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In software engineering, double-checked locking (also known as "double-checked locking optimization" [1]) is a software design pattern used to reduce the overhead of acquiring a lock by testing the locking criterion (the "lock hint") before acquiring the lock. Locking occurs only if the locking criterion check indicates that locking is required.
First, the async keyword indicates to C# that the method is asynchronous, meaning that it may use an arbitrary number of await expressions and will bind the result to a promise. [1]: 165–168 The return type, Task<T>, is C#'s analogue to the concept of a promise, and here is indicated to have a result value of type int.
A mutex is a locking mechanism that sometimes uses the same basic implementation as the binary semaphore. However, they differ in how they are used. While a binary semaphore may be colloquially referred to as a mutex, a true mutex has a more specific use-case and definition, in that only the task that locked the mutex is supposed to unlock it ...
lock contention: this occurs whenever one process or thread attempts to acquire a lock held by another process or thread. The more fine-grained the available locks, the less likely one process/thread will request a lock held by the other. (For example, locking a row rather than the entire table, or locking a cell rather than the entire row);
In multithreaded computer programming, asynchronous method invocation (AMI), also known as asynchronous method calls or the asynchronous pattern is a design pattern in which the call site is not blocked while waiting for the called code to finish. Instead, the calling thread is notified when the reply arrives.
C#, since .NET Framework 4.5, [22] via the keywords async and await [23] Kotlin, however kotlin.native.concurrent.Future is only usually used when writing Kotlin that is intended to run natively [35] Nim; Oxygene; Oz version 3 [36] Python concurrent.futures, since 3.2, [37] as proposed by the PEP 3148, and Python 3.5 added async and await [38]
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Theoretically, the worst case space and time complexity of n concurrent transactions is O(n). Actual needs depend on implementation details (one can make transactions fail early enough to avoid overhead), but there will also be cases, albeit rare, where lock-based algorithms have better time complexity than software transactional memory.