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Functions with promises also have promise aggregation methods that allow the program to await multiple promises at once or in some special pattern (such as C#'s Task.WhenAll(), [1]: 174–175 [13]: 664–665 which returns a valueless Task that resolves when all of the tasks in the arguments have resolved). Many promise types also have ...
The goal here is to model processes that happen concurrently, like multiple clients accessing a server at the same time. Structuring software systems as composed of multiple concurrent, communicating parts can be useful for tackling complexity, regardless of whether the parts can be executed in parallel.
The terms future, promise, delay, and deferred are often used interchangeably, although some differences in usage between future and promise are treated below. Specifically, when usage is distinguished, a future is a read-only placeholder view of a variable, while a promise is a writable, single assignment container which sets the value of the ...
This is an accepted version of this page This is the latest accepted revision, reviewed on 23 February 2025. High-level programming language Not to be confused with Java (programming language), Javanese script, or ECMAScript. JavaScript Screenshot of JavaScript source code Paradigm Multi-paradigm: event-driven, functional, imperative, procedural, object-oriented Designed by Brendan Eich of ...
At any specific time, processes can be grouped into two categories: those that are waiting for input or output (called "I/O bound"), and those that are fully utilizing the CPU ("CPU bound"). In primitive systems, the software would often "poll", or "busywait" while waiting for requested input (such as disk, keyboard or network input). During ...
Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. [1] [2] It is also the name of the resulting set of database transaction schedules (histories).
Discrete time views values of variables as occurring at distinct, separate "points in time", or equivalently as being unchanged throughout each non-zero region of time ("time period")—that is, time is viewed as a discrete variable. Thus a non-time variable jumps from one value to another as time moves from one time period to the next.