Smart Programming Spot

ASP.NET 4.0 Features

2012-01-21T02:23:00.001-08:00

ASP.NET 4.0 Features

The focus of Microsoft’s latest ASP.NET 4has mainly been on improving the performance and Search-engine Optimization (SEO). In this article, I'll be taking a look at what I think are the most important new features in ASP.NET 4.

Output cache extensibility
Session state compression
View state mode for individual control
Page.MetaKeyword and Page.MetaDescription properties
Response.RedirectPermanent method
Routing in ASP.NET
Increase the URL character length
New syntax for Html Encode
Predictable Client IDs
Web.config file refactoring
Auto-Start ASP.NET applications
Improvements on Microsoft Ajax Library

I’ll describe the details of each of these features in the following sections.

Output Cache extensibility

Output caching, or Page-Level Caching, caches the entire rendered markup of an ASP.NET web page for a specific time-period. This has always been one of the essential features for ASP.NET that is used extensively to increase application performance. However there have been some limitations on the feasible extent of caching, because cached content always had to be stored in-memory.

But with ASP.NET 4.0 developers can extend their caching by using Output-cache providers. Developers can now create ‘output-cache providers’ that store the cache contents to any persistence mechanism such as databases, disks, cloud storage and distributed cache engines.

To create a custom output-cache provider, a class which derived fromSystem.Web.Caching.OutputCacheProvider has to be created in ASP.NET 4.0. There are four public methods which you have to override in order to provide your own implementation for add, remove, retrieve and update functionality. Also, the output-cache provider has to be registered in the web.config file as shown in the following screen capture.

You can also set this custom output-cache provider as your default cache mechanism. So once you add the page cache directives all of your contents will be stored using the custom output-cache provider.

Moreover, developers can also dynamically configure which output-cache Provider is used. For example you might want to cache the frequently access pages in the memory for faster access and less frequent pages on disk. By overriding the GetOutputCacheProviderName() method you can configure which output cache provider to use for different requests. These additions to the output-cache can enable developers to write extensible and more efficient cache mechanisms to their web application and thereby improve its responsiveness.

Session State compression

The ASP.NET session state is a mechanism to maintain session-specific data through subsequent requests. In some instances, you may wish to store your session state data in a session-state server or in Microsoft SQL server. However, these two options require you to store data out of the web application’s worker process. To send across to the relevant sources, (State server or Microsoft SQL Server), session-state data has to be serialized. This can take a significant time if the size of the data to be serialized grows significantly. This will increase the latency of the application.

This latency can be reduced if the size of the data is lessened by compression. ASP.NET 4.0 introduces a new mechanism to compress your session state data for both Session-state server and Microsoft SQL server. Compression can be enabled by setting the compressionEnable to true in the web.config file. In this example, the session-state data will be serialized/desterilized using System.IO.Compression.GZipStream.

  mode="SqlServer"  sqlConnectionString="data source=DB;Initial Catalog=LudmalDB"  allowCustomSqlDatabase="true"  compressionEnabled="true"/>

With this compression feature, developers can often reduce the time it takes for a web application to respond by reducing the size of session data.

View State mode for Individual Controls

View state is a mechanism to maintain page controls’ state on subsequent post backs. ASP.NET stores the view state data for controls that are in the page, even if it’s not necessary. Since the view state data is stored in the pages’ html, the size of the request object will be increased, and make performance worse.

In ASP.NET 4.0, each web control will include a ViewStateMode property which lets developers disable view-state by default, and enable it just for the controls for which a persistence of state is required. ViewStateMode has the following three values;

Enabled – enables the view state for this control and any child control.
Disabled – disable the view state.
Inherits – this specify the control uses the settings from its parent control.

By setting these values in page controls accordingly, a significant performance improvement can be gained in response-time.

Page.MetaKeywords and Page.MetaDescription properties

To increase the relevance of pages in searches, developers should include relevant “keyword” and “description” meta tags in the html section. Unfortunately, it takes some time to add these tags for each and every page, and the alternative of adding these tags programmatically was difficult.

But with ASP.NET 4.0, there are two new properties in the code behind file;

Page.MetaDescription – equivalent to meta name “description”
Page.MetaKeywords – equivalent to meta name “keywords”

This will enable developers to easily and programmatically add the relevant keywords and description.

This will even be useful for Master pages—where you only have to add these properties in the master page. In addition to “keywords” and “description” settings in the code behind, developers can also set these values within the @Page directive.

Response.RedirectPermanent Method

ASP.NET 4.0 has improved SEO (Search-engine Optimization) facilities. Typically developers useResponse.Redirect(string url) to handle requests for old URLs. However, this leads to an extra round trip to access the old URLs and so will negatively affect your page-ranking in search-engines.

ASP.NET 4.0 introduces a new Response.RedirectPermanent(string url) helper method to be used as HTTP 301 (Moved permanently) to handle requests. This will enable search-engines to index URLs and content efficiently and thus improve the page rankings.

Routing in ASP.NET

Routing will let developers serve meaningful URLs to users and map them with the actual physical files. This URL-rewriting mechanism enables developers to write high ranking, search-engine optimized web applications. For example, URL for a page which displays an actual product might look like the following;

http://www.ludmal.net/showproducts.aspx?prodId=24

By using routing the URL will look like the following

http://www.ludmal.net/products/ipod

In this way, the URLs will be more easily remembered by users. It will also significantly improve the search-engine page rankings of the web site.

The following example shows how to implement routing behavior in ASP.NET 4 using new MapPageRoute inRoute class.

public class Global : System.Web.HttpApplication {   void Application_Start(object sender, EventArgs e)   {    RouteTable.Routes.MapPageRoute("ProductsRoute",       "product/{prodId}", "~/products.aspx");       } }

Increase the URL character length

In previous versions of ASP.NET, URLs were limited to 260 characters in length. But in ASP.NET 4.0 developers have the option of increasing or decreasing the length of URLs by using the new maxRequestPathLength andmaxQueryStringLength. I’ll illustrate this in an example.

In previous versions of ASP.NET you were limited to a fixed set of characters but in v4, developers can also validate the invalid characters by specifying values in the requestPathInvalidChars attribute.

New syntax for Html Encode

Html Encode method encodes a particular string to be displayed in a browser. It is important to encode strings prior it’s rendering in the page, mainly to avoid cross-site script injection (XSS) and HTML injection attacks. However, developers so often forget to call the encode function.

In previous .NET versions, Server.HtmlEncode() or HttpUtility.Encode() methods has been used for string encoding as shown in the following example.

ASP.NET 4.0 introduced new code expression syntax for encoding a particular string. While the syntax will render the output it also encodes the relevant string as shown below. Note “:” character after opening tag (“<%”).

The new encoding syntax provides an easy and concise way of encoding a particular string.

Predictable Client IDs

ASP.NET 4 now supports a new ClientIDMode property for server control. This property indicates how the Client ID should be generated to a particular control when they render. Client ID has been an important property of the server controls recently—especially with the success of jQuery and other Ajax scripting technologies. The ClientIDMode property has four values;

AutoID – This renders the output as it was before (example:ctl00_ContentPlaceholder1_ListView1_ctrl0_Label1)
Predictable (Default)– Trims all “ctl00” strings in the Client Id property.
Static – Full control over the Client ID (developer can set the Client Id and it will not be changed after the control renders)
Inherit – Allow control to inherit the behavior from its parent control

Client ID property can be set in three different ways;

Directly on individual control
On the container control. (All the child controls will inherit the settings from parent/container control)
Page or User Control level using <%@ Page%> or <%@ Control %> directives.
Directly in the web.config file. All the controls within the web application will inherit the settings.

New ClientIDRowSuffix property on databound controls also gives a similar functionality when rendering an each data item. Once you set the relevant databound property to ClientIDRowSuffix, the value will be added as a suffix to individual row elements.

After the control renders the “State” value will be added as a suffix to each data row element.

Web.config refactoring

Over the past few years web.config file has grown significantly as ASP.NET has used it for more and more features such as routing, Ajax, IIS 7 and version compatibility. This has made it trickier to maintain even with the Visual Studio environment.

With ASP.NET 4, most of the major elements have been moved to the machine.config file. This has enabled developers to maintain a cleaner, less cluttered, web.config file. The new web.config file is either empty, or includes just the .NET framework version details as shown in the following example.

Auto-Start ASP.NET Applications

Most application requires initial data load or caching operations to be done before serving the client requests. Typical this happens only when the first user request a page. However, often developers and web administrators write fake requests to keep the application alive to increase the response time. To overcome this issue, ASP.NET 4 introduce new Auto-Start feature. Auto-start feature available with IIS 7.5 and it initialize the ASP.NET application to accept requests.

To configure the Auto-start, you need to configure the “Application pool” worker process by setting the startMode attribute to “AlwaysRunning” in the applicationHost.config file. (C:\Windows\System32\inetsrv\config\applicationHost.config)

As soon you save the applicationHost.config file the worker process will start and initialize the required application operations before the first user has been served.

Improvements on Microsoft Ajax Library

Microsoft Ajax library is client side library which includes high performance server –based user controls and asynchronous page rendering controls. Ajax Library enables developers to easily and quickly write responsive database-driven applications.

There are some significant improvements in the Ajax Library in the ASP.NET 4. of which the most important seem to be...

Scrip Loader – the new script loader control enable developers to load all the required scripts only once, thereby eliminating the unnecessary subsequent requests to the server. It supports the ‘lazy load’ pattern which loads scripts only when necessary, and loads scripts in combination, in order to increase the performance of loading a page. It also supports the jQuery script and custom scripts.
JQuery Integration – JQuery is very popular third party javascript library. ASP.NET 4 extensively supports the integration for jQuery by mixing the jQuery and Ajax plug-ins seamlessly.
Client Data Access – by using pre-defined client controls inside the Ajax Library, developers can easily build asynchronous data-driven applications. For example client DataView control will display one or more records by consuming a WCF service. All the relevant time-consuming operations will be handled by the Ajax library asynchronously.

Conclusion
ASP.NET 4 includes plethora of new features which will enable developers to write high performance, search-engine friendly web application quickly. The features I’ve mentioned seem to be the most important of all the new features in ASP.NET 4. By upgrading your existing web applications to up-coming ASP.NET 4, you are likely to see an improvement in performance and search-engine optimization.

Interview Questions on Java

2011-11-06T23:45:00.000-08:00

What if the main method is declared as private?

The program compiles properly but at runtime it will give “Main method not public.” message.

What is meant by pass by reference and pass by value in Java?

Pass by reference means, passing the address itself rather than passing the value. Pass by value means passing a copy of the value.

If you’re overriding the method equals() of an object, which other method you might also consider?

hashCode()

What is Byte Code?

What gives java it’s “write once and run anywhere” nature?

All Java programs are compiled into class files that contain bytecodes. These byte codes can be run in any platform and hence java is said to be platform independent.

Expain the reason for each keyword of public static void main(String args[])?

public- main(..) is the first method called by java environment when a program is executed so it has to accessible from java environment. Hence the access specifier has to be public.

static: Java environment should be able to call this method without creating an instance of the class , so this method must be declared as static.

void: main does not return anything so the return type must be void

The argument String indicates the argument type which is given at the command line and arg is an array for string given during command line.

What are the differences between == and .equals() ?

what is difference between == and equals

Difference between == and equals method

What would you use to compare two String variables – the operator == or the method equals()?

How is it possible for two String objects with identical values not to be equal under the == operator?

The == operator compares two objects to determine if they are the same object in memory i.e. present in the same memory location. It is possible for two String objects to have the same value, but located in different areas of memory.

== compares references while .equals compares contents. The method public boolean equals(Object obj) is provided by the Object class and can be overridden. The default implementation returns true only if the object is compared with itself, which is equivalent to the equality operator == being used to compare aliases to the object. String, BitSet, Date, and File override the equals() method. For two String objects, value equality means that they contain the same character sequence. For the Wrapper classes, value equality means that the primitive values are equal.

 public class EqualsTest {

 public static void main(String[] args) {

  String s1 = "abc";
  String s2 = s1;
  String s5 = "abc";
  String s3 = new String("abc");
  String s4 = new String("abc");
  System.out.println("== comparison : " + (s1 == s5));
  System.out.println("== comparison : " + (s1 == s2));
  System.out.println("Using equals method : " + s1.equals(s2));
  System.out.println("== comparison : " + s3 == s4);
  System.out.println("Using equals method : " + s3.equals(s4));
 }
}

Output
== comparison : true
== comparison : true
Using equals method : true
false
Using equals method : true

What if the static modifier is removed from the signature of the main method?

What if I do not provide the String array as the argument to the method?

Program compiles. But at runtime throws an error “NoSuchMethodError”.

Why oracle Type 4 driver is named as oracle thin driver?

Oracle provides a Type 4 JDBC driver, referred to as the Oracle “thin” driver. This driver includes its own implementation of a TCP/IP version of Oracle’s Net8 written entirely in Java, so it is platform independent, can be downloaded to a browser at runtime, and does not require any Oracle software on the client side. This driver requires a TCP/IP listener on the server side, and the client connection string uses the TCP/IP port address, not the TNSNAMES entry for the database name.

What is the difference between final, finally and finalize? What do you understand by the java final keyword?

What is final, finalize() and finally?

What is finalize() method?

What is the difference between final, finally and finalize?

What does it mean that a class or member is final?

o final – declare constant
o finally – handles exception
o finalize – helps in garbage collection

Variables defined in an interface are implicitly final. A final class can’t be extended i.e., final class may not be subclassed. This is done for security reasons with basic classes like String and Integer. It also allows the compiler to make some optimizations, and makes thread safety a little easier to achieve. A final method can’t be overridden when its class is inherited. You can’t change value of a final variable (is a constant). finalize() method is used just before an object is destroyed and garbage collected. finally, a key word used in exception handling and will be executed whether or not an exception is thrown. For example, closing of open connections is done in the finally method.

What is the Java API?

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets.

What is the GregorianCalendar class?

The GregorianCalendar provides support for traditional Western calendars.

What is the ResourceBundle class?

The ResourceBundle class is used to store locale-specific resources that can be loaded by a program to tailor the program’s appearance to the particular locale in which it is being run.

Why there are no global variables in Java?

Global variables are globally accessible. Java does not support globally accessible variables due to following reasons:

The global variables breaks the referential transparency
Global variables creates collisions in namespace.

How to convert String to Number in java program?

The valueOf() function of Integer class is is used to convert string to Number. Here is the code example:
String numString = “1000″;
int id=Integer.valueOf(numString).intValue();

What is the SimpleTimeZone class?

The SimpleTimeZone class provides support for a Gregorian calendar.

What is the difference between a while statement and a do statement?

A while statement (pre test) checks at the beginning of a loop to see whether the next loop iteration should occur. A do while statement (post test) checks at the end of a loop to see whether the next iteration of a loop should occur. The do statement will always execute the loop body at least once.

What is the Locale class?

The Locale class is used to tailor a program output to the conventions of a particular geographic, political, or cultural region.

Describe the principles of OOPS.

There are three main principals of oops which are called Polymorphism, Inheritance and Encapsulation.

Explain the Inheritance principle.

Inheritance is the process by which one object acquires the properties of another object. Inheritance allows well-tested procedures to be reused and enables changes to make once and have effect in all relevant places

What is implicit casting?

Implicit casting is the process of simply assigning one entity to another without any transformation guidance to the compiler. This type of casting is not permitted in all kinds of transformations and may not work for all scenarios.

Example

int i = 1000;

long j = i; //Implicit casting

Is sizeof a keyword in java?

The sizeof operator is not a keyword.

What is a native method?

A native method is a method that is implemented in a language other than Java.

In System.out.println(), what is System, out and println?

System is a predefined final class, out is a PrintStream object and println is a built-in overloaded method in the out object.

What are Encapsulation, Inheritance and Polymorphism

Explain the Polymorphism principle. Explain the different forms of Polymorphism.

Polymorphism in simple terms means one name many forms. Polymorphism enables one entity to be used as a general category for different types of actions. The specific action is determined by the exact nature of the situation.

