Today, everyone wants to protect their future as life is full of uncertainties.
For example, if you Wait on a ManualResetEventSlim and specify a cancellation token, another thread can Cancel its wait. This is much tidier and safer than calling Interrupt on the blocked thread. A common problem in threading is how to lazily initialize a shared field in a thread-safe fashion.
The obvious answer is to construct the instance on demand: As this may lead to subtle errors, we would say, in general, that this code is not thread-safe. The solution to the problem is to lock around checking and initializing the object: If instantiated with an argument of true, it implements the thread-safe initialization pattern just described.
Double-checked locking performs an additional volatile read to avoid the cost of obtaining a lock if the object is already initialized. Then access its value via the Value property: Its functionality is exposed through a static method that operates directly on a field in your own type.
This avoids a level of indirection, improving performance in cases where you need extreme optimization. It offers another mode of initialization that has multiple threads race to initialize. To use LazyInitializer, call EnsureInitialized before accessing the field, passing a reference to the field and the factory delegate: This sounds similar to our original thread-unsafe example, except that the first thread to finish always wins — and so you end up with only one instance.
This is an extreme optimization that you rarely need, and one that comes at a cost: It potentially wastes CPU resources performing redundant initialization. Sometimes, however, you want to keep data isolated, ensuring that each thread has a separate copy.
Local variables achieve exactly this, but they are useful only with transient data.
The solution is thread-local storage. You might be hard-pressed to think of a requirement: Passing such data around in method parameters is extremely clumsy and alienates all but your own methods; storing such information in ordinary static fields means sharing it among all threads.
Thread-local storage can also be useful in optimizing parallel code. It allows each thread to exclusively access its own version of a thread-unsafe object without needing locks — and without needing to reconstruct that object between method calls.
There are three ways to implement thread-local storage. The easiest approach to thread-local storage is to mark a static field with the ThreadStatic attribute: It provides thread-local storage for both static and instance fields — and allows you to specify default values.
A bonus of using ThreadLocal is that values are lazily evaluated: For example, consider the problem of generating random numbers in a multithreaded environment.
The Random class is not thread-safe, so we have to either lock around using Random limiting concurrency or generate a separate Random object for each thread. SetData writes to it. Both methods require a LocalDataStoreSlot object to identify the slot. In this instance, we called Thread.
GetNamedDataSlot, which creates a named slot — this allows sharing of that slot across the application. FreeNamedDataSlot will release a named data slot across all threads, but only once all references to that LocalDataStoreSlot have dropped out of scope and have been garbage-collected.
If you need to execute some method repeatedly at regular intervals, the easiest way is with a timer. Timers are convenient and efficient in their use of memory and resources — compared with techniques such as the following: Start ; Not only does this permanently tie up a thread resource, but without additional coding, DoSomeAction will happen at a later time each day.
Timers solve these problems. NET Framework provides four timers. Two of these are general-purpose multithreaded timers: Timer The other two are special-purpose single-threaded timers:Patterns of Enterprise Application Architecture [Martin Fowler] on initiativeblog.com *FREE* shipping on qualifying offers.
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Most of the examples quoted for usage of Dependency Injection, we can solve using the factory pattern as well. Looks like when it comes to usage/design the difference between dependency injection and factory is blurred or thin.
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