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Circular dependencies can cause many unwanted effects in software programs. Most problematic from a software design point of view is the tight coupling of the mutually dependent modules which reduces or makes impossible the separate re-use of a single module.
Dependency hell is a colloquial term for the frustration of some software users who have installed software packages which have dependencies on specific versions of other software packages. [ 1 ] The dependency issue arises when several packages have dependencies on the same shared packages or libraries, but they depend on different and ...
Software dependencies can either be explicit or implicit. Examples of explicit dependencies includes: Include statements, such as #include in C/C++, using in C# and import in Java. Dependencies stated in the build system (e.g. dependency tags in Maven configuration). Examples of implicit dependencies includes: [3]
32 or 64 bits add,shift,xor MurmurHash: 32, 64, or 128 bits product/rotation Fast-Hash [3] 32 or 64 bits xorshift operations SpookyHash 32, 64, or 128 bits see Jenkins hash function: CityHash [4] 32, 64, 128, or 256 bits FarmHash [5] 32, 64 or 128 bits MetroHash [6] 64 or 128 bits numeric hash (nhash) [7] variable division/modulo xxHash [8] 32 ...
In object-oriented design, the dependency inversion principle is a specific methodology for loosely coupled software modules.When following this principle, the conventional dependency relationships established from high-level, policy-setting modules to low-level, dependency modules are reversed, thus rendering high-level modules independent of the low-level module implementation details.
An early package manager was SMIT (and its backend installp) from IBM AIX. SMIT was introduced with AIX 3.0 in 1989. [citation needed]Early package managers, from around 1994, had no automatic dependency resolution [3] but could already drastically simplify the process of adding and removing software from a running system.
In computing, reactive programming is a declarative programming paradigm concerned with data streams and the propagation of change. With this paradigm, it is possible to express static (e.g., arrays) or dynamic (e.g., event emitters) data streams with ease, and also communicate that an inferred dependency within the associated execution model exists, which facilitates the automatic propagation ...
When using update coalescing with an appropriate treatment of new objects, more than 99% of the counter updates are eliminated for typical Java benchmarks. Interestingly, update coalescing also eliminates the need to employ atomic operations during pointer updates in a concurrent setting, this solving reference counting issues in a concurrent ...