Polymorphism exists in three distinct forms in Java:
• Method overloading
• Method overriding through inheritance
• Method overriding through the Java interface

What is explicit casting?

Explicit casting in the process in which the complier are specifically informed to about transforming the object.

Example

long i = 700.20;

int j = (int) i; //Explicit casting

What is the Java Virtual Machine (JVM)?

The Java Virtual Machine is software that can be ported onto various hardware-based platforms

What do you understand by downcasting?

The process of Downcasting refers to the casting from a general to a more specific type, i.e. casting down the hierarchy

What are Java Access Specifiers?

What is the difference between public, private, protected and default Access Specifiers?

What are different types of access modifiers?

Access specifiers are keywords that determine the type of access to the member of a class. These keywords are for allowing
privileges to parts of a program such as functions and variables. These are:
• Public : accessible to all classes
• Protected : accessible to the classes within the same package and any subclasses.
• Private : accessible only to the class to which they belong
• Default : accessible to the class to which they belong and to subclasses within the same package

Which class is the superclass of every class?

Object.

Name primitive Java types.

The 8 primitive types are byte, char, short, int, long, float, double, and boolean.

What is the difference between static and non-static variables?

What are class variables?

What is static in java?

What is a static method?

A static variable is associated with the class as a whole rather than with specific instances of a class. Each object will share a common copy of the static variables i.e. there is only one copy per class, no matter how many objects are created from it. Class variables or static variables are declared with the static keyword in a class. These are declared outside a class and stored in static memory. Class variables are mostly used for constants. Static variables are always called by the class name. This variable is created when the program starts and gets destroyed when the programs stops. The scope of the class variable is same an instance variable. Its initial value is same as instance variable and gets a default value when its not initialized corresponding to the data type. Similarly, a static method is a method that belongs to the class rather than any object of the class and doesn’t apply to an object or even require that any objects of the class have been instantiated.
Static methods are implicitly final, because overriding is done based on the type of the object, and static methods are attached to a class, not an object. A static method in a superclass can be shadowed by another static method in a subclass, as long as the original method was not declared final. However, you can’t override a static method with a non-static method. In other words, you can’t change a static method into an instance method in a subclass.

Non-static variables take on unique values with each object instance.

What is the difference between the boolean & operator and the && operator?

If an expression involving the boolean & operator is evaluated, both operands are evaluated, whereas the && operator is a short cut operator. When an expression involving the && operator is evaluated, the first operand is evaluated. If the first operand returns a value of true then the second operand is evaluated. If the first operand evaluates to false, the evaluation of the second operand is skipped.

How does Java handle integer overflows and underflows?

It uses those low order bytes of the result that can fit into the size of the type allowed by the operation.

What if I write static public void instead of public static void?

Program compiles and runs properly.

What is the difference between declaring a variable and defining a variable?

In declaration we only mention the type of the variable and its name without initializing it. Defining means declaration + initialization. E.g. String s; is just a declaration while String s = new String (“bob”); Or String s = “bob”; are both definitions.

What type of parameter passing does Java support?

In Java the arguments (primitives and objects) are always passed by value. With objects, the object reference itself is passed by value and so both the original reference and parameter copy both refer to the same object.

Explain the Encapsulation principle.

Encapsulation is a process of binding or wrapping the data and the codes that operates on the data into a single entity. This keeps the data safe from outside interface and misuse. Objects allow procedures to be encapsulated with their data to reduce potential interference. One way to think about encapsulation is as a protective wrapper that prevents code and data from being arbitrarily accessed by other code defined outside the wrapper.

What do you understand by a variable?

Variable is a named memory location that can be easily referred in the program. The variable is used to hold the data and it can be changed during the course of the execution of the program.

What do you understand by numeric promotion?

The Numeric promotion is the conversion of a smaller numeric type to a larger numeric type, so that integral and floating-point operations may take place. In the numerical promotion process the byte, char, and short values are converted to int values. The int values are also converted to long values, if necessary. The long and float values are converted to double values, as required.

What do you understand by casting in java language? What are the types of casting?

The process of converting one data type to another is called Casting. There are two types of casting in Java; these are implicit casting and explicit casting.

What is the first argument of the String array in main method?

The String array is empty. It does not have any element. This is unlike C/C++ where the first element by default is the program name. If we do not provide any arguments on the command line, then the String array of main method will be empty but not null.

How can one prove that the array is not null but empty?

Print array.length. It will print 0. That means it is empty. But if it would have been null then it would have thrown a NullPointerException on attempting to print array.length.

Can an application have multiple classes having main method?

Yes. While starting the application we mention the class name to be run. The JVM will look for the main method only in the class whose name you have mentioned. Hence there is not conflict amongst the multiple classes having main method.

When is static variable loaded? Is it at compile time or runtime? When exactly a static block is loaded in Java?

Static variable are loaded when classloader brings the class to the JVM. It is not necessary that an object has to be created. Static variables will be allocated memory space when they have been loaded. The code in a static block is loaded/executed only once i.e. when the class is first initialized. A class can have any number of static blocks. Static block is not member of a class, they do not have a return statement and they cannot be called directly. Cannot contain this or super. They are primarily used to initialize static fields.

Can I have multiple main methods in the same class?

We can have multiple overloaded main methods but there can be only one main method with the following signature :

public static void main(String[] args) {}

No the program fails to compile. The compiler says that the main method is already defined in the class.

Explain working of Java Virtual Machine (JVM)?

JVM is an abstract computing machine like any other real computing machine which first converts .java file into .class file by using Compiler (.class is nothing but byte code file.) and Interpreter reads byte codes.

How can I swap two variables without using a third variable?

Add two variables and assign the value into First variable. Subtract the Second value with the result Value. and assign to Second variable. Subtract the Result of First Variable With Result of Second Variable and Assign to First Variable. Example:

int a=5,b=10;a=a+b; b=a-b; a=a-b;

An other approach to the same question

You use an XOR swap.

for example:
int a = 5; int b = 10;
a = a ^ b;
b = a ^ b;
a = a ^ b;

What is data encapsulation?

Encapsulation may be used by creating ‘get’ and ‘set’ methods in a class (JAVABEAN) which are used to access the fields of the object. Typically the fields are made private while the get and set methods are public. Encapsulation can be used to validate the data that is to be stored, to do calculations on data that is stored in a field or fields, or for use in introspection (often the case when using javabeans in Struts, for instance). Wrapping of data and function into a single unit is called as data encapsulation. Encapsulation is nothing but wrapping up the data and associated methods into a single unit in such a way that data can be accessed with the help of associated methods. Encapsulation provides data security. It is nothing but data hiding.

What is reflection API? How are they implemented?

Reflection is the process of introspecting the features and state of a class at runtime and dynamically manipulate at run time. This is supported using Reflection API with built-in classes like Class, Method, Fields, Constructors etc. Example: Using Java Reflection API we can get the class name, by using the getName method.

Does JVM maintain a cache by itself? Does the JVM allocate objects in heap? Is this the OS heap or the heap maintained by the JVM? Why

Yes, the JVM maintains a cache by itself. It creates the Objects on the HEAP, but references to those objects are on the STACK.

What is phantom memory?

Phantom memory is false memory. Memory that does not exist in reality.

Can a method be static and synchronized?

A static method can be synchronized. If you do so, the JVM will obtain a lock on the java.lang.
Class instance associated with the object. It is similar to saying:

synchronized(XYZ.class) {

}

What is difference between String and StringTokenizer?

A StringTokenizer is utility class used to break up string.

Example:

StringTokenizer st = new StringTokenizer(“Hello World”);

while (st.hasMoreTokens()) {

System.out.println(st.nextToken());

}

Output:

Hello

World

7 Surprising Trends That Show What Tech Skills You Need to Succeed

2011-05-12T08:28:00.000-07:00

7 Surprising Trends That Show What Tech Skills You Need to Succeed

IT salaries are up after a two year decline, according to CIO.com. That's good news, considering the level of dissatisfaction IT professionals are experiencing.

Meanwhile, hiring is up as well. Dennis B. Moore has analyzed listings from Dice.com and found an overall increase of 6.1% in the past three months, and a 46.2% increase over the past year. Moore looked at four areas: database, applications, languages and platforms. Moore found some surprising results.
Here are some of the biggest surprises. Keep in mind that these are just the results for one job board and may not be representative of the industry overall:

Demand for Hadoop knowledge grew slower than other NoSQL related technologies. However, there were still more Hadoop jobs than there were jobs in every other NoSQL technology combined. Also, traditional RDBMS technologies are still the most popular, with the most jobs and strong growth.
Demand for Oracle eBusiness Suite skills dipped. Skills on Oracle's database, unsurprisingly, remained strong. SAP hiring experienced the most growth in the applications area in the past three months, followed by PeopleSoft.
Silverlight overtook Flash. Silverlight jobs experienced 12.6% growth in the past three months, while Flash experienced just 2.2%. Silverlight also surpassed Flash in total number of jobs, with 982 job listings for Silverlight and 646 for Flash.
Demand for iPad skills decreased by 3.5%. However, iOS demand increased by 24.9%. Moore didn't tracked Macintosh or OSX demand in the past so he couldn't make a comparison.
Android had 1,019 jobs, which beat iOS' 832 jobs. But iOS is growing faster, at a rate of 24.9% to Android's 19.8%.
There was an increased demand for skills in Facebook and Twitter.
Azure was the fastest growing platform, with 80.7% growth. But it still trailed Amazon in total number of jobs 1,019 to 103.

The five programming languages with the top growth in the past three months were:

HTML5 (45.2%)
SAP Sybase PowerBuilder (26.0%)
Ruby (15.8%)
Python (15.8%)
Silverlight (12.6%)

PowerBuilder demand grew quickly, but only had 155 jobs total. Assembler also experienced strong growth at 12.2% in the past three months, but only had 212 jobs. Moore didn't rank Node.js separately from JavaScript, so there's no indication of how quickly demand for it is growing.
"There was such strong demand growth for all skills, that it is more useful in this category to speak about the area of weakest growth - Adobe Flash," Moore wrote.
The top languages, by total number of jobs, were:

Java (16,152 jobs)
HTML (9,736 jobs)
XML (9,651)
JavaScript (9,618)
C# (7,940)

Take aways:

Microsoft professionals are doing well, with strong growth in C#, Silverlight and Azure.
Java is still sitting pretty, as expected.
SAP is rebounding.
Oracle database skills remain vital, but other Oracle applications are questionable.
Demand for CRM skills are in decline, with Siebel, Salesforce.com and Microsoft Dynamics all taking hits in the past three months.
NoSQL is a small but growing niche.
You can't go wrong with HTML5 and JavaScript.

Programming is…

2011-05-12T08:27:00.000-07:00

Programming is…

Myth: Programmers get to write code all day.
Truth: Most programmers spend a ton of time (in no particular order):

Carefully composing e-mails to other programmers/mailing lists/non-technical folks
Sitting in on meetings, working on mockups and DB schemas, worrying about performance implications of proposed features
Writing bug reports and searching through bug DBs
Scrambling to figure out why systems with numerous opaque layers are failing, digging through multi-GB log files with command line tools
Explaining downtime to users/higher ups
Contributing solutions to strangers’ problems
Reading documentation/books/programming blogs/release notes/vulnerability announcements
Searching for existing code that does what you want, maybe without knowing what that’s called
Evaluating if code you found solves your problem/would perform acceptably/fits in your environment/has a compatible license/has a lasting support community
Installing, configuring, and testing a codebase then finding it won’t work for you
Googling error messages
Digging through public code repositories to see “how [some open source project] does it”
Learning source control tools, bash, GNU utilities, and Linux file permissions (and/or the Windows equivalents)
Configuring IDEs, virtual machines, web servers, databases
Figuring out how to shoehorn together codebases that weren’t designed to coexist
Determining which tasks to prioritize from an endless supply

ASP.NET Tutorial - Membership and Roles - Part2

2011-04-06T05:19:00.000-07:00

Personalization and Profiles in ASP.NET 3.5 Tutorial

2011-04-06T05:13:00.000-07:00

SQL – Simple Questions with Answers

2011-01-11T05:42:00.000-08:00

SQL INTERVIEW QUESTIONS

Q: What is SQL?
A: SQL stands for ‘Structured Query Language’.
Q: What is SELECT statement?
A: The SELECT statement lets you select a set of values from a table in a database. The values selected from the database table would depend on the various conditions that are specified in the SQL query.
Q: How can you compare a part of the name rather than the entire name?
A: SELECT * FROM people WHERE empname LIKE ‘%ab%’
Would return a recordset with records consisting empname the sequence ‘ab’ in empname .
Q: What is the INSERT statement?
A: The INSERT statement lets you insert information into a database.
Q: How do you delete a record from a database?
A: Use the DELETE statement to remove records or any particular column values from a database.
Q: How could I get distinct entries from a table?
A: The SELECT statement in conjunction with DISTINCT lets you select a set of distinct values from a table in a database. The values selected from the database table would of course depend on the various conditions that are specified in the SQL query. Example
SELECT DISTINCT empname FROM emptable
Q: How to get the results of a Query sorted in any order?
A: You can sort the results and return the sorted results to your program by using ORDER BY keyword thus saving you the pain of carrying out the sorting yourself. The ORDER BY keyword is used for sorting.
SELECT empname, age, city FROM emptable ORDER BY empname
Q: How can I find the total number of records in a table?
A: You could use the COUNT keyword , example
SELECT COUNT(*) FROM emp WHERE age>40
Q: What is GROUP BY?
A: The GROUP BY keywords have been added to SQL because aggregate functions (like SUM) return the aggregate of all column values every time they are called. Without the GROUP BY functionality, finding the sum for each individual group of column values was not possible.
Q: What is the difference among “dropping a table”, “truncating a table” and “deleting all records” from a table.
A: Dropping : (Table structure + Data are deleted), Invalidates the dependent objects ,Drops the indexes
Truncating: (Data alone deleted), Performs an automatic commit, Faster than delete
Delete : (Data alone deleted), Doesn’t perform automatic commit
Q: What are the Large object types suported by Oracle?
A: Blob and Clob.
Q: Difference between a “where” clause and a “having” clause.
A: Having clause is used only with group functions whereas Where is not used with.
Q: What’s the difference between a primary key and a unique key?
A: Both primary key and unique enforce uniqueness of the column on which they are defined. But by default primary key creates a clustered index on the column, where are unique creates a nonclustered index by default. Another major difference is that, primary key doesn’t allow NULLs, but unique key allows one NULL only.
Q: What are cursors? Explain different types of cursors. What are the disadvantages of cursors? How can you avoid cursors?
A: Cursors allow row-by-row prcessing of the resultsets.
Types of cursors: Static, Dynamic, Forward-only, Keyset-driven. See books online for more information.
Disadvantages of cursors: Each time you fetch a row from the cursor, it results in a network roundtrip, where as a normal SELECT query makes only one rowundtrip, however large the resultset is. Cursors are also costly because they require more resources and temporary storage (results in more IO operations). Furthere, there are restrictions on the SELECT statements that can be used with some types of cursors.
Most of the times, set based operations can be used instead of cursors.
Q: What are triggers? How to invoke a trigger on demand?
A: Triggers are special kind of stored procedures that get executed automatically when an INSERT, UPDATE or DELETE operation takes place on a table.
Triggers can’t be invoked on demand. They get triggered only when an associated action (INSERT, UPDATE, DELETE) happens on the table on which they are defined.
Triggers are generally used to implement business rules, auditing. Triggers can also be used to extend the referential integrity checks, but wherever possible, use constraints for this purpose, instead of triggers, as constraints are much faster.
Q: What is a join and explain different types of joins.
A: Joins are used in queries to explain how different tables are related. Joins also let you select data from a table depending upon data from another table.
Types of joins: INNER JOINs, OUTER JOINs, CROSS JOINs. OUTER JOINs are further classified as LEFT OUTER JOINS, RIGHT OUTER JOINS and FULL OUTER JOINS.
Q: What is a self join?
A: Self join is just like any other join, except that two instances of the same table will be joined in the query.
How would you find out the total number of rows in a table?
Use SELECT COUNT(*) … in query
How do you eliminate duplicate values in SELECT ?
Use SELECT DISTINCT … in SQL query
How you insert records into a table
Using SQL INSERT statement
How do you delete record from a table ?
Using DELETE statement
Example : DELETE FROM EMP
How do you select a row using indexes?
Specify the indexed columns in the WHERE clause of query.
How do you find the maximum value in a column?
Use SELECT MAX(…) .. in query
How do you retrieve the first 5 characters of FIRSTNAME column of table EMP ?
SELECT SUBSTR(FIRSTNAME,1,5) FROM EMP
My SQL statement SELECT AVG(SALARY) FROM EMP yields inaccurate results. Why?
Because SALARY is not declared to have NULLs and the employees for whom the
salary is not known are also counted.
How do you concatenate the FIRSTNAME and LASTNAME from EMP table to give a complete name?
SELECT FIRSTNAME || ‘ ‘ || LASTNAME FROM EMP
What is UNION,UNION ALL in SQL?
UNION : eliminates duplicates
UNION ALL: retains duplicates
Both these are used to combine the results of different SELECT statements.
Suppose I have five SQL SELECT statements connected by UNION/UNION ALL, how many times
should I specify UNION to eliminate the duplicate rows?
Once.
In the WHERE clause what is BETWEEN and IN?
BETWEEN supplies a range of values while IN supplies a list of values.
Is BETWEEN inclusive of the range values specified?
Yes.
What is ‘LIKE’ used for in WHERE clause? What are the wildcard characters?
LIKE is used for partial string matches. ‘%’ ( for a string of any character )
and ‘_’ (for any single character ) are the two wild card characters.
When do you use a LIKE statement?
To do partial search e.g. to search employee by name, you need not specify
the complete name; using LIKE, you can search for partial string matches.
Example SQL : SELECT EMPNO FROM EMP
WHERE EMPNAME LIKE ‘RAMESH%’
% is used to represent remaining all characters in the name.
This query fetches all records contains RAMESH in six characters.
What do you accomplish by GROUP BY … HAVING clause?
GROUP BY partitions the selected rows on the distinct values of the column on
which you group by. HAVING selects GROUPs which match the criteria specified
Consider the employee table with column PROJECT nullable. How can you get a list
of employees who are not assigned to any project?
SQL : SELECT EMPNO
FROM EMP
WHERE PROJECT IS null;
What are the large objects supported by oracle and db2?
Blob , Clob ( Binary Large Objects, Character Large Objects)
What’s the difference between a primary key and a unique key?
Primary key wont allow nulls, unique key allow nulls.
Both Primary key and Unique key enforce the uniqueness of the column on which they are defined.
What is a join and explain different types of joins?
INNER JOIN
OUTER JOIN
LEFT OUTER JOIN
RIGHT OUTER JOIN
FULL OUTER JOIN
What is a self join?
Joining two instances of a same table.
Sample SQL : SELECT A.EMPNAME , B.EMPNAME
FROM EMP A, EMP B
WHERE A.MGRID = B.EMPID
What is a transaction and ACID?
Transaction – A transaction is a logicl unint of work. All steps must be commited or rolled back.
ACID – Atomicity, Consistency, Isolation and Duralbility, these are properties of a transaction.
Materialized Query Tables in db2 ( This feature might not be available in oracle) ?
Materialized Query Tables or MQTs are also known as automatic summary
tables. A materialized query table (MQT) is a table whose definition is based upon the result of a
query. The data that is contained in an MQT is derived from one or more tables on which the materialized
query table definition is based. MQT improve the query performance.
Sample SQL to creat MQT.
CREATE TABLE CUSTOMER_ORDER AS
(SELECT SUM(AMOUNT) AS TOTAL_SUM,
TRANS_DT,
STATUS
FROM DB2INST2.CUSTOMER_ORDER
WHERE TRANS_DT BETWEEN ’1/1/2001′ AND ’12/31/2001′
GROUP BY TRANS_DT,
STATUS)
DATA INITIALLY DEFERRED REFRESH DEFERRED;

