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  • C#/.NET Little Wonders: A Redux

    - by James Michael Hare
    I gave my Little Wonders presentation to the Topeka Dot Net Users' Group today, so re-posting the links to all the previous posts for them. The Presentation: C#/.NET Little Wonders: A Presentation The Original Trilogy: C#/.NET Five Little Wonders (part 1) C#/.NET Five More Little Wonders (part 2) C#/.NET Five Final Little Wonders (part 3) The Subsequent Sequels: C#/.NET Little Wonders: ToDictionary() and ToList() C#/.NET Little Wonders: DateTime is Packed With Goodies C#/.NET Little Wonders: Fun With Enum Methods C#/.NET Little Wonders: Cross-Calling Constructors C#/.NET Little Wonders: Constraining Generics With Where Clause C#/.NET Little Wonders: Comparer<T>.Default C#/.NET Little Wonders: The Useful (But Overlooked) Sets The Concurrent Wonders: C#/.NET Little Wonders: The Concurrent Collections (1 of 3) - ConcurrentQueue and ConcurrentStack C#/.NET Little Wonders: The Concurrent Collections (2 of 3) - ConcurrentDictionary Tweet   Technorati Tags: .NET,C#,Little Wonders

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  • C#/.NET Little Wonders: Getting Caller Information

    - by James Michael Hare
    Originally posted on: http://geekswithblogs.net/BlackRabbitCoder/archive/2013/07/25/c.net-little-wonders-getting-caller-information.aspx Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. There are times when it is desirable to know who called the method or property you are currently executing.  Some applications of this could include logging libraries, or possibly even something more advanced that may server up different objects depending on who called the method. In the past, we mostly relied on the System.Diagnostics namespace and its classes such as StackTrace and StackFrame to see who our caller was, but now in C# 5, we can also get much of this data at compile-time. Determining the caller using the stack One of the ways of doing this is to examine the call stack.  The classes that allow you to examine the call stack have been around for a long time and can give you a very deep view of the calling chain all the way back to the beginning for the thread that has called you. You can get caller information by either instantiating the StackTrace class (which will give you the complete stack trace, much like you see when an exception is generated), or by using StackFrame which gets a single frame of the stack trace.  Both involve examining the call stack, which is a non-trivial task, so care should be done not to do this in a performance-intensive situation. For our simple example let's say we are going to recreate the wheel and construct our own logging framework.  Perhaps we wish to create a simple method Log which will log the string-ified form of an object and some information about the caller.  We could easily do this as follows: 1: static void Log(object message) 2: { 3: // frame 1, true for source info 4: StackFrame frame = new StackFrame(1, true); 5: var method = frame.GetMethod(); 6: var fileName = frame.GetFileName(); 7: var lineNumber = frame.GetFileLineNumber(); 8: 9: // we'll just use a simple Console write for now 10: Console.WriteLine("{0}({1}):{2} - {3}", 11: fileName, lineNumber, method.Name, message); 12: } So, what we are doing here is grabbing the 2nd stack frame (the 1st is our current method) using a 2nd argument of true to specify we want source information (if available) and then taking the information from the frame.  This works fine, and if we tested it out by calling from a file such as this: 1: // File c:\projects\test\CallerInfo\CallerInfo.cs 2:  3: public class CallerInfo 4: { 5: Log("Hello Logger!"); 6: } We'd see this: 1: c:\projects\test\CallerInfo\CallerInfo.cs(5):Main - Hello Logger! This works well, and in fact CallStack and StackFrame are still the best ways to examine deeper into the call stack.  But if you only want to get information on the caller of your method, there is another option… Determining the caller at compile-time In C# 5 (.NET 4.5) they added some attributes that can be supplied to optional parameters on a method to receive caller information.  These attributes can only be applied to methods with optional parameters with explicit defaults.  Then, as the compiler determines who is calling your method with these attributes, it will fill in the values at compile-time. These are the currently supported attributes available in the  System.Runtime.CompilerServices namespace": CallerFilePathAttribute – The path and name of the file that is calling your method. CallerLineNumberAttribute – The line number in the file where your method is being called. CallerMemberName – The member that is calling your method. So let’s take a look at how our Log method would look using these attributes instead: 1: static int Log(object message, 2: [CallerMemberName] string memberName = "", 3: [CallerFilePath] string fileName = "", 4: [CallerLineNumber] int lineNumber = 0) 5: { 6: // we'll just use a simple Console write for now 7: Console.WriteLine("{0}({1}):{2} - {3}", 8: fileName, lineNumber, memberName, message); 9: } Again, calling this from our sample Main would give us the same result: 1: c:\projects\test\CallerInfo\CallerInfo.cs(5):Main - Hello Logger! However, though this seems the same, there are a few key differences. First of all, there are only 3 supported attributes (at this time) that give you the file path, line number, and calling member.  Thus, it does not give you as rich of detail as a StackFrame (which can give you the calling type as well and deeper frames, for example).  Also, these are supported through optional parameters, which means we could call our new Log method like this: 1: // They're defaults, why not fill 'em in 2: Log("My message.", "Some member", "Some file", -13); In addition, since these attributes require optional parameters, they cannot be used in properties, only in methods. These caveats aside, they do let you get similar information inside of methods at a much greater speed!  How much greater?  Well lets crank through 1,000,000 iterations of each.  instead of logging to console, I’ll return the formatted string length of each.  Doing this, we get: 1: Time for 1,000,000 iterations with StackTrace: 5096 ms 2: Time for 1,000,000 iterations with Attributes: 196 ms So you see, using the attributes is much, much faster!  Nearly 25x faster in fact.  Summary There are a few ways to get caller information for a method.  The StackFrame allows you to get a comprehensive set of information spanning the whole call stack, but at a heavier cost.  On the other hand, the attributes allow you to quickly get at caller information baked in at compile-time, but to do so you need to create optional parameters in your methods to support it. Technorati Tags: Little Wonders,CSharp,C#,.NET,StackFrame,CallStack,CallerFilePathAttribute,CallerLineNumberAttribute,CallerMemberName

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  • TechEd 2012: A Little Cloud And Too Little Windows Phone

    - by Tim Murphy
    It is Monday afternoon and the last couple of sessions have been disappointing.  I started out in the Nokia: Learning to Tile session.  I guess I should have read the summary more closely because it turned out to be more of a Nokia/WP7 history and sales pitch. “I’m outa here!” I made a quick venue change and now we are learning about Private Cloud Architecture.  The topic and the material were very informative.  The speaker even had a couple of quotable statements. The first quote was “You can trust me … I’m a doctor”.  The second was a new acronym (at least for me): CAVE – committee against virtually everything.  I am sure I have dealt with them more than once in my career. Unfortunately he didn’t just have a doctorate, the presentation was overdone like a medical journal.  While I didn’t enjoy the presentation, I am looking forward to getting my hands on the slides to review. Here is looking forward to the next sessions. del.icio.us Tags: Windows Phone,Cloud,Architecture

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  • Removing Little Snitch completely (Mac OS X Snow Leopard)

    - by Mathias Bynens
    I uninstalled Little Snitch months ago. Or so, I thought. When opening Console.app, I see something like this: Here’s a textual log: 21/11/09 22:05:31 com.apple.launchd[1] (at.obdev.littlesnitchd[10045]) Exited with exit code: 1 21/11/09 22:05:31 com.apple.launchd[1] (at.obdev.littlesnitchd) Throttling respawn: Will start in 10 seconds 21/11/09 22:05:33 Little Snitch UIAgent[10046] 2.0.4.385: m65968c1c 21/11/09 22:05:33 Little Snitch UIAgent[10046] 2.0.4.385: m579328b9 21/11/09 22:05:33 Little Snitch UIAgent[10046] 2.0.4.385: m41531ded 21/11/09 22:05:33 com.apple.launchd.peruser.501[170] (at.obdev.LittleSnitchUIAgent) Throttling respawn: Will start in 10 seconds 21/11/09 22:05:41 com.apple.launchd[1] (at.obdev.littlesnitchd[10049]) Exited with exit code: 1 21/11/09 22:05:41 com.apple.launchd[1] (at.obdev.littlesnitchd) Throttling respawn: Will start in 10 seconds 21/11/09 22:05:43 Little Snitch UIAgent[10050] 2.0.4.385: m65968c1c 21/11/09 22:05:43 Little Snitch UIAgent[10050] 2.0.4.385: m579328b9 21/11/09 22:05:43 Little Snitch UIAgent[10050] 2.0.4.385: m41531ded 21/11/09 22:05:43 com.apple.launchd.peruser.501[170] (at.obdev.LittleSnitchUIAgent) Throttling respawn: Will start in 10 seconds Spotlight searches for ‘little snitch’ or ‘littlesnitch’ yield no results. Yet, it seems like I didn’t get rid of Little Snitch entirely, since it’s still using up my CPU. Any ideas?

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  • Explore Historic Sites from the Comfort of Your Desktop with Google’s ‘World Wonders Project’

    - by Asian Angel
    Have you always wanted to explore historic sites across the world but lack the extra time and/or funds to do so? Then take heart! Now you can visit historic sites to your heart’s content from home with Google’s ‘World Wonders Project’. Note: The screenshot shown above is from the ‘Archaeological Areas of Pompei’ site. You can explore exotic locations such as Pompei, the Palace and Park of Versailles, Shark Bay, the Tenryu-ji-Temple in Ancient Kyoto, and more. The World Wonders Project Homepage The World Wonders Project YouTube Channel HTG Explains: Learn How Websites Are Tracking You Online Here’s How to Download Windows 8 Release Preview Right Now HTG Explains: Why Linux Doesn’t Need Defragmenting

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  • A Trio of Presentations: Little Wonders, StyleCop, and LINQ/Lambdas

    - by James Michael Hare
    This week is a busy week for me.  First of all I’m giving another presentation on a LINQ/Lambda primer for the rest of the developers in my company.  Of Lambdas and LINQ View more presentations from BlackRabbitCoder Then this Saturday the 25th of June I’ll be reprising my Little Wonders presentation for the Kansas City Developers Camp.  If you are in the area I highly recommend attending and seeing the other great presentations as well.  Their link is here. Little Wonders View more presentations from BlackRabbitCoder Finally, this Monday the 27th I’ll be speaking at the Saint Louis .NET Users group, giving my Automating Code Standards Using StyleCop and FxCop presentation.  If you are in the Saint Louis area stop by!  There’s two other simultaneous presentations as well if they’re more suited to your interests.  The link for the SLDNUG is here. Automating C# Coding Standards using StyleCop and FxCop View more presentations from BlackRabbitCoder Tweet Technorati Tags: C#,.NET,LINQ,Lambda,StyleCop,FxCop,Little Wonders

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  • Substituting Java for Groovy Little By Little

    - by yar
    I have been checking out Groovy a bit and I feel that moving a Java program to Groovy little by little -- grabbing a class and making it a Groovy class, then converting the method guts a bit at a time -- might be a relatively sane way to take advantage of some of the Groovy language features. I would also do new classes in Groovy. Questions: Is this a reasonable way to convert? Can I keep all of my public methods and and fields in Java? Groovy is "just" a superset, right? What kinds of things would you not do in Groovy, but prefer Java instead?

