Build and Dependency Management

Most projects these days are written in high-level languages that do not require compilation. This simplifies things, but it can still be complicated to package your software in a way that makes it easy for another person to install in on their computer from scratch.

C/C++

If you have a multi-file project coded natively in C/C++, using a tool to help other build code is a necessity. Below is a quick summary, but more detailed information is here.

• The Big Picture
  • No single standard: C/C++ has the most fragmented build ecosystem of any major language
  • CMake dominates: ~60-70% of modern C++ projects use CMake despite its complexity
  • Context matters: The “best” build system depends heavily on project size, platform needs, and team experience
• Meta-build vs. Direct
  • Meta-build systems (CMake, Meson, Premake): Generate native build files
  • Direct systems (Make, Ninja, Bazel): Execute builds themselves
• Quick Recommendations
  • Small/medium projects: Use Meson (simpler) or CMake (more ecosystem support)
  • Large monorepos: Use Bazel if you need extreme scale
  • Cross-platform GUI apps: Use CMake for best IDE integration
• Managing Dependencies
  • Build systems traditionally only build code
  • Dependency management is separate (Conan, vcpkg, system packages)
  • Unlike Python/Julia/R, no unified solution for both
  • Emerging trend: Tools like build2 and xmake integrate both
  • Linux
    • The native package manager is usually sufficient for installing dependencies.
    • Docker can help to easily create an isolated, clean Linux environment.
    • Conda with msys2-bash is a nice alternative for using Unix tools in Windows/OSX.
    • [`linuxbrew’] also provides a way of installing dependencies thst is distribution-independent
  • Windows: Conda with msys2-bash is a nice alternative for using Unix tools in Windows/OSX.
  • OS/X: Homebrew

• When to Use What

  Build System	Best For	Avoid If
  CMake	Cross-platform, ecosystem support, industry standard	You want simple syntax, small hobby projects
  Meson	New projects, fast builds, clean syntax	Need Windows-specific features, established CMake workflow
  Make	Simple Unix projects, understanding legacy code	Cross-platform needs, large projects
  Bazel	Monorepos, massive scale, Google-like setups	Small teams, simple projects, Windows-primary
  Autotools	Traditional Unix distribution	Modern projects, Windows support, maintainability
  xmake/Premake	Game development, Lua enthusiasts	Need mature ecosystem, enterprise support

Python

Highlights of Python’s dependency mechanism are here. More details available here.

• Fragmented Ecosystem
  • Multiple competing standards: requirements.txt, setup.py, pyproject.toml, Pipfile, etc.
  • No built-in solution: Unlike Julia, Python requires third-party tools for advanced dependency management
  • Evolution over time: The ecosystem has gradually moved from setup.py → pyproject.toml
• Modern Best Practices
  • pyproject.toml: Standardized format (PEP 518, 621) for project configuration and dependencies
  • Lock files: Poetry, Pipenv, or pip-tools provide deterministic, reproducible builds
  • Virtual environments: venv or virtualenv for project isolation (separate from dependency specification)
• Tool Landscape
  • pip + requirements.txt: Simple, ubiquitous, but lacks advanced features
  • Poetry: All-in-one solution with dependency resolution, virtual env management, and publishing
  • Pipenv: Combines Pipfile + lock file with virtual environment management
  • pip-tools: Minimalist approach to generate lock files from requirements.in
• Version Specification
  • Rich syntax: ==, >=, ~=, != with multiple constraints
  • Dependency resolution: Third-party tools handle complex version conflicts
  • Optional dependencies: extras_require or optional-dependencies for feature-specific packages
• Key Differences from Other Languages
  • Not built into the language: Requires external package manager (pip) and tools
  • Separation of concerns: Virtual environments (isolation) vs. dependency files (specification)
  • Massive ecosystem: PyPI hosts 500,000+ packages with minimal curation
  • Ongoing standardization: Community still converging on best practicesPython offers several methods for specifying project dependencies, each suited for different use cases:

Julia

Highlights of Python’s dependency mechanism are here. More details available here.

• Built-in and Unified
  • Native package manager (Pkg): Fully integrated into Julia, no external tools needed
  • Single standard: No fragmentation—everyone uses the same system
  • Batteries included: Dependency resolution, environments, and versioning work out of the box
• Two-File System
  • Project.toml: Human-readable file specifying direct dependencies and compatibility
  • Manifest.toml: Auto-generated lock file with complete dependency graph and exact versions
  • Both committed to version control: Ensures reproducibility across machines
• Sophisticated Resolution
  • Automatic conflict resolution: Built-in solver handles complex dependency graphs
  • Semantic versioning: First-class support with [compat] specifications
  • Transitive dependencies: Automatically resolved and tracked in Manifest.toml
• UUID-Based Identification
  • No naming conflicts: Packages identified by UUID, not just name
  • Multiple registries: Can use packages from different sources without collision
  • Global uniqueness: UUIDs prevent the “namespace problem”
• Environment Management
  • Project-specific environments: Easy switching with Pkg.activate()
  • Isolated by default: Each project can have different package versions
  • No separate tool needed: Unlike Python’s venv/virtualenv, it’s built into Pkg
• Developer Experience
  • Precompilation: Packages are precompiled for faster loading
  • Development mode: Pkg.develop() for working on packages locally
  • Integrated testing: Pkg.test() runs package tests in isolated environments
• Key Advantages
  • Zero configuration: Works perfectly without setup or config files
  • Reproducible by default: Lock files are generated automatically
  • Scientific computing focus: Designed with reproducibility in mind from day one
  • Fast and efficient: Modern resolver is quick even with large dependency trees

R

Highlights of R’s dependency mechanism are here. More details available here.

• Built-in Tools
  • Simple but limited: install.packages() and library() work globally without isolation
  • Automatic dependency installation: Dependencies listed in Imports and Depends are installed automatically
  • No sophisticated version resolution: Uses latest compatible versions from repositories
• Modern Standard: renv
  • Project-specific libraries: Each project has isolated package installations
  • Reproducible environments: renv.lock captures exact package versions
  • Global package cache: Saves disk space by sharing packages across projects
  • Simple workflow: init(), snapshot(), restore() cover most use cases
• Package Repositories
  • CRAN: Primary repository with ~20,000 packages and strict quality control
  • Bioconductor: Specialized repository for bioinformatics packages
  • GitHub: Access to development versions and custom packages
• For Package Development
  • DESCRIPTION file: Specifies Imports, Suggests, and Depends
  • devtools integration: Streamlined development workflow
  • Version specifications: Support for minimum versions and ranges
• Differences from Python/Julia
  • Environment isolation was added later (not built-in from the start)
  • Less sophisticated dependency resolution compared to Julia’s Pkg
  • More centralized ecosystem (CRAN curation vs. PyPI’s open model)
  • renv fills the role of Python’s Poetry or Julia’s built-in Pkg