Threading in C# (Part 5)

2010-12-29T05:41:00.000-08:00

Part 5: Parallel Programming

Parallel LINQ or PLINQ
The Parallel class
The task parallelism constructs
The concurrent collections
SpinLock and SpinWait

These APIs are collectively known (loosely) as PFX (Parallel Framework). The Parallel class together with the task parallelism constructs is called the Task Parallel Library or TPL.
Framework 4.0 also adds a number of lower-level threading constructs that are aimed equally at traditional multithreading. We covered these previously:

The low-latency signaling constructs (SemaphoreSlim, ManualResetEventSlim, CountdownEvent and Barrier)
Cancellation tokens for cooperative cancellation
The lazy initialization classes
ThreadLocal

You’ll need to be comfortable with the fundamentals in Parts 1-4 before continuing — particularly locking and thread safety.

All the code listings in the parallel programming sections are available as interactive samples in LINQPad. To access these samples, click Download More Samples in LINQPad's Samples tab in the bottom left, and select C# 4.0 in a Nutshell: More Chapters.

Why PFX?

In recent times, CPU clock speeds have stagnated and manufacturers have shifted their focus to increasing core counts. This is problematic for us as programmers because our standard single-threaded code will not automatically run faster as a result of those extra cores.
Leveraging multiple cores is easy for most server applications, where each thread can independently handle a separate client request, but is harder on the desktop — because it typically requires that you take your computationally intensive code and do the following:

Partition it into small chunks.
Execute those chunks in parallel via multithreading.
Collate the results as they become available, in a thread-safe and performant manner.

Although you can do all of this with the classic multithreading constructs, it’s awkward — particularly the steps of partitioning and collating. A further problem is that the usual strategy of locking for thread safety causes a lot of contention when many threads work on the same data at once.
The PFX libraries have been designed specifically to help in these scenarios.

Programming to leverage multicores or multiple processors is called parallel programming. This is a subset of the broader concept of multithreading.

PFX Concepts

There are two strategies for partitioning work among threads: data parallelism and task parallelism.
When a set of tasks must be performed on many data values, we can parallelize by having each thread perform the (same) set of tasks on a subset of values. This is called data parallelism because we are partitioning the data between threads. In contrast, with task parallelism we partition the tasks; in other words, we have each thread perform a different task.
In general, data parallelism is easier and scales better to highly parallel hardware, because it reduces or eliminates shared data (thereby reducing contention and thread-safety issues). Also, data parallelism leverages the fact that there are often more data values than discrete tasks, increasing the parallelism potential.
Data parallelism is also conducive to structured parallelism, which means that parallel work units start and finish in the same place in your program. In contrast, task parallelism tends to be unstructured, meaning that parallel work units may start and finish in places scattered across your program. Structured parallelism is simpler and less error-prone and allows you to farm the difficult job of partitioning and thread coordination (and even result collation) out to libraries.

PFX Components

PFX comprises two layers of functionality. The higher layer consists of two structured data parallelism APIs: PLINQ and the Parallel class. The lower layer contains the task parallelism classes — plus a set of additional constructs to help with parallel programming activities.

PLINQ offers the richest functionality: it automates all the steps of parallelization — including partitioning the work into tasks, executing those tasks on threads, and collating the results into a single output sequence. It’s called declarative — because you simply declare that you want to parallelize your work (which you structure as a LINQ query), and let the Framework take care of the implementation details. In contrast, the other approaches are imperative, in that you need to explicitly write code to partition or collate. In the case of the Parallel class, you must collate results yourself; with the task parallelism constructs, you must partition the work yourself, too:

	Partitions work	Collates results
PLINQ	Yes	Yes
The `Parallel` class	Yes	No
PFX’s task parallelism	No	No

The concurrent collections and spinning primitives help you with lower-level parallel programming activities. These are important because PFX has been designed to work not only with today’s hardware, but also with future generations of processors with far more cores. If you want to move a pile of chopped wood and you have 32 workers to do the job, the biggest challenge is moving the wood without the workers getting in each other's way. It’s the same with dividing an algorithm among 32 cores: if ordinary locks are used to protect common resources, the resultant blocking may mean that only a fraction of those cores are ever actually busy at once. The concurrent collections are tuned specifically for highly concurrent access, with the focus on minimizing or eliminating blocking. PLINQ and the Parallel class themselves rely on the concurrent collections and on spinning primitives for efficient management of work.

PFX and Traditional Multithreading

A traditional multithreading scenario is one where multithreading can be of benefit even on a single-core machine — with no true parallelization taking place. We covered these previously: they include such tasks as maintaining a responsive user interface and downloading two web pages at once.
Some of the constructs that we’ll cover in the parallel programming sections are also sometimes useful in traditional multithreading. In particular:

PLINQ and the Parallel class are useful whenever you want to execute operations in parallel and then wait for them to complete (structured parallelism). This includes non-CPU-intensive tasks such as calling a web service.
The task parallelism constructs are useful when you want to run some operation on a pooled thread, and also to manage a task’s workflow through continuations and parent/child tasks.
The concurrent collections are sometimes appropriate when you want a thread-safe queue, stack, or dictionary.
BlockingCollection provides an easy means to implement producer/consumer structures.

When to Use PFX

The primary use case for PFX is parallel programming: leveraging multicore processors to speed up computationally intensive code.
A challenge in leveraging multicores is Amdahl's law, which states that the maximum performance improvement from parallelization is governed by the portion of the code that must execute sequentially. For instance, if only two-thirds of an algorithm’s execution time is parallelizable, you can never exceed a threefold performance gain — even with an infinite number of cores.
So, before proceeding, it’s worth verifying that the bottleneck is in parallelizable code. It’s also worth considering whether your code needs to be computationally intensive — optimization is often the easiest and most effective approach. There’s a trade-off, though, in that some optimization techniques can make it harder to parallelize code.
The easiest gains come with what’s called embarrassingly parallel problems — where a job can be divided easily into tasks that execute efficiently on their own (structured parallelism is very well suited to such problems). Examples include many image processing tasks, ray tracing, and brute force approaches in mathematics or cryptography. An example of a nonembarrassingly parallel problem is implementing an optimized version of the quicksort algorithm — a good result takes some thought and may require unstructured parallelism.

PLINQ

PLINQ automatically parallelizes local LINQ queries. PLINQ has the advantage of being easy to use in that it offloads the burden of both work partitioning and result collation to the Framework.
To use PLINQ, simply call AsParallel() on the input sequence and then continue the LINQ query as usual. The following query calculates the prime numbers between 3 and 100,000 — making full use of all cores on the target machine:

// Calculate prime numbers using a simple (unoptimized) algorithm.
 
IEnumerable numbers = Enumerable.Range (3, 100000-3);
 
var parallelQuery = 
  from n in numbers.AsParallel()
  where Enumerable.Range (2, (int) Math.Sqrt (n)).All (i => n % i > 0)
  select n;
 
int[] primes = parallelQuery.ToArray();

AsParallel is an extension method in System.Linq.ParallelEnumerable. It wraps the input in a sequence based on ParallelQuery, which causes the LINQ query operators that you subsequently call to bind to an alternate set of extension methods defined in ParallelEnumerable. These provide parallel implementations of each of the standard query operators. Essentially, they work by partitioning the input sequence into chunks that execute on different threads, collating the results back into a single output sequence for consumption:

Calling AsSequential() unwraps a ParallelQuery sequence so that subsequent query operators bind to the standard query operators and execute sequentially. This is necessary before calling methods that have side effects or are not thread-safe.
For query operators that accept two input sequences (Join, GroupJoin, Concat, Union, Intersect, Except, and Zip), you must apply AsParallel() to both input sequences (otherwise, an exception is thrown). You don’t, however, need to keep applying AsParallel to a query as it progresses, because PLINQ’s query operators output another ParallelQuery sequence. In fact, calling AsParallel again introduces inefficiency in that it forces merging and repartitioning of the query:

mySequence.AsParallel()           // Wraps sequence in ParallelQuery
          .Where (n => n > 100)   // Outputs another ParallelQuery
          .AsParallel()           // Unnecessary - and inefficient!
          .Select (n => n * n)

Not all query operators can be effectively parallelized. For those that cannot, PLINQ implements the operator sequentially instead. PLINQ may also operate sequentially if it suspects that the overhead of parallelization will actually slow a particular query.
PLINQ is only for local collections: it doesn’t work with LINQ to SQL or Entity Framework because in those cases the LINQ translates into SQL which then executes on a database server. However, you can use PLINQ to perform additional local querying on the result sets obtained from database queries.

If a PLINQ query throws an exception, it’s rethrown as an AggregateException whose InnerExceptions property contains the real exception (or exceptions). See Working with AggregateException for details.

Why Isn’t AsParallel the Default?

Given that AsParallel transparently parallelizes LINQ queries, the question arises, “Why didn’t Microsoft simply parallelize the standard query operators and make PLINQ the default?”
There are a number of reasons for the opt-in approach. First, for PLINQ to be useful there has to be a reasonable amount of computationally intensive work for it to farm out to worker threads. Most LINQ to Objects queries execute very quickly, and not only would parallelization be unnecessary, but the overhead of partitioning, collating, and coordinating the extra threads may actually slow things down.
Additionally:

The output of a PLINQ query (by default) may differ from a LINQ query with respect to element ordering.
PLINQ wraps exceptions in an AggregateException (to handle the possibility of multiple exceptions being thrown).
PLINQ will give unreliable results if the query invokes thread-unsafe methods.

Finally, PLINQ offers quite a few hooks for tuning and tweaking. Burdening the standard LINQ to Objects API with such nuances would add distraction.

Parallel Execution Ballistics

Like ordinary LINQ queries, PLINQ queries are lazily evaluated. This means that execution is triggered only when you begin consuming the results — typically via a foreach loop (although it may also be via a conversion operator such as ToArray or an operator that returns a single element or value).
As you enumerate the results, though, execution proceeds somewhat differently from that of an ordinary sequential query. A sequential query is powered entirely by the consumer in a “pull” fashion: each element from the input sequence is fetched exactly when required by the consumer. A parallel query ordinarily uses independent threads to fetch elements from the input sequence slightly ahead of when they’re needed by the consumer (rather like a teleprompter for newsreaders, or an antiskip buffer in CD players). It then processes the elements in parallel through the query chain, holding the results in a small buffer so that they’re ready for the consumer on demand. If the consumer pauses or breaks out of the enumeration early, the query processor also pauses or stops so as not to waste CPU time or memory.

You can tweak PLINQ’s buffering behavior by calling WithMergeOptions after AsParallel. The default value of AutoBuffered generally gives the best overall results. NotBuffered disables the buffer and is useful if you want to see results as soon as possible; FullyBuffered caches the entire result set before presenting it to the consumer (the OrderBy and Reverse operators naturally work this way, as do the element, aggregation, and conversion operators).

PLINQ and Ordering

A side effect of parallelizing the query operators is that when the results are collated, it’s not necessarily in the same order that they were submitted, as illustrated in the previous diagram. In other words, LINQ’s normal order-preservation guarantee for sequences no longer holds.
If you need order preservation, you can force it by calling AsOrdered() after AsParallel():

myCollection.AsParallel().AsOrdered()...

Calling AsOrdered incurs a performance hit with large numbers of elements because PLINQ must keep track of each element’s original position.
You can negate the effect of AsOrdered later in a query by calling AsUnordered: this introduces a “random shuffle point” which allows the query to execute more efficiently from that point on. So if you wanted to preserve input-sequence ordering for just the first two query operators, you’d do this:

inputSequence.AsParallel().AsOrdered()
  .QueryOperator1()
  .QueryOperator2()
  .AsUnordered()       // From here on, ordering doesn’t matter
  .QueryOperator3()
  ...

AsOrdered is not the default because for most queries, the original input ordering doesn’t matter. In other words, if AsOrdered was the default, you’d have to apply AsUnordered to the majority of your parallel queries to get the best performance, which would be burdensome.