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  • C#/.NET Little Wonders: The Timeout static class

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. When I started the “Little Wonders” series, I really wanted to pay homage to parts of the .NET Framework that are often small but can help in big ways.  The item I have to discuss today really is a very small item in the .NET BCL, but once again I feel it can help make the intention of code much clearer and thus is worthy of note. The Problem - Magic numbers aren’t very readable or maintainable In my first Little Wonders Post (Five Little Wonders That Make Code Better) I mention the TimeSpan factory methods which, I feel, really help the readability of constructed TimeSpan instances. Just to quickly recap that discussion, ask yourself what the TimeSpan specified in each case below is 1: // Five minutes? Five Seconds? 2: var fiveWhat1 = new TimeSpan(0, 0, 5); 3: var fiveWhat2 = new TimeSpan(0, 0, 5, 0); 4: var fiveWhat3 = new TimeSpan(0, 0, 5, 0, 0); You’d think they’d all be the same unit of time, right?  After all, most overloads tend to tack additional arguments on the end.  But this is not the case with TimeSpan, where the constructor forms are:     TimeSpan(int hours, int minutes, int seconds);     TimeSpan(int days, int hours, int minutes, int seconds);     TimeSpan(int days, int hours, int minutes, int seconds, int milliseconds); Notice how in the 4 and 5 parameter version we suddenly have the parameter days slipping in front of hours?  This can make reading constructors like those above much harder.  Fortunately, there are TimeSpan factory methods to help make your intention crystal clear: 1: // Ah! Much clearer! 2: var fiveSeconds = TimeSpan.FromSeconds(5); These are great because they remove all ambiguity from the reader!  So in short, magic numbers in constructors and methods can be ambiguous, and anything we can do to clean up the intention of the developer will make the code much easier to read and maintain. Timeout – Readable identifiers for infinite timeout values In a similar way to TimeSpan, let’s consider specifying timeouts for some of .NET’s (or our own) many methods that allow you to specify timeout periods. For example, in the TPL Task class, there is a family of Wait() methods that can take TimeSpan or int for timeouts.  Typically, if you want to specify an infinite timeout, you’d just call the version that doesn’t take a timeout parameter at all: 1: myTask.Wait(); // infinite wait But there are versions that take the int or TimeSpan for timeout as well: 1: // Wait for 100 ms 2: myTask.Wait(100); 3:  4: // Wait for 5 seconds 5: myTask.Wait(TimeSpan.FromSeconds(5); Now, if we want to specify an infinite timeout to wait on the Task, we could pass –1 (or a TimeSpan set to –1 ms), which what the .NET BCL methods with timeouts use to represent an infinite timeout: 1: // Also infinite timeouts, but harder to read/maintain 2: myTask.Wait(-1); 3: myTask.Wait(TimeSpan.FromMilliseconds(-1)); However, these are not as readable or maintainable.  If you were writing this code, you might make the mistake of thinking 0 or int.MaxValue was an infinite timeout, and you’d be incorrect.  Also, reading the code above it isn’t as clear that –1 is infinite unless you happen to know that is the specified behavior. To make the code like this easier to read and maintain, there is a static class called Timeout in the System.Threading namespace which contains definition for infinite timeouts specified as both int and TimeSpan forms: Timeout.Infinite An integer constant with a value of –1 Timeout.InfiniteTimeSpan A static readonly TimeSpan which represents –1 ms (only available in .NET 4.5+) This makes our calls to Task.Wait() (or any other calls with timeouts) much more clear: 1: // intention to wait indefinitely is quite clear now 2: myTask.Wait(Timeout.Infinite); 3: myTask.Wait(Timeout.InfiniteTimeSpan); But wait, you may say, why would we care at all?  Why not use the version of Wait() that takes no arguments?  Good question!  When you’re directly calling the method with an infinite timeout that’s what you’d most likely do, but what if you are just passing along a timeout specified by a caller from higher up?  Or perhaps storing a timeout value from a configuration file, and want to default it to infinite? For example, perhaps you are designing a communications module and want to be able to shutdown gracefully, but if you can’t gracefully finish in a specified amount of time you want to force the connection closed.  You could create a Shutdown() method in your class, and take a TimeSpan or an int for the amount of time to wait for a clean shutdown – perhaps waiting for client to acknowledge – before terminating the connection.  So, assume we had a pub/sub system with a class to broadcast messages: 1: // Some class to broadcast messages to connected clients 2: public class Broadcaster 3: { 4: // ... 5:  6: // Shutdown connection to clients, wait for ack back from clients 7: // until all acks received or timeout, whichever happens first 8: public void Shutdown(int timeout) 9: { 10: // Kick off a task here to send shutdown request to clients and wait 11: // for the task to finish below for the specified time... 12:  13: if (!shutdownTask.Wait(timeout)) 14: { 15: // If Wait() returns false, we timed out and task 16: // did not join in time. 17: } 18: } 19: } We could even add an overload to allow us to use TimeSpan instead of int, to give our callers the flexibility to specify timeouts either way: 1: // overload to allow them to specify Timeout in TimeSpan, would 2: // just call the int version passing in the TotalMilliseconds... 3: public void Shutdown(TimeSpan timeout) 4: { 5: Shutdown(timeout.TotalMilliseconds); 6: } Notice in case of this class, we don’t assume the caller wants infinite timeouts, we choose to rely on them to tell us how long to wait.  So now, if they choose an infinite timeout, they could use the –1, which is more cryptic, or use Timeout class to make the intention clear: 1: // shutdown the broadcaster, waiting until all clients ack back 2: // without timing out. 3: myBroadcaster.Shutdown(Timeout.Infinite); We could even add a default argument using the int parameter version so that specifying no arguments to Shutdown() assumes an infinite timeout: 1: // Modified original Shutdown() method to add a default of 2: // Timeout.Infinite, works because Timeout.Infinite is a compile 3: // time constant. 4: public void Shutdown(int timeout = Timeout.Infinite) 5: { 6: // same code as before 7: } Note that you can’t default the ShutDown(TimeSpan) overload with Timeout.InfiniteTimeSpan since it is not a compile-time constant.  The only acceptable default for a TimeSpan parameter would be default(TimeSpan) which is zero milliseconds, which specified no wait, not infinite wait. Summary While Timeout.Infinite and Timeout.InfiniteTimeSpan are not earth-shattering classes in terms of functionality, they do give you very handy and readable constant values that you can use in your programs to help increase readability and maintainability when specifying infinite timeouts for various timeouts in the BCL and your own applications. Technorati Tags: C#,CSharp,.NET,Little Wonders,Timeout,Task

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  • C#/.NET Little Wonders: Of LINQ and Lambdas - A Presentation

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Today I’m giving a brief beginner’s guide to LINQ and Lambdas at the St. Louis .NET User’s Group so I thought I’d post the presentation here as well.  I updated the presentation a bit as well as added some notes on the query syntax.  Enjoy! The C#/.NET Fundaments: Of Lambdas and LINQ Presentation Of Lambdas and LINQ View more presentations from BlackRabbitCoder   Technorati Tags: C#, CSharp, .NET, Little Wonders, LINQ, Lambdas

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  • C++ Little Wonders: The C++11 auto keyword redux

    - by James Michael Hare
    I’ve decided to create a sub-series of my Little Wonders posts to focus on C++.  Just like their C# counterparts, these posts will focus on those features of the C++ language that can help improve code by making it easier to write and maintain.  The index of the C# Little Wonders can be found here. This has been a busy week with a rollout of some new website features here at my work, so I don’t have a big post for this week.  But I wanted to write something up, and since lately I’ve been renewing my C++ skills in a separate project, it seemed like a good opportunity to start a C++ Little Wonders series.  Most of my development work still tends to focus on C#, but it was great to get back into the saddle and renew my C++ knowledge.  Today I’m going to focus on a new feature in C++11 (formerly known as C++0x, which is a major move forward in the C++ language standard).  While this small keyword can seem so trivial, I feel it is a big step forward in improving readability in C++ programs. The auto keyword If you’ve worked on C++ for a long time, you probably have some passing familiarity with the old auto keyword as one of those rarely used C++ keywords that was almost never used because it was the default. That is, in the code below (before C++11): 1: int foo() 2: { 3: // automatic variables (allocated and deallocated on stack) 4: int x; 5: auto int y; 6:  7: // static variables (retain their value across calls) 8: static int z; 9:  10: return 0; 11: } The variable x is assumed to be auto because that is the default, thus it is unnecessary to specify it explicitly as in the declaration of y below that.  Basically, an auto variable is one that is allocated and de-allocated on the stack automatically.  Contrast this to static variables, that are allocated statically and exist across the lifetime of the program. Because auto was so rarely (if ever) used since it is the norm, they decided to remove it for this purpose and give it new meaning in C++11.  The new meaning of auto: implicit typing Now, if your compiler supports C++ 11 (or at least a good subset of C++11 or 0x) you can take advantage of type inference in C++.  For those of you from the C# world, this means that the auto keyword in C++ now behaves a lot like the var keyword in C#! For example, many of us have had to declare those massive type declarations for an iterator before.  Let’s say we have a std::map of std::string to int which will map names to ages: 1: std::map<std::string, int> myMap; And then let’s say we want to find the age of a given person: 1: // Egad that's a long type... 2: std::map<std::string, int>::const_iterator pos = myMap.find(targetName); Notice that big ugly type definition to declare variable pos?  Sure, we could shorten this by creating a typedef of our specific map type if we wanted, but now with the auto keyword there’s no need: 1: // much shorter! 2: auto pos = myMap.find(targetName); The auto now tells the compiler to determine what type pos should be based on what it’s being assigned to.  This is not dynamic typing, it still determines the type as if it were explicitly declared and once declared that type cannot be changed.  That is, this is invalid: 1: // x is type int 2: auto x = 42; 3:  4: // can't assign string to int 5: x = "Hello"; Once the compiler determines x is type int it is exactly as if we typed int x = 42; instead, so don’t' confuse it with dynamic typing, it’s still very type-safe. An interesting feature of the auto keyword is that you can modify the inferred type: 1: // declare method that returns int* 2: int* GetPointer(); 3:  4: // p1 is int*, auto inferred type is int 5: auto *p1 = GetPointer(); 6:  7: // ps is int*, auto inferred type is int* 8: auto p2 = GetPointer(); Notice in both of these cases, p1 and p2 are determined to be int* but in each case the inferred type was different.  because we declared p1 as auto *p1 and GetPointer() returns int*, it inferred the type int was needed to complete the declaration.  In the second case, however, we declared p2 as auto p2 which means the inferred type was int*.  Ultimately, this make p1 and p2 the same type, but which type is inferred makes a difference, if you are chaining multiple inferred declarations together.  In these cases, the inferred type of each must match the first: 1: // Type inferred is int 2: // p1 is int* 3: // p2 is int 4: // p3 is int& 5: auto *p1 = GetPointer(), p2 = 42, &p3 = p2; Note that this works because the inferred type was int, if the inferred type was int* instead: 1: // syntax error, p1 was inferred to be int* so p2 and p3 don't make sense 2: auto p1 = GetPointer(), p2 = 42, &p3 = p2; You could also use const or static to modify the inferred type: 1: // inferred type is an int, theAnswer is a const int 2: const auto theAnswer = 42; 3:  4: // inferred type is double, Pi is a static double 5: static auto Pi = 3.1415927; Thus in the examples above it inferred the types int and double respectively, which were then modified to const and static. Summary The auto keyword has gotten new life in C++11 to allow you to infer the type of a variable from it’s initialization.  This simple little keyword can be used to cut down large declarations for complex types into a much more readable form, where appropriate.   Technorati Tags: C++, C++11, Little Wonders, auto

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  • C#/.NET Little Wonders: Static Char Methods

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Often times in our code we deal with the bigger classes and types in the BCL, and occasionally forgot that there are some nice methods on the primitive types as well.  Today we will discuss some of the handy static methods that exist on the char (the C# alias of System.Char) type. The Background I was examining a piece of code this week where I saw the following: 1: // need to get the 5th (offset 4) character in upper case 2: var type = symbol.Substring(4, 1).ToUpper(); 3:  4: // test to see if the type is P 5: if (type == "P") 6: { 7: // ... do something with P type... 8: } Is there really any error in this code?  No, but it still struck me wrong because it is allocating two very short-lived throw-away strings, just to store and manipulate a single char: The call to Substring() generates a new string of length 1 The call to ToUpper() generates a new upper-case version of the string from Step 1. In my mind this is similar to using ToUpper() to do a case-insensitive compare: it isn’t wrong, it’s just much heavier than it needs to be (for more info on case-insensitive compares, see #2 in 5 More Little Wonders). One of my favorite books is the C++ Coding Standards: 101 Rules, Guidelines, and Best Practices by Sutter and Alexandrescu.  True, it’s about C++ standards, but there’s also some great general programming advice in there, including two rules I love:         8. Don’t Optimize Prematurely         9. Don’t Pessimize Prematurely We all know what #8 means: don’t optimize when there is no immediate need, especially at the expense of readability and maintainability.  I firmly believe this and in the axiom: it’s easier to make correct code fast than to make fast code correct.  Optimizing code to the point that it becomes difficult to maintain often gains little and often gives you little bang for the buck. But what about #9?  Well, for that they state: “All other things being equal, notably code complexity and readability, certain efficient design patterns and coding idioms should just flow naturally from your fingertips and are no harder to write then the pessimized alternatives. This is not premature optimization; it is avoiding gratuitous pessimization.” Or, if I may paraphrase: “where it doesn’t increase the code complexity and readability, prefer the more efficient option”. The example code above was one of those times I feel where we are violating a tacit C# coding idiom: avoid creating unnecessary temporary strings.  The code creates temporary strings to hold one char, which is just unnecessary.  I think the original coder thought he had to do this because ToUpper() is an instance method on string but not on char.  What he didn’t know, however, is that ToUpper() does exist on char, it’s just a static method instead (though you could write an extension method to make it look instance-ish). This leads me (in a long-winded way) to my Little Wonders for the day… Static Methods of System.Char So let’s look at some of these handy, and often overlooked, static methods on the char type: IsDigit(), IsLetter(), IsLetterOrDigit(), IsPunctuation(), IsWhiteSpace() Methods to tell you whether a char (or position in a string) belongs to a category of characters. IsLower(), IsUpper() Methods that check if a char (or position in a string) is lower or upper case ToLower(), ToUpper() Methods that convert a single char to the lower or upper equivalent. For example, if you wanted to see if a string contained any lower case characters, you could do the following: 1: if (symbol.Any(c => char.IsLower(c))) 2: { 3: // ... 4: } Which, incidentally, we could use a method group to shorten the expression to: 1: if (symbol.Any(char.IsLower)) 2: { 3: // ... 4: } Or, if you wanted to verify that all of the characters in a string are digits: 1: if (symbol.All(char.IsDigit)) 2: { 3: // ... 4: } Also, for the IsXxx() methods, there are overloads that take either a char, or a string and an index, this means that these two calls are logically identical: 1: // check given a character 2: if (char.IsUpper(symbol[0])) { ... } 3:  4: // check given a string and index 5: if (char.IsUpper(symbol, 0)) { ... } Obviously, if you just have a char, then you’d just use the first form.  But if you have a string you can use either form equally well. As a side note, care should be taken when examining all the available static methods on the System.Char type, as some seem to be redundant but actually have very different purposes.  For example, there are IsDigit() and IsNumeric() methods, which sound the same on the surface, but give you different results. IsDigit() returns true if it is a base-10 digit character (‘0’, ‘1’, … ‘9’) where IsNumeric() returns true if it’s any numeric character including the characters for ½, ¼, etc. Summary To come full circle back to our opening example, I would have preferred the code be written like this: 1: // grab 5th char and take upper case version of it 2: var type = char.ToUpper(symbol[4]); 3:  4: if (type == 'P') 5: { 6: // ... do something with P type... 7: } Not only is it just as readable (if not more so), but it performs over 3x faster on my machine:    1,000,000 iterations of char method took: 30 ms, 0.000050 ms/item.    1,000,000 iterations of string method took: 101 ms, 0.000101 ms/item. It’s not only immediately faster because we don’t allocate temporary strings, but as an added bonus there less garbage to collect later as well.  To me this qualifies as a case where we are using a common C# performance idiom (don’t create unnecessary temporary strings) to make our code better. Technorati Tags: C#,CSharp,.NET,Little Wonders,char,string

