Demystifying Build Systems: A Comprehensive Framework for Understanding the Foundation of Software Development

In the intricate world of software development, build systems serve as the unsung heroes that transform source code into functional applications. Yet, despite their critical role, many developers operate with only a partial understanding of how these systems work under the hood. Andrew Nesbitt's recent post attempts to remedy this by providing a systematic vocabulary and conceptual framework for understanding build systems in all their complexity.

The Big Picture

At its core, a build system is a tool or library that defines and executes a series of transformations from input data to output data, memoizing these transformations in an object store. These transformations, called steps or rules, define how to generate zero or more outputs from zero or more inputs.

Rules form the fundamental unit of caching in build systems, with cache points typically aligned with rule outputs. When a rule's dependencies change—either directly or transitively—the outputs become "dirty" or "stale," invalidating the cache and requiring rebuilding. This dependency tracking creates a directed graph structure, where circular dependencies are generally prohibited.

Build systems operate through various invocation patterns:
- A full build occurs when the cache is empty and all transformations are executed
- An incremental build happens when the cache is partially full but some outputs are outdated
- A clean build involves deleting the cache entirely

The quality of a build system is measured by its correctness (soundness) and efficiency. A build is sound if all possible incremental builds produce the same result as a full build. It's minimal if rules are rerun at most once per build and only when necessary.

Specifying Dependencies

Build systems handle dependencies in two primary ways: inter-process and intra-process. In inter-process builds, tasks typically involve single process executions with file inputs and outputs. Intra-process builds, conversely, involve function calls with arguments and return values.

Dependency tracking can occur through:
1. Declaration: All inputs and outputs specified in advance
2. Inference: Dependencies determined from runtime behavior (tracing)

Tracing, the process of inferring dependencies from execution, presents unique challenges. When a traced rule depends on an unbuilt dependency, the build system may error, suspend the task, or abort and restart it later. While inter-process builds often declare dependencies and intra-process builds often infer them, this isn't a strict dichotomy.

Architectural Patterns: Applicative vs. Monadic

Build graphs can be classified based on when their components are known:

An applicative build graph has all inputs, outputs, and rules declared ahead of time, making the graph statically known. Purely applicative systems are rare, with most providing some form of escape hatch.

A monadic build graph allows outputs to be unknown at runtime or enables rules to generate other rules dynamically. Inputs not known ahead of time are called dynamic dependencies. Build systems that don't require declaring rules are inherently monadic.

This architectural distinction has practical implications:
- Applicative systems: Make (with recursive make disallowed), Bazel (excluding native rules)
- Monadic systems: Shake, ninja dyndeps, Cargo build scripts

Optimizing Builds: Early Cutoff and Rebuild Detection

When a dirty rule reruns and produces an output matching the previous version, build systems can optimize by avoiding execution of dependent rules—a technique called early cutoff. This optimization is particularly valuable in large codebases where rebuilds can be time-consuming.

Not all build systems accurately detect when rebuilds are necessary. In unsound systems, developers may need to force-rerun targets, often by modifying file timestamps to invalidate the cache.

The Build Executor

At the heart of every build system lies the executor, responsible for:
- Scheduling tasks in dependency-respecting order
- Detecting when inputs have changed (rebuild detection)
- Managing task suspension or restart in systems that support it
- Providing progress reporting and dependency graph querying

Executors may also trace inputs to verify they match declared dependencies or to automatically expand the dependency graph.

Inter-Process Builds: The Traditional Approach

In inter-process builds, artifacts are output files generated by rules. The build process involves several key components:

• Source files: Input files specific to the current project
• Build files: Containing rule definitions, input/output declarations, and metadata
• Environment: All inputs not classified as source files or command-line arguments

Inputs are typically categorized as:
- Explicit: Passed directly to the spawned process
- Implicit: Tracked by the build system but not used in the task definition
- Order-only: Must exist before execution but don't invalidate the cache when modified

Processes depend on more than just files—they also rely on environment variables, working directory, current time, and potentially network services or local daemons. System dependencies further expand this scope, including compilers, language libraries, runtime environments, and system configuration files.

