Compilation Speed in application development

 In recent weeks, I've had several intriguing discussions with colleagues and peers about how the choice of programming language and compiler affects software performance on a broader level. These conversations have highlighted how often overlooked factors, like compilation speed, play a crucial role in the development process. Many developers may not think twice about the time it takes to compile their code, but this seemingly minor detail can make a significant difference in certain contexts.

When we think of performance, we tend to focus on runtime efficiency—how fast the software runs, manages memory, and scales under load. But what about the speed of getting that code to run in the first place? Compilation speed, the time it takes for code to transform from the source into an executable program, directly impacts developer productivity, team workflows, and ultimately, the agility of the software development lifecycle.

The importance of compilation speed varies based on factors such as project scale, development workflow, and team priorities. For small projects or rapid prototyping, fast compilation can enable quick iterations and testing of ideas. For larger projects or teams, it becomes even more crucial, as slow compilation can lead to delays in CI/CD pipelines, disrupted workflows, and increased wait times for developers.

In this blog post, I’ll explore the importance of compilation speed, why it’s a key consideration in modern development environments, and how the choice of language and compiler can either accelerate or hinder the development process. We'll dive into when and why compilation speed matters, the trade-offs involved, and practical tips for optimizing compile times in different project setups.

Why Compilation Speed Matters

Below are some key benefits that a faster compilation speed can offer both applications and the software development process:

• Developer Productivity and Flow: Fast compilation means that developers spend less time waiting and more time coding, testing, and debugging. Frequent pauses during coding disrupt the "flow," making it harder for developers to stay focused and engaged. This can be especially noticeable in iterative development workflows or with large teams working on complex codebases.
• Continuous Integration (CI) and Deployment: In modern CI/CD pipelines, code is frequently compiled, tested, and deployed. Fast compilation shortens CI/CD cycles, enabling quicker feedback, which is especially beneficial for large teams working on shared codebases. Slow compile times can delay feedback and slow down the entire pipeline, impacting overall delivery speed.
• Rapid Prototyping: During the early stages of development, when rapid prototyping and experimentation are common, fast compilation can accelerate testing new ideas. Languages like Go, for example, are preferred in some cases because they allow developers to see changes almost immediately.
• Refactoring and Code Maintenance: Developers often need to refactor or optimize code, making small changes that require re-compilation. If compiling takes a long time, this process can become frustrating and discourage frequent code improvements, impacting code quality over time.
• Feedback Loops in Test-Driven Development (TDD): TDD involves frequent cycles of writing code, compiling, and running tests. Faster compilation speeds can make these cycles more efficient and practical, while slow compilation might discourage the use of TDD or slow down developers working within this methodology.
• Team Collaboration: In larger teams, especially with microservices architectures or modularized monoliths, different modules might need to be compiled separately or together. Slow compilation times can impede collaborative workflows by increasing the time required to merge, review, and test code.

When Compilation Speed Is Less Critical

Compilation speed has its advantages, but it’s not something that should be overly focused on all the time. There are occasions when the ability to deliver quickly is preferable and specific types of applications where compilation speed may not be as effective and something worth spending too significant a time on:

• Small or Single-Developer Projects: For smaller codebases or projects where one developer is managing all components, compilation speed may not be as critical. The wait times are generally shorter, and the impact on productivity is less severe.
• Early Stages of Project Setup: During the initial setup or design phase, developers may not compile as frequently. Therefore, compilation speed may be less of a priority compared to the overall design, architecture, or choice of language features.
• Certain Application Types: For some applications—especially those where performance, security, or robustness are prioritized over development speed—longer compile times may be acceptable if they lead to a more performant or stable product. Embedded systems, scientific computing, and applications with strict performance requirements sometimes fall into this category.
• Scripting and Interpreted Languages: In development contexts where scripting or interpreted languages (like Python or JavaScript) are used, compilation speed may not be a factor at all, as these languages are not compiled in the traditional sense.

Balancing Compilation Speed and Other Factors

In application development, a balance is often sought between compilation speed and other critical factors like language features, ecosystem support, runtime performance, and ease of use. Here’s how some teams approach this balance:

• Optimizing for Incremental Builds: Many modern compilers and build tools support incremental builds, which recompile only the parts of the code that have changed. This approach reduces total build times and allows developers to work effectively in languages that might otherwise compile slowly.
• Tooling and Build Automation: Tools like Bazel, Gradle, and CMake offer powerful optimizations that can make builds faster, allowing teams to keep using languages or frameworks that might otherwise be slower to compile. Investing in effective build tooling can minimize the drawbacks of slower compilation times.
• Using Parallel Compilation: Many build systems support parallel compilation, where different parts of the codebase compile simultaneously. This can improve build times and allow development on complex, large-scale applications that need fast feedback.

