Semantic Versioning 2.0.0 – Semantic Versioning

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  • semver

    Semantic Versioning Specification

  • libc

    Raw bindings to platform APIs for Rust

  • Even if coexistence of multiple library versions is supported, there are scenarios where things break.

    For example, the "libc apocalypse" situation in Rust https://github.com/rust-lang/libc/issues/547

    Here after the "libc" module released a major version, the definition for the `void` C type in two versions of the lib are considered by the compiler as two different types, resulting in breakages everywhere around the library ecosystem.

    There are also scenarios for dynamic languages / runtime errors in statically typed languages.

    My main problem with the current SemVer spec, is that it does not mention multiple lib versions problem, and promises the dependency hell issues can be solved simply by updating major version number. Thus encouraging to break backward compatibility freely.

    Also note, it's not the case that SemVer is intended only for languages supporting multiple library versions. The SemVer is a product of Ruby community, and Ruby has a global namespace for classes and unable to have several versions of a lib simultaneously.

    In 2000s they were breaking compatibility left and right, neglecting elementary compatibility practices. If you were working on an application, practically every time when you update dependencies, something would break.

    So (in 2011 ?) they came out with this "manifesto" (Why such a big name? This scheme of versioning was well established in linkers and sonames of all Unix-like systems for decades - it goes back to at least 1987 paper "Shared Libraries in SunOS").

    It's a good thing SemVer acknowledges finally that compatibility is a serious matter. Only that it's better to discourage compatibility breakages. An in cases when it's really needed (I agree such cases exists), there are things to take care of in addition to simply increase major version num.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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