PLINQ Limitations

There are currently some practical limitations on what PLINQ can parallelize. These limitations may loosen with subsequent service packs and Framework versions.
The following query operators prevent a query from being parallelized, unless the source elements are in their original indexing position:

Take, TakeWhile, Skip, and SkipWhile
The indexed versions of Select, SelectMany, and ElementAt

Most query operators change the indexing position of elements (including those that remove elements, such as Where). This means that if you want to use the preceding operators, they’ll usually need to be at the start of the query.
The following query operators are parallelizable, but use an expensive partitioning strategy that can sometimes be slower than sequential processing:

Join, GroupBy, GroupJoin, Distinct, Union, Intersect, and Except

The Aggregate operator’s seeded overloads in their standard incarnations are not parallelizable — PLINQ provides special overloads to deal with this.
All other operators are parallelizable, although use of these operators doesn’t guarantee that your query will be parallelized. PLINQ may run your query sequentially if it suspects that the overhead of parallelization will slow down that particular query. You can override this behavior and force parallelism by calling the following after AsParallel():

.WithExecutionMode (ParallelExecutionMode.ForceParallelism)

Example: Parallel Spellchecker

Suppose we want to write a spellchecker that runs quickly with very large documents by leveraging all available cores. By formulating our algorithm into a LINQ query, we can very easily parallelize it.
The first step is to download a dictionary of English words into a HashSet for efficient lookup:

if (!File.Exists ("WordLookup.txt"))    // Contains about 150,000 words
  new WebClient().DownloadFile (
    "http://www.albahari.com/ispell/allwords.txt", "WordLookup.txt");
 
var wordLookup = new HashSet<string> (
  File.ReadAllLines ("WordLookup.txt"),
  StringComparer.InvariantCultureIgnoreCase);

We’ll then use our word lookup to create a test “document” comprising an array of a million random words. After building the array, we’ll introduce a couple of spelling mistakes:

var random = new Random();
string[] wordList = wordLookup.ToArray();
 
string[] wordsToTest = Enumerable.Range (0, 1000000)
  .Select (i => wordList [random.Next (0, wordList.Length)])
  .ToArray();
 
wordsToTest [12345] = "woozsh";     // Introduce a couple
wordsToTest [23456] = "wubsie";     // of spelling mistakes.

Now we can perform our parallel spellcheck by testing wordsToTest against wordLookup. PLINQ makes this very easy:

var query = wordsToTest
  .AsParallel()
  .Select  ((word, index) => new IndexedWord { Word=word, Index=index })
  .Where   (iword => !wordLookup.Contains (iword.Word))
  .OrderBy (iword => iword.Index);
 
query.Dump();     // Display output in LINQPad

Here's the output, as displayed in LINQPad:

Word	Index
OrderedParallelQuery (2 items)
woozsh	12345
wubsie	23456

IndexedWord is a custom struct that we define as follows:

struct IndexedWord { public string Word; public int Index; }

The wordLookup.Contains method in the predicate gives the query some “meat” and makes it worth parallelizing.

We could simplify the query slightly by using an anonymous type instead of the IndexedWord struct. However, this would degrade performance because anonymous types (being classes and therefore reference types) incur the cost of heap-based allocation and subsequent garbage collection.
The difference might not be enough to matter with sequential queries, but with parallel queries, favoring stack-based allocation can be quite advantageous. This is because stack-based allocation is highly parallelizable (as each thread has its own stack), whereas all threads must compete for the same heap — managed by a single memory manager and garbage collector.

Using ThreadLocal

Let’s extend our example by parallelizing the creation of the random test-word list itself. We structured this as a LINQ query, so it should be easy. Here’s the sequential version:

string[] wordsToTest = Enumerable.Range (0, 1000000)
  .Select (i => wordList [random.Next (0, wordList.Length)])
  .ToArray();

Unfortunately, the call to random.Next is not thread-safe, so it’s not as simple as inserting AsParallel() into the query. A potential solution is to write a function that locks around random.Next; however, this would limit concurrency. The better option is to use ThreadLocal to create a separate Random object for each thread. We can then parallelize the query as follows:

var localRandom = new ThreadLocal<Random>
 ( () => new Random (Guid.NewGuid().GetHashCode()) );
 
string[] wordsToTest = Enumerable.Range (0, 1000000).AsParallel()
  .Select (i => wordList [localRandom.Value.Next (0, wordList.Length)])
  .ToArray();

In our factory function for instantiating a Random object, we pass in a Guid’s hashcode to ensure that if two Random objects are created within a short period of time, they’ll yield different random number sequences.

When to Use PLINQ

It’s tempting to search your existing applications for LINQ queries and experiment with parallelizing them. This is usually unproductive, because most problems for which LINQ is obviously the best solution tend to execute very quickly and so don’t benefit from parallelization. A better approach is to find a CPU-intensive bottleneck and then consider, “Can this be expressed as a LINQ query?” (A welcome side effect of such restructuring is that LINQ typically makes code smaller and more readable.)
PLINQ is well suited to embarrassingly parallel problems. It also works well for structured blocking tasks, such as calling several web services at once (see Calling Blocking or I/O-Intensive Functions).
PLINQ can be a poor choice for imaging, because collating millions of pixels into an output sequence creates a bottleneck. Instead, it’s better to write pixels directly to an array or unmanaged memory block and use the Parallel class or task parallelism to manage the multithreading. (It is possible, however, to defeat result collation using ForAll. Doing so makes sense if the image processing algorithm naturally lends itself to LINQ.)

Functional Purity

Because PLINQ runs your query on parallel threads, you must be careful not to perform thread-unsafe operations. In particular, writing to variables is side-effecting and therefore thread-unsafe:

// The following query multiplies each element by its position.
// Given an input of Enumerable.Range(0,999), it should output squares.
int i = 0;
var query = from n in Enumerable.Range(0,999).AsParallel() select n * i++;

We could make incrementing i thread-safe by using locks or Interlocked, but the problem would still remain that i won’t necessarily correspond to the position of the input element. And adding AsOrdered to the query wouldn’t fix the latter problem, because AsOrdered ensures only that the elements are output in an order consistent with them having been processed sequentially — it doesn’t actually process them sequentially.
Instead, this query should be rewritten to use the indexed version of Select:

var query = Enumerable.Range(0,999).AsParallel().Select ((n, i) => n * i);

For best performance, any methods called from query operators should be thread-safe by virtue of not writing to fields or properties (non-side-effecting, or functionally pure). If they’re thread-safe by virtue of locking, the query’s parallelism potential will be limited — by the duration of the lock divided by the total time spent in that function.

Calling Blocking or I/O-Intensive Functions

Sometimes a query is long-running not because it’s CPU-intensive, but because it waits on something — such as a web page to download or some hardware to respond. PLINQ can effectively parallelize such queries, providing that you hint it by calling WithDegreeOfParallelism after AsParallel. For instance, suppose we want to ping six websites simultaneously. Rather than using clumsy asynchronous delegates or manually spinning up six threads, we can accomplish this effortlessly with a PLINQ query:

from site in new[]
{
  "www.albahari.com",
  "www.linqpad.net",
  "www.oreilly.com",
  "www.google.com",
  "www.takeonit.com",
  "stackoverflow.com"
}
.AsParallel().WithDegreeOfParallelism(6)
let p = new Ping().Send (site)
select new
{
  site,
  Result = p.Status,
  Time = p.RoundtripTime
}

WithDegreeOfParallelism forces PLINQ to run the specified number of tasks simultaneously. This is necessary when calling blocking functions such as Ping.Send because PLINQ otherwise assumes that the query is CPU-intensive and allocates tasks accordingly. On a two-core machine, for instance, PLINQ may default to running only two tasks at once, which is clearly undesirable in this situation.

PLINQ typically serves each task with a thread, subject to allocation by the thread pool. You can accelerate the initial ramping up of threads by calling ThreadPool.SetMinThreads.

To give another example, suppose we were writing a surveillance system and wanted to repeatedly combine images from four security cameras into a single composite image for display on a CCTV. We’ll represent a camera with the following class:

class Camera
{
  public readonly int CameraID;
  public Camera (int cameraID) { CameraID = cameraID; }
 
  // Get image from camera: return a simple string rather than an image
  public string GetNextFrame()
  {
    Thread.Sleep (123);       // Simulate time taken to get snapshot
    return "Frame from camera " + CameraID;
  }
}

To obtain a composite image, we must call GetNextFrame on each of four camera objects. Assuming the operation is I/O-bound, we can quadruple our frame rate with parallelization — even on a single-core machine. PLINQ makes this possible with minimal programming effort:

Camera[] cameras = Enumerable.Range (0, 4)    // Create 4 camera objects.
  .Select (i => new Camera (i))
  .ToArray();
 
while (true)
{
  string[] data = cameras
    .AsParallel().AsOrdered().WithDegreeOfParallelism (4)
    .Select (c => c.GetNextFrame()).ToArray();
 
  Console.WriteLine (string.Join (", ", data));   // Display data...
}

GetNextFrame is a blocking method, so we used WithDegreeOfParallelism to get the desired concurrency. In our example, the blocking happens when we call Sleep; in real life it would block because fetching an image from a camera is I/O- rather than CPU-intensive.

Calling AsOrdered ensures the images are displayed in a consistent order. Because there are only four elements in the sequence, this would have a negligible effect on performance.

Changing the degree of parallelism

You can call WithDegreeOfParallelism only once within a PLINQ query. If you need to call it again, you must force merging and repartitioning of the query by calling IsParallel() again within the query:

"The Quick Brown Fox"
  .AsParallel().WithDegreeOfParallelism (2)
  .Where (c => !char.IsWhiteSpace (c))
  .AsParallel().WithDegreeOfParallelism (3)   // Forces Merge + Partition
  .Select (c => char.ToUpper (c))

Cancellation

Canceling a PLINQ query whose results you’re consuming in a foreach loop is easy: simply break out of the foreach and the query will be automatically canceled as the enumerator is implicitly disposed.
For a query that terminates with a conversion, element, or aggregation operator, you can cancel it from another thread via a cancellation token. To insert a token, call WithCancellation after calling AsParallel, passing in the Token property of a CancellationTokenSource object. Another thread can then call Cancel on the token source, which throws an OperationCanceledException on the query’s consumer:

IEnumerable million = Enumerable.Range (3, 1000000);
 
var cancelSource = new CancellationTokenSource(); 
var primeNumberQuery = 
  from n in million.AsParallel().WithCancellation (cancelSource.Token)
  where Enumerable.Range (2, (int) Math.Sqrt (n)).All (i => n % i > 0)
  select n;
 
new Thread (() => {
                    Thread.Sleep (100);      // Cancel query after
                    cancelSource.Cancel();   // 100 milliseconds.
                  }
           ).Start();
try 
{
  // Start query running:
  int[] primes = primeNumberQuery.ToArray();
  // We'll never get here because the other thread will cancel us.
}
catch (OperationCanceledException)
{
  Console.WriteLine ("Query canceled");
}

PLINQ doesn’t preemptively abort threads, because of the danger of doing so. Instead, upon cancellation it waits for each worker thread to finish with its current element before ending the query. This means that any external methods that the query calls will run to completion.

Optimizing PLINQ

Output-side optimization

One of PLINQ’s advantages is that it conveniently collates the results from parallelized work into a single output sequence. Sometimes, though, all that you end up doing with that sequence is running some function once over each element:

foreach (int n in parallelQuery)
  DoSomething (n);

If this is the case — and you don’t care about the order in which the elements are processed — you can improve efficiency with PLINQ’s ForAll method.
The ForAll method runs a delegate over every output element of a ParallelQuery. It hooks right into PLINQ’s internals, bypassing the steps of collating and enumerating the results. To give a trivial example:

"abcdef".AsParallel().Select (c => char.ToUpper(c)).ForAll (Console.Write);

Collating and enumerating results is not a massively expensive operation, so the ForAll optimization yields the greatest gains when there are large numbers of quickly executing input elements.

Input-side optimization

PLINQ has three partitioning strategies for assigning input elements to threads:

Strategy	Element allocation	Relative performance
Chunk partitioning	Dynamic	Average
Range partitioning	Static	Poor to excellent
Hash partitioning	Static	Poor

For query operators that require comparing elements (GroupBy, Join, GroupJoin, Intersect, Except, Union, and Distinct), you have no choice: PLINQ always uses hash partitioning. Hash partitioning is relatively inefficient in that it must precalculate the hashcode of every element (so that elements with identical hashcodes can be processed on the same thread). If you find this too slow, your only option is to call AsSequential to disable parallelization.
For all other query operators, you have a choice as to whether to use range or chunk partitioning. By default:

If the input sequence is indexable (if it’s an array or implements IList), PLINQ chooses range partitioning.
Otherwise, PLINQ chooses chunk partitioning.

In a nutshell, range partitioning is faster with long sequences for which every element takes a similar amount of CPU time to process. Otherwise, chunk partitioning is usually faster.
To force range partitioning:

If the query starts with Enumerable.Range, replace the latter with ParallelEnumerable.Range.
Otherwise, simply call ToList or ToArray on the input sequence (obviously, this incurs a performance cost in itself which you should take into account).

ParallelEnumerable.Range is not simply a shortcut for calling Enumerable.Range(…).AsParallel(). It changes the performance of the query by activating range partitioning.

To force chunk partitioning, wrap the input sequence in a call to Partitioner.Create (in System.Collection.Concurrent) as follows:

int[] numbers = { 3, 4, 5, 6, 7, 8, 9 };
var parallelQuery =
  Partitioner.Create (numbers, true).AsParallel()
  .Where (...)

The second argument to Partitioner.Create indicates that you want to load-balance the query, which is another way of saying that you want chunk partitioning.
Chunk partitioning works by having each worker thread periodically grab small “chunks” of elements from the input sequence to process. PLINQ starts by allocating very small chunks (one or two elements at a time), then increases the chunk size as the query progresses: this ensures that small sequences are effectively parallelized and large sequences don’t cause excessive round-tripping. If a worker happens to get “easy” elements (that process quickly) it will end up getting more chunks. This system keeps every thread equally busy (and the cores “balanced”); the only downside is that fetching elements from the shared input sequence requires synchronization (typically an exclusive lock) — and this can result in some overhead and contention.

Range partitioning bypasses the normal input-side enumeration and preallocates an equal number of elements to each worker, avoiding contention on the input sequence. But if some threads happen to get easy elements and finish early, they sit idle while the remaining threads continue working. Our earlier prime number calculator might perform poorly with range partitioning. An example of when range partitioning would do well is in calculating the sum of the square roots of the first 10 million integers:

ParallelEnumerable.Range (1, 10000000).Sum (i => Math.Sqrt (i))

ParallelEnumerable.Range returns a ParallelQuery, so you don’t need to subsequently call AsParallel.

Range partitioning doesn’t necessarily allocate element ranges in contiguous blocks — it might instead choose a “striping” strategy. For instance, if there are two workers, one worker might process odd-numbered elements while the other processes even-numbered elements. The TakeWhile operator is almost certain to trigger a striping strategy to avoid unnecessarily processing elements later in the sequence.

Parallelizing Custom Aggregations

PLINQ parallelizes the Sum, Average, Min, and Max operators efficiently without additional intervention. The Aggregate operator, though, presents special challenges for PLINQ.
If you’re unfamiliar with this operator, you can think of Aggregate as a generalized version of Sum, Average, Min, and Max — in other words, an operator that lets you plug in a custom accumulation algorithm for implementing unusual aggregations. The following demonstrates how Aggregate can do the work of Sum:

int[] numbers = { 2, 3, 4 };
int sum = numbers.Aggregate (0, (total, n) => total + n);   // 9

The first argument to Aggregate is the seed, from which accumulation starts. The second argument is an expression to update the accumulated value, given a fresh element. You can optionally supply a third argument to project the final result value from the accumulated value.

Most problems for which Aggregate has been designed can be solved as easily with a foreach loop — and with more familiar syntax. The advantage of Aggregate is precisely that large or complex aggregations can be parallelized declaratively with PLINQ.