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  • C#/.NET Little Wonders: The Nullable static class

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Today we’re going to look at an interesting Little Wonder that can be used to mitigate what could be considered a Little Pitfall.  The Little Wonder we’ll be examining is the System.Nullable static class.  No, not the System.Nullable<T> class, but a static helper class that has one useful method in particular that we will examine… but first, let’s look at the Little Pitfall that makes this wonder so useful. Little Pitfall: Comparing nullable value types using <, >, <=, >= Examine this piece of code, without examining it too deeply, what’s your gut reaction as to the result? 1: int? x = null; 2:  3: if (x < 100) 4: { 5: Console.WriteLine("True, {0} is less than 100.", 6: x.HasValue ? x.ToString() : "null"); 7: } 8: else 9: { 10: Console.WriteLine("False, {0} is NOT less than 100.", 11: x.HasValue ? x.ToString() : "null"); 12: } Your gut would be to say true right?  It would seem to make sense that a null integer is less than the integer constant 100.  But the result is actually false!  The null value is not less than 100 according to the less-than operator. It looks even more outrageous when you consider this also evaluates to false: 1: int? x = null; 2:  3: if (x < int.MaxValue) 4: { 5: // ... 6: } So, are we saying that null is less than every valid int value?  If that were true, null should be less than int.MinValue, right?  Well… no: 1: int? x = null; 2:  3: // um... hold on here, x is NOT less than min value? 4: if (x < int.MinValue) 5: { 6: // ... 7: } So what’s going on here?  If we use greater than instead of less than, we see the same little dilemma: 1: int? x = null; 2:  3: // once again, null is not greater than anything either... 4: if (x > int.MinValue) 5: { 6: // ... 7: } It turns out that four of the comparison operators (<, <=, >, >=) are designed to return false anytime at least one of the arguments is null when comparing System.Nullable wrapped types that expose the comparison operators (short, int, float, double, DateTime, TimeSpan, etc.).  What’s even odder is that even though the two equality operators (== and !=) work correctly, >= and <= have the same issue as < and > and return false if both System.Nullable wrapped operator comparable types are null! 1: DateTime? x = null; 2: DateTime? y = null; 3:  4: if (x <= y) 5: { 6: Console.WriteLine("You'd think this is true, since both are null, but it's not."); 7: } 8: else 9: { 10: Console.WriteLine("It's false because <=, <, >, >= don't work on null."); 11: } To make matters even more confusing, take for example your usual check to see if something is less than, greater to, or equal: 1: int? x = null; 2: int? y = 100; 3:  4: if (x < y) 5: { 6: Console.WriteLine("X is less than Y"); 7: } 8: else if (x > y) 9: { 10: Console.WriteLine("X is greater than Y"); 11: } 12: else 13: { 14: // We fall into the "equals" assumption, but clearly null != 100! 15: Console.WriteLine("X is equal to Y"); 16: } Yes, this code outputs “X is equal to Y” because both the less-than and greater-than operators return false when a Nullable wrapped operator comparable type is null.  This violates a lot of our assumptions because we assume is something is not less than something, and it’s not greater than something, it must be equal.  So keep in mind, that the only two comparison operators that work on Nullable wrapped types where at least one is null are the equals (==) and not equals (!=) operators: 1: int? x = null; 2: int? y = 100; 3:  4: if (x == y) 5: { 6: Console.WriteLine("False, x is null, y is not."); 7: } 8:  9: if (x != y) 10: { 11: Console.WriteLine("True, x is null, y is not."); 12: } Solution: The Nullable static class So we’ve seen that <, <=, >, and >= have some interesting and perhaps unexpected behaviors that can trip up a novice developer who isn’t expecting the kinks that System.Nullable<T> types with comparison operators can throw.  How can we easily mitigate this? Well, obviously, you could do null checks before each check, but that starts to get ugly: 1: if (x.HasValue) 2: { 3: if (y.HasValue) 4: { 5: if (x < y) 6: { 7: Console.WriteLine("x < y"); 8: } 9: else if (x > y) 10: { 11: Console.WriteLine("x > y"); 12: } 13: else 14: { 15: Console.WriteLine("x == y"); 16: } 17: } 18: else 19: { 20: Console.WriteLine("x > y because y is null and x isn't"); 21: } 22: } 23: else if (y.HasValue) 24: { 25: Console.WriteLine("x < y because x is null and y isn't"); 26: } 27: else 28: { 29: Console.WriteLine("x == y because both are null"); 30: } Yes, we could probably simplify this logic a bit, but it’s still horrendous!  So what do we do if we want to consider null less than everything and be able to properly compare Nullable<T> wrapped value types? The key is the System.Nullable static class.  This class is a companion class to the System.Nullable<T> class and allows you to use a few helper methods for Nullable<T> wrapped types, including a static Compare<T>() method of the. What’s so big about the static Compare<T>() method?  It implements an IComparer compatible comparison on Nullable<T> types.  Why do we care?  Well, if you look at the MSDN description for how IComparer works, you’ll read: Comparing null with any type is allowed and does not generate an exception when using IComparable. When sorting, null is considered to be less than any other object. This is what we probably want!  We want null to be less than everything!  So now we can change our logic to use the Nullable.Compare<T>() static method: 1: int? x = null; 2: int? y = 100; 3:  4: if (Nullable.Compare(x, y) < 0) 5: { 6: // Yes! x is null, y is not, so x is less than y according to Compare(). 7: Console.WriteLine("x < y"); 8: } 9: else if (Nullable.Compare(x, y) > 0) 10: { 11: Console.WriteLine("x > y"); 12: } 13: else 14: { 15: Console.WriteLine("x == y"); 16: } Summary So, when doing math comparisons between two numeric values where one of them may be a null Nullable<T>, consider using the System.Nullable.Compare<T>() method instead of the comparison operators.  It will treat null less than any value, and will avoid logic consistency problems when relying on < returning false to indicate >= is true and so on. Tweet   Technorati Tags: C#,C-Sharp,.NET,Little Wonders,Little Pitfalls,Nulalble

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  • Little Wheel Is An Atmospheric and Engaging Point-and-Click Adventure

    - by Jason Fitzpatrick
    If you’re a fan of the resurgence of highly stylized and atmospheric adventure games–such as Spirit, World of Goo, and the like–you’ll definitely want to check out this well executed, free, and more than a little bit charming browser-based game. Little Wheel is set in a world of robots where, 10,000 years ago, a terrible accident at the central power plant left all the robots without power. The entire robot world went into a deep sleep and now, thanks to a freak lightning strike, one little robot has woken up. Your job, as that little robot, is to navigate the world of Little Wheel and help bring it back to life. Hit up the link below to play the game for free–the quality of the visual and audio design make going full screen and turning the speakers on a must. Little Wheel [via Freeware Genuis] How to Make Your Laptop Choose a Wired Connection Instead of Wireless HTG Explains: What Is Two-Factor Authentication and Should I Be Using It? HTG Explains: What Is Windows RT and What Does It Mean To Me?

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  • What's the closest equivalent of Little Snitch (Mac program) on Windows?

    - by Charles Scowcroft
    I'm using Windows 7 and would like to have a feature like Little Snitch on the Mac that alerts you whenever a program on your computer makes an outgoing connection. Description of Little Snitch from its website: Little Snitch informs you whenever a program attempts to establish an outgoing Internet connection. You can then choose to allow or deny this connection, or define a rule how to handle similar, future connection attempts. This reliably prevents private data from being sent out without your knowledge. Little Snitch runs inconspicuously in the background and it can also detect network related activity of viruses, trojans and other malware. Little Snitch provides flexible configuration options, allowing you to grant specific permissions to your trusted applications or to prevent others from establishing particular Internet connections at all. So you will only be warned in those cases that really need your attention. Is there a program like Little Snitch for Windows?

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  • C#/.NET Little Wonders: The Generic Func Delegates

