When should you prefer App::cpanminus (cpanm), and when should you avoid it?

App::cpanminus (also known as cpanm) is a popular Perl module management tool that simplifies the installation of Perl modules. It prioritizes ease of use and quick installations. However, there are scenarios where choosing cpanm may not be ideal.
cpanminus, perl module management, easy installations, cpanm advantages, cpanm disadvantages

Here are some scenarios to consider:

When to Prefer App::cpanminus (cpanm)

  • Simple Installations: If you need to quickly install a module without dealing with dependencies and configurations, cpanm is a great choice.
  • Local::Lib Use: cpanm can easily work with local Perl environments without requiring root access, making it ideal for shared hosting environments.
  • Minimal Configuration: cpanm doesn’t require extensive configuration, making it suitable for users who want a hassle-free experience.
  • Fast Installation: cpanm is generally faster than traditional CPAN because it uses pre-compiled packages when available.

When to Avoid App::cpanminus (cpanm)

  • Complex Dependencies: In projects that rely on extensive dependencies, manually tracking and managing them may be better suited to CPAN, especially with its dependency resolution mechanisms.
  • Custom Builds: If you need to apply specific build options or configurations, traditional CPAN gives you more control over the installation process.
  • Corporate or Controlled Environments: For strict environments where compliance and logging of installations are required, traditional methods may be favored.
  • Legacy Systems: Older systems or environments that depend on system Perl installations may not work well with cpanm.

Example of using cpanm to install a Perl module:

cpanm Some::Module

cpanminus perl module management easy installations cpanm advantages cpanm disadvantages

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?