Unseeded aggregations

You can omit the seed value when calling Aggregate, in which case the first element becomes the implicit seed, and aggregation proceeds from the second element. Here’s the preceding example, unseeded:

int[] numbers = { 1, 2, 3 };
int sum = numbers.Aggregate ((total, n) => total + n);   // 6

This gives the same result as before, but we’re actually doing a different calculation. Before, we were calculating 0+1+2+3; now we’re calculating 1+2+3. We can better illustrate the difference by multiplying instead of adding:

int[] numbers = { 1, 2, 3 };
int x = numbers.Aggregate (0, (prod, n) => prod * n);   // 0*1*2*3 = 0
int y = numbers.Aggregate (   (prod, n) => prod * n);   //   1*2*3 = 6

As we’ll see shortly, unseeded aggregations have the advantage of being parallelizable without requiring the use of special overloads. However, there is a trap with unseeded aggregations: the unseeded aggregation methods are intended for use with delegates that are commutative and associative. If used otherwise, the result is either unintuitive (with ordinary queries) or nondeterministic (in the case that you parallelize the query with PLINQ). For example, consider the following function:

(total, n) => total + n * n

This is neither commutative nor associative. (For example, 1+2*2 != 2+1*1). Let’s see what happens when we use it to sum the square of the numbers 2, 3, and 4:

int[] numbers = { 2, 3, 4 };
int sum = numbers.Aggregate ((total, n) => total + n * n);    // 27

Instead of calculating:

2*2 + 3*3 + 4*4    // 29

it calculates:

2 + 3*3 + 4*4      // 27

We can fix this in a number of ways. First, we could include 0 as the first element:

int[] numbers = { 0, 2, 3, 4 };

Not only is this inelegant, but it will still give incorrect results if parallelized — because PLINQ leverages the function’s assumed associativity by selecting multiple elements as seeds. To illustrate, if we denote our aggregation function as follows:

f(total, n) => total + n * n

then LINQ to Objects would calculate this:

f(f(f(0, 2),3),4)

whereas PLINQ may do this:

f(f(0,2),f(3,4))

with the following result:

First partition:   a = 0 + 2*2  (= 4)
Second partition:  b = 3 + 4*4  (= 19)
Final result:          a + b*b  (= 365)
OR EVEN:               b + a*a  (= 35)

There are two good solutions. The first is to turn this into a seeded aggregation — with zero as the seed. The only complication is that with PLINQ, we’d need to use a special overload in order for the query not to execute sequentially (as we’ll see soon).
The second solution is to restructure the query such that the aggregation function is commutative and associative:

int sum = numbers.Select (n => n * n).Aggregate ((total, n) => total + n);

Of course, in such simple scenarios you can (and should) use the Sum operator instead of Aggregate:

int sum = numbers.Sum (n => n * n);

You can actually go quite far just with Sum and Average. For instance, you can use Average to calculate a root-mean-square:

Math.Sqrt (numbers.Average (n => n * n))

and even standard deviation:

double mean = numbers.Average();
double sdev = Math.Sqrt (numbers.Average (n =>
              {
                double dif = n - mean;
                return dif * dif;
              }));

Both are safe, efficient and fully parallelizable.

Parallelizing Aggregate

We just saw that for unseeded aggregations, the supplied delegate must be associative and commutative. PLINQ will give incorrect results if this rule is violated, because it draws multiple seeds from the input sequence in order to aggregate several partitions of the sequence simultaneously.
Explicitly seeded aggregations might seem like a safe option with PLINQ, but unfortunately these ordinarily execute sequentially because of the reliance on a single seed. To mitigate this, PLINQ provides another overload of Aggregate that lets you specify multiple seeds — or rather, a seed factory function. For each thread, it executes this function to generate a separate seed, which becomes a thread-local accumulator into which it locally aggregates elements.
You must also supply a function to indicate how to combine the local and main accumulators. Finally, this Aggregate overload (somewhat gratuitously) expects a delegate to perform any final transformation on the result (you can achieve this as easily by running some function on the result yourself afterward). So, here are the four delegates, in the order they are passed:

seedFactory: Returns a new local accumulator
updateAccumulatorFunc: Aggregates an element into a local accumulator
combineAccumulatorFunc: Combines a local accumulator with the main accumulator
resultSelector: Applies any final transformation on the end result

In simple scenarios, you can specify a seed value instead of a seed factory. This tactic fails when the seed is a reference type that you wish to mutate, because the same instance will then be shared by each thread.

To give a very simple example, the following sums the values in a numbers array:

numbers.AsParallel().Aggregate (
  () => 0,                                     // seedFactory
  (localTotal, n) => localTotal + n,           // updateAccumulatorFunc
  (mainTot, localTot) => mainTot + localTot,   // combineAccumulatorFunc
  finalResult => finalResult)                  // resultSelector

This example is contrived in that we could get the same answer just as efficiently using simpler approaches (such as an unseeded aggregate, or better, the Sum operator). To give a more realistic example, suppose we wanted to calculate the frequency of each letter in the English alphabet in a given string. A simple sequential solution might look like this:

string text = "Let’s suppose this is a really long string";
var letterFrequencies = new int[26];
foreach (char c in text)
{
  int index = char.ToUpper (c) - 'A';
  if (index >= 0 && index <= 26) letterFrequencies [index]++;
};

An example of when the input text might be very long is in gene sequencing. The “alphabet” would then consist of the letters a, c, g, and t.

To parallelize this, we could replace the foreach statement with a call to Parallel.ForEach (as we’ll cover in the following section), but this will leave us to deal with concurrency issues on the shared array. And locking around accessing that array would all but kill the potential for parallelization.
Aggregate offers a tidy solution. The accumulator, in this case, is an array just like the letterFrequencies array in our preceding example. Here’s a sequential version using Aggregate:

int[] result =
  text.Aggregate (
    new int[26],                // Create the "accumulator"
    (letterFrequencies, c) =>   // Aggregate a letter into the accumulator
    {
      int index = char.ToUpper (c) - 'A';
      if (index >= 0 && index <= 26) letterFrequencies [index]++;
      return letterFrequencies;
    });

And now the parallel version, using PLINQ’s special overload:

int[] result =
  text.AsParallel().Aggregate (
    () => new int[26],             // Create a new local accumulator
 
    (localFrequencies, c) =>       // Aggregate into the local accumulator
    {
      int index = char.ToUpper (c) - 'A';
      if (index >= 0 && index <= 26) localFrequencies [index]++;
      return localFrequencies;
    },
                                   // Aggregate local->main accumulator
    (mainFreq, localFreq) =>
      mainFreq.Zip (localFreq, (f1, f2) => f1 + f2).ToArray(),
 
    finalResult => finalResult     // Perform any final transformation
  );                               // on the end result.

Notice that the local accumulation function mutates the localFrequencies array. This ability to perform this optimization is important — and is legitimate because localFrequencies is local to each thread.

The Parallel Class

PFX provides a basic form of structured parallelism via three static methods in the Parallel class:

Parallel.Invoke: Executes an array of delegates in parallel
Parallel.For: Performs the parallel equivalent of a C# for loop
Parallel.ForEach: Performs the parallel equivalent of a C# foreach loop

All three methods block until all work is complete. As with PLINQ, after an unhandled exception, remaining workers are stopped after their current iteration and the exception (or exceptions) are thrown back to the caller — wrapped in an AggregateException.

Parallel.Invoke

Parallel.Invoke executes an array of Action delegates in parallel, and then waits for them to complete. The simplest version of the method is defined as follows:

public static void Invoke (params Action[] actions);

Here’s how we can use Parallel.Invoke to download two web pages at once:

Parallel.Invoke (
 () => new WebClient().DownloadFile ("http://www.linqpad.net", "lp.html"),
 () => new WebClient().DownloadFile ("http://www.jaoo.dk", "jaoo.html"));

On the surface, this seems like a convenient shortcut for creating and waiting on two Task objects (or asynchronous delegates). But there’s an important difference: Parallel.Invoke still works efficiently if you pass in an array of a million delegates. This is because it partitions large numbers of elements into batches which it assigns to a handful of underlying Tasks — rather than creating a separate Task for each delegate.
As with all of Parallel’s methods, you’re on your own when it comes to collating the results. This means you need to keep thread safety in mind. The following, for instance, is thread-unsafe:

var data = new List<string>();
Parallel.Invoke (
 () => data.Add (new WebClient().DownloadString ("http://www.foo.com")),
 () => data.Add (new WebClient().DownloadString ("http://www.far.com")));

Locking around adding to the list would resolve this, although locking would create a bottleneck if you had a much larger array of quickly executing delegates. A better solution is to use a thread-safe collection such as ConcurrentBag would be ideal in this case.
Parallel.Invoke is also overloaded to accept a ParallelOptions object:

public static void Invoke (ParallelOptions options,
                           params Action[] actions);

With ParallelOptions, you can insert a cancellation token, limit the maximum concurrency, and specify a custom task scheduler. A cancellation token is relevant when you’re executing (roughly) more tasks than you have cores: upon cancellation, any unstarted delegates will be abandoned. Any already-executing delegates will, however, continue to completion. See Cancellation for an example of how to use cancellation tokens.

Parallel.For and Parallel.ForEach

Parallel.For and Parallel.ForEach perform the equivalent of a C# for and foreach loop, but with each iteration executing in parallel instead of sequentially. Here are their (simplest) signatures:

public static ParallelLoopResult For (
  int fromInclusive, int toExclusive, Action body)
 
public static ParallelLoopResult ForEach<TSource> (
  IEnumerable source, Action body)

The following sequential for loop:

for (int i = 0; i < 100; i++)
  Foo (i);

is parallelized like this:

Parallel.For (0, 100, i => Foo (i));

or more simply:

Parallel.For (0, 100, Foo);

And the following sequential foreach:

foreach (char c in "Hello, world")
  Foo (c);

is parallelized like this:

Parallel.ForEach ("Hello, world", Foo);

To give a practical example, if we import the System.Security.Cryptography namespace, we can generate six public/private key-pair strings in parallel as follows:

var keyPairs = new string[6];
 
Parallel.For (0, keyPairs.Length,
              i => keyPairs[i] = RSA.Create().ToXmlString (true));

As with Parallel.Invoke, we can feed Parallel.For and Parallel.ForEach a large number of work items and they’ll be efficiently partitioned onto a few tasks.

The latter query could also be done with PLINQ:

string[] keyPairs =
  ParallelEnumerable.Range (0, 6)
  .Select (i => RSA.Create().ToXmlString (true))
  .ToArray();

Outer versus inner loops

Parallel.For and Parallel.ForEach usually work best on outer rather than inner loops. This is because with the former, you’re offering larger chunks of work to parallelize, diluting the management overhead. Parallelizing both inner and outer loops is usually unnecessary. In the following example, we’d typically need more than 100 cores to benefit from the inner parallelization:

Parallel.For (0, 100, i =>
{
  Parallel.For (0, 50, j => Foo (i, j));   // Sequential would be better
});                                        // for the inner loop.

Indexed Parallel.ForEach

Sometimes it’s useful to know the loop iteration index. With a sequential foreach, it’s easy:

int i = 0;
foreach (char c in "Hello, world")
  Console.WriteLine (c.ToString() + i++);

Incrementing a shared variable, however, is not thread-safe in a parallel context. You must instead use the following version of ForEach:

public static ParallelLoopResult ForEach<TSource> (
  IEnumerable source, Action,long> body)

We’ll ignore ParallelLoopState (which we’ll cover in the following section). For now, we’re interested in Action’s third type parameter of type long, which indicates the loop index:

Parallel.ForEach ("Hello, world", (c, state, i) =>
{
   Console.WriteLine (c.ToString() + i);
});

To put this into a practical context, we’ll revisit the spellchecker that we wrote with PLINQ. The following code loads up a dictionary along with an array of a million words to test:

if (!File.Exists ("WordLookup.txt"))    // Contains about 150,000 words
  new WebClient().DownloadFile (
    "http://www.albahari.com/ispell/allwords.txt", "WordLookup.txt");
 
var wordLookup = new HashSet<string> (
  File.ReadAllLines ("WordLookup.txt"),
  StringComparer.InvariantCultureIgnoreCase);
 
var random = new Random();
string[] wordList = wordLookup.ToArray();
 
string[] wordsToTest = Enumerable.Range (0, 1000000)
  .Select (i => wordList [random.Next (0, wordList.Length)])
  .ToArray();
 
wordsToTest [12345] = "woozsh";     // Introduce a couple
wordsToTest [23456] = "wubsie";     // of spelling mistakes.

We can perform the spellcheck on our wordsToTest array using the indexed version of Parallel.ForEach as follows:

var misspellings = new ConcurrentBag<Tuple<int,string>>();
 
Parallel.ForEach (wordsToTest, (word, state, i) =>
{
  if (!wordLookup.Contains (word))
    misspellings.Add (Tuple.Create ((int) i, word));
});

Notice that we had to collate the results into a thread-safe collection: having to do this is the disadvantage when compared to using PLINQ. The advantage over PLINQ is that we avoid the cost of applying an indexed Select query operator — which is less efficient than an indexed ForEach.

ParallelLoopState: Breaking early out of loops

Because the loop body in a parallel For or ForEach is a delegate, you can’t exit the loop early with a break statement. Instead, you must call Break or Stop on a ParallelLoopState object:

public class ParallelLoopState
{
  public void Break();
  public void Stop();
 
  public bool IsExceptional { get; }
  public bool IsStopped { get; }
  public long? LowestBreakIteration { get; }
  public bool ShouldExitCurrentIteration { get; }
}

Obtaining a ParallelLoopState is easy: all versions of For and ForEach are overloaded to accept loop bodies of type Action. So, to parallelize this:

foreach (char c in "Hello, world")
  if (c == ',')
    break;
  else
    Console.Write (c);

do this:

Parallel.ForEach ("Hello, world", (c, loopState) =>
{
  if (c == ',')
    loopState.Break();
  else
    Console.Write (c);
});

Hlloe

You can see from the output that loop bodies may complete in a random order. Aside from this difference, calling Break yields at least the same elements as executing the loop sequentially: this example will always output at least the letters H, e, l, l, and o in some order. In contrast, calling Stop instead of Break forces all threads to finish right after their current iteration. In our example, calling Stop could give us a subset of the letters H, e, l, l, and o if another thread was lagging behind. Calling Stop is useful when you’ve found something that you’re looking for — or when something has gone wrong and you won’t be looking at the results.

The Parallel.For and Parallel.ForEach methods return a ParallelLoopResult object that exposes properties called IsCompleted and LowestBreakIteration. These tell you whether the loop ran to completion, and if not, at what cycle the loop was broken.
If LowestBreakIteration returns null, it means that you called Stop (rather than Break) on the loop.

If your loop body is long, you might want other threads to break partway through the method body in case of an early Break or Stop. You can do this by polling the ShouldExitCurrentIteration property at various places in your code; this property becomes true immediately after a Stop — or soon after a Break.

ShouldExitCurrentIteration also becomes true after a cancellation request — or if an exception is thrown in the loop.

IsExceptional lets you know whether an exception has occurred on another thread. Any unhandled exception will cause the loop to stop after each thread’s current iteration: to avoid this, you must explicitly handle exceptions in your code.

Optimization with local values

Parallel.For and Parallel.ForEach each offer a set of overloads that feature a generic type argument called TLocal. These overloads are designed to help you optimize the collation of data with iteration-intensive loops. The simplest is this:

public static ParallelLoopResult For <TLocal> (
  int fromInclusive,
  int toExclusive,
  Func <TLocal> localInit,  Func <int, ParallelLoopState, TLocal, TLocal> body,
  Action <TLocal> localFinally);

These methods are rarely needed in practice because their target scenarios are covered mostly by PLINQ (which is fortunate because these overloads are somewhat intimidating!).
Essentially, the problem is this: suppose we want to sum the square roots of the numbers 1 through 10,000,000. Calculating 10 million square roots is easily parallelizable, but summing their values is troublesome because we must lock around updating the total:

object locker = new object();
double total = 0;
Parallel.For (1, 10000000,
              i => { lock (locker) total += Math.Sqrt (i); });

The gain from parallelization is more than offset by the cost of obtaining 10 million locks — plus the resultant blocking.
The reality, though, is that we don’t actually need 10 million locks. Imagine a team of volunteers picking up a large volume of litter. If all workers shared a single trash can, the travel and contention would make the process extremely inefficient. The obvious solution is for each worker to have a private or “local” trash can, which is occasionally emptied into the main bin.
The TLocal versions of For and ForEach work in exactly this way. The volunteers are internal worker threads, and the local value represents a local trash can. In order for Parallel to do this job, you must feed it two additional delegates that indicate:

How to initialize a new local value
How to combine a local aggregation with the master value

Additionally, instead of the body delegate returning void, it should return the new aggregate for the local value. Here’s our example refactored:

object locker = new object();
double grandTotal = 0;
 
Parallel.For (1, 10000000,
 
  () => 0.0,                        // Initialize the local value.
 