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Back in one of my three original “Little Wonders” Trilogy of posts, I had listed generic delegates as one of the Little Wonders of .NET.  Later, someone posted a comment saying said that they would love more detail on the generic delegates and their uses, since my original entry just scratched the surface of them. Last week, I began our look at some of the handy generic delegates built into .NET with a description of delegates in general, and the Action family of delegates.  For this week, I’ll launch into a look at the Func family of generic delegates and how they can be used to support generic, reusable algorithms and classes. Quick Delegate Recap Delegates are similar to function pointers in C++ in that they allow you to store a reference to a method.  They can store references to either static or instance methods, and can actually be used to chain several methods together in one delegate. Delegates are very type-safe and can be satisfied with any standard method, anonymous method, or a lambda expression.  They can also be null as well (refers to no method), so care should be taken to make sure that the delegate is not null before you invoke it. Delegates are defined using the keyword delegate, where the delegate’s type name is placed where you would typically place the method name: 1: // This delegate matches any method that takes string, returns nothing 2: public delegate void Log(string message); This delegate defines a delegate type named Log that can be used to store references to any method(s) that satisfies its signature (whether instance, static, lambda expression, etc.). Delegate instances then can be assigned zero (null) or more methods using the operator = which replaces the existing delegate chain, or by using the operator += which adds a method to the end of a delegate chain: 1: // creates a delegate instance named currentLogger defaulted to Console.WriteLine (static method) 2: Log currentLogger = Console.Out.WriteLine; 3:  4: // invokes the delegate, which writes to the console out 5: currentLogger("Hi Standard Out!"); 6:  7: // append a delegate to Console.Error.WriteLine to go to std error 8: currentLogger += Console.Error.WriteLine; 9:  10: // invokes the delegate chain and writes message to std out and std err 11: currentLogger("Hi Standard Out and Error!"); While delegates give us a lot of power, it can be cumbersome to re-create fairly standard delegate definitions repeatedly, for this purpose the generic delegates were introduced in various stages in .NET.  These support various method types with particular signatures. Note: a caveat with generic delegates is that while they can support multiple parameters, they do not match methods that contains ref or out parameters. If you want to a delegate to represent methods that takes ref or out parameters, you will need to create a custom delegate. We’ve got the Func… delegates Just like it’s cousin, the Action delegate family, the Func delegate family gives us a lot of power to use generic delegates to make classes and algorithms more generic.  Using them keeps us from having to define a new delegate type when need to make a class or algorithm generic. Remember that the point of the Action delegate family was to be able to perform an “action” on an item, with no return results.  Thus Action delegates can be used to represent most methods that take 0 to 16 arguments but return void.  You can assign a method The Func delegate family was introduced in .NET 3.5 with the advent of LINQ, and gives us the power to define a function that can be called on 0 to 16 arguments and returns a result.  Thus, the main difference between Action and Func, from a delegate perspective, is that Actions return nothing, but Funcs return a result. The Func family of delegates have signatures as follows: Func<TResult> – matches a method that takes no arguments, and returns value of type TResult. Func<T, TResult> – matches a method that takes an argument of type T, and returns value of type TResult. Func<T1, T2, TResult> – matches a method that takes arguments of type T1 and T2, and returns value of type TResult. Func<T1, T2, …, TResult> – and so on up to 16 arguments, and returns value of type TResult. These are handy because they quickly allow you to be able to specify that a method or class you design will perform a function to produce a result as long as the method you specify meets the signature. For example, let’s say you were designing a generic aggregator, and you wanted to allow the user to define how the values will be aggregated into the result (i.e. Sum, Min, Max, etc…).  To do this, we would ask the user of our class to pass in a method that would take the current total, the next value, and produce a new total.  A class like this could look like: 1: public sealed class Aggregator<TValue, TResult> 2: { 3: // holds method that takes previous result, combines with next value, creates new result 4: private Func<TResult, TValue, TResult> _aggregationMethod; 5:  6: // gets or sets the current result of aggregation 7: public TResult Result { get; private set; } 8:  9: // construct the aggregator given the method to use to aggregate values 10: public Aggregator(Func<TResult, TValue, TResult> aggregationMethod = null) 11: { 12: if (aggregationMethod == null) throw new ArgumentNullException("aggregationMethod"); 13:  14: _aggregationMethod = aggregationMethod; 15: } 16:  17: // method to add next value 18: public void Aggregate(TValue nextValue) 19: { 20: // performs the aggregation method function on the current result and next and sets to current result 21: Result = _aggregationMethod(Result, nextValue); 22: } 23: } Of course, LINQ already has an Aggregate extension method, but that works on a sequence of IEnumerable<T>, whereas this is designed to work more with aggregating single results over time (such as keeping track of a max response time for a service). We could then use this generic aggregator to find the sum of a series of values over time, or the max of a series of values over time (among other things): 1: // creates an aggregator that adds the next to the total to sum the values 2: var sumAggregator = new Aggregator<int, int>((total, next) => total + next); 3:  4: // creates an aggregator (using static method) that returns the max of previous result and next 5: var maxAggregator = new Aggregator<int, int>(Math.Max); So, if we were timing the response time of a web method every time it was called, we could pass that response time to both of these aggregators to get an idea of the total time spent in that web method, and the max time spent in any one call to the web method: 1: // total will be 13 and max 13 2: int responseTime = 13; 3: sumAggregator.Aggregate(responseTime); 4: maxAggregator.Aggregate(responseTime); 5:  6: // total will be 20 and max still 13 7: responseTime = 7; 8: sumAggregator.Aggregate(responseTime); 9: maxAggregator.Aggregate(responseTime); 10:  11: // total will be 40 and max now 20 12: responseTime = 20; 13: sumAggregator.Aggregate(responseTime); 14: maxAggregator.Aggregate(responseTime); The Func delegate family is useful for making generic algorithms and classes, and in particular allows the caller of the method or user of the class to specify a function to be performed in order to generate a result. What is the result of a Func delegate chain? If you remember, we said earlier that you can assign multiple methods to a delegate by using the += operator to chain them.  So how does this affect delegates such as Func that return a value, when applied to something like the code below? 1: Func<int, int, int> combo = null; 2:  3: // What if we wanted to aggregate the sum and max together? 4: combo += (total, next) => total + next; 5: combo += Math.Max; 6:  7: // what is the result? 8: var comboAggregator = new Aggregator<int, int>(combo); Well, in .NET if you chain multiple methods in a delegate, they will all get invoked, but the result of the delegate is the result of the last method invoked in the chain.  Thus, this aggregator would always result in the Math.Max() result.  The other chained method (the sum) gets executed first, but it’s result is thrown away: 1: // result is 13 2: int responseTime = 13; 3: comboAggregator.Aggregate(responseTime); 4:  5: // result is still 13 6: responseTime = 7; 7: comboAggregator.Aggregate(responseTime); 8:  9: // result is now 20 10: responseTime = 20; 11: comboAggregator.Aggregate(responseTime); So remember, you can chain multiple Func (or other delegates that return values) together, but if you do so you will only get the last executed result. Func delegates and co-variance/contra-variance in .NET 4.0 Just like the Action delegate, as of .NET 4.0, the Func delegate family is contra-variant on its arguments.  In addition, it is co-variant on its return type.  To support this, in .NET 4.0 the signatures of the Func delegates changed to: Func<out TResult> – matches a method that takes no arguments, and returns value of type TResult (or a more derived type). Func<in T, out TResult> – matches a method that takes an argument of type T (or a less derived type), and returns value of type TResult(or a more derived type). Func<in T1, in T2, out TResult> – matches a method that takes arguments of type T1 and T2 (or less derived types), and returns value of type TResult (or a more derived type). Func<in T1, in T2, …, out TResult> – and so on up to 16 arguments, and returns value of type TResult (or a more derived type). Notice the addition of the in and out keywords before each of the generic type placeholders.  As we saw last week, the in keyword is used to specify that a generic type can be contra-variant -- it can match the given type or a type that is less derived.  However, the out keyword, is used to specify that a generic type can be co-variant -- it can match the given type or a type that is more derived. On contra-variance, if you are saying you need an function that will accept a string, you can just as easily give it an function that accepts an object.  In other words, if you say “give me an function that will process dogs”, I could pass you a method that will process any animal, because all dogs are animals.  On the co-variance side, if you are saying you need a function that returns an object, you can just as easily pass it a function that returns a string because any string returned from the given method can be accepted by a delegate expecting an object result, since string is more derived.  Once again, in other words, if you say “give me a method that creates an animal”, I can pass you a method that will create a dog, because all dogs are animals. It really all makes sense, you can pass a more specific thing to a less specific parameter, and you can return a more specific thing as a less specific result.  In other words, pay attention to the direction the item travels (parameters go in, results come out).  Keeping that in mind, you can always pass more specific things in and return more specific things out. For example, in the code below, we have a method that takes a Func<object> to generate an object, but we can pass it a Func<string> because the return type of object can obviously accept a return value of string as well: 1: // since Func<object> is co-variant, this will access Func<string>, etc... 2: public static string Sequence(int count, Func<object> generator) 3: { 4: var builder = new StringBuilder(); 5:  6: for (int i=0; i<count; i++) 7: { 8: object value = generator(); 9: builder.Append(value); 10: } 11:  12: return builder.ToString(); 13: } Even though the method above takes a Func<object>, we can pass a Func<string> because the TResult type placeholder is co-variant and accepts types that are more derived as well: 1: // delegate that's typed to return string. 2: Func<string> stringGenerator = () => DateTime.Now.ToString(); 3:  4: // This will work in .NET 4.0, but not in previous versions 5: Sequence(100, stringGenerator); Previous versions of .NET implemented some forms of co-variance and contra-variance before, but .NET 4.0 goes one step further and allows you to pass or assign an Func<A, BResult> to a Func<Y, ZResult> as long as A is less derived (or same) as Y, and BResult is more derived (or same) as ZResult. Sidebar: The Func and the Predicate A method that takes one argument and returns a bool is generally thought of as a predicate.  Predicates are used to examine an item and determine whether that item satisfies a particular condition.  Predicates are typically unary, but you may also have binary and other predicates as well. Predicates are often used to filter results, such as in the LINQ Where() extension method: 1: var numbers = new[] { 1, 2, 4, 13, 8, 10, 27 }; 2:  3: // call Where() using a predicate which determines if the number is even 4: var evens = numbers.Where(num => num % 2 == 0); As of .NET 3.5, predicates are typically represented as Func<T, bool> where T is the type of the item to examine.  Previous to .NET 3.5, there was a Predicate<T> type that tended to be used (which we’ll discuss next week) and is still supported, but most developers recommend using Func<T, bool> now, as it prevents confusion with overloads that accept unary predicates and binary predicates, etc.: 1: // this seems more confusing as an overload set, because of Predicate vs Func 2: public static SomeMethod(Predicate<int> unaryPredicate) { } 3: public static SomeMethod(Func<int, int, bool> binaryPredicate) { } 4:  5: // this seems more consistent as an overload set, since just uses Func 6: public static SomeMethod(Func<int, bool> unaryPredicate) { } 7: public static SomeMethod(Func<int, int, bool> binaryPredicate) { } Also, even though Predicate<T> and Func<T, bool> match the same signatures, they are separate types!  Thus you cannot assign a Predicate<T> instance to a Func<T, bool> instance and vice versa: 1: // the same method, lambda expression, etc can be assigned to both 2: Predicate<int> isEven = i => (i % 2) == 0; 3: Func<int, bool> alsoIsEven = i => (i % 2) == 0; 4:  5: // but the delegate instances cannot be directly assigned, strongly typed! 6: // ERROR: cannot convert type... 7: isEven = alsoIsEven; 8:  9: // however, you can assign by wrapping in a new instance: 10: isEven = new Predicate<int>(alsoIsEven); 11: alsoIsEven = new Func<int, bool>(isEven); So, the general advice that seems to come from most developers is that Predicate<T> is still supported, but we should use Func<T, bool> for consistency in .NET 3.5 and above. Sidebar: Func as a Generator for Unit Testing One area of difficulty in unit testing can be unit testing code that is based on time of day.  We’d still want to unit test our code to make sure the logic is accurate, but we don’t want the results of our unit tests to be dependent on the time they are run. One way (of many) around this is to create an internal generator that will produce the “current” time of day.  This would default to returning result from DateTime.Now (or some other method), but we could inject specific times for our unit testing.  Generators are typically methods that return (generate) a value for use in a class/method. For example, say we are creating a CacheItem<T> class that represents an item in the cache, and we want to make sure the item shows as expired if the age is more than 30 seconds.  Such a class could look like: 1: // responsible for maintaining an item of type T in the cache 2: public sealed class CacheItem<T> 3: { 4: // helper method that returns the current time 5: private static Func<DateTime> _timeGenerator = () => DateTime.Now; 6:  7: // allows internal access to the time generator 8: internal static Func<DateTime> TimeGenerator 9: { 10: get { return _timeGenerator; } 11: set { _timeGenerator = value; } 12: } 13:  14: // time the item was cached 15: public DateTime CachedTime { get; private set; } 16:  17: // the item cached 18: public T Value { get; private set; } 19:  20: // item is expired if older than 30 seconds 21: public bool IsExpired 22: { 23: get { return _timeGenerator() - CachedTime > TimeSpan.FromSeconds(30.0); } 24: } 25:  26: // creates the new cached item, setting cached time to "current" time 27: public CacheItem(T value) 28: { 29: Value = value; 30: CachedTime = _timeGenerator(); 31: } 32: } Then, we can use this construct to unit test our CacheItem<T> without any time dependencies: 1: var baseTime = DateTime.Now; 2:  3: // start with current time stored above (so doesn't drift) 4: CacheItem<int>.TimeGenerator = () => baseTime; 5:  6: var target = new CacheItem<int>(13); 7:  8: // now add 15 seconds, should still be non-expired 9: CacheItem<int>.TimeGenerator = () => baseTime.AddSeconds(15); 10:  11: Assert.IsFalse(target.IsExpired); 12:  13: // now add 31 seconds, should now be expired 14: CacheItem<int>.TimeGenerator = () => baseTime.AddSeconds(31); 15:  16: Assert.IsTrue(target.IsExpired); Now we can unit test for 1 second before, 1 second after, 1 millisecond before, 1 day after, etc.  Func delegates can be a handy tool for this type of value generation to support more testable code.  Summary Generic delegates give us a lot of power to make truly generic algorithms and classes.  The Func family of delegates is a great way to be able to specify functions to calculate a result based on 0-16 arguments.  Stay tuned in the weeks that follow for other generic delegates in the .NET Framework!   Tweet Technorati Tags: .NET, C#, CSharp, Little Wonders, Generics, Func, Delegates

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  • little oh notation as the limit of n goes to infinity

    - by Tony
    Hi all, I'm just trying to understand how in little o notation this is true: f(n)/g(n) as n goes to infinity = infinity? Can someone explain that to me? I do get the idea that f(n) = o(g(n)) means that f(n) grows no faster then cg(n) for all constants c 0. I just don't get the bit in bold above.