Compilation speed is important, especially in modern iterative, collaborative development environments. It can impact developer productivity, CI/CD workflows, and the ability to implement best practices like TDD. For large teams, fast compilation helps maintain high productivity and keeps the focus on quality and speed of delivery. However, other factors may take precedence in certain projects, especially if the language’s benefits outweigh the drawbacks of slower compilation.

The impact programming languages have on compilation speed

The speed of compilation can vary significantly based on the language's design, the complexity of the code being compiled, and the compiler's efficiency. Here’s a general overview of some languages known for fast compilation times, along with those that tend to compile slower:

Fastest-Compiling Languages

• Go: Go was designed with fast compilation in mind. Its minimalistic design, clear syntax, and lack of complex features like generics (until recently) contribute to extremely fast compile times. Go compiles large codebases quickly, which makes it ideal for large projects that need frequent builds.
• Rust: While Rust is known for high performance, it often takes longer to compile than simpler languages due to its complex type-checking, memory safety checks, and borrow checker. However, it's still generally faster than languages with extensive runtime requirements or complex tooling setups.
• C/C++ (with optimizations): C and C++ can compile relatively quickly, especially with simpler codebases. However, if heavy optimizations are enabled or the codebase is very large, the compilation process can slow down significantly.
• D Language: Known for being both fast to compile and powerful, D balances the ease of a high-level language with the performance of low-level programming. D’s incremental compilation helps keep compile times down.
• Swift (with incremental builds): While Swift can take some time for initial builds, it uses an incremental compilation process that speeds up subsequent compilations, making it efficient in large projects with frequent rebuilds.

Slower-Compiling Languages

• Java (especially with large codebases): Java relies on the Java Virtual Machine (JVM) and bytecode interpretation, which can make the compilation slightly slower than languages like Go. However, Java’s Just-In-Time (JIT) compilation offers runtime performance benefits.
• C++ (with heavy templates): Although C++ can compile quickly, extensive use of templates and certain libraries can cause a noticeable slowdown in compile times.
• Scala: Scala’s powerful type system and compatibility with the JVM make it versatile but slower to compile than many other languages.
• Haskell: Due to its lazy evaluation model and extensive optimizations for purely functional code, Haskell can take a while to compile, especially for large projects.
• Rust (with optimizations): Rust is designed with memory safety and high performance in mind, but these features add to compile time. Complex projects or those with heavy optimizations can take time to compile.
• Kotlin: Kotlin, also on the JVM, tends to compile slightly slower than Java due to its advanced language features and interoperability with Java, although incremental compilation can help.

Factors That Impact Compilation Speed

It’s not just all about the language though. Several factors affect compilation time, regardless of language:

• Codebase Size: Larger codebases naturally take longer to compile.
• Use of Templates or Generics: Heavy use of templates (C++) or generics (in languages like Java or Kotlin) can significantly increase compile time.
• Optimizations: Higher optimization levels generally slow down the compile process as the compiler spends more time analyzing and optimizing code.
• Incremental Compilation: Languages like Swift, Kotlin, and Java support incremental compilation, which speeds up re-compilation by only compiling modified code.
• Compiler and Build Tools: The efficiency of the compiler (e.g., Clang, GCC, Rust’s Cargo, or Go’s go build) and the build system used (e.g., Make, CMake, Bazel, Gradle) greatly influences build time.

In general, Go is one of the fastest to compile from scratch, while languages with more complex type systems, like Rust or C++ with templates, tend to compile slower.

Things that can be done to improve compilation speed

So while each programming language has its advantages balancing specific features and libraries with performance, there are a lot of things that can be done to improve compilation speed and application performance regardless of the programming language you are using.

Here are some additional factors and practices that can significantly improve compilation speeds:

Incremental Builds and Caching

• Incremental Builds: This approach recompiles only modified files rather than the entire codebase, which can speed up builds tremendously.
• Build Caching: Tools like Gradle, Bazel, and others use caching to store compiled results for unchanged code or dependencies, so re-compilation isn't necessary unless changes are detected. 