  (i, state, localTotal) =>         // Body delegate. Notice that it
     localTotal + Math.Sqrt (i),    // returns the new local total.
 
  localTotal =>                                    // Add the local value
    { lock (locker) grandTotal += localTotal; }    // to the master value.
);

We must still lock, but only around aggregating the local value to the grand total. This makes the process dramatically more efficient.

As stated earlier, PLINQ is often a good fit in these scenarios. Our example could be parallelized with PLINQ simply like this:

ParallelEnumerable.Range(1, 10000000)
                  .Sum (i => Math.Sqrt (i))

(Notice that we used ParallelEnumerable to force range partitioning: this improves performance in this case because all numbers will take equally long to process.)
In more complex scenarios, you might use LINQ’s Aggregate operator instead of Sum. If you supplied a local seed factory, the situation would be somewhat analogous to providing a local value function with Parallel.For.

Task Parallelism

Task parallelism is the lowest-level approach to parallelization with PFX. The classes for working at this level are defined in the System.Threading.Tasks namespace and comprise the following:

Class	Purpose
`Task`	For managing a unit for work
`Task`	For managing a unit for work with a return value
`TaskFactory`	For creating tasks
`TaskFactory`	For creating tasks and continuations with the same return type
`TaskScheduler`	For managing the scheduling of tasks
TaskCompletionSource	For manually controlling a task’s workflow

Essentially, a task is a lightweight object for managing a parallelizable unit of work. A task avoids the overhead of starting a dedicated thread by using the CLR’s thread pool: this is the same thread pool used by ThreadPool.QueueUserWorkItem, tweaked in CLR 4.0 to work more efficiently with Tasks (and more efficiently in general).
Tasks can be used whenever you want to execute something in parallel. However, they’re tuned for leveraging multicores: in fact, the Parallel class and PLINQ are internally built on the task parallelism constructs.
Tasks do more than just provide an easy and efficient way into the thread pool. They also provide some powerful features for managing units of work, including the ability to:

Tune a task’s scheduling
Establish a parent/child relationship when one task is started from another
Implement cooperative cancellation
Wait on a set of tasks — without a signaling construct
Attach “continuation” task(s)
Schedule a continuation based on multiple antecedent tasks
Propagate exceptions to parents, continuations, and task consumers

Tasks also implement local work queues, an optimization that allows you to efficiently create many quickly executing child tasks without incurring the contention overhead that would otherwise arise with a single work queue.

The Task Parallel Library lets you create hundreds (or even thousands) of tasks with minimal overhead. But if you want to create millions of tasks, you’ll need to partition those tasks into larger work units to maintain efficiency. The Parallel class and PLINQ do this automatically.

Visual Studio 2010 provides a new window for monitoring tasks (Debug | Window | Parallel Tasks). This is equivalent to the Threads window, but for tasks. The Parallel Stacks window also has a special mode for tasks.

Creating and Starting Tasks

As we described in Part 1 in our discussion of thread pooling, you can create and start a Task by calling Task.Factory.StartNew, passing in an Action delegate:

Task.Factory.StartNew (() => Console.WriteLine ("Hello from a task!"));

The generic version, Task (a subclass of Task), lets you get data back from a task upon completion:

Task task = Task.Factory.StartNew<string> (() =>    // Begin task
{
  using (var wc = new System.Net.WebClient())
    return wc.DownloadString ("http://www.linqpad.net");
});
 
RunSomeOtherMethod();         // We can do other work in parallel...
 
string result = task.Result;  // Wait for task to finish and fetch result.

Task.Factory.StartNew creates and starts a task in one step. You can decouple these operations by first instantiating a Task object, and then calling Start:

var task = new Task (() => Console.Write ("Hello"));
...
task.Start();

A task that you create in this manner can also be run synchronously (on the same thread) by calling RunSynchronously instead of Start.

You can track a task’s execution status via its Status property.

Specifying a state object

When instantiating a task or calling Task.Factory.StartNew, you can specify a state object, which is passed to the target method. This is useful should you want to call a method directly rather than using a lambda expression:

static void Main()
{
  var task = Task.Factory.StartNew (Greet, "Hello");
  task.Wait();  // Wait for task to complete.
}
 
static void Greet (object state) { Console.Write (state); }   // Hello

Given that we have lambda expressions in C#, we can put the state object to better use, which is to assign a meaningful name to the task. We can then use the AsyncState property to query its name:

static void Main()
{
  var task = Task.Factory.StartNew (state => Greet ("Hello"), "Greeting");
  Console.WriteLine (task.AsyncState);   // Greeting
  task.Wait();
}
 
static void Greet (string message) { Console.Write (message); }

Visual Studio displays each task’s AsyncState in the Parallel Tasks window, so having a meaningful name here can ease debugging considerably.

TaskCreationOptions

You can tune a task’s execution by specifying a TaskCreationOptions enum when calling StartNew (or instantiating a Task). TaskCreationOptions is a flags enum with the following (combinable) values:

LongRunning
PreferFairness
AttachedToParent

LongRunning suggests to the scheduler to dedicate a thread to the task. This is beneficial for long-running tasks because they might otherwise “hog” the queue, and force short-running tasks to wait an unreasonable amount of time before being scheduled. LongRunning is also good for blocking tasks.

The task queuing problem arises because the task scheduler ordinarily tries to keep just enough tasks active on threads at once to keep each CPU core busy. Not oversubscribing the CPU with too many active threads avoids the degradation in performance that would occur if the operating system was forced to perform a lot of expensive time slicing and context switching.

PreferFairness tells the scheduler to try to ensure that tasks are scheduled in the order they were started. It may ordinarily do otherwise, because it internally optimizes the scheduling of tasks using local work-stealing queues. This optimization is of practical benefit with very small (fine-grained) tasks.
AttachedToParent is for creating child tasks.

Child tasks

When one task starts another, you can optionally establish a parent-child relationship by specifying TaskCreationOptions.AttachedToParent:

Task parent = Task.Factory.StartNew (() =>
{
  Console.WriteLine ("I am a parent");
 
  Task.Factory.StartNew (() =>        // Detached task
  {
    Console.WriteLine ("I am detached");
  });
 
  Task.Factory.StartNew (() =>        // Child task
  {
    Console.WriteLine ("I am a child");
  }, TaskCreationOptions.AttachedToParent);
});

A child task is special in that when you wait for the parent task to complete, it waits for any children as well. This can be particularly useful when a child task is a continuation, as we’ll see shortly.

Waiting on Tasks

You can explicitly wait for a task to complete in two ways:

Calling its Wait method (optionally with a timeout)
Accessing its Result property (in the case of Task)

You can also wait on multiple tasks at once — via the static methods Task.WaitAll (waits for all the specified tasks to finish) and Task.WaitAny (waits for just one task to finish).
WaitAll is similar to waiting out each task in turn, but is more efficient in that it requires (at most) just one context switch. Also, if one or more of the tasks throw an unhandled exception, WaitAll still waits out every task — and then rethrows a single AggregateException that accumulates the exceptions from each faulted task. It’s equivalent to doing this:

// Assume t1, t2 and t3 are tasks:
var exceptions = new List<Exception>();
try { t1.Wait(); } catch (AggregateException ex) { exceptions.Add (ex); }
try { t2.Wait(); } catch (AggregateException ex) { exceptions.Add (ex); }
try { t3.Wait(); } catch (AggregateException ex) { exceptions.Add (ex); }
if (exceptions.Count > 0) throw new AggregateException (exceptions);

Calling WaitAny is equivalent to waiting on a ManualResetEventSlim that’s signaled by each task as it finishes.
As well as a timeout, you can also pass in a cancellation token to the Wait methods: this lets you cancel the wait — not the task itself.

Exception-Handling Tasks

When you wait for a task to complete (by calling its Wait method or accessing its Result property), any unhandled exceptions are conveniently rethrown to the caller, wrapped in an AggregateException object. This usually avoids the need to write code within task blocks to handle unexpected exceptions; instead we can do this:

int x = 0;
Task calc = Task.Factory.StartNew (() => 7 / x);
try
{
  Console.WriteLine (calc.Result);
}
catch (AggregateException aex)
{
  Console.Write (aex.InnerException.Message);  // Attempted to divide by 0
}

You still need to exception-handle detached autonomous tasks (unparented tasks that are not waited upon) in order to prevent an unhandled exception taking down the application when the task drops out of scope and is garbage-collected (subject to the following note). The same applies for tasks waited upon with a timeout, because any exception thrown after the timeout interval will otherwise be “unhandled.”

The static TaskScheduler.UnobservedTaskException event provides a final last resort for dealing with unhandled task exceptions. By handling this event, you can intercept task exceptions that would otherwise end the application — and provide your own logic for dealing with them.

For parented tasks, waiting on the parent implicitly waits on the children — and any child exceptions then bubble up:

TaskCreationOptions atp = TaskCreationOptions.AttachedToParent;
var parent = Task.Factory.StartNew (() => 
{
  Task.Factory.StartNew (() =>   // Child
  {
    Task.Factory.StartNew (() => { throw null; }, atp);   // Grandchild
  }, atp);
});
 
// The following call throws a NullReferenceException (wrapped
// in nested AggregateExceptions):
parent.Wait();

Interestingly, if you check a task’s Exception property after it has thrown an exception, the act of reading that property will prevent the exception from subsequently taking down your application. The rationale is that PFX’s designers don’t want you ignoring exceptions — as long as you acknowledge them in some way, they won’t punish you by terminating your program.

An unhandled exception on a task doesn’t cause immediate application termination: instead, it’s delayed until the garbage collector catches up with the task and calls its finalizer. Termination is delayed because it can’t be known for certain that you don’t plan to call Wait or check its Result or Exception property until the task is garbage-collected. This delay can sometimes mislead you as to the original source of the error (although Visual Studio’s debugger can assist if you enable breaking on first-chance exceptions).

As we’ll see soon, an alternative strategy for dealing with exceptions is with continuations.

Canceling Tasks

You can optionally pass in a cancellation token when starting a task. This lets you cancel tasks via the cooperative cancellation pattern described previously:

var cancelSource = new CancellationTokenSource();
CancellationToken token = cancelSource.Token;
 
Task task = Task.Factory.StartNew (() => 
{
  // Do some stuff...
  token.ThrowIfCancellationRequested();  // Check for cancellation request
  // Do some stuff...
}, token);
...
cancelSource.Cancel();

To detect a canceled task, catch an AggregateException and check the inner exception as follows:

try 
{
  task.Wait();
}
catch (AggregateException ex)
{
  if (ex.InnerException is OperationCanceledException)
    Console.Write ("Task canceled!");
}

If you want to explicitly throw an OperationCanceledException (rather than calling token.ThrowIfCancellationRequested), you must pass the cancellation token into OperationCanceledException’s constructor. If you fail to do this, the task won’t end up with a TaskStatus.Canceled status and won’t trigger OnlyOnCanceled continuations.

If the task is canceled before it has started, it won’t get scheduled — an OperationCanceledException will instead be thrown on the task immediately.
Because cancellation tokens are recognized by other APIs, you can pass them into other constructs and cancellations will propagate seamlessly:

var cancelSource = new CancellationTokenSource();
CancellationToken token = cancelSource.Token;
 
Task task = Task.Factory.StartNew (() =>
{
  // Pass our cancellation token into a PLINQ query:
  var query = someSequence.AsParallel().WithCancellation (token)...
  ... enumerate query ...
});

Calling Cancel on cancelSource in this example will cancel the PLINQ query, which will throw an OperationCanceledException on the task body, which will then cancel the task.

The cancellation tokens that you can pass into methods such as Wait and CancelAndWait allow you to cancel the wait operation and not the task itself.

Continuations

Sometimes it’s useful to start a task right after another one completes (or fails). The ContinueWith method on the Task class does exactly this:

Task task1 = Task.Factory.StartNew (() => Console.Write ("antecedant.."));
Task task2 = task1.ContinueWith (ant => Console.Write ("..continuation"));

As soon as task1 (the antecedent) finishes, fails, or is canceled, task2 (the continuation) automatically starts. (If task1 had completed before the second line of code ran, task2 would be scheduled to execute right away.) The ant argument passed to the continuation’s lambda expression is a reference to the antecedent task.
Our example demonstrated the simplest kind of continuation, and is functionally similar to the following:

Task task = Task.Factory.StartNew (() =>
{
  Console.Write ("antecedent..");
  Console.Write ("..continuation");
});

The continuation-based approach, however, is more flexible in that you could first wait on task1, and then later wait on task2. This is particularly useful if task1 returns data.

Another (subtler) difference is that by default, antecedent and continuation tasks may execute on different threads. You can force them to execute on the same thread by specifying TaskContinuationOptions.ExecuteSynchronously when calling ContinueWith: this can improve performance in very fine-grained continuations by lessening indirection.

Continuations and Task

Just like ordinary tasks, continuations can be of type Task and return data. In the following example, we calculate Math.Sqrt(8*2) using a series of chained tasks and then write out the result:

Task.Factory.StartNew<int> (() => 8)
  .ContinueWith (ant => ant.Result * 2)
  .ContinueWith (ant => Math.Sqrt (ant.Result))
  .ContinueWith (ant => Console.WriteLine (ant.Result));   // 4

Our example is somewhat contrived for simplicity; in real life, these lambda expressions would call computationally intensive functions.

Continuations and exceptions

A continuation can find out if an exception was thrown by the antecedent via the antecedent task’s Exception property. The following writes the details of a NullReferenceException to the console:

Task task1 = Task.Factory.StartNew (() => { throw null; });
Task task2 = task1.ContinueWith (ant => Console.Write (ant.Exception));

If an antecedent throws and the continuation fails to query the antecedent’s Exception property (and the antecedent isn’t otherwise waited upon), the exception is considered unhandled and the application dies (unless handled by TaskScheduler.UnobservedTaskException).

A safe pattern is to rethrow antecedent exceptions. As long as the continuation is Waited upon, the exception will be propagated and rethrown to the Waiter:

Task continuation = Task.Factory.StartNew     (()  => { throw null; })
                                .ContinueWith (ant =>
  {
    if (ant.Exception != null) throw ant.Exception;    // Continue processing...
  });
 
continuation.Wait();    // Exception is now thrown back to caller.

