema evolution more complex. For example, a change to a base class property results in changes to multiple columns. It also makes it much more difficult to implement database integrity constraints that apply to all subclasses. Generated SQLLet's examine SQL output for polymorphic queries in TPC mapping. For example, consider this polymorphic query for all BillingDetails and the resulting SQL statements that being executed in the database: var query = from b in context.BillingDetails select b; Just like the SQL query generated by TPT mapping, the CASE statements that you see in the beginning of the query is merely to ensure columns that are irrelevant for a particular row have NULL values in the returning flattened table. (e.g. BankName for a row that represents a CreditCard type). TPC's SQL Queries are Union Based As you can see in the above screenshot, the first SELECT uses a FROM-clause subquery (which is selected with a red rectangle) to retrieve all instances of BillingDetails from all concrete class tables. The tables are combined with a UNION operator, and a literal (in this case, 0 and 1) is inserted into the intermediate result; (look at the lines highlighted in yellow.) EF reads this to instantiate the correct class given the data from a particular row. A union requires that the queries that are combined, project over the same columns; hence, EF has to pad and fill up nonexistent columns with NULL. This query will really perform well since here we can let the database optimizer find the best execution plan to combine rows from several tables. There is also no Joins involved so it has a better performance than the SQL queries generated by TPT where a Join is required between the base and subclasses tables. Choosing Strategy GuidelinesBefore we get into this discussion, I want to emphasize that there is no one single "best strategy fits all scenarios" exists. As you saw, each of the approaches have their own advantages and drawbacks. Here are some rules of thumb to identify the best strategy in a particular scenario: If you don’t require polymorphic associations or queries, lean toward TPC—in other words, if you never or rarely query for BillingDetails and you have no class that has an association to BillingDetail base class. I recommend TPC (only) for the top level of your class hierarchy, where polymorphism isn’t usually required, and when modification of the base class in the future is unlikely. If you do require polymorphic associations or queries, and subclasses declare relatively few properties (particularly if the main difference between subclasses is in their behavior), lean toward TPH. Your goal is to minimize the number of nullable columns and to convince yourself (and your DBA) that a denormalized schema won’t create problems in the long run. If you do require polymorphic associations or queries, and subclasses declare many properties (subclasses differ mainly by the data they hold), lean toward TPT. Or, depending on the width and depth of your inheritance hierarchy and the possible cost of joins versus unions, use TPC. By default, choose TPH only for simple problems. For more complex cases (or when you’re overruled by a data modeler insisting on the importance of nullability constraints and normalization), you should consider the TPT strategy. But at that point, ask yourself whether it may not be better to remodel inheritance as delegation in the object model (delegation is a way of making composition as powerful for reuse as inheritance). Complex inheritance is often best avoided for all sorts of reasons unrelated to persistence or ORM. EF acts as a buffer between the domain and relational models, but that doesn’t mean you can ignore persistence concerns when designing your classes. SummaryIn this series, we focused on one of the main structural aspect of the object/relational paradigm mismatch which is inheritance and discussed how EF solve this problem as an ORM solution. We learned about the three well-known inheritance mapping strategies and their implementations in EF Code First. Hopefully it gives you a better insight about the mapping of inheritance hierarchies as well as choosing the best strategy for your particular scenario. Happy New Year and Happy Code-Firsting! References ADO.NET team blog Java Persistence with Hibernate book a { color: #5A99FF; } a:visited { color: #5A99FF; } .title { padding-bottom: 5px; font-family: Segoe UI; font-size: 11pt; font-weight: bold; padding-top: 15px; } .code, .typeName { font-family: consolas; } .typeName { color: #2b91af; } .padTop5 { padding-top: 5px; } .padTop10 { padding-top: 10px; } .exception { background-color: #f0f0f0; font-style: italic; padding-bottom: 5px; padding-left: 5px; padding-top: 5px; padding-right: 5px; }

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• Using R to Analyze G1GC Log Files

  - by user12620111

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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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• Twitter Bootstrap styling conflicts with plug-ins like jqGrid and other third part libraries

  - by Renso

  Issues:The concern is that the Twitter Bootstrap framework is that some of their css selectors are simply too generic and have incompatibility issues and conflicts with most third party plug-ins and css libraries, like jQuery-UI and jqGrid.My most pressing concern is only with the generic selector for the styling of "INPUT" controls.Some concerns:So basically anyone using BS (Bootstrap) will have to override styling 100% of the time on all input controls on all their web pages for all the plug-ins they use that render their own styling for input controls. This seems to chisel away any reason for using Bootstrap. Overriding Bootstrap css in this case seems illogical at best as it implies the BS styling is not correct or as granular as it is supposed to be. It also suggests you realize there is an issue here. Any person who has written a fair amount of css will realize that it is a mammoth task to to take an existing app, converting it to BS and then having to find all non-BS input controls and styling them all. The worst part is that there is no generic styling for this as each input control has a different source/context, some are regular tags and some belong to plug-ins, each with their own flavor of styling. For new web apps the challenge is not that different, each time you add a new plug-in you will have to test all facets of it, and I mean all of it, pop-ups, etc, that contain any kind of input control to make sure it is styled correctly. I am having a hard time seeing the benefits of BS in this context. So until the BS team addresses the issue, or not, you may be wondering what is the easiest solution.Help the community to drive this issue home by creating a new issue on github, see my entry here: https://github.com/twitter/bootstrap/issues/4008. As you can see I got some good and some negative feedback, but we all agree it is an issue. I do believe my solution below should be reverse compatible if the proper class declarations were followed as recommended by Bootstrap.The solution:Add a higher-level qualifier to the input selector, which may not break anything.  