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  • C#/.NET Little Wonders: Fun With Enum Methods

    - by James Michael Hare
    Once again lets dive into the Little Wonders of .NET, those small things in the .NET languages and BCL classes that make development easier by increasing readability, maintainability, and/or performance. So probably every one of us has used an enumerated type at one time or another in a C# program.  The enumerated types we create are a great way to represent that a value can be one of a set of discrete values (or a combination of those values in the case of bit flags). But the power of enum types go far beyond simple assignment and comparison, there are many methods in the Enum class (that all enum types “inherit” from) that can give you even more power when dealing with them. IsDefined() – check if a given value exists in the enum Are you reading a value for an enum from a data source, but are unsure if it is actually a valid value or not?  Casting won’t tell you this, and Parse() isn’t guaranteed to balk either if you give it an int or a combination of flags.  So what can we do? Let’s assume we have a small enum like this for result codes we want to return back from our business logic layer: 1: public enum ResultCode 2: { 3: Success, 4: Warning, 5: Error 6: } In this enum, Success will be zero (unless given another value explicitly), Warning will be one, and Error will be two. So what happens if we have code like this where perhaps we’re getting the result code from another data source (could be database, could be web service, etc)? 1: public ResultCode PerformAction() 2: { 3: // set up and call some method that returns an int. 4: int result = ResultCodeFromDataSource(); 5:  6: // this will suceed even if result is < 0 or > 2. 7: return (ResultCode) result; 8: } So what happens if result is –1 or 4?  Well, the cast does not fail, so what we end up with would be an instance of a ResultCode that would have a value that’s outside of the bounds of the enum constants we defined. This means if you had a block of code like: 1: switch (result) 2: { 3: case ResultType.Success: 4: // do success stuff 5: break; 6:  7: case ResultType.Warning: 8: // do warning stuff 9: break; 10:  11: case ResultType.Error: 12: // do error stuff 13: break; 14: } That you would hit none of these blocks (which is a good argument for always having a default in a switch by the way). So what can you do?  Well, there is a handy static method called IsDefined() on the Enum class which will tell you if an enum value is defined.  1: public ResultCode PerformAction() 2: { 3: int result = ResultCodeFromDataSource(); 4:  5: if (!Enum.IsDefined(typeof(ResultCode), result)) 6: { 7: throw new InvalidOperationException("Enum out of range."); 8: } 9:  10: return (ResultCode) result; 11: } In fact, this is often recommended after you Parse() or cast a value to an enum as there are ways for values to get past these methods that may not be defined. If you don’t like the syntax of passing in the type of the enum, you could clean it up a bit by creating an extension method instead that would allow you to call IsDefined() off any isntance of the enum: 1: public static class EnumExtensions 2: { 3: // helper method that tells you if an enum value is defined for it's enumeration 4: public static bool IsDefined(this Enum value) 5: { 6: return Enum.IsDefined(value.GetType(), value); 7: } 8: }   HasFlag() – an easier way to see if a bit (or bits) are set Most of us who came from the land of C programming have had to deal extensively with bit flags many times in our lives.  As such, using bit flags may be almost second nature (for a quick refresher on bit flags in enum types see one of my old posts here). However, in higher-level languages like C#, the need to manipulate individual bit flags is somewhat diminished, and the code to check for bit flag enum values may be obvious to an advanced developer but cryptic to a novice developer. For example, let’s say you have an enum for a messaging platform that contains bit flags: 1: // usually, we pluralize flags enum type names 2: [Flags] 3: public enum MessagingOptions 4: { 5: None = 0, 6: Buffered = 0x01, 7: Persistent = 0x02, 8: Durable = 0x04, 9: Broadcast = 0x08 10: } We can combine these bit flags using the bitwise OR operator (the ‘|’ pipe character): 1: // combine bit flags using 2: var myMessenger = new Messenger(MessagingOptions.Buffered | MessagingOptions.Broadcast); Now, if we wanted to check the flags, we’d have to test then using the bit-wise AND operator (the ‘&’ character): 1: if ((options & MessagingOptions.Buffered) == MessagingOptions.Buffered) 2: { 3: // do code to set up buffering... 4: // ... 5: } While the ‘|’ for combining flags is easy enough to read for advanced developers, the ‘&’ test tends to be easy for novice developers to get wrong.  First of all you have to AND the flag combination with the value, and then typically you should test against the flag combination itself (and not just for a non-zero)!  This is because the flag combination you are testing with may combine multiple bits, in which case if only one bit is set, the result will be non-zero but not necessarily all desired bits! Thanks goodness in .NET 4.0 they gave us the HasFlag() method.  This method can be called from an enum instance to test to see if a flag is set, and best of all you can avoid writing the bit wise logic yourself.  Not to mention it will be more readable to a novice developer as well: 1: if (options.HasFlag(MessagingOptions.Buffered)) 2: { 3: // do code to set up buffering... 4: // ... 5: } It is much more concise and unambiguous, thus increasing your maintainability and readability. It would be nice to have a corresponding SetFlag() method, but unfortunately generic types don’t allow you to specialize on Enum, which makes it a bit more difficult.  It can be done but you have to do some conversions to numeric and then back to the enum which makes it less of a payoff than having the HasFlag() method.  But if you want to create it for symmetry, it would look something like this: 1: public static T SetFlag<T>(this Enum value, T flags) 2: { 3: if (!value.GetType().IsEquivalentTo(typeof(T))) 4: { 5: throw new ArgumentException("Enum value and flags types don't match."); 6: } 7:  8: // yes this is ugly, but unfortunately we need to use an intermediate boxing cast 9: return (T)Enum.ToObject(typeof (T), Convert.ToUInt64(value) | Convert.ToUInt64(flags)); 10: } Note that since the enum types are value types, we need to assign the result to something (much like string.Trim()).  Also, you could chain several SetFlag() operations together or create one that takes a variable arg list if desired. Parse() and ToString() – transitioning from string to enum and back Sometimes, you may want to be able to parse an enum from a string or convert it to a string - Enum has methods built in to let you do this.  Now, many may already know this, but may not appreciate how much power are in these two methods. For example, if you want to parse a string as an enum, it’s easy and works just like you’d expect from the numeric types: 1: string optionsString = "Persistent"; 2:  3: // can use Enum.Parse, which throws if finds something it doesn't like... 4: var result = (MessagingOptions)Enum.Parse(typeof (MessagingOptions), optionsString); 5:  6: if (result == MessagingOptions.Persistent) 7: { 8: Console.WriteLine("It worked!"); 9: } Note that Enum.Parse() will throw if it finds a value it doesn’t like.  But the values it likes are fairly flexible!  You can pass in a single value, or a comma separated list of values for flags and it will parse them all and set all bits: 1: // for string values, can have one, or comma separated. 2: string optionsString = "Persistent, Buffered"; 3:  4: var result = (MessagingOptions)Enum.Parse(typeof (MessagingOptions), optionsString); 5:  6: if (result.HasFlag(MessagingOptions.Persistent) && result.HasFlag(MessagingOptions.Buffered)) 7: { 8: Console.WriteLine("It worked!"); 9: } Or you can parse in a string containing a number that represents a single value or combination of values to set: 1: // 3 is the combination of Buffered (0x01) and Persistent (0x02) 2: var optionsString = "3"; 3:  4: var result = (MessagingOptions) Enum.Parse(typeof (MessagingOptions), optionsString); 5:  6: if (result.HasFlag(MessagingOptions.Persistent) && result.HasFlag(MessagingOptions.Buffered)) 7: { 8: Console.WriteLine("It worked again!"); 9: } And, if you really aren’t sure if the parse will work, and don’t want to handle an exception, you can use TryParse() instead: 1: string optionsString = "Persistent, Buffered"; 2: MessagingOptions result; 3:  4: // try parse returns true if successful, and takes an out parm for the result 5: if (Enum.TryParse(optionsString, out result)) 6: { 7: if (result.HasFlag(MessagingOptions.Persistent) && result.HasFlag(MessagingOptions.Buffered)) 8: { 9: Console.WriteLine("It worked!"); 10: } 11: } So we covered parsing a string to an enum, what about reversing that and converting an enum to a string?  The ToString() method is the obvious and most basic choice for most of us, but did you know you can pass a format string for enum types that dictate how they are written as a string?: 1: MessagingOptions value = MessagingOptions.Buffered | MessagingOptions.Persistent; 2:  3: // general format, which is the default, 4: Console.WriteLine("Default : " + value); 5: Console.WriteLine("G (default): " + value.ToString("G")); 6:  7: // Flags format, even if type does not have Flags attribute. 8: Console.WriteLine("F (flags) : " + value.ToString("F")); 9:  10: // integer format, value as number. 11: Console.WriteLine("D (num) : " + value.ToString("D")); 12:  13: // hex format, value as hex 14: Console.WriteLine("X (hex) : " + value.ToString("X")); Which displays: 1: Default : Buffered, Persistent 2: G (default): Buffered, Persistent 3: F (flags) : Buffered, Persistent 4: D (num) : 3 5: X (hex) : 00000003 Now, you may not really see a difference here between G and F because I used a [Flags] enum, the difference is that the “F” option treats the enum as if it were flags even if the [Flags] attribute is not present.  Let’s take a non-flags enum like the ResultCode used earlier: 1: // yes, we can do this even if it is not [Flags] enum. 2: ResultCode value = ResultCode.Warning | ResultCode.Error; And if we run that through the same formats again we get: 1: Default : 3 2: G (default): 3 3: F (flags) : Warning, Error 4: D (num) : 3 5: X (hex) : 00000003 Notice that since we had multiple values combined, but it was not a [Flags] marked enum, the G and default format gave us a number instead of a value name.  This is because the value was not a valid single-value constant of the enum.  However, using the F flags format string, it broke out the value into its component flags even though it wasn’t marked [Flags]. So, if you want to get an enum to display appropriately for whether or not it has the [Flags] attribute, use G which is the default.  If you always want it to attempt to break down the flags, use F.  For numeric output, obviously D or  X are the best choice depending on whether you want decimal or hex. Summary Hopefully, you learned a couple of new tricks with using the Enum class today!  I’ll add more little wonders as I think of them and thanks for all the invaluable input!   Technorati Tags: C#,.NET,Little Wonders,Enum,BlackRabbitCoder

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  • Visual Studio Little Wonders: Quick Launch / Quick Access

    - by James Michael Hare
    Once again, in this series of posts I look at features of Visual Studio that may seem trivial, but can help improve your efficiency as a developer. The index of all my past little wonders posts can be found here. Well, my friends, this post will be a bit short because I’m in the middle of a bit of a move at the moment.  But, that said, I didn’t want to let the blog go completely silent this week, so I decided to add another Little Wonder to the list for the Visual Studio IDE. How often have you wanted to change an option or execute a command in Visual Studio, but can’t remember where the darn thing is in the menu, settings, etc.?  If so, Quick Launch in VS2012 (or Quick Access in VS2010 with the Productivity Power Tools extension) is just for you! Quick Launch / Quick Access – find a command or option quickly For those of you using Visual Studio 2012, Quick Launch is built right into the IDE at the top of the title bar, near the minimize, maximize, and close buttons: But do not despair if you are using Visual Studio 2010, you can get Quick Access from the Productivity Power Tools extension.  To do this, you can go to the extension manager: And then go to the gallery and search for Productivity Power Tools and install it.  If you don’t have VS2012 yet, then the Productivity Power Tools is the next best thing.  This extension updates VS2010 with features such as Quick Access, the Solution Navigator, searchable Add Reference Dialog, better tab wells, etc.  I highly recommend it! But back to the topic at hand!  In VS2012 Quick Launch is built into the IDE and can be accessed by clicking in the Quick Launch area of the title bar, or by pressing CTRL+Q.  If you have VS2010 with the PPT installed, though, it is called Quick Access and is accessible through View –> Quick Access: Regardless of which IDE you are using, the feature behaves mostly the same.  It allows you to search all of Visual Studio’s commands and options for a particular topic.  For example, let’s say you want to change from tabs to tabs expanded to spaces, but don’t remember where that option is buried.  You can bring up Quick Launch / Quick Access and type in “tabs”: And it brings up a list of all options on tabs, you can then choose the one appropriate to you and click on it and it will take you right there! A lot easier than diving through the options tree to find what you are looking for!  It also works on menu commands, for example if you can’t remember how to open the Output window: It shows you the menu items that will get you to the Output window, and (if applicable) the keyboard shortcuts.  Again, clicking on one of these will perform the action for you as well. There are also some tasks you can perform directly from Quick Launch / Quick Access.  For example, perhaps you are one of those people who like to have the line numbers in your editor (I do), so let’s bring up Quick Launch / Quick Access and type “line numbers”: And let’s select Turn Line Numbers On, and now our editor looks like: And Voila!  We have line numbers in VS2010.  You can do this in VS2012 too, but it takes you to the option settings instead of directly turning them off and on.  There are bound to be differences between the way the two editors organize settings and commands, but you get the point. So, as you can see, the Quick Launch / Quick Access feature in Visual Studio makes it easy to jump right to the options, commands, or tasks you are interested in without all the digging. Summary An IDE as powerful as Visual Studio has so many options and commands that it can be confusing to remember how to find and invoke them.  Quick Launch (Quick Access in VS2010 with Productivity Power Tools extension) is a quick and handy way to jump to any of these options, commands, or tasks quickly without having to remember in what menu or screen they are buried!  Technorati Tags: C#,CSharp,.NET,Little Wonders,Visual Studio,Quick Access,Quick Launch

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  • C#/.NET Little Wonders: The Joy of Anonymous Types

