What is *Software Engineering at Google* about?

*Software Engineering at Google* explores how Google manages large-scale software development through processes, tools, and culture. It emphasizes sustainability, codebase maintenance, and adaptability over time, framing software engineering as "programming integrated over time." Key topics include testing, code reviews, monorepos, and trade-offs in system design, with insights tailored to organizations operating at unprecedented scale.

Who should read *Software Engineering at Google*?

This book is ideal for software engineers, technical leads, and engineering managers in large organizations seeking strategies for scalable code management. While many practices are Google-specific, the principles around testing, culture, and process optimization offer value to anyone interested in long-term software sustainability.

Is *Software Engineering at Google* worth reading?

Yes, for its candid look at scaling challenges and innovative solutions like automated testing pipelines and monorepo workflows. However, readers should critically evaluate which practices apply to their context, as Google’s scale and resources are uncommon.

What are Google’s key principles for software engineering?

Google prioritizes three principles: - **Time**: Building systems that evolve sustainably. - **Scale**: Addressing challenges like codebase size and testing flakiness. - **Trade-offs**: Balancing completeness, latency, and expressiveness in design. These principles guide decisions at the intersection of engineering and organizational needs.

How does Google approach testing and code reviews?

Google employs rigorous testing practices, including probabilistic testing to minimize flakiness, and enforces mandatory code reviews to ensure consistency and correctness. Reviews are streamlined through automated tooling and clear ownership rules, fostering collaboration across teams.

What is the “monorepo” concept discussed in the book?

Google’s monorepo (a single repository for most code) enables centralized dependency management and cross-team collaboration. While efficient at scale, it requires robust tooling to handle issues like build times and access controls, making it less practical for smaller organizations.

What does “programming over time” mean in the book?

The phrase underscores software engineering’s focus on maintaining and evolving codebases across years or decades. It highlights the importance of adaptability, documentation, and processes that outlast individual contributors.

What critiques exist about *Software Engineering at Google*?

Critics note that solutions like stacked diffs or monorepos may not translate to smaller teams. The book’s emphasis on Google’s unique scale can limit applicability, though its core principles remain insightful.

Googleのソフトウェアエンジニアリング

# 長年のプログラミングから学んだ教訓プログラミングは単にコードを書くことではありません。それは、時間の経過とともに進化する問題解決、継続的な学習、そして考え方の変化を伴う旅です。以下は、私が長年の経験から学んだ重要な教訓です。 ## 1. 読みやすさは賢さよりも重要かつては、複雑なワンライナーや難解なアルゴリズムを書くことがスキルの証だと思っていました。しかし、本当のスキルは、**他の人が理解し、保守できるコードを書くこと**にあります。 * **変数名:** 明確で説明的な名前を使用しましょう。 * **シンプルさ:** 可能な限り、単純なアプローチを選択しましょう。 * **ドキュメント:** 「何」をしているかではなく、「なぜ」そうしているかを説明しましょう。 ## 2. ツールよりも基礎を優先するフレームワークやライブラリは移り変わりが激しいですが、コンピュータサイエンスの基礎は永遠です。 1. **データ構造とアルゴリズム:** 効率的なコードの基盤となります。 2. **デザインパターン:** 共通の問題に対する再利用可能な解決策を理解しましょう。 3. **ネットワーキングとOS:** システムが内部でどのように動作するかを知ることは、デバッグにおいて非常に重要です。 ## 3. デバッグは科学である当てずっぽうにコードを変更してうまくいくのを期待するのは、戦略ではありません。 * **問題を再現する:** 修正を試みる前に、バグを確実に発生させられるようにします。 * **仮説を立てる:** 何が間違っているのかを推測し、それを検証します。 * **ラバーダック・デバッグ:** 誰か（あるいは何か）に問題を説明することで、解決策が見つかることがよくあります。 ## 4. 完璧主義は進歩の敵完璧なアーキテクチャを待つよりも、まずは動くものを作り、後でリファクタリングする方が良い場合がほとんどです。 > "まず動かし、次に正しくし、最後に速くする。" — *Kent Beck* ## 5. ソフトスキルはハードスキルと同じくらい重要優れたエンジニアであるだけでは不十分です。効果的に働くためには、以下の能力が必要です。 * **共感:** ユーザーとチームメイトのニーズを理解すること。 * **コミュニケーション:** 複雑な概念を非技術者にも説明できること。 * **コラボレーション:** フィードバックを受け入れ、共通の目標に向かって協力すること。 --- ### 結論プログラミングは生涯続く学習のプロセスです。新しい技術に対してオープンであり続け、常に謙虚さを忘れず、そして最も重要なこととして、**構築することを楽しんでください。** 詳細については、[プログラミングのベストプラクティス](https://example.com/best-practices)をご覧ください。