Add "control-group" and "controls" classes as higher-level selectors, as they have to be declared inside those classes anyway as far as I understand the design approach of BS. So in my example below can modify the css without possible breaking anything, see the css at the bottom. I tested this briefly and seems to render just as expected. May not be complete as I only spent a few minutes on the css. Your feedback will be greatly appreciated. <div class="control-group">    <label title="" for="Contact_FirstName" class="control-label">First Name</label>    <div class="controls">        <input type="text" value="" name="Contact.FirstName" id="Contact_FirstName" data-val-required="The Reader Contact&amp;#39;s First Name is required" data-val-length-min="2" data-val-length-max="250" data-val-length="The maximum length allowed for the Reader Contact&amp;#39;s First Name is 250 characters and must be two or more characters long" data-val="true" class="input-medium">        <span data-valmsg-replace="true" data-valmsg-for="Contact.FirstName" class="field-validation-valid"></span>    </div></div>Here are the SCSS (SASS) updates. In stead of just including the updates I decided to include the entire bootstrap SCSS file so you can just copy-and-paste it in stead of trying to figure out what selectors have changed./*! * Bootstrap v2.0.4 * Enhacement by Renso Hollhumer * Copyright 2012 Twitter, Inc * Licensed under the Apache License v2.0 * http://www.apache.org/licenses/LICENSE-2.0 * * Designed and built with all the love in the world @twitter by @mdo and @fat. * Enhancement by Renso Hollhumer: To isolate styling of INPUT tags to the Bootstrap context only */.clearfix {  *zoom: 1;}.clearfix:before,.clearfix:after {  display: table;  content: "";}.clearfix:after {  clear: both;}.hide-text {  font: 0/0 a;  color: transparent;  text-shadow: none;  background-color: transparent;  border: 0;}.input-block-level {  display: block;  width: 100%;  min-height: 28px;  -webkit-box-sizing: border-box;  -moz-box-sizing: border-box;  -ms-box-sizing: border-box;  box-sizing: border-box;}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section {  display: block;}audio,canvas,video {  display: inline-block;  *display: inline;  *zoom: 1;}audio:not([controls]) {  display: none;}html {  font-size: 100%;  -webkit-text-size-adjust: 100%;  -ms-text-size-adjust: 100%;}a:focus {  outline: thin dotted #333;  outline: 5px auto -webkit-focus-ring-color;  outline-offset: -2px;}a:hover,a:active {  outline: 0;}sub,sup {  position: relative;  font-size: 75%;  line-height: 0;  vertical-align: baseline;}sup {  top: -0.5em;}sub {  bottom: -0.25em;}img {  max-width: 100%;  vertical-align: middle;  border: 0;  -ms-interpolation-mode: bicubic;}#map_canvas img {  max-width: none;}button,input,select,textarea {  margin: 0;  font-size: 100%;  vertical-align: middle;}button,input {  *overflow: visible;  line-height: normal;}button::-moz-focus-inner,input::-moz-focus-inner {  padding: 0;  border: 0;}button,input[type="button"],input[type="reset"],input[type="submit"] {  cursor: pointer;  -webkit-appearance: button;}input[type="search"] {  -webkit-box-sizing: content-box;  -moz-box-sizing: content-box;  box-sizing: content-box;  -webkit-appearance: textfield;}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button {  -webkit-appearance: none;}textarea {  overflow: auto;  vertical-align: top;}body {  margin: 0;  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;  font-size: 13px;  line-height: 18px;  color: #333333;  background-color: #ffffff;}a {  color: #0088cc;  text-decoration: none;}a:hover {  color: #005580;  text-decoration: underline;}.row {  margin-left: -20px;  *zoom: 1;}.row:before,.row:after {  display: table;  content: "";}.row:after {  clear: both;}[class*="span"] {  float: left;  margin-left: 20px;}.container,.navbar-fixed-top .container,.navbar-fixed-bottom .container {  width: 940px;}.span12 {  width: 940px;}.span11 {  width: 860px;}.span10 {  width: 780px;}.span9 {  width: 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2.0744680846382977%;}.row-fluid [class*="span"]:first-child {  margin-left: 0;}.row-fluid .span12 {  width: 99.99999998999999%;  *width: 99.94680850063828%;}.row-fluid .span11 {  width: 91.489361693%;  *width: 91.4361702036383%;}.row-fluid .span10 {  width: 82.97872339599999%;  *width: 82.92553190663828%;}.row-fluid .span9 {  width: 74.468085099%;  *width: 74.4148936096383%;}.row-fluid .span8 {  width: 65.95744680199999%;  *width: 65.90425531263828%;}.row-fluid .span7 {  width: 57.446808505%;  *width: 57.3936170156383%;}.row-fluid .span6 {  width: 48.93617020799999%;  *width: 48.88297871863829%;}.row-fluid .span5 {  width: 40.425531911%;  *width: 40.3723404216383%;}.row-fluid .span4 {  width: 31.914893614%;  *width: 31.8617021246383%;}.row-fluid .span3 {  width: 23.404255317%;  *width: 23.3510638276383%;}.row-fluid .span2 {  width: 14.89361702%;  *width: 14.8404255306383%;}.row-fluid .span1 {  width: 6.382978723%;  *width: 6.329787233638298%;}.container {  margin-right: auto;  margin-left: auto;  *zoom: 1;}.container:before,.container:after {  display: table;  content: "";}.container:after {  clear: both;}.container-fluid {  padding-right: 20px;  padding-left: 20px;  *zoom: 1;}.container-fluid:before,.container-fluid:after {  display: table;  content: "";}.container-fluid:after {  clear: both;}p {  margin: 0 0 9px;}p small {  font-size: 11px;  color: #999999;}.lead {  margin-bottom: 18px;  font-size: 20px;  font-weight: 200;  line-height: 27px;}h1,h2,h3,h4,h5,h6 {  margin: 0;  font-family: inherit;  font-weight: bold;  color: inherit;  text-rendering: optimizelegibility;}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small {  font-weight: normal;  color: #999999;}h1 {  font-size: 30px;  line-height: 36px;}h1 small {  font-size: 18px;}h2 {  font-size: 24px;  line-height: 36px;}h2 small {  font-size: 18px;}h3 {  font-size: 18px;  line-height: 27px;}h3 small {  font-size: 14px;}h4,h5,h6 {  line-height: 18px;}h4 {  font-size: 14px;}h4 small {  font-size: 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border-radius: 3px;}code {  padding: 2px 4px;  color: #d14;  background-color: #f7f7f9;  border: 1px solid #e1e1e8;}pre {  display: block;  padding: 8.5px;  margin: 0 0 9px;  font-size: 12.025px;  line-height: 18px;  word-break: break-all;  word-wrap: break-word;  white-space: pre;  white-space: pre-wrap;  background-color: #f5f5f5;  border: 1px solid #ccc;  border: 1px solid rgba(0, 0, 0, 0.15);  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}pre.prettyprint {  margin-bottom: 18px;}pre code {  padding: 0;  color: inherit;  background-color: transparent;  border: 0;}.pre-scrollable {  max-height: 340px;  overflow-y: scroll;}.label,.badge {  font-size: 10.998px;  font-weight: bold;  line-height: 14px;  color: #ffffff;  vertical-align: baseline;  white-space: nowrap;  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);  background-color: #999999;}.label {  padding: 1px 4px 2px;  -webkit-border-radius: 3px;  -moz-border-radius: 3px;  border-radius: 3px;}.badge {  padding: 1px 9px 2px;  -webkit-border-radius: 9px;  -moz-border-radius: 9px;  border-radius: 9px;}a.label:hover,a.badge:hover {  color: #ffffff;  text-decoration: none;  cursor: pointer;}.label-important,.badge-important {  background-color: #b94a48;}.label-important[href],.badge-important[href] {  background-color: #953b39;}.label-warning,.badge-warning {  background-color: #f89406;}.label-warning[href],.badge-warning[href] {  background-color: #c67605;}.label-success,.badge-success {  background-color: #468847;}.label-success[href],.badge-success[href] {  background-color: #356635;}.label-info,.badge-info {  background-color: #3a87ad;}.label-info[href],.badge-info[href] {  background-color: #2d6987;}.label-inverse,.badge-inverse {  background-color: #333333;}.label-inverse[href],.badge-inverse[href] {  background-color: #1a1a1a;}table {  max-width: 100%;  background-color: transparent;  border-collapse: collapse;  border-spacing: 0;}.table {  width: 100%;  margin-bottom: 18px;}.table th,.table td {  padding: 8px;  line-height: 18px;  text-align: left;  vertical-align: top;  border-top: 1px solid #dddddd;}.table th {  font-weight: bold;}.table thead th {  vertical-align: bottom;}.table caption + thead tr:first-child th,.table caption + thead tr:first-child td,.table colgroup + thead tr:first-child th,.table colgroup + thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td {  border-top: 0;}.table tbody + tbody {  border-top: 2px solid #dddddd;}.table-condensed th,.table-condensed td {  padding: 4px 5px;}.table-bordered {  border: 1px solid #dddddd;  border-collapse: separate;  *border-collapse: collapsed;  border-left: 0;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.table-bordered th,.table-bordered td {  border-left: 1px solid #dddddd;}.table-bordered caption + thead tr:first-child