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. In the .NET 3 Framework, Microsoft introduced the concept of anonymous types, which provide a way to create a quick, compiler-generated types at the point of instantiation.  These may seem trivial, but are very handy for concisely creating lightweight, strongly-typed objects containing only read-only properties that can be used within a given scope. Creating an Anonymous Type In short, an anonymous type is a reference type that derives directly from object and is defined by its set of properties base on their names, number, types, and order given at initialization.  In addition to just holding these properties, it is also given appropriate overridden implementations for Equals() and GetHashCode() that take into account all of the properties to correctly perform property comparisons and hashing.  Also overridden is an implementation of ToString() which makes it easy to display the contents of an anonymous type instance in a fairly concise manner. To construct an anonymous type instance, you use basically the same initialization syntax as with a regular type.  So, for example, if we wanted to create an anonymous type to represent a particular point, we could do this: 1: var point = new { X = 13, Y = 7 }; Note the similarity between anonymous type initialization and regular initialization.  The main difference is that the compiler generates the type name and the properties (as readonly) based on the names and order provided, and inferring their types from the expressions they are assigned to. It is key to remember that all of those factors (number, names, types, order of properties) determine the anonymous type.  This is important, because while these two instances share the same anonymous type: 1: // same names, types, and order 2: var point1 = new { X = 13, Y = 7 }; 3: var point2 = new { X = 5, Y = 0 }; These similar ones do not: 1: var point3 = new { Y = 3, X = 5 }; // different order 2: var point4 = new { X = 3, Y = 5.0 }; // different type for Y 3: var point5 = new {MyX = 3, MyY = 5 }; // different names 4: var point6 = new { X = 1, Y = 2, Z = 3 }; // different count Limitations on Property Initialization Expressions The expression for a property in an anonymous type initialization cannot be null (though it can evaluate to null) or an anonymous function.  For example, the following are illegal: 1: // Null can't be used directly. Null reference of what type? 2: var cantUseNull = new { Value = null }; 3:  4: // Anonymous methods cannot be used. 5: var cantUseAnonymousFxn = new { Value = () => Console.WriteLine(“Can’t.”) }; Note that the restriction on null is just that you can’t use it directly as the expression, because otherwise how would it be able to determine the type?  You can, however, use it indirectly assigning a null expression such as a typed variable with the value null, or by casting null to a specific type: 1: string str = null; 2: var fineIndirectly = new { Value = str }; 3: var fineCast = new { Value = (string)null }; All of the examples above name the properties explicitly, but you can also implicitly name properties if they are being set from a property, field, or variable.  In these cases, when a field, property, or variable is used alone, and you don’t specify a property name assigned to it, the new property will have the same name.  For example: 1: int variable = 42; 2:  3: // creates two properties named varriable and Now 4: var implicitProperties = new { variable, DateTime.Now }; Is the same type as: 1: var explicitProperties = new { variable = variable, Now = DateTime.Now }; But this only works if you are using an existing field, variable, or property directly as the expression.  If you use a more complex expression then the name cannot be inferred: 1: // can't infer the name variable from variable * 2, must name explicitly 2: var wontWork = new { variable * 2, DateTime.Now }; In the example above, since we typed variable * 2, it is no longer just a variable and thus we would have to assign the property a name explicitly. ToString() on Anonymous Types One of the more trivial overrides that an anonymous type provides you is a ToString() method that prints the value of the anonymous type instance in much the same format as it was initialized (except actual values instead of expressions as appropriate of course). For example, if you had: 1: var point = new { X = 13, Y = 42 }; And then print it out: 1: Console.WriteLine(point.ToString()); You will get: 1: { X = 13, Y = 42 } While this isn’t necessarily the most stunning feature of anonymous types, it can be handy for debugging or logging values in a fairly easy to read format. Comparing Anonymous Type Instances Because anonymous types automatically create appropriate overrides of Equals() and GetHashCode() based on the underlying properties, we can reliably compare two instances or get hash codes.  For example, if we had the following 3 points: 1: var point1 = new { X = 1, Y = 2 }; 2: var point2 = new { X = 1, Y = 2 }; 3: var point3 = new { Y = 2, X = 1 }; If we compare point1 and point2 we’ll see that Equals() returns true because they overridden version of Equals() sees that the types are the same (same number, names, types, and order of properties) and that the values are the same.   In addition, because all equal objects should have the same hash code, we’ll see that the hash codes evaluate to the same as well: 1: // true, same type, same values 2: Console.WriteLine(point1.Equals(point2)); 3:  4: // true, equal anonymous type instances always have same hash code 5: Console.WriteLine(point1.GetHashCode() == point2.GetHashCode()); However, if we compare point2 and point3 we get false.  Even though the names, types, and values of the properties are the same, the order is not, thus they are two different types and cannot be compared (and thus return false).  And, since they are not equal objects (even though they have the same value) there is a good chance their hash codes are different as well (though not guaranteed): 1: // false, different types 2: Console.WriteLine(point2.Equals(point3)); 3:  4: // quite possibly false (was false on my machine) 5: Console.WriteLine(point2.GetHashCode() == point3.GetHashCode()); Using Anonymous Types Now that we’ve created instances of anonymous types, let’s actually use them.  The property names (whether implicit or explicit) are used to access the individual properties of the anonymous type.  The main thing, once again, to keep in mind is that the properties are readonly, so you cannot assign the properties a new value (note: this does not mean that instances referred to by a property are immutable – for more information check out C#/.NET Fundamentals: Returning Data Immutably in a Mutable World). Thus, if we have the following anonymous type instance: 1: var point = new { X = 13, Y = 42 }; We can get the properties as you’d expect: 1: Console.WriteLine(“The point is: ({0},{1})”, point.X, point.Y); But we cannot alter the property values: 1: // compiler error, properties are readonly 2: point.X = 99; Further, since the anonymous type name is only known by the compiler, there is no easy way to pass anonymous type instances outside of a given scope.  The only real choices are to pass them as object or dynamic.  But really that is not the intention of using anonymous types.  If you find yourself needing to pass an anonymous type outside of a given scope, you should really consider making a POCO (Plain Old CLR Type – i.e. a class that contains just properties to hold data with little/no business logic) instead. Given that, why use them at all?  Couldn’t you always just create a POCO to represent every anonymous type you needed?  Sure you could, but then you might litter your solution with many small POCO classes that have very localized uses. It turns out this is the key to when to use anonymous types to your advantage: when you just need a lightweight type in a local context to store intermediate results, consider an anonymous type – but when that result is more long-lived and used outside of the current scope, consider a POCO instead. So what do we mean by intermediate results in a local context?  Well, a classic example would be filtering down results from a LINQ expression.  For example, let’s say we had a List<Transaction>, where Transaction is defined something like: 1: public class Transaction 2: { 3: public string UserId { get; set; } 4: public DateTime At { get; set; } 5: public decimal Amount { get; set; } 6: // … 7: } And let’s say we had this data in our List<Transaction>: 1: var transactions = new List<Transaction> 2: { 3: new Transaction { UserId = "Jim", At = DateTime.Now, Amount = 2200.00m }, 4: new Transaction { UserId = "Jim", At = DateTime.Now, Amount = -1100.00m }, 5: new Transaction { UserId = "Jim", At = DateTime.Now.AddDays(-1), Amount = 900.00m }, 6: new Transaction { UserId = "John", At = DateTime.Now.AddDays(-2), Amount = 300.00m }, 7: new Transaction { UserId = "John", At = DateTime.Now, Amount = -10.00m }, 8: new Transaction { UserId = "Jane", At = DateTime.Now, Amount = 200.00m }, 9: new Transaction { UserId = "Jane", At = DateTime.Now, Amount = -50.00m }, 10: new Transaction { UserId = "Jaime", At = DateTime.Now.AddDays(-3), Amount = -100.00m }, 11: new Transaction { UserId = "Jaime", At = DateTime.Now.AddDays(-3), Amount = 300.00m }, 12: }; So let’s say we wanted to get the transactions for each day for each user.  That is, for each day we’d want to see the transactions each user performed.  We could do this very simply with a nice LINQ expression, without the need of creating any POCOs: 1: // group the transactions based on an anonymous type with properties UserId and Date: 2: byUserAndDay = transactions 3: .GroupBy(tx => new { tx.UserId, tx.At.Date }) 4: .OrderBy(grp => grp.Key.Date) 5: .ThenBy(grp => grp.Key.UserId); Now, those of you who have attempted to use custom classes as a grouping type before (such as GroupBy(), Distinct(), etc.) may have discovered the hard way that LINQ gets a lot of its speed by utilizing not on Equals(), but also GetHashCode() on the type you are grouping by.  Thus, when you use custom types for these purposes, you generally end up having to write custom Equals() and GetHashCode() implementations or you won’t get the results you were expecting (the default implementations of Equals() and GetHashCode() are reference equality and reference identity based respectively). As we said before, it turns out that anonymous types already do these critical overrides for you.  This makes them even more convenient to use!  Instead of creating a small POCO to handle this grouping, and then having to implement a custom Equals() and GetHashCode() every time, we can just take advantage of the fact that anonymous types automatically override these methods with appropriate implementations that take into account the values of all of the properties. Now, we can look at our results: 1: foreach (var group in byUserAndDay) 2: { 3: // the group’s Key is an instance of our anonymous type 4: Console.WriteLine("{0} on {1:MM/dd/yyyy} did:", group.Key.UserId, group.Key.Date); 5:  6: // each grouping contains a sequence of the items. 7: foreach (var tx in group) 8: { 9: Console.WriteLine("\t{0}", tx.Amount); 10: } 11: } And see: 1: Jaime on 06/18/2012 did: 2: -100.00 3: 300.00 4:  5: John on 06/19/2012 did: 6: 300.00 7:  8: Jim on 06/20/2012 did: 9: 900.00 10:  11: Jane on 06/21/2012 did: 12: 200.00 13: -50.00 14:  15: Jim on 06/21/2012 did: 16: 2200.00 17: -1100.00 18:  19: John on 06/21/2012 did: 20: -10.00 Again, sure we could have just built a POCO to do this, given it an appropriate Equals() and GetHashCode() method, but that would have bloated our code with so many extra lines and been more difficult to maintain if the properties change.  Summary Anonymous types are one of those Little Wonders of the .NET language that are perfect at exactly that time when you need a temporary type to hold a set of properties together for an intermediate result.  While they are not very useful beyond the scope in which they are defined, they are excellent in LINQ expressions as a way to create and us intermediary values for further expressions and analysis. Anonymous types are defined by the compiler based on the number, type, names, and order of properties created, and they automatically implement appropriate Equals() and GetHashCode() overrides (as well as ToString()) which makes them ideal for LINQ expressions where you need to create a set of properties to group, evaluate, etc. Technorati Tags: C#,CSharp,.NET,Little Wonders,Anonymous Types,LINQ

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  • C#/.NET Little Wonders: ConcurrentBag and BlockingCollection

