Build All the Things with Pants Build System

Episode Deep Dive

Guest Introduction and Background

Benjy Weinberger is a seasoned software developer with over two decades of experience. He’s been at companies such as Twitter, where he helped develop large-scale engineering solutions and cultivated deep expertise in developer tooling. Benjy is a co-founder at Toolchain, where he leads the creation and support of Pants Build—a fast, scalable, user-friendly build system for Python and other languages. His passion for making developers’ lives easier and for empowering teams of all sizes to have enterprise-grade build tooling is a driving force behind Pants Build’s continuous evolution.

What to Know If You're New to Python

If you’re new to Python and want to get the most out of this episode, it helps to understand basic package management, testing, and version control with Git. Knowing how to install and run third-party libraries (e.g., using pip or virtual environments) will also help you follow references to Python tooling.

Key Points and Takeaways

1. Pants Build System for Large or Growing Codebases Pants is a build system that automates testing, packaging, and code quality checks, especially valuable for teams with substantial Python code. It focuses on dependency inference, caching, concurrency, and minimal setup overhead so you can quickly scale workflows without manual complexity.
   • Links and Tools:
     • Pants Build homepage
     • Toolchain (Benjy’s company offering Pants Build services)
2. Seamless Integration with Python Tooling Pants automatically orchestrates popular Python tools such as pytest, mypy, Black, Bandit, and more. Instead of manually installing and remembering to run each tool in the correct order, Pants can unify these steps, speeding up routine tasks and ensuring consistent standards.
   • Links and Tools:
     • pytest.org
     • mypy on GitHub
     • Black code formatter
     • Bandit security linter
3. Caching and Parallelism for Speed The Pants engine creates a dependency graph for each action—like testing or linting—and runs them with maximum concurrency while caching all intermediate steps. If part of your codebase is unchanged since the last run, Pants reuses prior results to cut down build and test times. This design greatly accelerates repetitive tasks in both local development and CI.
   • Links and Tools:
     • Pants Build cache docs (for details on how caching works)
     • GitHub Actions (common CI platform Pants can integrate with)
4. Monorepo Support and Cross-Team Collaboration Pants excels in larger codebases or monorepos where multiple services or apps live side by side. By analyzing imports and dependencies, Pants lets developers only run checks or tests relevant to changed files. This ensures teams don’t break each other’s code and can scale safely without excessive overhead or complicated publishing workflows.
   • Links and Tools:
     • Monorepo discussion in Pants docs
5. Multi-Language and Heterogeneous Environments While Python is a major focus, Pants also supports languages like Go, Java, Scala, Kotlin, Shell, and Docker. This is beneficial when you have microservices or components in multiple languages but want a single, coherent build tool.
   • Links and Tools:
     • Supported Languages in Pants
6. Supporting Packaging and Deployments (AWS Lambda, GCP, Docker) Pants can package code for AWS Lambda, Google Cloud Functions, or Docker images, helping you maintain many deployable artifacts from one codebase. You specify how to build each target once, and Pants takes care of packaging only what’s needed to produce minimal, consistent deploys.
   • Links and Tools:
     • AWS Lambda docs
     • Google Cloud Functions
7. Adoption Without Overhauling Your Workflow Benjy emphasized that Pants is easy to adopt incrementally. You can begin with a small part of your project or basic tasks (e.g., test running, linting) and expand as you see the benefits. Teams often discover that adding more tools (like new linters or type-checkers) becomes trivial once Pants is in place.
   • Links and Tools:
     • Pants Setup and Installation
8. Dependency Inference and Lock Files Pants infers dependencies by examining your import statements, reducing the need for explicit “build metadata.” It also supports universal lock files, giving you stable, reproducible environments across teams. This approach safeguards against supply-chain issues and lowers friction for new developers on your team.
   • Links and Tools:
     • Hermetic environment docs in Pants
9. Evolution from Pants v1 to Pants v2 Pants v2 is a full re-implementation focusing on Python first (with strong cross-language support). The engine is written in Rust, with a Python 3 async interface for extensibility. It’s also designed for broad community use instead of being specialized for a single large company’s monolithic codebase.
   • Links and Tools:
     • Pants v2 Overview
10. Benefits for Both Large Enterprises and Small Teams One of the core themes Benjy highlighted is that a tool like Pants shouldn’t just be for tech giants. Whether you’re a 10-person startup or a 10,000-person organization, having a robust build system means saving time, preventing mistakes, and improving code quality.

• Links and Tools:
  • Toolchain - Enterprise Pants Support

Interesting Quotes and Stories

• Benjy on childhood and coding: “It's absolutely unbelievable that your childhood hobby can become your grown-up profession. We get to do it all the time, for fun and for our jobs.”
• On concurrency and caching: “The system can look at your code, look at the dependencies, perform all this analysis, and say, well, that means I actually need to invoke pytest on these tests. And if that means I need to first install pytest in a hermetic environment, I'll do that. Then everything is cached.”

Key Definitions and Terms

• Monorepo: A single repository that holds the code and assets for multiple projects or services.
• Hermetic Environment: A build or run environment sealed off from the rest of your system, ensuring consistent, reproducible results with no hidden dependencies.
• Caching: Storing computed results so they can be reused rather than recalculated, thus speeding up subsequent runs.
• Concurrency: Executing multiple tasks or checks in parallel, typically leveraging all CPU cores or even distributed systems.

Learning Resources

• Python for Absolute Beginners: Start here if you need a foundational Python course to build up your programming skills.
• Getting started with pytest: Deepen your testing knowledge and see how tools like Pants can supercharge your test workflow.
• Up and Running with Git: Learn Git fundamentals so you can leverage Pants’ Git-based features and compare changes confidently.

Overall Takeaway

Pants Build aims to bring top-tier build automation to every Python team—no matter its size. By harmonizing code checks, testing, packaging, and deployment under a single smart system, Pants reduces tedious work, speeds up feedback loops, and lets developers focus on delivering features confidently. It is an inclusive solution that handles single-language or multilingual projects, supports modern workflows like microservices and serverless, and can be adopted incrementally to fit any organization's culture.