th,.table-bordered caption + tbody tr:first-child th,.table-bordered caption + tbody tr:first-child 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.span24 {  float: none;  width: 1884px;  margin-left: 0;}form {  margin: 0 0 18px;}fieldset {  padding: 0;  margin: 0;  border: 0;}legend {  display: block;  width: 100%;  padding: 0;  margin-bottom: 27px;  font-size: 19.5px;  line-height: 36px;  color: #333333;  border: 0;  border-bottom: 1px solid #e5e5e5;}legend small {  font-size: 13.5px;  color: #999999;}.control-group .controls {    label,    input,    button,    select,    textarea {      font-size: 13px;      font-weight: normal;      line-height: 18px;    }}.control-group .controls {    input,    button,    select,    textarea {      font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;    }}label {  display: block;  margin-bottom: 5px;}.control-group .controls {    select,    textarea,    input[type="text"],    input[type="password"],    input[type="datetime"],    input[type="datetime-local"],    input[type="date"],    input[type="month"],    input[type="time"],    input[type="week"],    input[type="number"],    input[type="email"],    input[type="url"],    input[type="search"],    input[type="tel"],    input[type="color"],    .uneditable-input {      display: inline-block;      height: 18px;      padding: 4px;      margin-bottom: 9px;      font-size: 13px;      line-height: 18px;      color: #555555;    }}.control-group .controls {    input,    textarea {      width: 210px;    }}.control-group .controls {    textarea {      height: auto;    }}.control-group .controls {    textarea,    input[type="text"],    input[type="password"],    input[type="datetime"],    input[type="datetime-local"],    input[type="date"],    input[type="month"],    input[type="time"],    input[type="week"],    input[type="number"],    input[type="email"],    input[type="url"],    input[type="search"],    input[type="tel"],    input[type="color"],    .uneditable-input {      background-color: #ffffff;      border: 1px solid #cccccc;      -webkit-border-radius: 3px;      -moz-border-radius: 3px;      border-radius: 3px;      -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);      -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);      box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);      -webkit-transition: border linear 0.2s, box-shadow linear 0.2s;      -moz-transition: border linear 0.2s, box-shadow linear 0.2s;      -ms-transition: border linear 0.2s, box-shadow linear 0.2s;      -o-transition: border linear 0.2s, box-shadow linear 0.2s;      transition: border linear 0.2s, box-shadow linear 0.2s;    }}.control-group .controls {    textarea:focus,    input[type="text"]:focus,    input[type="password"]:focus,    input[type="datetime"]:focus,    input[type="datetime-local"]:focus,    input[type="date"]:focus,    input[type="month"]:focus,    input[type="time"]:focus,    input[type="week"]:focus,    input[type="number"]:focus,    input[type="email"]:focus,    input[type="url"]:focus,    input[type="search"]:focus,    input[type="tel"]:focus,    input[type="color"]:focus,    .uneditable-input:focus {      border-color: rgba(82, 168, 236, 0.8);      outline: 0;      outline: thin dotted \9;      /* IE6-9 */      -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);      -moz-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);      box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);    }}.control-group .controls {    input[type="radio"],    input[type="checkbox"] {      margin: 3px 0;      *margin-top: 0;      /* IE7 */      line-height: normal;      cursor: pointer;    }}.control-group .controls {    input[type="submit"],    input[type="reset"],    input[type="button"],    input[type="radio"],    input[type="checkbox"] {      width: auto;    }}.uneditable-textarea {  width: auto;  height: auto;}.control-group .controls {    select,    input[type="file"] {      height: 28px;      /* In IE7, the height of the select element cannot be changed by height, only font-size */      *margin-top: 4px;      /* For IE7, add top margin to align select with labels */      line-height: 28px;    }}.control-group .controls {    select {      width: 220px;      border: 1px solid #bbb;    }}.control-group .controls {    select[multiple],    select[size] {      height: auto;    }}.control-group .controls {    select:focus,    input[type="file"]:focus,    input[type="radio"]:focus,    input[type="checkbox"]:focus {      outline: thin dotted #333;      outline: 5px auto -webkit-focus-ring-color;      outline-offset: -2px;    }}.radio,.checkbox {  min-height: 18px;  padding-left: 18px;}.radio input[type="radio"],.checkbox input[type="checkbox"] {  float: left;  margin-left: -18px;}.controls > .radio:first-child,.controls > .checkbox:first-child {  padding-top: 5px;}.radio.inline,.checkbox.inline {  display: inline-block;  padding-top: 5px;  margin-bottom: 0;  vertical-align: middle;}.radio.inline + .radio.inline,.checkbox.inline + .checkbox.inline {  margin-left: 10px;}.control-group .controls {    .input-mini {      width: 60px;    }}.control-group .controls {    .input-small {      width: 90px;    }}.control-group .controls {    .input-medium {      width: 150px;    }}.control-group .controls {    .input-large {      width: 210px;    }}.input-xlarge {    .input-xlarge {      width: 270px;    }}.input-xxlarge {    .input-xxlarge {      width: 530px;    }}.control-group .controls {    input[class*="span"],    select[class*="span"],    textarea[class*="span"],    .uneditable-input[class*="span"],    .row-fluid input[class*="span"],    .row-fluid select[class*="span"],    .row-fluid textarea[class*="span"],    .row-fluid .uneditable-input[class*="span"] {      float: none;      margin-left: 0;    }}.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"] {  display: inline-block;}.control-group .controls {    input,    textarea,    .uneditable-input {      margin-left: 0;    }}input.span12, textarea.span12, .uneditable-input.span12 {  width: 930px;}input.span11, textarea.span11, .uneditable-input.span11 {  width: 850px;}input.span10, textarea.span10, .uneditable-input.span10 {  width: 770px;}input.span9, textarea.span9, .uneditable-input.span9 {  width: 690px;}input.span8, textarea.span8, .uneditable-input.span8 {  width: 610px;}input.span7, textarea.span7, .uneditable-input.span7 {  width: 530px;}input.span6, textarea.span6, .uneditable-input.span6 {  width: 450px;}input.span5, textarea.span5, .uneditable-input.span5 {  width: 370px;}input.span4, textarea.span4, .uneditable-input.span4 {  width: 290px;}input.span3, textarea.span3, .uneditable-input.span3 {  width: 210px;}input.span2, textarea.span2, .uneditable-input.span2 {  width: 130px;}input.span1, textarea.span1, .uneditable-input.span1 {  width: 50px;}input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly] {  cursor: not-allowed;  background-color: #eeeeee;  border-color: #ddd;}input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly] {  background-color: transparent;}.control-group.warning > label,.control-group.warning .help-block,.control-group.warning .help-inline {  color: #c09853;}.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea {  color: #c09853;  border-color: #c09853;}.control-group.warning .checkbox:focus,.control-group.warning .radio:focus,.