    - by James Michael Hare
    In the first week of concurrent collections, began with a general introduction and discussed the ConcurrentStack<T> and ConcurrentQueue<T>.  The last post discussed the ConcurrentDictionary<T> .  Finally this week, we shall close with a discussion of the ConcurrentBag<T> and BlockingCollection<T>. For more of the "Little Wonders" posts, see C#/.NET Little Wonders: A Redux. Recap As you'll recall from the previous posts, the original collections were object-based containers that accomplished synchronization through a Synchronized member.  With the advent of .NET 2.0, the original collections were succeeded by the generic collections which are fully type-safe, but eschew automatic synchronization.  With .NET 4.0, a new breed of collections was born in the System.Collections.Concurrent namespace.  Of these, the final concurrent collection we will examine is the ConcurrentBag and a very useful wrapper class called the BlockingCollection. For some excellent information on the performance of the concurrent collections and how they perform compared to a traditional brute-force locking strategy, see this informative whitepaper by the Microsoft Parallel Computing Platform team here. ConcurrentBag<T> – Thread-safe unordered collection. Unlike the other concurrent collections, the ConcurrentBag<T> has no non-concurrent counterpart in the .NET collections libraries.  Items can be added and removed from a bag just like any other collection, but unlike the other collections, the items are not maintained in any order.  This makes the bag handy for those cases when all you care about is that the data be consumed eventually, without regard for order of consumption or even fairness – that is, it’s possible new items could be consumed before older items given the right circumstances for a period of time. So why would you ever want a container that can be unfair?  Well, to look at it another way, you can use a ConcurrentQueue and get the fairness, but it comes at a cost in that the ordering rules and synchronization required to maintain that ordering can affect scalability a bit.  Thus sometimes the bag is great when you want the fastest way to get the next item to process, and don’t care what item it is or how long its been waiting. The way that the ConcurrentBag works is to take advantage of the new ThreadLocal<T> type (new in System.Threading for .NET 4.0) so that each thread using the bag has a list local to just that thread.  This means that adding or removing to a thread-local list requires very low synchronization.  The problem comes in where a thread goes to consume an item but it’s local list is empty.  In this case the bag performs “work-stealing” where it will rob an item from another thread that has items in its list.  This requires a higher level of synchronization which adds a bit of overhead to the take operation. So, as you can imagine, this makes the ConcurrentBag good for situations where each thread both produces and consumes items from the bag, but it would be less-than-idea in situations where some threads are dedicated producers and the other threads are dedicated consumers because the work-stealing synchronization would outweigh the thread-local optimization for a thread taking its own items. Like the other concurrent collections, there are some curiosities to keep in mind: IsEmpty(), Count, ToArray(), and GetEnumerator() lock collection Each of these needs to take a snapshot of whole bag to determine if empty, thus they tend to be more expensive and cause Add() and Take() operations to block. ToArray() and GetEnumerator() are static snapshots Because it is based on a snapshot, will not show subsequent updates after snapshot. Add() is lightweight Since adding to the thread-local list, there is very little overhead on Add. TryTake() is lightweight if items in thread-local list As long as items are in the thread-local list, TryTake() is very lightweight, much more so than ConcurrentStack() and ConcurrentQueue(), however if the local thread list is empty, it must steal work from another thread, which is more expensive. Remember, a bag is not ideal for all situations, it is mainly ideal for situations where a process consumes an item and either decomposes it into more items to be processed, or handles the item partially and places it back to be processed again until some point when it will complete.  The main point is that the bag works best when each thread both takes and adds items. For example, we could create a totally contrived example where perhaps we want to see the largest power of a number before it crosses a certain threshold.  Yes, obviously we could easily do this with a log function, but bare with me while I use this contrived example for simplicity. So let’s say we have a work function that will take a Tuple out of a bag, this Tuple will contain two ints.  The first int is the original number, and the second int is the last multiple of that number.  So we could load our bag with the initial values (let’s say we want to know the last multiple of each of 2, 3, 5, and 7 under 100. 1: var bag = new ConcurrentBag<Tuple<int, int>> 2: { 3: Tuple.Create(2, 1), 4: Tuple.Create(3, 1), 5: Tuple.Create(5, 1), 6: Tuple.Create(7, 1) 7: }; Then we can create a method that given the bag, will take out an item, apply the multiplier again, 1: public static void FindHighestPowerUnder(ConcurrentBag<Tuple<int,int>> bag, int threshold) 2: { 3: Tuple<int,int> pair; 4:  5: // while there are items to take, this will prefer local first, then steal if no local 6: while (bag.TryTake(out pair)) 7: { 8: // look at next power 9: var result = Math.Pow(pair.Item1, pair.Item2 + 1); 10:  11: if (result < threshold) 12: { 13: // if smaller than threshold bump power by 1 14: bag.Add(Tuple.Create(pair.Item1, pair.Item2 + 1)); 15: } 16: else 17: { 18: // otherwise, we're done 19: Console.WriteLine("Highest power of {0} under {3} is {0}^{1} = {2}.", 20: pair.Item1, pair.Item2, Math.Pow(pair.Item1, pair.Item2), threshold); 21: } 22: } 23: } Now that we have this, we can load up this method as an Action into our Tasks and run it: 1: // create array of tasks, start all, wait for all 2: var tasks = new[] 3: { 4: new Task(() => FindHighestPowerUnder(bag, 100)), 5: new Task(() => FindHighestPowerUnder(bag, 100)), 6: }; 7:  8: Array.ForEach(tasks, t => t.Start()); 9:  10: Task.WaitAll(tasks); Totally contrived, I know, but keep in mind the main point!  When you have a thread or task that operates on an item, and then puts it back for further consumption – or decomposes an item into further sub-items to be processed – you should consider a ConcurrentBag as the thread-local lists will allow for quick processing.  However, if you need ordering or if your processes are dedicated producers or consumers, this collection is not ideal.  As with anything, you should performance test as your mileage will vary depending on your situation! BlockingCollection<T> – A producers & consumers pattern collection The BlockingCollection<T> can be treated like a collection in its own right, but in reality it adds a producers and consumers paradigm to any collection that implements the interface IProducerConsumerCollection<T>.  If you don’t specify one at the time of construction, it will use a ConcurrentQueue<T> as its underlying store. If you don’t want to use the ConcurrentQueue, the ConcurrentStack and ConcurrentBag also implement the interface (though ConcurrentDictionary does not).  In addition, you are of course free to create your own implementation of the interface. So, for those who don’t remember the producers and consumers classical computer-science problem, the gist of it is that you have one (or more) processes that are creating items (producers) and one (or more) processes that are consuming these items (consumers).  Now, the crux of the problem is that there is a bin (queue) where the produced items are placed, and typically that bin has a limited size.  Thus if a producer creates an item, but there is no space to store it, it must wait until an item is consumed.  Also if a consumer goes to consume an item and none exists, it must wait until an item is produced. The BlockingCollection makes it trivial to implement any standard producers/consumers process set by providing that “bin” where the items can be produced into and consumed from with the appropriate blocking operations.  In addition, you can specify whether the bin should have a limited size or can be (theoretically) unbounded, and you can specify timeouts on the blocking operations. As far as your choice of “bin”, for the most part the ConcurrentQueue is the right choice because it is fairly light and maximizes fairness by ordering items so that they are consumed in the same order they are produced.  You can use the concurrent bag or stack, of course, but your ordering would be random-ish in the case of the former and LIFO in the case of the latter. So let’s look at some of the methods of note in BlockingCollection: BoundedCapacity returns capacity of the “bin” If the bin is unbounded, the capacity is int.MaxValue. Count returns an internally-kept count of items This makes it O(1), but if you modify underlying collection directly (not recommended) it is unreliable. CompleteAdding() is used to cut off further adds. This sets IsAddingCompleted and begins to wind down consumers once empty. IsAddingCompleted is true when producers are “done”. Once you are done producing, should complete the add process to alert consumers. IsCompleted is true when producers are “done” and “bin” is empty. Once you mark the producers done, and all items removed, this will be true. Add() is a blocking add to collection. If bin is full, will wait till space frees up Take() is a blocking remove from collection. If bin is empty, will wait until item is produced or adding is completed. GetConsumingEnumerable() is used to iterate and consume items. Unlike the standard enumerator, this one consumes the items instead of iteration. TryAdd() attempts add but does not block completely If adding would block, returns false instead, can specify TimeSpan to wait before stopping. TryTake() attempts to take but does not block completely Like TryAdd(), if taking would block, returns false instead, can specify TimeSpan to wait. Note the use of CompleteAdding() to signal the BlockingCollection that nothing else should be added.  This means that any attempts to TryAdd() or Add() after marked completed will throw an InvalidOperationException.  In addition, once adding is complete you can still continue to TryTake() and Take() until the bin is empty, and then Take() will throw the InvalidOperationException and TryTake() will return false. So let’s create a simple program to try this out.  Let’s say that you have one process that will be producing items, but a slower consumer process that handles them.  This gives us a chance to peek inside what happens when the bin is bounded (by default, the bin is NOT bounded). 1: var bin = new BlockingCollection<int>(5); Now, we create a method to produce items: 1: public static void ProduceItems(BlockingCollection<int> bin, int numToProduce) 2: { 3: for (int i = 0; i < numToProduce; i++) 4: { 5: // try for 10 ms to add an item 6: while (!bin.TryAdd(i, TimeSpan.FromMilliseconds(10))) 7: { 8: Console.WriteLine("Bin is full, retrying..."); 9: } 10: } 11:  12: // once done producing, call CompleteAdding() 13: Console.WriteLine("Adding is completed."); 14: bin.CompleteAdding(); 15: } And one to consume them: 1: public static void ConsumeItems(BlockingCollection<int> bin) 2: { 3: // This will only be true if CompleteAdding() was called AND the bin is empty. 4: while (!bin.IsCompleted) 5: { 6: int item; 7:  8: if (!bin.TryTake(out item, TimeSpan.FromMilliseconds(10))) 9: { 10: Console.WriteLine("Bin is empty, retrying..."); 11: } 12: else 13: { 14: Console.WriteLine("Consuming item {0}.", item); 15: Thread.Sleep(TimeSpan.FromMilliseconds(20)); 16: } 17: } 18: } Then we can fire them off: 1: // create one producer and two consumers 2: var tasks = new[] 3: { 4: new Task(() => ProduceItems(bin, 20)), 5: new Task(() => ConsumeItems(bin)), 6: new Task(() => ConsumeItems(bin)), 7: }; 8:  9: Array.ForEach(tasks, t => t.Start()); 10:  11: Task.WaitAll(tasks); Notice that the producer is faster than the consumer, thus it should be hitting a full bin often and displaying the message after it times out on TryAdd(). 1: Consuming item 0. 2: Consuming item 1. 3: Bin is full, retrying... 4: Bin is full, retrying... 5: Consuming item 3. 6: Consuming item 2. 7: Bin is full, retrying... 8: Consuming item 4. 9: Consuming item 5. 10: Bin is full, retrying... 11: Consuming item 6. 12: Consuming item 7. 13: Bin is full, retrying... 14: Consuming item 8. 15: Consuming item 9. 16: Bin is full, retrying... 17: Consuming item 10. 18: Consuming item 11. 19: Bin is full, retrying... 20: Consuming item 12. 21: Consuming item 13. 22: Bin is full, retrying... 23: Bin is full, retrying... 24: Consuming item 14. 25: Adding is completed. 26: Consuming item 15. 27: Consuming item 16. 28: Consuming item 17. 29: Consuming item 19. 30: Consuming item 18. Also notice that once CompleteAdding() is called and the bin is empty, the IsCompleted property returns true, and the consumers will exit. Summary The ConcurrentBag is an interesting collection that can be used to optimize concurrency scenarios where tasks or threads both produce and consume items.  In this way, it will choose to consume its own work if available, and then steal if not.  However, in situations where you want fair consumption or ordering, or in situations where the producers and consumers are distinct processes, the bag is not optimal. The BlockingCollection is a great wrapper around all of the concurrent queue, stack, and bag that allows you to add producer and consumer semantics easily including waiting when the bin is full or empty. That’s the end of my dive into the concurrent collections.  I’d also strongly recommend, once again, you read this excellent Microsoft white paper that goes into much greater detail on the efficiencies you can gain using these collections judiciously (here). Tweet Technorati Tags: C#,.NET,Concurrent Collections,Little Wonders

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  • C#/.NET Little Wonders: Interlocked CompareExchange()