Another way to deal with exceptions is to specify different continuations for exceptional versus nonexceptional outcomes. This is done with TaskContinuationOptions:

Task task1 = Task.Factory.StartNew (() => { throw null; });
 
Task error = task1.ContinueWith (ant => Console.Write (ant.Exception),
                                 TaskContinuationOptions.OnlyOnFaulted);
 
Task ok = task1.ContinueWith (ant => Console.Write ("Success!"),
                              TaskContinuationOptions.NotOnFaulted);

This pattern is particularly useful in conjunction with child tasks, as we’ll see very soon.
The following extension method “swallows” a task’s unhandled exceptions:

public static void IgnoreExceptions (this Task task)
{
  task.ContinueWith (t => { var ignore = t.Exception; },
    TaskContinuationOptions.OnlyOnFaulted);
}

(This could be improved by adding code to log the exception.) Here’s how it would be used:

Task.Factory.StartNew (() => { throw null; }).IgnoreExceptions();

Continuations and child tasks

A powerful feature of continuations is that they kick off only when all child tasks have completed. At that point, any exceptions thrown by the children are marshaled to the continuation.
In the following example, we start three child tasks, each throwing a NullReferenceException. We then catch all of them in one fell swoop via a continuation on the parent:

TaskCreationOptions atp = TaskCreationOptions.AttachedToParent;
Task.Factory.StartNew (() =>
{
  Task.Factory.StartNew (() => { throw null; }, atp);
  Task.Factory.StartNew (() => { throw null; }, atp);
  Task.Factory.StartNew (() => { throw null; }, atp);
})
.ContinueWith (p => Console.WriteLine (p.Exception),
                    TaskContinuationOptions.OnlyOnFaulted);

Conditional continuations

By default, a continuation is scheduled unconditionally — whether the antecedent completes, throws an exception, or is canceled. You can alter this behavior via a set of (combinable) flags included within the TaskContinuationOptions enum. The three core flags that control conditional continuation are:

NotOnRanToCompletion = 0x10000,
NotOnFaulted = 0x20000,
NotOnCanceled = 0x40000,

These flags are subtractive in the sense that the more you apply, the less likely the continuation is to execute. For convenience, there are also the following precombined values:

OnlyOnRanToCompletion = NotOnFaulted | NotOnCanceled,
OnlyOnFaulted = NotOnRanToCompletion | NotOnCanceled,
OnlyOnCanceled = NotOnRanToCompletion | NotOnFaulted

(Combining all the Not* flags [NotOnRanToCompletion, NotOnFaulted, NotOnCanceled] is nonsensical, as it would result in the continuation always being canceled.)
“RanToCompletion” means the antecedent succeeded — without cancellation or unhandled exceptions.
“Faulted” means an unhandled exception was thrown on the antecedent.
“Canceled” means one of two things:

The antecedent was canceled via its cancellation token. In other words, an OperationCanceledException was thrown on the antecedent — whose CancellationToken property matched that passed to the antecedent when it was started.
The antecedent was implicitly canceled because it didn’t satisfy a conditional continuation predicate.

It’s essential to grasp that when a continuation doesn’t execute by virtue of these flags, the continuation is not forgotten or abandoned — it’s canceled. This means that any continuations on the continuation itself will then run — unless you predicate them with NotOnCanceled. For example, consider this:

Task t1 = Task.Factory.StartNew (...);
 
Task fault = t1.ContinueWith (ant => Console.WriteLine ("fault"),
                              TaskContinuationOptions.OnlyOnFaulted);
 
Task t3 = fault.ContinueWith (ant => Console.WriteLine ("t3"));

As it stands, t3 will always get scheduled — even if t1 doesn’t throw an exception. This is because if t1 succeeds, the fault task will be canceled, and with no continuation restrictions placed on t3, t3 will then execute unconditionally.

If we want t3 to execute only if fault actually runs, we must instead do this:

Task t3 = fault.ContinueWith (ant => Console.WriteLine ("t3"),
                              TaskContinuationOptions.NotOnCanceled);

(Alternatively, we could specify OnlyOnRanToCompletion; the difference is that t3 would not then execute if an exception was thrown within fault.)

Continuations with multiple antecedents

Another useful feature of continuations is that you can schedule them to execute based on the completion of multiple antecedents. ContinueWhenAll schedules execution when all antecedents have completed; ContinueWhenAny schedules execution when one antecedent completes. Both methods are defined in the TaskFactory class:

var task1 = Task.Factory.StartNew (() => Console.Write ("X"));
var task2 = Task.Factory.StartNew (() => Console.Write ("Y"));
 
var continuation = Task.Factory.ContinueWhenAll (
  new[] { task1, task2 }, tasks => Console.WriteLine ("Done"));

This writes “Done” after writing “XY” or “YX”. The tasks argument in the lambda expression gives you access to the array of completed tasks, which is useful when the antecedents return data. The following example adds together numbers returned from two antecedent tasks:

// task1 and task2 would call complex functions in real life:
Task task1 = Task.Factory.StartNew (() => 123);
Task task2 = Task.Factory.StartNew (() => 456);
 
Task task3 = Task<int>.Factory.ContinueWhenAll (
  new[] { task1, task2 }, tasks => tasks.Sum (t => t.Result));
 
Console.WriteLine (task3.Result);           // 579

We’ve included the type argument in our call to Task.Factory in this example to clarify that we’re obtaining a generic task factory. The type argument is unnecessary, though, as it will be inferred by the compiler.

Multiple continuations on a single antecedent

Calling ContinueWith more than once on the same task creates multiple continuations on a single antecedent. When the antecedent finishes, all continuations will start together (unless you specify TaskContinuationOptions.ExecuteSynchronously, in which case the continuations will execute sequentially).
The following waits for one second, and then writes either “XY” or “YX”:

var t = Task.Factory.StartNew (() => Thread.Sleep (1000));
t.ContinueWith (ant => Console.Write ("X"));
t.ContinueWith (ant => Console.Write ("Y"));

Task Schedulers and UIs

A task scheduler allocates tasks to threads. All tasks are associated with a task scheduler, which is represented by the abstract TaskScheduler class. The Framework provides two concrete implementations: the default scheduler that works in tandem with the CLR thread pool, and the synchronization context scheduler. The latter is designed (primarily) to help you with the threading model of WPF and Windows Forms, which requires that UI elements and controls are accessed only from the thread that created them. For example, suppose we wanted to fetch some data from a web service in the background, and then update a WPF label called lblResult with its result. We can divide this into two tasks:

Call a method to get data from the web service (antecedent task).
Update lblResult with the results (continuation task).

If, for a continuation task, we specify the synchronization context scheduler obtained when the window was constructed, we can safely update lblResult:

public partial class MyWindow : Window
{
  TaskScheduler _uiScheduler;   // Declare this as a field so we can use
                                // it throughout our class.
  public MyWindow()
  {    
    InitializeComponent();
 
    // Get the UI scheduler for the thread that created the form:
    _uiScheduler = TaskScheduler.FromCurrentSynchronizationContext();
 
    Task.Factory.StartNew<string> (SomeComplexWebService)
      .ContinueWith (ant => lblResult.Content = ant.Result, _uiScheduler);
  }
 
  string SomeComplexWebService() { ... }
}

It’s also possible to write our own task scheduler (by subclassing TaskScheduler), although this is something you’d do only in very specialized scenarios. For custom scheduling, you’d more commonly use TaskCompletionSource, which we’ll cover soon.

TaskFactory

When you call Task.Factory, you’re calling a static property on Task that returns a default TaskFactory object. The purpose of a task factory is to create tasks — specifically, three kinds of tasks:

“Ordinary” tasks (via StartNew)
Continuations with multiple antecedents (via ContinueWhenAll and ContinueWhenAny)
Tasks that wrap methods that follow the asynchronous programming model (via FromAsync)

Interestingly, TaskFactory is the only way to achieve the latter two goals. In the case of StartNew, TaskFactory is purely a convenience and technically redundant in that you can simply instantiate Task objects and call Start on them.

Creating your own task factories

TaskFactory is not an abstract factory: you can actually instantiate the class, and this is useful when you want to repeatedly create tasks using the same (nonstandard) values for TaskCreationOptions, TaskContinuationOptions, or TaskScheduler. For example, if we wanted to repeatedly create long-running parented tasks, we could create a custom factory as follows:

var factory = new TaskFactory (
  TaskCreationOptions.LongRunning | TaskCreationOptions.AttachedToParent,
  TaskContinuationOptions.None);

Creating tasks is then simply a matter of calling StartNew on the factory:

Task task1 = factory.StartNew (Method1);
Task task2 = factory.StartNew (Method2);
...

The custom continuation options are applied when calling ContinueWhenAll and ContinueWhenAny.

TaskCompletionSource

The Task class achieves two distinct things:

It schedules a delegate to run on a pooled thread.
It offers a rich set of features for managing work items (continuations, child tasks, exception marshaling, etc.).

Interestingly, these two things are not joined at the hip: you can leverage a task’s features for managing work items without scheduling anything to run on the thread pool. The class that enables this pattern of use is called TaskCompletionSource.
To use TaskCompletionSource you simply instantiate the class. It exposes a Task property that returns a task upon which you can wait and attach continuations—just like any other task. The task, however, is entirely controlled by the TaskCompletionSource object via the following methods:

public class TaskCompletionSource<TResult>
{
  public void SetResult (TResult result);
  public void SetException (Exception exception);
  public void SetCanceled();
 
  public bool TrySetResult (TResult result);
  public bool TrySetException (Exception exception);
  public bool TrySetCanceled();
  ...
}

If called more than once, SetResult, SetException, or SetCanceled throws an exception; the Try* methods instead return false.

TResult corresponds to the task’s result type, so TaskCompletionSource gives you a Task. If you want a task with no result, create a TaskCompletionSource of object and pass in null when calling SetResult. You can then cast the Task to Task.

The following example prints 123 after waiting for five seconds:

var source = new TaskCompletionSource<int>();
 
new Thread (() => { Thread.Sleep (5000); source.SetResult (123); })
  .Start();
 
Task task = source.Task;      // Our "slave" task.
Console.WriteLine (task.Result);   // 123

Later on, we'll show how BlockingCollection can be used to write a producer/consumer queue. We then demonstrate how TaskCompletionSource improves the solution by allowing queued work items to be waited upon and canceled.

Working with AggregateException

As we’ve seen, PLINQ, the Parallel class, and Tasks automatically marshal exceptions to the consumer. To see why this is essential, consider the following LINQ query, which throws a DivideByZeroException on the first iteration:

try
{
  var query = from i in Enumerable.Range (0, 1000000)
              select 100 / i;
  ...
}
catch (DivideByZeroException)
{
  ...
}

If we asked PLINQ to parallelize this query and it ignored the handling of exceptions, a DivideByZeroException would probably be thrown on a separate thread, bypassing our catch block and causing the application to die.
Hence, exceptions are automatically caught and rethrown to the caller. But unfortunately, it’s not quite as simple as catching a DivideByZeroException. Because these libraries leverage many threads, it’s actually possible for two or more exceptions to be thrown simultaneously. To ensure that all exceptions are reported, exceptions are therefore wrapped in an AggregateException container, which exposes an InnerExceptions property containing each of the caught exception(s):

try
{
  var query = from i in ParallelEnumerable.Range (0, 1000000)
              select 100 / i;
  // Enumerate query
  ...
}
catch (AggregateException aex)
{
  foreach (Exception ex in aex.InnerExceptions)
    Console.WriteLine (ex.Message);
}

Both PLINQ and the Parallel class end the query or loop execution upon encountering the first exception — by not processing any further elements or loop bodies. More exceptions might be thrown, however, before the current cycle is complete. The first exception in AggregateException is visible in the InnerException property.

Flatten and Handle

The AggregateException class provides a couple of methods to simplify exception handling: Flatten and Handle.

Flatten

AggregateExceptions will quite often contain other AggregateExceptions. An example of when this might happen is if a child task throws an exception. You can eliminate any level of nesting to simplify handling by calling Flatten. This method returns a new AggregateException with a simple flat list of inner exceptions:

catch (AggregateException aex)
{
  foreach (Exception ex in aex.Flatten().InnerExceptions)
    myLogWriter.LogException (ex);
}

Handle

Sometimes it’s useful to catch only specific exception types, and have other types rethrown. The Handle method on AggregateException provides a shortcut for doing this. It accepts an exception predicate which it runs over every inner exception:

public void Handle (Func predicate)

If the predicate returns true, it considers that exception “handled.” After the delegate has run over every exception, the following happens:

If all exceptions were “handled” (the delegate returned true), the exception is not rethrown.
If there were any exceptions for which the delegate returned false (“unhandled”), a new AggregateException is built up containing those exceptions, and is rethrown.

For instance, the following ends up rethrowing another AggregateException that contains a single NullReferenceException:

var parent = Task.Factory.StartNew (() => 
{
  // We’ll throw 3 exceptions at once using 3 child tasks:
 
  int[] numbers = { 0 };
 
  var childFactory = new TaskFactory
   (TaskCreationOptions.AttachedToParent, TaskContinuationOptions.None);
 
  childFactory.StartNew (() => 5 / numbers[0]);   // Division by zero
  childFactory.StartNew (() => numbers [1]);      // Index out of range
  childFactory.StartNew (() => { throw null; });  // Null reference
});
 
try { parent.Wait(); }
catch (AggregateException aex)
{
  aex.Flatten().Handle (ex =>   // Note that we still need to call Flatten
  {
    if (ex is DivideByZeroException)
    {
      Console.WriteLine ("Divide by zero");
      return true;                           // This exception is "handled"
    }
    if (ex is IndexOutOfRangeException)
    {
      Console.WriteLine ("Index out of range");
      return true;                           // This exception is "handled"   
    }
    return false;    // All other exceptions will get rethrown
  });
}

Concurrent Collections

Framework 4.0 provides a set of new collections in the System.Collections.Concurrent namespace. All of these are fully thread-safe:

Concurrent collection	Nonconcurrent equivalent
`ConcurrentStack`	`Stack`
`ConcurrentQueue`	`Queue`
`ConcurrentBag`	(none)
BlockingCollection	(none)
`ConcurrentDictionary`	`Dictionary`

The concurrent collections can sometimes be useful in general multithreading when you need a thread-safe collection. However, there are some caveats:

The concurrent collections are tuned for parallel programming. The conventional collections outperform them in all but highly concurrent scenarios.
A thread-safe collection doesn’t guarantee that the code using it will be thread-safe.
If you enumerate over a concurrent collection while another thread is modifying it, no exception is thrown. Instead, you get a mixture of old and new content.
There’s no concurrent version of List.
The concurrent stack, queue, and bag classes are implemented internally with linked lists. This makes them less memory-efficient than the nonconcurrent Stack and Queue classes, but better for concurrent access because linked lists are conducive to lock-free or low-lock implementations. (This is because inserting a node into a linked list requires updating just a couple of references, while inserting an element into a List-like structure may require moving thousands of existing elements.)

In other words, these collections don’t merely provide shortcuts for using an ordinary collection with a lock. To demonstrate, if we execute the following code on a single thread:

var d = new ConcurrentDictionary<int,int>();
for (int i = 0; i < 1000000; i++) d[i] = 123;

it runs three times more slowly than this:

var d = new Dictionary<int,int>();
for (int i = 0; i < 1000000; i++) lock (d) d[i] = 123;

(Reading from a ConcurrentDictionary, however, is fast because reads are lock-free.)
The concurrent collections also differ from conventional collections in that they expose special methods to perform atomic test-and-act operations, such as TryPop. Most of these methods are unified via the IProducerConsumerCollection interface.

IProducerConsumerCollection

A producer/consumer collection is one for which the two primary use cases are:

Adding an element (“producing”)
Retrieving an element while removing it (“consuming”)

The classic examples are stacks and queues. Producer/consumer collections are significant in parallel programming because they’re conducive to efficient lock-free implementations.
The IProducerConsumerCollection interface represents a thread-safe producer/consumer collection. The following classes implement this interface:

ConcurrentStack<T>
ConcurrentQueue<T>
ConcurrentBag<T>

IProducerConsumerCollection extends ICollection, adding the following methods:

void CopyTo (T[] array, int index);
T[] ToArray();
bool TryAdd (T item);
bool TryTake (out T item);

The TryAdd and TryTake methods test whether an add/remove operation can be performed, and if so, they perform the add/remove. The testing and acting are performed atomically, eliminating the need to lock as you would around a conventional collection:

int result;
lock (myStack) if (myStack.Count > 0) result = myStack.Pop();

TryTake returns false if the collection is empty. TryAdd always succeeds and returns true in the three implementations provided. If you wrote your own concurrent collection that prohibited duplicates, however, you’d make TryAdd return false if the element already existed (an example would be if you wrote a concurrent set).
The particular element that TryTake removes is defined by the subclass:

With a stack, TryTake removes the most recently added element.
With a queue, TryTake removes the least recently added element.
With a bag, TryTake removes whatever element it can remove most efficiently.

The three concrete classes mostly implement the TryTake and TryAdd methods explicitly, exposing the same functionality through more specifically named public methods such as TryDequeue and TryPop.