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus {  border-color: #a47e3c;  -webkit-box-shadow: 0 0 6px #dbc59e;  -moz-box-shadow: 0 0 6px #dbc59e;  box-shadow: 0 0 6px #dbc59e;}.control-group.warning 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#ccc;}.input-prepend .add-on,.input-append .add-on,.input-prepend .btn,.input-append .btn {  margin-left: -1px;  -webkit-border-radius: 0;  -moz-border-radius: 0;  border-radius: 0;}.input-prepend .active,.input-append .active {  background-color: #a9dba9;  border-color: #46a546;}.input-prepend .add-on,.input-prepend .btn {  margin-right: -1px;}.input-prepend .add-on:first-child,.input-prepend .btn:first-child {  -webkit-border-radius: 3px 0 0 3px;  -moz-border-radius: 3px 0 0 3px;  border-radius: 3px 0 0 3px;}.input-append input,.input-append select,.input-append .uneditable-input {  -webkit-border-radius: 3px 0 0 3px;  -moz-border-radius: 3px 0 0 3px;  border-radius: 3px 0 0 3px;}.input-append .uneditable-input {  border-right-color: #ccc;  border-left-color: #eee;}.input-append .add-on:last-child,.input-append .btn:last-child {  -webkit-border-radius: 0 3px 3px 0;  -moz-border-radius: 0 3px 3px 0;  border-radius: 0 3px 3px 0;}.input-prepend.input-append 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*display: inline;  /* IE7 inline-block hack */  *zoom: 1;  padding: 4px 10px 4px;  margin-bottom: 0;  font-size: 13px;  line-height: 18px;  *line-height: 20px;  color: #333333;  text-align: center;  text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75);  vertical-align: middle;  cursor: pointer;  background-color: #f5f5f5;  background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6));  background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -o-linear-gradient(top, #ffffff, #e6e6e6);  background-image: linear-gradient(top, #ffffff, #e6e6e6);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffff', endColorstr='#e6e6e6', GradientType=0);  border-color: #e6e6e6 #e6e6e6 #bfbfbf;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #e6e6e6;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);  border: 1px solid #cccccc;  *border: 0;  border-bottom-color: #b3b3b3;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;  *margin-left: .3em;  -webkit-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);}.btn:hover,.btn:active,.btn.active,.btn.disabled,.btn[disabled] {  background-color: #e6e6e6;  *background-color: #d9d9d9;}.btn:active,.btn.active {  background-color: #cccccc \9;}.btn:first-child {  *margin-left: 0;}.btn:hover {  color: #333333;  text-decoration: none;  background-color: #e6e6e6;  *background-color: #d9d9d9;  /* Buttons in IE7 don't get borders, so darken on hover */  background-position: 0 -15px;  -webkit-transition: background-position 0.1s linear;  -moz-transition: background-position 0.1s linear;  -ms-transition: background-position 0.1s linear;  -o-transition: background-position 0.1s linear;  transition: background-position 0.1s linear;}.btn:focus {  outline: thin dotted #333;  outline: 5px auto -webkit-focus-ring-color;  outline-offset: -2px;}.btn.active,.btn:active {  background-color: #e6e6e6;  background-color: #d9d9d9 \9;  background-image: none;  outline: 0;  -webkit-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);}.btn.disabled,.btn[disabled] {  cursor: default;  background-color: #e6e6e6;  background-image: none;  opacity: 0.65;  filter: alpha(opacity=65);  -webkit-box-shadow: none;  -moz-box-shadow: none;  box-shadow: none;}.btn-large {  padding: 9px 14px;  font-size: 15px;  line-height: normal;  -webkit-border-radius: 5px;  -moz-border-radius: 5px;  border-radius: 5px;}.btn-large [class^="icon-"] {  margin-top: 1px;}.btn-small {  padding: 5px 9px;  font-size: 11px;  line-height: 16px;}.btn-small [class^="icon-"] {  margin-top: -1px;}.btn-mini {  padding: 2px 6px;  font-size: 11px;  line-height: 14px;}.btn-primary,.btn-primary:hover,.btn-warning,.btn-warning:hover,.btn-danger,.btn-danger:hover,.btn-success,.btn-success:hover,.btn-info,.btn-info:hover,.btn-inverse,.btn-inverse:hover {  color: #ffffff;  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);}.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active {  color: rgba(255, 255, 255, 0.75);}.btn {  border-color: #ccc;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);}.btn-primary {  background-color: #0074cc;  background-image: -moz-linear-gradient(top, #0088cc, #0055cc);  background-image: -ms-linear-gradient(top, #0088cc, #0055cc);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0055cc));  background-image: -webkit-linear-gradient(top, #0088cc, #0055cc);  background-image: -o-linear-gradient(top, #0088cc, #0055cc);  background-image: linear-gradient(top, #0088cc, #0055cc);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#0088cc', endColorstr='#0055cc', GradientType=0);  border-color: #0055cc #0055cc #003580;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #0055cc;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-primary:hover,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled] {  background-color: #0055cc;  *background-color: #004ab3;}.btn-primary:active,.btn-primary.active {  background-color: #004099 \9;}.btn-warning {  background-color: #faa732;  background-image: -moz-linear-gradient(top, #fbb450, #f89406);  background-image: -ms-linear-gradient(top, #fbb450, #f89406);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);  background-image: -o-linear-gradient(top, #fbb450, #f89406);  background-image: linear-gradient(top, #fbb450, #f89406);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0);  border-color: #f89406 #f89406 #ad6704;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #f89406;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-warning:hover,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled] {  background-color: #f89406;  *background-color: #df8505;}.btn-warning:active,.btn-warning.active {  background-color: #c67605 \9;}.btn-danger {  background-color: #da4f49;  background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -ms-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));  background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -o-linear-gradient(top, #ee5f5b, #bd362f);  background-image: linear-gradient(top, #ee5f5b, #bd362f);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#bd362f', GradientType=0);  border-color: #bd362f #bd362f #802420;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #bd362f;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-danger:hover,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled] {  background-color: #bd362f;  *background-color: #a9302a;}.btn-danger:active,.btn-danger.active {  background-color: #942a25 \9;}.btn-success {  background-color: #5bb75b;  background-image: -moz-linear-gradient(top, #62c462, #51a351);  background-image: -ms-linear-gradient(top, #62c462, #51a351);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));  background-image: -webkit-linear-gradient(top, #62c462, #51a351);  background-image: -o-linear-gradient(top, #62c462, #51a351);  background-image: linear-gradient(top, #62c462, #51a351);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#62c462', endColorstr='#51a351', GradientType=0);  border-color: #51a351 #51a351 #387038;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #51a351;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-success:hover,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled] {  background-color: #51a351;  *background-color: #499249;}.btn-success:active,.btn-success.active {  background-color: #408140 \9;}.btn-info {  background-color: #49afcd;  background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -ms-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));  background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -o-linear-gradient(top, #5bc0de, #2f96b4);  background-image: linear-gradient(top, #5bc0de, #2f96b4);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#5bc0de', endColorstr='#2f96b4', GradientType=0);  border-color: #2f96b4 #2f96b4 #1f6377;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #2f96b4;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-info:hover,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled] {  background-color: #2f96b4;  *background-color: #2a85a0;}.btn-info:active,.btn-info.active {  background-color: #24748c \9;}.btn-inverse {  background-color: #414141;  background-image: -moz-linear-gradient(top, #555555, #222222);  background-image: -ms-linear-gradient(top, #555555, #222222);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#555555), to(#222222));  background-image: -webkit-linear-gradient(top, #555555, #222222);  background-image: -o-linear-gradient(top, #555555, #222222);  background-image: linear-gradient(top, #555555, #222222);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#555555', endColorstr='#222222', GradientType=0);  border-color: #222222 #222222 #000000;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #222222;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-inverse:hover,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled] {  background-color: #222222;  *background-color: #151515;}.btn-inverse:active,.btn-inverse.active {  background-color: #080808 \9;}button.btn,input[type="submit"].btn {  *padding-top: 2px;  *padding-bottom: 2px;}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner {  padding: 0;  border: 0;}button.btn.btn-large,input[type="submit"].btn.btn-large {  *padding-top: 7px;  *padding-bottom: 7px;}button.btn.btn-small,input[type="submit"].btn.btn-small {  *padding-top: 3px;  *padding-bottom: 3px;}button.btn.btn-mini,input[type="submit"].btn.btn-mini {  *padding-top: 1px;  *padding-bottom: 1px;}.btn-group {  position: relative;  *zoom: 1;  *margin-left: .3em;}.btn-group:before,.btn-group:after {  display: table;  content: "";}.btn-group:after {  clear: both;}.btn-group:first-child {  *margin-left: 0;}.btn-group + .btn-group {  margin-left: 5px;}.btn-toolbar {  margin-top: 9px;  margin-bottom: 9px;}.btn-toolbar .btn-group {  display: inline-block;  *display: inline;  /* IE7 inline-block hack */  *zoom: 1;}.btn-group > .btn {  position: relative;  float: left;  margin-left: -1px;  -webkit-border-radius: 0;  -moz-border-radius: 0;  border-radius: 0;}.btn-group > .btn:first-child {  margin-left: 0;  -webkit-border-top-left-radius: 4px;  -moz-border-radius-topleft: 4px;  border-top-left-radius: 4px;  -webkit-border-bottom-left-radius: 4px;  -moz-border-radius-bottomleft: 4px;  border-bottom-left-radius: 4px;}.btn-group > .btn:last-child,.btn-group > .dropdown-toggle {  -webkit-border-top-right-radius: 4px;  -moz-border-radius-topright: 4px;  border-top-right-radius: 4px;  -webkit-border-bottom-right-radius: 4px;  -moz-border-radius-bottomright: 4px;  border-bottom-right-radius: 4px;}.btn-group > .btn.large:first-child {  margin-left: 0;  -webkit-border-top-left-radius: 6px;  -moz-border-radius-topleft: 6px;  border-top-left-radius: 6px;  -webkit-border-bottom-left-radius: 6px;  -moz-border-radius-bottomleft: 6px;  border-bottom-left-radius: 6px;}.btn-group > .btn.large:last-child,.btn-group > .large.dropdown-toggle {  -webkit-border-top-right-radius: 6px;  -moz-border-radius-topright: 6px;  border-top-right-radius: 6px;  -webkit-border-bottom-right-radius: 6px;  -moz-border-radius-bottomright: 6px;  border-bottom-right-radius: 6px;}.btn-group > .btn:hover,.btn-group > .btn:focus,.btn-group > .btn:active,.btn-group > 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1;}.thumbnails:before,.thumbnails:after {  display: table;  content: "";}.thumbnails:after {  clear: both;}.row-fluid .thumbnails {  margin-left: 0;}.thumbnails > li {  float: left;  margin-bottom: 18px;  margin-left: 20px;}.thumbnail {  display: block;  padding: 4px;  line-height: 1;  border: 1px solid #ddd;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075);  -moz-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075);  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075);}a.thumbnail:hover {  border-color: #0088cc;  -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);  -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);  box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);}.thumbnail > img {  display: block;  max-width: 100%;  margin-left: auto;  margin-right: auto;}.thumbnail .caption {  padding: 9px;}.alert {  padding: 8px 35px 8px 14px;  margin-bottom: 18px;  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5);  background-color: #fcf8e3;  border: 1px solid #fbeed5;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;  color: #c09853;}.alert-heading {  color: inherit;}.alert .close {  position: relative;  top: -2px;  right: -21px;  line-height: 18px;}.alert-success {  background-color: #dff0d8;  border-color: #d6e9c6;  color: #468847;}.alert-danger,.alert-error {  background-color: #f2dede;  border-color: #eed3d7;  color: #b94a48;}.alert-info {  background-color: #d9edf7;  border-color: #bce8f1;  color: #3a87ad;}.alert-block {  padding-top: 14px;  padding-bottom: 14px;}.alert-block > p,.alert-block > ul {  margin-bottom: 0;}.alert-block p + p {  margin-top: 5px;}@-webkit-keyframes progress-bar-stripes {  from {    background-position: 40px 0;  }  to {    background-position: 0 0;  }}@-moz-keyframes progress-bar-stripes {  from {    background-position: 40px 0;  }  to {    background-position: 0 0;  }}@-ms-keyframes progress-bar-stripes {  from {    background-position: 40px 0;  }  to {    background-position: 0 0;  }}@-o-keyframes progress-bar-stripes {  from {    background-position: 0 0;  }  to {    background-position: 40px 0;  }}@keyframes progress-bar-stripes {  from {    background-position: 40px 0;  }  to {    background-position: 0 0;  }}.progress {  overflow: hidden;  height: 18px;  margin-bottom: 18px;  background-color: #f7f7f7;  background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -ms-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));  background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: linear-gradient(top, #f5f5f5, #f9f9f9);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#f5f5f5', endColorstr='#f9f9f9', GradientType=0);  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.progress .bar {  width: 0%;  height: 18px;  color: #ffffff;  font-size: 12px;  text-align: center;  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);  background-color: #0e90d2;  background-image: -moz-linear-gradient(top, #149bdf, #0480be);  background-image: -ms-linear-gradient(top, #149bdf, #0480be);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));  background-image: -webkit-linear-gradient(top, #149bdf, #0480be);  background-image: -o-linear-gradient(top, #149bdf, #0480be);  background-image: linear-gradient(top, #149bdf, #0480be);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#149bdf', endColorstr='#0480be', GradientType=0);  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  -webkit-box-sizing: border-box;  -moz-box-sizing: border-box;  -ms-box-sizing: border-box;  box-sizing: border-box;  -webkit-transition: width 0.6s ease;  -moz-transition: width 0.6s ease;  -ms-transition: width 0.6s ease;  -o-transition: width 0.6s ease;  transition: width 0.6s ease;}.progress-striped .bar {  background-color: #149bdf;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  -webkit-background-size: 40px 40px;  -moz-background-size: 40px 40px;  -o-background-size: 40px 40px;  background-size: 40px 40px;}.progress.active .bar {  -webkit-animation: progress-bar-stripes 2s linear infinite;  -moz-animation: progress-bar-stripes 2s linear infinite;  -ms-animation: progress-bar-stripes 2s linear infinite;  -o-animation: progress-bar-stripes 2s linear infinite;  animation: progress-bar-stripes 2s linear infinite;}.progress-danger .bar {  background-color: #dd514c;  background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -ms-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));  background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -o-linear-gradient(top, #ee5f5b, #c43c35);  background-image: linear-gradient(top, #ee5f5b, #c43c35);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#c43c35', GradientType=0);}.progress-danger.progress-striped .bar {  background-color: #ee5f5b;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-success .bar {  background-color: #5eb95e;  background-image: -moz-linear-gradient(top, #62c462, #57a957);  background-image: -ms-linear-gradient(top, #62c462, #57a957);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));  background-image: -webkit-linear-gradient(top, #62c462, #57a957);  background-image: -o-linear-gradient(top, #62c462, #57a957);  background-image: linear-gradient(top, #62c462, #57a957);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#62c462', endColorstr='#57a957', GradientType=0);}.progress-success.progress-striped .bar {  background-color: #62c462;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-info .bar {  background-color: #4bb1cf;  background-image: -moz-linear-gradient(top, #5bc0de, #339bb9);  background-image: -ms-linear-gradient(top, #5bc0de, #339bb9);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));  background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9);  background-image: -o-linear-gradient(top, #5bc0de, #339bb9);  background-image: linear-gradient(top, #5bc0de, #339bb9);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#5bc0de', endColorstr='#339bb9', GradientType=0);}.progress-info.progress-striped .bar {  background-color: #5bc0de;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-warning .bar {  background-color: #faa732;  background-image: -moz-linear-gradient(top, #fbb450, #f89406);  background-image: -ms-linear-gradient(top, #fbb450, #f89406);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);  background-image: -o-linear-gradient(top, #fbb450, #f89406);  background-image: linear-gradient(top, #fbb450, #f89406);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0);}.progress-warning.progress-striped .bar {  background-color: #fbb450;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.hero-unit {  padding: 60px;  margin-bottom: 30px;  background-color: #eeeeee;  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;}.hero-unit h1 {  margin-bottom: 0;  font-size: 60px;  line-height: 1;  color: inherit;  letter-spacing: -1px;}.hero-unit p {  font-size: 18px;  font-weight: 200;  line-height: 27px;  color: inherit;}.tooltip {  position: absolute;  z-index: 1020;  display: block;  visibility: visible;  padding: 5px;  font-size: 11px;  opacity: 0;  filter: alpha(opacity=0);}.tooltip.in {  opacity: 0.8;  filter: alpha(opacity=80);}.tooltip.top {  margin-top: -2px;}.tooltip.right {  margin-left: 2px;}.tooltip.bottom {  margin-top: 2px;}.tooltip.left {  margin-left: -2px;}.tooltip.top .tooltip-arrow {  bottom: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-top: 5px solid #000000;}.tooltip.left .tooltip-arrow {  top: 50%;  right: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-left: 5px solid #000000;}.tooltip.bottom .tooltip-arrow {  top: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-bottom: 5px solid #000000;}.tooltip.right .tooltip-arrow {  top: 50%;  left: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-right: 5px solid #000000;}.tooltip-inner {  max-width: 200px;  padding: 3px 8px;  color: #ffffff;  text-align: center;  text-decoration: none;  background-color: #000000;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.tooltip-arrow {  position: absolute;  width: 0;  height: 0;}.popover {  position: absolute;  top: 0;  left: 0;  z-index: 1010;  display: none;  padding: 5px;}.popover.top {  margin-top: -5px;}.popover.right {  margin-left: 5px;}.popover.bottom {  margin-top: 5px;}.popover.left {  margin-left: -5px;}.popover.top .arrow {  bottom: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-top: 5px solid #000000;}.popover.right .arrow {  top: 50%;  left: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-right: 5px solid #000000;}.popover.bottom .arrow {  top: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-bottom: 5px solid #000000;}.popover.left .arrow {  top: 50%;  right: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-left: 5px solid #000000;}.popover .arrow {  position: absolute;  width: 0;  height: 0;}.popover-inner {  padding: 3px;  width: 280px;  overflow: hidden;  background: #000000;  background: rgba(0, 0, 0, 0.8);  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;  -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);}.popover-title {  padding: 9px 15px;  line-height: 1;  background-color: #f5f5f5;  border-bottom: 1px solid #eee;  -webkit-border-radius: 3px 3px 0 0;  -moz-border-radius: 3px 3px 0 0;  border-radius: 3px 3px 0 0;}.popover-content {  padding: 14px;  background-color: #ffffff;  -webkit-border-radius: 0 0 3px 3px;  -moz-border-radius: 0 0 3px 3px;  border-radius: 0 0 3px 3px;  -webkit-background-clip: padding-box;  -moz-background-clip: padding-box;  background-clip: padding-box;}.popover-content p,.popover-content ul,.popover-content ol {  margin-bottom: 0;}.modal-open .dropdown-menu {  z-index: 2050;}.modal-open .dropdown.open {  *z-index: 2050;}.modal-open .popover {  z-index: 2060;}.modal-open .tooltip {  z-index: 2070;}.modal-backdrop {  position: fixed;  top: 0;  right: 0;  bottom: 0;  left: 0;  z-index: 1040;  background-color: #000000;}.modal-backdrop.fade {  opacity: 0;}.modal-backdrop,.modal-backdrop.fade.in {  opacity: 0.8;  filter: alpha(opacity=80);}.modal {  position: fixed;  top: 50%;  left: 50%;  z-index: 1050;  overflow: auto;  width: 560px;  margin: -250px 0 0 -280px;  background-color: #ffffff;  border: 1px solid #999;  border: 1px solid rgba(0, 0, 0, 0.3);  *border: 1px solid #999;  /* IE6-7 */  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;  -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -webkit-background-clip: padding-box;  -moz-background-clip: padding-box;  background-clip: padding-box;}.modal.fade {  -webkit-transition: opacity .3s linear, top .3s ease-out;  -moz-transition: opacity .3s linear, top .3s ease-out;  -ms-transition: opacity .3s linear, top .3s ease-out;  -o-transition: opacity .3s linear, top .3s ease-out;  transition: opacity .3s linear, top .3s ease-out;  top: -25%;}.modal.fade.in {  top: 50%;}.modal-header {  padding: 9px 15px;  border-bottom: 1px solid #eee;}.modal-header .close {  margin-top: 2px;}.modal-body {  overflow-y: auto;  max-height: 400px;  padding: 15px;}.modal-form {  margin-bottom: 0;}.modal-footer {  padding: 14px 15px 15px;  margin-bottom: 0;  text-align: right;  background-color: #f5f5f5;  border-top: 1px solid #ddd;  -webkit-border-radius: 0 0 6px 6px;  -moz-border-radius: 0 0 6px 6px;  border-radius: 0 0 6px 6px;  -webkit-box-shadow: inset 0 1px 0 #ffffff;  -moz-box-shadow: inset 0 1px 0 #ffffff;  box-shadow: inset 0 1px 0 #ffffff;  *zoom: 1;}.modal-footer:before,.modal-footer:after {  display: table;  content: "";}.modal-footer:after {  clear: both;}.modal-footer .btn + .btn {  margin-left: 5px;  margin-bottom: 0;}.modal-footer .btn-group .btn + .btn {  margin-left: -1px;}.dropup,.dropdown {  position: relative;}.dropdown-toggle {  *margin-bottom: -3px;}.dropdown-toggle:active,.open .dropdown-toggle {  outline: 0;}.caret {  display: inline-block;  width: 0;  height: 0;  vertical-align: top;  border-top: 4px solid #000000;  border-right: 4px solid transparent;  border-left: 4px solid transparent;  content: "";  opacity: 0.3;  filter: alpha(opacity=30);}.dropdown .caret {  margin-top: 8px;  margin-left: 2px;}.dropdown:hover .caret,.open .caret {  opacity: 1;  filter: alpha(opacity=100);}.dropdown-menu {  position: absolute;  top: 100%;  left: 0;  z-index: 1000;  display: none;  float: left;  min-width: 160px;  padding: 4px 0;  margin: 1px 0 0;  list-style: none;  background-color: #ffffff;  border: 1px solid #ccc;  border: 1px solid rgba(0, 0, 0, 0.2);  *border-right-width: 2px;  *border-bottom-width: 2px;  -webkit-border-radius: 5px;  -moz-border-radius: 5px;  border-radius: 5px;  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  -webkit-background-clip: padding-box;  -moz-background-clip: padding;  background-clip: padding-box;}.dropdown-menu.pull-right {  right: 0;  left: auto;}.dropdown-menu .divider {  *width: 100%;  height: 1px;  margin: 8px 1px;  *margin: -5px 0 5px;  overflow: hidden;  background-color: #e5e5e5;  border-bottom: 1px solid #ffffff;}.dropdown-menu a {  display: block;  padding: 3px 15px;  clear: both;  font-weight: normal;  line-height: 18px;  color: #333333;  white-space: nowrap;}.dropdown-menu li > a:hover,.dropdown-menu .active > a,.dropdown-menu .active > a:hover {  color: #ffffff;  text-decoration: none;  background-color: #0088cc;}.open {  *z-index: 1000;}.open  > .dropdown-menu {  display: block;}.pull-right > .dropdown-menu {  right: 0;  left: auto;}.dropup .caret,.navbar-fixed-bottom .dropdown .caret {  border-top: 0;  border-bottom: 4px