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Two posts ago, I discussed the Interlocked Add(), Increment(), and Decrement() methods (here) for adding and subtracting values in a thread-safe, lightweight manner.  Then, last post I talked about the Interlocked Read() and Exchange() methods (here) for safely and efficiently reading and setting 32 or 64 bit values (or references).  This week, we’ll round out the discussion by talking about the Interlocked CompareExchange() method and how it can be put to use to exchange a value if the current value is what you expected it to be. Dirty reads can lead to bad results Many of the uses of Interlocked that we’ve explored so far have centered around either reading, setting, or adding values.  But what happens if you want to do something more complex such as setting a value based on the previous value in some manner? Perhaps you were creating an application that reads a current balance, applies a deposit, and then saves the new modified balance, where of course you’d want that to happen atomically.  If you read the balance, then go to save the new balance and between that time the previous balance has already changed, you’ll have an issue!  Think about it, if we read the current balance as $400, and we are applying a new deposit of $50.75, but meanwhile someone else deposits $200 and sets the total to $600, but then we write a total of $450.75 we’ve lost $200! Now, certainly for int and long values we can use Interlocked.Add() to handles these cases, and it works well for that.  But what if we want to work with doubles, for example?  Let’s say we wanted to add the numbers from 0 to 99,999 in parallel.  We could do this by spawning several parallel tasks to continuously add to a total: 1: double total = 0; 2:  3: Parallel.For(0, 10000, next => 4: { 5: total += next; 6: }); Were this run on one thread using a standard for loop, we’d expect an answer of 4,999,950,000 (the sum of all numbers from 0 to 99,999).  But when we run this in parallel as written above, we’ll likely get something far off.  The result of one of my runs, for example, was 1,281,880,740.  That is way off!  If this were banking software we’d be in big trouble with our clients.  So what happened?  The += operator is not atomic, it will read in the current value, add the result, then store it back into the total.  At any point in all of this another thread could read a “dirty” current total and accidentally “skip” our add.   So, to clean this up, we could use a lock to guarantee concurrency: 1: double total = 0.0; 2: object locker = new object(); 3:  4: Parallel.For(0, count, next => 5: { 6: lock (locker) 7: { 8: total += next; 9: } 10: }); Which will give us the correct result of 4,999,950,000.  One thing to note is that locking can be heavy, especially if the operation being locked over is trivial, or the life of the lock is a high percentage of the work being performed concurrently.  In the case above, the lock consumes pretty much all of the time of each parallel task – and the task being locked on is relatively trivial. Now, let me put in a disclaimer here before we go further: For most uses, lock is more than sufficient for your needs, and is often the simplest solution!    So, if lock is sufficient for most needs, why would we ever consider another solution?  The problem with locking is that it can suspend execution of your thread while it waits for the signal that the lock is free.  Moreover, if the operation being locked over is trivial, the lock can add a very high level of overhead.  This is why things like Interlocked.Increment() perform so well, instead of locking just to perform an increment, we perform the increment with an atomic, lockless method. As with all things performance related, it’s important to profile before jumping to the conclusion that you should optimize everything in your path.  If your profiling shows that locking is causing a high level of waiting in your application, then it’s time to consider lighter alternatives such as Interlocked. CompareExchange() – Exchange existing value if equal some value So let’s look at how we could use CompareExchange() to solve our problem above.  The general syntax of CompareExchange() is: T CompareExchange<T>(ref T location, T newValue, T expectedValue) If the value in location == expectedValue, then newValue is exchanged.  Either way, the value in location (before exchange) is returned. Actually, CompareExchange() is not one method, but a family of overloaded methods that can take int, long, float, double, pointers, or references.  It cannot take other value types (that is, can’t CompareExchange() two DateTime instances directly).  Also keep in mind that the version that takes any reference type (the generic overload) only checks for reference equality, it does not call any overridden Equals(). So how does this help us?  Well, we can grab the current total, and exchange the new value if total hasn’t changed.  This would look like this: 1: // grab the snapshot 2: double current = total; 3:  4: // if the total hasn’t changed since I grabbed the snapshot, then 5: // set it to the new total 6: Interlocked.CompareExchange(ref total, current + next, current); So what the code above says is: if the amount in total (1st arg) is the same as the amount in current (3rd arg), then set total to current + next (2nd arg).  This check and exchange pair is atomic (and thus thread-safe). This works if total is the same as our snapshot in current, but the problem, is what happens if they aren’t the same?  Well, we know that in either case we will get the previous value of total (before the exchange), back as a result.  Thus, we can test this against our snapshot to see if it was the value we expected: 1: // if the value returned is != current, then our snapshot must be out of date 2: // which means we didn't (and shouldn't) apply current + next 3: if (Interlocked.CompareExchange(ref total, current + next, current) != current) 4: { 5: // ooops, total was not equal to our snapshot in current, what should we do??? 6: } So what do we do if we fail?  That’s up to you and the problem you are trying to solve.  It’s possible you would decide to abort the whole transaction, or perhaps do a lightweight spin and try again.  Let’s try that: 1: double current = total; 2:  3: // make first attempt... 4: if (Interlocked.CompareExchange(ref total, current + i, current) != current) 5: { 6: // if we fail, go into a spin wait, spin, and try again until succeed 7: var spinner = new SpinWait(); 8:  9: do 10: { 11: spinner.SpinOnce(); 12: current = total; 13: } 14: while (Interlocked.CompareExchange(ref total, current + i, current) != current); 15: } 16:  This is not trivial code, but it illustrates a possible use of CompareExchange().  What we are doing is first checking to see if we succeed on the first try, and if so great!  If not, we create a SpinWait and then repeat the process of SpinOnce(), grab a fresh snapshot, and repeat until CompareExchnage() succeeds.  You may wonder why not a simple do-while here, and the reason it’s more efficient to only create the SpinWait until we absolutely know we need one, for optimal efficiency. Though not as simple (or maintainable) as a simple lock, this will perform better in many situations.  Comparing an unlocked (and wrong) version, a version using lock, and the Interlocked of the code, we get the following average times for multiple iterations of adding the sum of 100,000 numbers: 1: Unlocked money average time: 2.1 ms 2: Locked money average time: 5.1 ms 3: Interlocked money average time: 3 ms So the Interlocked.CompareExchange(), while heavier to code, came in lighter than the lock, offering a good compromise of safety and performance when we need to reduce contention. CompareExchange() - it’s not just for adding stuff… So that was one simple use of CompareExchange() in the context of adding double values -- which meant we couldn’t have used the simpler Interlocked.Add() -- but it has other uses as well. If you think about it, this really works anytime you want to create something new based on a current value without using a full lock.  For example, you could use it to create a simple lazy instantiation implementation.  In this case, we want to set the lazy instance only if the previous value was null: 1: public static class Lazy<T> where T : class, new() 2: { 3: private static T _instance; 4:  5: public static T Instance 6: { 7: get 8: { 9: // if current is null, we need to create new instance 10: if (_instance == null) 11: { 12: // attempt create, it will only set if previous was null 13: Interlocked.CompareExchange(ref _instance, new T(), (T)null); 14: } 15:  16: return _instance; 17: } 18: } 19: } So, if _instance == null, this will create a new T() and attempt to exchange it with _instance.  If _instance is not null, then it does nothing and we discard the new T() we created. This is a way to create lazy instances of a type where we are more concerned about locking overhead than creating an accidental duplicate which is not used.  In fact, the BCL implementation of Lazy<T> offers a similar thread-safety choice for Publication thread safety, where it will not guarantee only one instance was created, but it will guarantee that all readers get the same instance.  Another possible use would be in concurrent collections.  Let’s say, for example, that you are creating your own brand new super stack that uses a linked list paradigm and is “lock free”.  We could use Interlocked.CompareExchange() to be able to do a lockless Push() which could be more efficient in multi-threaded applications where several threads are pushing and popping on the stack concurrently. Yes, there are already concurrent collections in the BCL (in .NET 4.0 as part of the TPL), but it’s a fun exercise!  So let’s assume we have a node like this: 1: public sealed class Node<T> 2: { 3: // the data for this node 4: public T Data { get; set; } 5:  6: // the link to the next instance 7: internal Node<T> Next { get; set; } 8: } Then, perhaps, our stack’s Push() operation might look something like: 1: public sealed class SuperStack<T> 2: { 3: private volatile T _head; 4:  5: public void Push(T value) 6: { 7: var newNode = new Node<int> { Data = value, Next = _head }; 8:  9: if (Interlocked.CompareExchange(ref _head, newNode, newNode.Next) != newNode.Next) 10: { 11: var spinner = new SpinWait(); 12:  13: do 14: { 15: spinner.SpinOnce(); 16: newNode.Next = _head; 17: } 18: while (Interlocked.CompareExchange(ref _head, newNode, newNode.Next) != newNode.Next); 19: } 20: } 21:  22: // ... 23: } Notice a similar paradigm here as with adding our doubles before.  What we are doing is creating the new Node with the data to push, and with a Next value being the original node referenced by _head.  This will create our stack behavior (LIFO – Last In, First Out).  Now, we have to set _head to now refer to the newNode, but we must first make sure it hasn’t changed! So we check to see if _head has the same value we saved in our snapshot as newNode.Next, and if so, we set _head to newNode.  This is all done atomically, and the result is _head’s original value, as long as the original value was what we assumed it was with newNode.Next, then we are good and we set it without a lock!  If not, we SpinWait and try again. Once again, this is much lighter than locking in highly parallelized code with lots of contention.  If I compare the method above with a similar class using lock, I get the following results for pushing 100,000 items: 1: Locked SuperStack average time: 6 ms 2: Interlocked SuperStack average time: 4.5 ms So, once again, we can get more efficient than a lock, though there is the cost of added code complexity.  Fortunately for you, most of the concurrent collection you’d ever need are already created for you in the System.Collections.Concurrent (here) namespace – for more information, see my Little Wonders – The Concurent Collections Part 1 (here), Part 2 (here), and Part 3 (here). Summary We’ve seen before how the Interlocked class can be used to safely and efficiently add, increment, decrement, read, and exchange values in a multi-threaded environment.  In addition to these, Interlocked CompareExchange() can be used to perform more complex logic without the need of a lock when lock contention is a concern. The added efficiency, though, comes at the cost of more complex code.  As such, the standard lock is often sufficient for most thread-safety needs.  But if profiling indicates you spend a lot of time waiting for locks, or if you just need a lock for something simple such as an increment, decrement, read, exchange, etc., then consider using the Interlocked class’s methods to reduce wait. Technorati Tags: C#,CSharp,.NET,Little Wonders,Interlocked,CompareExchange,threading,concurrency

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  • C#/.NET Little Wonders: Interlocked Read() and Exchange()

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Last time we discussed the Interlocked class and its Add(), Increment(), and Decrement() methods which are all useful for updating a value atomically by adding (or subtracting).  However, this begs the question of how do we set and read those values atomically as well? Read() – Read a value atomically Let’s begin by examining the following code: 1: public class Incrementor 2: { 3: private long _value = 0; 4:  5: public long Value { get { return _value; } } 6:  7: public void Increment() 8: { 9: Interlocked.Increment(ref _value); 10: } 11: } 12:  It uses an interlocked increment, as we discuss in my previous post (here), so we know that the increment will be thread-safe.  But, to realize what’s potentially wrong we have to know a bit about how atomic reads are in 32 bit and 64 bit .NET environments. When you are dealing with an item smaller or equal to the system word size (such as an int on a 32 bit system or a long on a 64 bit system) then the read is generally atomic, because it can grab all of the bits needed at once.  However, when dealing with something larger than the system word size (reading a long on a 32 bit system for example), it cannot grab the whole value at once, which can lead to some problems since this read isn’t atomic. For example, this means that on a 32 bit system we may read one half of the long before another thread increments the value, and the other half of it after the increment.  To protect us from reading an invalid value in this manner, we can do an Interlocked.Read() to force the read to be atomic (of course, you’d want to make sure any writes or increments are atomic also): 1: public class Incrementor 2: { 3: private long _value = 0; 4:  5: public long Value 6: { 7: get { return Interlocked.Read(ref _value); } 8: } 9:  10: public void Increment() 11: { 12: Interlocked.Increment(ref _value); 13: } 14: } Now we are guaranteed that we will read the 64 bit value atomically on a 32 bit system, thus ensuring our thread safety (assuming all other reads, writes, increments, etc. are likewise protected).  Note that as stated before, and according to the MSDN (here), it isn’t strictly necessary to use Interlocked.Read() for reading 64 bit values on 64 bit systems, but for those still working in 32 bit environments, it comes in handy when dealing with long atomically. Exchange() – Exchanges two values atomically Exchange() lets us store a new value in the given location (the ref parameter) and return the old value as a result. So just as Read() allows us to read atomically, one use of Exchange() is to write values atomically.  For example, if we wanted to add a Reset() method to our Incrementor, we could do something like this: 1: public void Reset() 2: { 3: _value = 0; 4: } But the assignment wouldn’t be atomic on 32 bit systems, since the word size is 32 bits and the variable is a long (64 bits).  Thus our assignment could have only set half the value when a threaded read or increment happens, which would put us in a bad state. So instead, we could write Reset() like this: 1: public void Reset() 2: { 3: Interlocked.Exchange(ref _value, 0); 4: } And we’d be safe again on a 32 bit system. But this isn’t the only reason Exchange() is valuable.  The key comes in realizing that Exchange() doesn’t just set a new value, it returns the old as well in an atomic step.  Hence the name “exchange”: you are swapping the value to set with the stored value. So why would we want to do this?  Well, anytime you want to set a value and take action based on the previous value.  An example of this might be a scheme where you have several tasks, and during every so often, each of the tasks may nominate themselves to do some administrative chore.  Perhaps you don’t want to make this thread dedicated for whatever reason, but want to be robust enough to let any of the threads that isn’t currently occupied nominate itself for the job.  An easy and lightweight way to do this would be to have a long representing whether someone has acquired the “election” or not.  So a 0 would indicate no one has been elected and 1 would indicate someone has been elected. We could then base our nomination strategy as follows: every so often, a thread will attempt an Interlocked.Exchange() on the long and with a value of 1.  The first thread to do so will set it to a 1 and return back the old value of 0.  We can use this to show that they were the first to nominate and be chosen are thus “in charge”.  Anyone who nominates after that will attempt the same Exchange() but will get back a value of 1, which indicates that someone already had set it to a 1 before them, thus they are not elected. Then, the only other step we need take is to remember to release the election flag once the elected thread accomplishes its task, which we’d do by setting the value back to 0.  In this way, the next thread to nominate with Exchange() will get back the 0 letting them know they are the new elected nominee. Such code might look like this: 1: public class Nominator 2: { 3: private long _nomination = 0; 4: public bool Elect() 5: { 6: return Interlocked.Exchange(ref _nomination, 1) == 0; 7: } 8: public bool Release() 9: { 10: return Interlocked.Exchange(ref _nomination, 0) == 1; 11: } 12: } There’s many ways to do this, of course, but you get the idea.  Running 5 threads doing some “sleep” work might look like this: 1: var nominator = new Nominator(); 2: var random = new Random(); 3: Parallel.For(0, 5, i => 4: { 5:  6: for (int j = 0; j < _iterations; ++j) 7: { 8: if (nominator.Elect()) 9: { 10: // elected 11: Console.WriteLine("Elected nominee " + i); 12: Thread.Sleep(random.Next(100, 5000)); 13: nominator.Release(); 14: } 15: else 16: { 17: // not elected 18: Console.WriteLine("Did not elect nominee " + i); 19: } 20: // sleep before check again 21: Thread.Sleep(1000); 22: } 23: }); And would spit out results like: 1: Elected nominee 0 2: Did not elect nominee 2 3: Did not elect nominee 1 4: Did not elect nominee 4 5: Did not elect nominee 3 6: Did not elect nominee 3 7: Did not elect nominee 1 8: Did not elect nominee 2 9: Did not elect nominee 4 10: Elected nominee 3 11: Did not elect nominee 2 12: Did not elect nominee 1 13: Did not elect nominee 4 14: Elected nominee 0 15: Did not elect nominee 2 16: Did not elect nominee 4 17: ... Another nice thing about the Interlocked.Exchange() is it can be used to thread-safely set pretty much anything 64 bits or less in size including references, pointers (in unsafe mode), floats, doubles, etc.  Summary So, now we’ve seen two more things we can do with Interlocked: reading and exchanging a value atomically.  Read() and Exchange() are especially valuable for reading/writing 64 bit values atomically in a 32 bit system.  Exchange() has value even beyond simply atomic writes by using the Exchange() to your advantage, since it reads and set the value atomically, which allows you to do lightweight nomination systems. There’s still a few more goodies in the Interlocked class which we’ll explore next time! Technorati Tags: C#,CSharp,.NET,Little Wonders,Interlocked

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