ConcurrentBag

ConcurrentBag stores an unordered collection of objects (with duplicates permitted). ConcurrentBag is suitable in situations when you don’t care which element you get when calling Take or TryTake.
The benefit of ConcurrentBag over a concurrent queue or stack is that a bag’s Add method suffers almost no contention when called by many threads at once. In contrast, calling Add in parallel on a queue or stack incurs some contention (although a lot less than locking around a nonconcurrent collection). Calling Take on a concurrent bag is also very efficient — as long as each thread doesn’t take more elements than it Added.
Inside a concurrent bag, each thread gets it own private linked list. Elements are added to the private list that belongs to the thread calling Add, eliminating contention. When you enumerate over the bag, the enumerator travels through each thread’s private list, yielding each of its elements in turn.
When you call Take, the bag first looks at the current thread’s private list. If there’s at least one element, it can complete the task easily and (in most cases) without contention. But if the list is empty, it must “steal” an element from another thread’s private list and incur the potential for contention.
So, to be precise, calling Take gives you the element added most recently on that thread; if there are no elements on that thread, it gives you the element added most recently on another thread, chosen at random.
Concurrent bags are ideal when the parallel operation on your collection mostly comprises Adding elements — or when the Adds and Takes are balanced on a thread. We saw an example of the former previously, when using Parallel.ForEach to implement a parallel spellchecker:

var misspellings = new ConcurrentBag<Tuple<int,string>>();
 
Parallel.ForEach (wordsToTest, (word, state, i) =>
{
  if (!wordLookup.Contains (word))
    misspellings.Add (Tuple.Create ((int) i, word));
});

A concurrent bag would be a poor choice for a producer/consumer queue, because elements are added and removed by different threads.

BlockingCollection

If you call TryTake on any of the producer/consumer collections we discussed previously:

ConcurrentStack
ConcurrentQueue
ConcurrentBag

and the collection is empty, the method returns false. Sometimes it would be more useful in this scenario to wait until an element is available.
Rather than overloading the TryTake methods with this functionality (which would have caused a blowout of members after allowing for cancellation tokens and timeouts), PFX’s designers encapsulated this functionality into a wrapper class called BlockingCollection. A blocking collection wraps any collection that implements IProducerConsumerCollection and lets you Take an element from the wrapped collection — blocking if no element is available.
A blocking collection also lets you limit the total size of the collection, blocking the producer if that size is exceeded. A collection limited in this manner is called a bounded blocking collection.
To use BlockingCollection:

Instantiate the class, optionally specifying the IProducerConsumerCollection to wrap and the maximum size (bound) of the collection.
Call Add or TryAdd to add elements to the underlying collection.
3.Call Take or TryTake to remove (consume) elements from the underlying collection.

If you call the constructor without passing in a collection, the class will automatically instantiate a ConcurrentQueue. The producing and consuming methods let you specify cancellation tokens and timeouts. Add and TryAdd may block if the collection size is bounded; Take and TryTake block while the collection is empty.
Another way to consume elements is to call GetConsumingEnumerable. This returns a (potentially) infinite sequence that yields elements as they become available. You can force the sequence to end by calling CompleteAdding: this method also prevents further elements from being enqueued.
Previously, we wrote a producer/consumer queue using Wait and Pulse. Here’s the same class refactored to use BlockingCollection (exception handling aside):

public class PCQueue : IDisposable
{
  BlockingCollection<Action> _taskQ = new BlockingCollection<Action>(); 
  public PCQueue (int workerCount)
  {
    // Create and start a separate Task for each consumer:
    for (int i = 0; i < workerCount; i++)
      Task.Factory.StartNew (Consume);
  }
 
  public void Dispose() { _taskQ.CompleteAdding(); }
 
  public void EnqueueTask (Action action) { _taskQ.Add (action); }
 
  void Consume()
  {
    // This sequence that we’re enumerating will block when no elements
    // are available and will end when CompleteAdding is called. 
    foreach (Action action in _taskQ.GetConsumingEnumerable())
      action();     // Perform task.
  }
}

Because we didn’t pass anything into BlockingCollection’s constructor, it instantiated a concurrent queue automatically. Had we passed in a ConcurrentStack, we’d have ended up with a producer/consumer stack.
BlockingCollection also provides static methods called AddToAny and TakeFromAny, which let you add or take an element while specifying several blocking collections. The action is then honored by the first collection able to service the request.

Leveraging TaskCompletionSource

The producer/consumer that we just wrote is inflexible in that we can’t track work items after they’ve been enqueued. It would be nice if we could:

Know when a work item has completed.
Cancel an unstarted work item.
Deal elegantly with any exceptions thrown by a work item.

An ideal solution would be to have the EnqueueTask method return some object giving us the functionality just described. The good news is that a class already exists to do exactly this — the Task class. All we need to do is to hijack control of the task via TaskCompletionSource:

public class PCQueue : IDisposable
{
  class WorkItem
  {
    public readonly TaskCompletionSource TaskSource;
    public readonly Action Action;
    public readonly CancellationToken? CancelToken;
 
    public WorkItem (
      TaskCompletionSource taskSource,
      Action action,
      CancellationToken? cancelToken)
    {
      TaskSource = taskSource;
      Action = action;
      CancelToken = cancelToken;
    }
  }
 
  BlockingCollection<WorkItem> _taskQ = new BlockingCollection<WorkItem>();
 
  public PCQueue (int workerCount)
  {
    // Create and start a separate Task for each consumer:
    for (int i = 0; i < workerCount; i++)
      Task.Factory.StartNew (Consume);
  }
 
  public void Dispose() { _taskQ.CompleteAdding(); }
 
  public Task EnqueueTask (Action action) 
  {
    return EnqueueTask (action, null);
  }
 
  public Task EnqueueTask (Action action, CancellationToken? cancelToken)
  {

    var tcs = new TaskCompletionSource<object>();
    _taskQ.Add (new WorkItem (tcs, action, cancelToken));
    return tcs.Task;
  }
 
  void Consume()
  {
    foreach (WorkItem workItem in _taskQ.GetConsumingEnumerable())
      if (workItem.CancelToken.HasValue && 
          workItem.CancelToken.Value.IsCancellationRequested)
      {
        workItem.TaskSource.SetCanceled();
      }
      else
        try
        {
          workItem.Action();
          workItem.TaskSource.SetResult (null);   // Indicate completion
        }
        catch (Exception ex)
        {
          workItem.TaskSource.SetException (ex);
        }
  }
}

In EnqueueTask, we enqueue a work item that encapsulates the target delegate and a task completion source — which lets us later control the task that we return to the consumer.
In Consume, we first check whether a task has been canceled after dequeuing the work item. If not, we run the delegate and then call SetResult on the task completion source to indicate its completion.
Here’s how we can use this class:

var pcQ = new PCQueue (1);
Task task = pcQ.EnqueueTask (() => Console.WriteLine ("Easy!"));
...

We can now wait on task, perform continuations on it, have exceptions propagate to continuations on parent tasks, and so on. In other words, we’ve got the richness of the task model while, in effect, implementing our own scheduler.

SpinLock and SpinWait

In parallel programming, a brief episode of spinning is often preferable to blocking, as it avoids the cost of context switching and kernel transitions. SpinLock and SpinWait are designed to help in such cases. Their main use is in writing custom synchronization constructs.

SpinLock and SpinWait are structs and not classes! This design decision was an extreme optimization technique to avoid the cost of indirection and garbage collection. It means that you must be careful not to unintentionally copy instances — by passing them to another method without the ref modifier, for instance, or declaring them as readonly fields. This is particularly important in the case of SpinLock.

SpinLock

The SpinLock struct lets you lock without incurring the cost of a context switch, at the expense of keeping a thread spinning (uselessly busy). This approach is valid in high-contention scenarios when locking will be very brief (e.g., in writing a thread-safe linked list from scratch).

If you leave a spinlock contended for too long (we’re talking milliseconds at most), it will yield its time slice, causing a context switch just like an ordinary lock. When rescheduled, it will yield again — in a continual cycle of “spin yielding.” This consumes far fewer CPU resources than outright spinning — but more than blocking.
On a single-core machine, a spinlock will start “spin yielding” immediately if contended.

Using a SpinLock is like using an ordinary lock, except:

Spinlocks are structs (as previously mentioned).
Spinlocks are not reentrant, meaning that you cannot call Enter on the same SpinLock twice in a row on the same thread. If you violate this rule, it will either throw an exception (if owner tracking is enabled) or deadlock (if owner tracking is disabled). You can specify whether to enable owner tracking when constructing the spinlock. Owner tracking incurs a performance hit.
SpinLock lets you query whether the lock is taken, via the properties IsHeld and, if owner tracking is enabled, IsHeldByCurrentThread.
There’s no equivalent to C#'s lock statement to provide SpinLock syntactic sugar.

Another difference is that when you call Enter, you must follow the robust pattern of providing a lockTaken argument (which is nearly always done within a try/finally block).
Here’s an example:

var spinLock = new SpinLock (true);   // Enable owner tracking
bool lockTaken = false;
try
{
  spinLock.Enter (ref lockTaken);
  // Do stuff...
}
finally
{
  if (lockTaken) spinLock.Exit();
}

As with an ordinary lock, lockTaken will be false after calling Enter if (and only if) the Enter method throws an exception and the lock was not taken. This happens in very rare scenarios (such as Abort being called on the thread, or an OutOfMemoryException being thrown) and lets you reliably know whether to subsequently call Exit.
SpinLock also provides a TryEnter method which accepts a timeout.

Given SpinLock’s ungainly value-type semantics and lack of language support, it’s almost as if they want you to suffer every time you use it! Think carefully before dismissing an ordinary lock.

A SpinLock makes the most sense when writing your own reusable synchronization constructs. Even then, a spinlock is not as useful as it sounds. It still limits concurrency. And it wastes CPU time doing nothing useful. Often, a better choice is to spend some of that time doing something speculative — with the help of SpinWait.

SpinWait

SpinWait helps you write lock-free code that spins rather than blocks. It works by implementing safeguards to avoid the dangers of resource starvation and priority inversion that might otherwise arise with spinning.

Lock-free programming with SpinWait is as hardcore as multithreading gets and is intended for when none of the higher-level constructs will do. A prerequisite is to understand Nonblocking Synchronization.

Why we need SpinWait

Suppose we wrote a spin-based signaling system based purely on a simple flag:

bool _proceed;
void Test()
{
  // Spin until another thread sets _proceed to true:
  while (!_proceed) Thread.MemoryBarrier();
  ...
}

This would be highly efficient if Test ran when _proceed was already true — or if _proceed became true within a few cycles. But now suppose that _proceed remained false for several seconds — and that four threads called Test at once. The spinning would then fully consume a quad-core CPU! This would cause other threads to run slowly (resource starvation) — including the very thread that might eventually set _proceed to true (priority inversion). The situation is exacerbated on single-core machines, where spinning will nearly always cause priority inversion. (And although single-core machines are rare nowadays, single-core virtual machines are not.)
SpinWait addresses these problems in two ways. First, it limits CPU-intensive spinning to a set number of iterations, after which it yields its time slice on every spin (by calling Thread.Yield and Thread.Sleep), lowering its resource consumption. Second, it detects whether it’s running on a single-core machine, and if so, it yields on every cycle.

How to use SpinWait

There are two ways to use SpinWait. The first is to call its static method, SpinUntil. This method accepts a predicate (and optionally, a timeout):

bool _proceed;
void Test()
{
  SpinWait.SpinUntil (() => { Thread.MemoryBarrier(); return _proceed; });
  ...
}

The other (more flexible) way to use SpinWait is to instantiate the struct and then to call SpinOnce in a loop:

bool _proceed;
void Test()
{
  var spinWait = new SpinWait();
  while (!_proceed) { Thread.MemoryBarrier(); spinWait.SpinOnce(); }
  ...
}

The former is a shortcut for the latter.

How SpinWait works

In its current implementation, SpinWait performs CPU-intensive spinning for 10 iterations before yielding. However, it doesn’t return to the caller immediately after each of those cycles: instead, it calls Thread.SpinWait to spin via the CLR (and ultimately the operating system) for a set time period. This time period is initially a few tens of nanoseconds, but doubles with each iteration until the 10 iterations are up. This ensures some predictability in the total time spent in the CPU-intensive spinning phase, which the CLR and operating system can tune according to conditions. Typically, it’s in the few-tens-of-microseconds region — small, but more than the cost of a context switch.
On a single-core machine, SpinWait yields on every iteration. You can test whether SpinWait will yield on the next spin via the property NextSpinWillYield.
If a SpinWait remains in “spin-yielding” mode for long enough (maybe 20 cycles) it will periodically sleep for a few milliseconds to further save resources and help other threads progress.

Lock-free updates with SpinWait and Interlocked.CompareExchange

SpinWait in conjunction with Interlocked.CompareExchange can atomically update fields with a value calculated from the original (read-modify-write). For example, suppose we want to multiply field x by 10. Simply doing the following is not thread-safe:

x = x * 10;

for the same reason that incrementing a field is not thread-safe, as we saw in Nonblocking Synchronization.
The correct way to do this without locks is as follows:

Take a “snapshot” of x into a local variable.
Calculate the new value (in this case by multiplying the snapshot by 10).
Write the calculated value back if the snapshot is still up-to-date (this step must be done atomically by calling Interlocked.CompareExchange).
If the snapshot was stale, spin and return to step 1.

For example:

int x;
 
void MultiplyXBy (int factor)
{
  var spinWait = new SpinWait();
  while (true)
  {
    int snapshot1 = x;
    Thread.MemoryBarrier();
    int calc = snapshot1 * factor;
    int snapshot2 = Interlocked.CompareExchange (ref x, calc, snapshot1);
    if (snapshot1 == snapshot2) return;   // No one preempted us.
    spinWait.SpinOnce();
  }
}

We can improve performance (slightly) by doing away with the call to Thread.MemoryBarrier. We can get away with this because CompareExchange generates a memory barrier anyway — so the worst that can happen is an extra spin if snapshot1 happens to read a stale value in its first iteration.

Interlocked.CompareExchange updates a field with a specified value if the field’s current value matches the third argument. It then returns the field’s old value, so you can test whether it succeeded by comparing that against the original snapshot. If the values differ, it means that another thread preempted you, in which case you spin and try again.
CompareExchange is overloaded to work with the object type too. We can leverage this overload by writing a lock-free update method that works with all reference types:

static void LockFreeUpdate (ref T field, Func  updateFunction)
  where T : class
{
  var spinWait = new SpinWait();
  while (true)
  {
    T snapshot1 = field;
    T calc = updateFunction (snapshot1);
    T snapshot2 = Interlocked.CompareExchange (ref field, calc, snapshot1);
    if (snapshot1 == snapshot2) return;
    spinWait.SpinOnce();
  }
}

Here’s how we can use this method to write a thread-safe event without locks (this is, in fact, what the C# 4.0 compiler now does by default with events):

EventHandler _someDelegate;
public event EventHandler SomeEvent
{
  add    { LockFreeUpdate (ref _someDelegate, d => d + value); }
  remove { LockFreeUpdate (ref _someDelegate, d => d - value); }
}

SpinWait Versus SpinLock

We could solve these problems instead by wrapping access to the shared field around a SpinLock. The problem with spin locking, though, is that it allows only one thread to proceed at a time — even though the spinlock (usually) eliminates the context-switching overhead. With SpinWait, we can proceed speculativelyand assume no contention. If we do get preempted, we simply try again. Spending CPU time doing something that might work is better than wasting CPU time in a spinlock!

Finally, consider the following class:

class Test
{
  ProgressStatus _status = new ProgressStatus (0, "Starting");
 
  class ProgressStatus    // Immutable class
  {
    public readonly int PercentComplete;
    public readonly string StatusMessage;
 
    public ProgressStatus (int percentComplete, string statusMessage)
    {
      PercentComplete = percentComplete;
      StatusMessage = statusMessage;
    }
  }
}

We can use our LockFreeUpdate method to “increment” the PercentComplete field in _status as follows:

LockFreeUpdate (ref _status,
  s => new ProgressStatus (s.PercentComplete + 1, s.StatusMessage));

Notice that we’re creating a new ProgressStatus object based on existing values. Thanks to the LockFreeUpdate method, the act of reading the existing PercentComplete value, incrementing it, and writing it back can’t get unsafely preempted: any preemption is reliably detected, triggering a spin and retry.