solid #000000;  content: "\2191";}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu {  top: auto;  bottom: 100%;  margin-bottom: 1px;}.typeahead {  margin-top: 2px;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.accordion {  margin-bottom: 18px;}.accordion-group {  margin-bottom: 2px;  border: 1px solid #e5e5e5;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.accordion-heading {  border-bottom: 0;}.accordion-heading .accordion-toggle {  display: block;  padding: 8px 15px;}.accordion-toggle {  cursor: pointer;}.accordion-inner {  padding: 9px 15px;  border-top: 1px solid #e5e5e5;}.carousel {  position: relative;  margin-bottom: 18px;  line-height: 1;}.carousel-inner {  overflow: hidden;  width: 100%;  position: relative;}.carousel .item {  display: none;  position: relative;  -webkit-transition: 0.6s ease-in-out left;  -moz-transition: 0.6s ease-in-out left;  -ms-transition: 0.6s ease-in-out left;  -o-transition: 0.6s ease-in-out left;  transition: 0.6s ease-in-out left;}.carousel .item > img {  display: block;  line-height: 1;}.carousel .active,.carousel .next,.carousel .prev {  display: block;}.carousel .active {  left: 0;}.carousel .next,.carousel .prev {  position: absolute;  top: 0;  width: 100%;}.carousel .next {  left: 100%;}.carousel .prev {  left: -100%;}.carousel .next.left,.carousel .prev.right {  left: 0;}.carousel .active.left {  left: -100%;}.carousel .active.right {  left: 100%;}.carousel-control {  position: absolute;  top: 40%;  left: 15px;  width: 40px;  height: 40px;  margin-top: -20px;  font-size: 60px;  font-weight: 100;  line-height: 30px;  color: #ffffff;  text-align: center;  background: #222222;  border: 3px solid #ffffff;  -webkit-border-radius: 23px;  -moz-border-radius: 23px;  border-radius: 23px;  opacity: 0.5;  filter: alpha(opacity=50);}.carousel-control.right {  left: auto;  right: 15px;}.carousel-control:hover {  color: #ffffff;  text-decoration: none;  opacity: 0.9;  filter: alpha(opacity=90);}.carousel-caption {  position: absolute;  left: 0;  right: 0;  bottom: 0;  padding: 10px 15px 5px;  background: #333333;  background: rgba(0, 0, 0, 0.75);}.carousel-caption h4,.carousel-caption p {  color: #ffffff;}.well {  min-height: 20px;  padding: 19px;  margin-bottom: 20px;  background-color: #f5f5f5;  border: 1px solid #eee;  border: 1px solid rgba(0, 0, 0, 0.05);  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);  -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);}.well blockquote {  border-color: #ddd;  border-color: rgba(0, 0, 0, 0.15);}.well-large {  padding: 24px;  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;}.well-small {  padding: 9px;  -webkit-border-radius: 3px;  -moz-border-radius: 3px;  border-radius: 3px;}.close {  float: right;  font-size: 20px;  font-weight: bold;  line-height: 18px;  color: #000000;  text-shadow: 0 1px 0 #ffffff;  opacity: 0.2;  filter: alpha(opacity=20);}.close:hover {  color: #000000;  text-decoration: none;  cursor: pointer;  opacity: 0.4;  filter: alpha(opacity=40);}button.close {  padding: 0;  cursor: pointer;  background: transparent;  border: 0;  -webkit-appearance: none;}.pull-right {  float: right;}.pull-left {  float: left;}.hide {  display: none;}.show {  display: block;}.invisible {  visibility: hidden;}.fade {  opacity: 0;  -webkit-transition: opacity 0.15s linear;  -moz-transition: opacity 0.15s linear;  -ms-transition: opacity 0.15s linear;  -o-transition: opacity 0.15s linear;  transition: opacity 0.15s linear;}.fade.in {  opacity: 1;}.collapse {  position: relative;  height: 0;  overflow: hidden;  -webkit-transition: height 0.35s ease;  -moz-transition: height 0.35s ease;  -ms-transition: height 0.35s ease;  -o-transition: height 0.35s ease;  transition: height 0.35s ease;}.collapse.in {  height: auto;}.hidden {  display: none;  visibility: hidden;}.visible-phone {  display: none !important;}.visible-tablet {  display: none !important;}.hidden-desktop {  display: none !important;}@media (max-width: 767px) {  .visible-phone {    display: inherit !important;  }  .hidden-phone {    display: none !important;  }  .hidden-desktop {    display: inherit !important;  }  .visible-desktop {    display: none !important;  }}@media (min-width: 768px) and (max-width: 979px) {  .visible-tablet {    display: inherit !important;  }  .hidden-tablet {    display: none !important;  }  .hidden-desktop {    display: inherit !important;  }  .visible-desktop {    display: none !important ;  }}@media (max-width: 480px) {  .nav-collapse {    -webkit-transform: translate3d(0, 0, 0);  }  .page-header h1 small {    display: block;    line-height: 18px;  }  input[type="checkbox"],  input[type="radio"] {    border: 1px solid #ccc;  }  .form-horizontal .control-group > label {    float: none;    width: auto;    padding-top: 0;    text-align: left;  }  .form-horizontal .controls {    margin-left: 0;  }  .form-horizontal .control-list {    padding-top: 0;  }  .form-horizontal .form-actions {    padding-left: 10px;    padding-right: 10px;  }  .modal {    position: absolute;    top: 10px;    left: 10px;    right: 10px;    width: auto;    margin: 0;  }  .modal.fade.in {    top: auto;  }  .modal-header .close {    padding: 10px;    margin: -10px;  }  .carousel-caption {    position: static;  }}@media (max-width: 767px) {  body {    padding-left: 20px;    padding-right: 20px;  }  .navbar-fixed-top,  .navbar-fixed-bottom {    margin-left: -20px;    margin-right: -20px;  }  .container-fluid {    padding: 0;  }  .dl-horizontal dt {    float: none;    clear: none;    width: auto;    text-align: left;  }  .dl-horizontal dd {    margin-left: 0;  }  .container {    width: auto;  }  .row-fluid {    width: 100%;  }  .row,  .thumbnails {    margin-left: 0;  }  [class*="span"],  .row-fluid [class*="span"] {    float: none;    display: block;    width: auto;    margin-left: 0;  }  .input-large,  .input-xlarge,  .input-xxlarge,  input[class*="span"],  select[class*="span"],  textarea[class*="span"],  .uneditable-input {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;  }  .input-prepend input,  .input-append input,  .input-prepend input[class*="span"],  .input-append input[class*="span"] {    display: inline-block;    width: auto;  }}@media (min-width: 768px) and (max-width: 979px) {  .row {    margin-left: -20px;    *zoom: 1;  }  .row:before,  .row:after {    display: table;    content: "";  }  .row:after {    clear: both;  }  [class*="span"] {    float: left;    margin-left: 20px;  }  .container,  .navbar-fixed-top .container,  .navbar-fixed-bottom .container {    width: 724px;  }  .span12 {    width: 724px;  }  .span11 {    width: 662px;  }  .span10 {    width: 600px;  }  .span9 {    width: 538px;  }  .span8 {    width: 476px;  }  .span7 {    width: 414px;  }  .span6 {    width: 352px;  }  .span5 {    width: 290px;  }  .span4 {    width: 228px;  }  .span3 {    width: 166px;  }  .span2 {    width: 104px;  }  .span1 {    width: 42px;  }  .offset12 {    margin-left: 764px;  }  .offset11 {    margin-left: 702px;  }  .offset10 {    margin-left: 640px;  }  .offset9 {    margin-left: 578px;  }  .offset8 {    margin-left: 516px;  }  .offset7 {    margin-left: 454px;  }  .offset6 {    margin-left: 392px;  }  .offset5 {    margin-left: 330px;  }  .offset4 {    margin-left: 268px;  }  .offset3 {    margin-left: 206px;  }  .offset2 {    margin-left: 144px;  }  .offset1 {    margin-left: 82px;  }  .row-fluid {    width: 100%;    *zoom: 1;  }  .row-fluid:before,  .row-fluid:after {    display: table;    content: "";  }  .row-fluid:after {    clear: both;  }  .row-fluid [class*="span"] {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;    float: left;    margin-left: 2.762430939%;    *margin-left: 2.709239449638298%;  }  .row-fluid [class*="span"]:first-child {    margin-left: 0;  }  .row-fluid .span12 {    width: 99.999999993%;    *width: 99.9468085036383%;  }  .row-fluid .span11 {    width: 91.436464082%;    *width: 91.38327259263829%;  }  .row-fluid .span10 {    width: 82.87292817100001%;    *width: 82.8197366816383%;  }  .row-fluid .span9 {    width: 74.30939226%;    *width: 