Optimized Compiler Flags and Settings

• Choosing Optimization Levels Carefully: In many compilers (e.g., GCC, Clang), higher optimization levels (-O2, -O3) produce faster code but increase compile times. For day-to-day development, using lower optimization levels (e.g., -O0 or -Og) can make compilation faster while retaining some debugging capabilities.
• Selective Optimization: In projects with modular builds, apply high optimization settings only to performance-critical parts and lower settings elsewhere to speed up compilation. 

Modularizing Code

• Breaking Down Code into Smaller Modules: Splitting large projects into smaller modules allows compilers to recompile only the modules that changed, not the entire codebase. This is especially useful in languages with longer compile times, like C++.
• Use of Static Libraries: Compiling reusable code as static libraries enables quicker linking and can avoid repetitive compilation across multiple binaries. 

Precompiled Headers (PCH)

• Header Consolidation: In languages like C++, headers are often included across multiple files, which slows down compilation. Using precompiled headers allows frequently-used headers to be compiled once and reused, reducing compile time.
• Reducing Header File Usage: Avoid unnecessary or overly large headers in frequently included files, as they can slow down builds considerably.

Distributed and Parallel Builds

• Parallel Compilation: Using multi-threading options (-j flag in Make, for example) allows the compiler to compile multiple files at once. Most modern machines handle several compilation jobs in parallel, which can dramatically improve build times.
• Distributed Builds: Tools like distcc (for C/C++) and Buck or Bazel allow for distributed builds across multiple machines, spreading the workload and speeding up overall compilation.

Build Tools and Systems

• Efficient Build Systems: Advanced build systems like Ninja, Bazel, and Gradle are often faster than traditional options (e.g., Make) and include features like caching and dependency management that improve speed.
• Dependency Management: Managing dependencies carefully and keeping them up-to-date reduces unnecessary re-compilation due to changes in external libraries.

Avoiding Template and Macro Overuse

• Minimize Heavy Templates and Metaprogramming: In languages like C++, excessive use of templates and metaprogramming can slow compilation significantly. Where possible, use simpler code structures.
• Limit Macros: Overuse of macros can lead to code bloat and longer compile times. Using inline functions or constexpr (in C++) can sometimes be a better choice for reducing compile time.

Continuous Integration (CI) Optimizations

• Selective Testing: Configure CI pipelines to skip compilation and testing for parts of the code that haven’t changed. Use “test impact analysis” to run only relevant tests and builds for each commit.
• Binary Caching in CI: Cache dependencies, tools, and build artifacts between CI runs. This can save time by avoiding re-downloading and re-compiling dependencies. 

Hardware Improvements

• CPU and RAM: Compilation benefits from high-performance CPUs with multiple cores and ample RAM, especially for parallel builds. More cores allow more parallel jobs, and higher RAM prevents swapping when handling large codebases.
• Solid-State Drives (SSDs): SSDs provide faster read/write speeds compared to traditional hard drives, which can reduce build times, particularly for large codebases and projects with many dependencies.

Minimizing Dependency Bloat

• Avoid Unnecessary Dependencies: Extraneous libraries and dependencies add to compile time, especially if these dependencies need frequent updating or recompiling.
• Minimize Intra-Project Dependencies: Reducing tight coupling between internal modules or components (where feasible) enables faster incremental builds, as fewer modules need recompiling after changes.

Hot Reloading and Interpreted Languages for Development

• Hot Reloading: For projects where instant feedback is important, using hot reloading frameworks or tools can avoid full recompilation for small changes, especially in front-end and mobile applications.
• Using Interpreted Languages for Early Prototyping: In cases where rapid prototyping is essential, using interpreted languages like Python or JavaScript can help iterate quickly on business logic and algorithms before moving to a compiled language if needed.

Summary

Compilation speed is a critical factor in enhancing the efficiency and scalability of software designs, particularly within modern cloud-based architectures. While it varies across different programming languages, balancing compilation speed with the requirements of the specific software being built is essential. In some cases, compilation speed may be less important, but when it matters, it can significantly impact development workflows and productivity.

Optimizing compilation speed involves a blend of refining code practices, configuring tools effectively, leveraging suitable hardware, and selecting architectures that reduce unnecessary work. By applying these strategies thoughtfully, teams can substantially decrease compile times and boost overall developer productivity.