The concepts of sandboxing (restricting process access) and hermeticity (eliminating external dependencies) represent orthogonal approaches to build isolation. For example:
- Docker builds are sandboxed but not hermetic
- Nix shells are hermetic but not sandboxed

Determinism and Reproducibility

A build is deterministic if it produces the same output repeatedly in a specific environment. It's reproducible if it maintains this consistency across different environments, provided system dependencies remain unchanged.

Determinism is crucial for debugging and verification, while reproducibility enables collaboration across different development environments. Achieving both requires careful management of all potential sources of variation.

Remote Caching and Shallow Builds

Caching can occur locally or remotely. Remote caching presents unique challenges—it's generally unsound unless builds are both hermetic and reproducible. Remote caches typically use content-addressed hashing in key-value stores to identify artifacts.

Materialization—downloading files from the remote cache—is often deferred as long as possible in large build graphs. Builds where the cache is never fully materialized are called "shallow builds," offering significant performance benefits for complex projects.

Interface Design: Targets and Meta-Build Systems

Build systems typically provide interfaces to run specific portions of the build. The identifiers used to specify which parts to execute are called targets, which may be filenames or abstract rule names. Different build systems have different conventions:
- Bazel-descended systems use "labels"
- Make-descended systems use "phony targets"
- Some systems like Cargo use subcommands with arguments

Many inter-process build systems separate into configuration and build steps. Systems that only run the configuration step, requiring another tool for building, are called meta-build systems. Examples include CMake, Meson, and Autotools, which discover rules, serialize them into action graphs, and allow configuration flags to affect generated rules.

Advanced Concepts: VFS and Beyond

Sophisticated build systems integrate with virtual file systems (VFS) to check out source control files on-demand rather than eagerly. This approach, exemplified by systems like EdenFS, optimizes performance by only retrieving files when needed.

Intra-Process Builds: A Different Paradigm

Intra-process builds operate within a single process, with dependencies manifesting as non-local state including environment variables, globals, and thread-locals. These systems face unique challenges:
- Function calls involving inter-process communication (IPC) are rarely cacheable
- Tracing dependencies is complex due to implicit state access
- Most object stores operate as in-memory caches

Persistence—the ability to save caches to disk—adds another layer of complexity. Systems like Salsa implement specialized database-like mechanisms for this purpose.

Tracing and Query Systems

Tracing intra-process build systems, sometimes called query systems, operate similarly to their inter-process counterparts. They track function call relationships to determine which functions need rerunning. The Rust compiler's query system exemplifies this approach.

Functional Reactive Programming Connections

Intra-process build systems with explicit dependency declarations often draw from functional reactive programming (FRP) traditions. While commonly associated with UI and frontend design, FRP principles share deep connections with build system architectures.

Unlike traditional build systems, FRP libraries allow examination of past output versions—a concept sometimes called "remembering state." To simplify reasoning about these systems, rules can be written as event handlers.

Beyond Traditional Systems: The Broad Definition of Build Systems

The definition of a build system extends far beyond traditional compilation tools. Any system that allows specifying dependencies on previous artifacts qualifies:
- GitHub Actions (jobs and workflows)
- Static site generators
- Docker-compose files
- Systemd unit files
- Even spreadsheets like Excel

Why Understanding Build Systems Matters

As software projects grow in complexity, the efficiency and reliability of build systems become increasingly critical. Understanding the principles outlined here provides developers with:

1. A vocabulary to discuss build system tradeoffs
2. Frameworks to evaluate different tools for specific needs
3. Insights to optimize existing build processes
4. Foundations to design better custom build solutions

In an era where continuous integration and deployment are standard, and where developer productivity is paramount, build systems are no longer just infrastructure—they're a competitive advantage.

Conclusion

Build systems represent a fascinating intersection of theory and practice, with concepts ranging from abstract mathematical models to concrete implementation details. By understanding the spectrum between applicative and monadic architectures, the tradeoffs between different dependency tracking strategies, and the implications of determinism and caching, developers can make more informed decisions about which tools to use and how to optimize their build processes.

As Andrew Nesbitt's comprehensive framework demonstrates, build systems are more than just compilation tools—they're the foundation upon which modern software development is built. Whether you're working with traditional systems like Make and Bazel, exploring newer approaches like FRP-inspired architectures, or even considering unconventional systems like GitHub Actions, the principles outlined here provide a common language and conceptual framework for understanding what makes a build system effective.