74.25620077063829%;  }  .row-fluid .span8 {    width: 65.74585634900001%;    *width: 65.6926648596383%;  }  .row-fluid .span7 {    width: 57.182320438000005%;    *width: 57.129128948638304%;  }  .row-fluid .span6 {    width: 48.618784527%;    *width: 48.5655930376383%;  }  .row-fluid .span5 {    width: 40.055248616%;    *width: 40.0020571266383%;  }  .row-fluid .span4 {    width: 31.491712705%;    *width: 31.4385212156383%;  }  .row-fluid .span3 {    width: 22.928176794%;    *width: 22.874985304638297%;  }  .row-fluid .span2 {    width: 14.364640883%;    *width: 14.311449393638298%;  }  .row-fluid .span1 {    width: 5.801104972%;    *width: 5.747913482638298%;  }  input,  textarea,  .uneditable-input {    margin-left: 0;  }  input.span12, textarea.span12, .uneditable-input.span12 {    width: 714px;  }  input.span11, textarea.span11, .uneditable-input.span11 {    width: 652px;  }  input.span10, textarea.span10, .uneditable-input.span10 {    width: 590px;  }  input.span9, textarea.span9, .uneditable-input.span9 {    width: 528px;  }  input.span8, textarea.span8, .uneditable-input.span8 {    width: 466px;  }  input.span7, textarea.span7, .uneditable-input.span7 {    width: 404px;  }  input.span6, textarea.span6, .uneditable-input.span6 {    width: 342px;  }  input.span5, textarea.span5, .uneditable-input.span5 {    width: 280px;  }  input.span4, textarea.span4, .uneditable-input.span4 {    width: 218px;  }  input.span3, textarea.span3, .uneditable-input.span3 {    width: 156px;  }  input.span2, textarea.span2, .uneditable-input.span2 {    width: 94px;  }  input.span1, textarea.span1, .uneditable-input.span1 {    width: 32px;  }}@media (min-width: 1200px) {  .row {    margin-left: -30px;    *zoom: 1;  }  .row:before,  .row:after {    display: table;    content: "";  }  .row:after {    clear: both;  }  [class*="span"] {    float: left;    margin-left: 30px;  }  .container,  .navbar-fixed-top .container,  .navbar-fixed-bottom .container {    width: 1170px;  }  .span12 {    width: 1170px;  }  .span11 {    width: 1070px;  }  .span10 {    width: 970px;  }  .span9 {    width: 870px;  }  .span8 {    width: 770px;  }  .span7 {    width: 670px;  }  .span6 {    width: 570px;  }  .span5 {    width: 470px;  }  .span4 {    width: 370px;  }  .span3 {    width: 270px;  }  .span2 {    width: 170px;  }  .span1 {    width: 70px;  }  .offset12 {    margin-left: 1230px;  }  .offset11 {    margin-left: 1130px;  }  .offset10 {    margin-left: 1030px;  }  .offset9 {    margin-left: 930px;  }  .offset8 {    margin-left: 830px;  }  .offset7 {    margin-left: 730px;  }  .offset6 {    margin-left: 630px;  }  .offset5 {    margin-left: 530px;  }  .offset4 {    margin-left: 430px;  }  .offset3 {    margin-left: 330px;  }  .offset2 {    margin-left: 230px;  }  .offset1 {    margin-left: 130px;  }  .row-fluid {    width: 100%;    *zoom: 1;  }  .row-fluid:before,  .row-fluid:after {    display: table;    content: "";  }  .row-fluid:after {    clear: both;  }  .row-fluid [class*="span"] {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;    float: left;    margin-left: 2.564102564%;    *margin-left: 2.510911074638298%;  }  .row-fluid [class*="span"]:first-child {    margin-left: 0;  }  .row-fluid .span12 {    width: 100%;    *width: 99.94680851063829%;  }  .row-fluid .span11 {    width: 91.45299145300001%;    *width: 91.3997999636383%;  }  .row-fluid .span10 {    width: 82.905982906%;    *width: 82.8527914166383%;  }  .row-fluid .span9 {    width: 74.358974359%;    *width: 74.30578286963829%;  }  .row-fluid .span8 {    width: 65.81196581200001%;    *width: 65.7587743226383%;  }  .row-fluid .span7 {    width: 57.264957265%;    *width: 57.2117657756383%;  }  .row-fluid .span6 {    width: 48.717948718%;    *width: 48.6647572286383%;  }  .row-fluid .span5 {    width: 40.170940171000005%;    *width: 40.117748681638304%;  }  .row-fluid .span4 {    width: 31.623931624%;    *width: 31.5707401346383%;  }  .row-fluid .span3 {    width: 23.076923077%;    *width: 23.0237315876383%;  }  .row-fluid .span2 {    width: 14.529914530000001%;    *width: 14.4767230406383%;  }  .row-fluid .span1 {    width: 5.982905983%;    *width: 5.929714493638298%;  }  input,  textarea,  .uneditable-input {    margin-left: 0;  }  input.span12, textarea.span12, .uneditable-input.span12 {    width: 1160px;  }  input.span11, textarea.span11, .uneditable-input.span11 {    width: 1060px;  }  input.span10, textarea.span10, .uneditable-input.span10 {    width: 960px;  }  input.span9, textarea.span9, .uneditable-input.span9 {    width: 860px;  }  input.span8, textarea.span8, .uneditable-input.span8 {    width: 760px;  }  input.span7, textarea.span7, .uneditable-input.span7 {    width: 660px;  }  input.span6, textarea.span6, .uneditable-input.span6 {    width: 560px;  }  input.span5, textarea.span5, .uneditable-input.span5 {    width: 460px;  }  input.span4, textarea.span4, .uneditable-input.span4 {    width: 360px;  }  input.span3, textarea.span3, .uneditable-input.span3 {    width: 260px;  }  input.span2, textarea.span2, .uneditable-input.span2 {    width: 160px;  }  input.span1, textarea.span1, .uneditable-input.span1 {    width: 60px;  }  .thumbnails {    margin-left: -30px;  }  .thumbnails > li {    margin-left: 30px;  }  .row-fluid .thumbnails {    margin-left: 0;  }}@media (max-width: 979px) {  body {    padding-top: 0;  }  .navbar-fixed-top,  .navbar-fixed-bottom {    position: static;  }  .navbar-fixed-top {    margin-bottom: 18px;  }  .navbar-fixed-bottom {    margin-top: 18px;  }  .navbar-fixed-top .navbar-inner,  .navbar-fixed-bottom .navbar-inner {    padding: 5px;  }  .navbar .container {    width: auto;    padding: 0;  }  .navbar .brand {    padding-left: 10px;    padding-right: 10px;    margin: 0 0 0 -5px;  }  .nav-collapse {    clear: both;  }  .nav-collapse .nav {    float: none;    margin: 0 0 9px;  }  .nav-collapse .nav > li {    float: none;  }  .nav-collapse .nav > li > a {    margin-bottom: 2px;  }  .nav-collapse .nav > .divider-vertical {    display: none;  }  .nav-collapse .nav .nav-header {    color: #999999;    text-shadow: none;  }  .nav-collapse .nav > li > a,  .nav-collapse .dropdown-menu a {    padding: 6px 15px;    font-weight: bold;    color: #999999;    -webkit-border-radius: 3px;    -moz-border-radius: 3px;    border-radius: 3px;  }  .nav-collapse .btn {    padding: 4px 10px 4px;    font-weight: normal;    -webkit-border-radius: 4px;    -moz-border-radius: 4px;    border-radius: 4px;  }  .nav-collapse .dropdown-menu li + li a {    margin-bottom: 2px;  }  .nav-collapse .nav > li > a:hover,  .nav-collapse .dropdown-menu a:hover {    background-color: #222222;  }  .nav-collapse.in .btn-group {    margin-top: 5px;    padding: 0;  }  .nav-collapse .dropdown-menu {    position: static;    top: auto;    left: auto;    float: none;    display: block;    max-width: none;    margin: 0 15px;    padding: 0;    background-color: transparent;    border: none;    -webkit-border-radius: 0;    -moz-border-radius: 0;    border-radius: 0;    -webkit-box-shadow: none;    -moz-box-shadow: none;    box-shadow: none;  }  .nav-collapse .dropdown-menu:before,  .nav-collapse .dropdown-menu:after {    display: none;  }  .nav-collapse .dropdown-menu .divider {    display: none;  }  .nav-collapse .navbar-form,  .nav-collapse .navbar-search {    float: none;    padding: 9px 15px;    margin: 9px 0;    border-top: 1px solid #222222;    border-bottom: 1px solid #222222;    -webkit-box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);    -moz-box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);    box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);  }  .navbar .nav-collapse .nav.pull-right {    float: none;    margin-left: 0;  }  .nav-collapse,  .nav-collapse.collapse {    overflow: hidden;    height: 0;  }  .navbar .btn-navbar {    display: block;  }  .navbar-static .navbar-inner {    padding-left: 10px;    padding-right: 10px;  }}@media (min-width: 980px) {  .nav-collapse.collapse {    height: auto !important;    overflow: visible !important;  }}

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