タイタス・ウィンタース

4.2(1901 Reviews)

Technology

『Googleのソフトウェアエンジニアリング』の概要

Googleのエンジニアリングの核心を覗いてみましょう。そこでは「巧妙（clever）」であることは、褒め言葉ではなく非難の対象です。業界の定義を塗り替えたこのガイドは、テクノロジー界で最も影響力のある企業が、いかにして時間と規模の試練に耐えうるソフトウェアを構築しているかを明らかにします。これらの実践は、現在、世界中のエリート開発者が持続可能なコードアーキテクチャに取り組む際の手法を再構築しています。

語り方を選ぶ

1冊の本。さまざまな聴き方。

Googleのソフトウェアエンジニアリングの同じアイデアを、あなたに合う声・深さ・ペースで学べます。いつでも切り替えられます。

第1章

Google's Billion-Dollar Engineering Secrets Revealed

Ever wonder how Google builds software that rarely crashes despite serving billions of users daily? "Software Engineering at Google" lifts the curtain on engineering practices that power the world's most reliable digital infrastructure. Written by Titus Winters, Google's "Large-Scale C++ Expert," whose technical decisions impact products used by over 2 billion people worldwide. The book emerged from Google's legendary internal engineering education program, where these principles helped scale from a simple search engine to a tech empire managing over 2 billion lines of code in a single repository. While most engineering books focus on coding, this one reveals Google's battle-tested systems for sustainability, knowledge sharing, and managing technical debt. You'll discover why Google engineers spend only 1/3 of their time writing new code (and what they're doing the rest of the time), how they make 40,000+ code changes daily without breaking systems, and the organizational psychology that enables continuous innovation at massive scale. This isn't just how Google codes - it's how they conquered the internet.

第2章

The Critical Distinction Between Programming and Engineering

Programming focuses on writing code, while software engineering encompasses maintaining that code over time. This fundamental distinction shapes Google's approach to software development. Engineering decisions must consider scale, policy, and sustainability-factors that become increasingly critical as projects grow. The challenge isn't just creating functional software but ensuring it remains valuable and adaptable for years or even decades. At Google's scale, with thousands of engineers and billions of lines of code, sustainability isn't optional-it's essential. But these principles apply universally. Whether you're managing a small project or an enterprise system, the same questions arise: How will this code evolve? Can we afford to maintain it? What trade-offs are we making? Consider Google Search-a product that must be perpetually modernized while maintaining reliability. The transition from creating code to maintaining it can be painful, especially when unplanned. Success comes when upgrades become routine rather than emergencies. This shift in mindset-from viewing software as a product to seeing it as an evolving entity-marks the difference between programming and engineering. Have you ever wondered why even small API changes sometimes break existing systems? This phenomenon is captured by Hyrum's Law: "With a sufficient number of users of an API, it does not matter what you promise in the contract: all observable behaviors of your system will be depended on by somebody." This reality makes change management both challenging and necessary.

聴きながら、すべての言葉を目で追えます

『Googleのソフトウェアエンジニアリング』の一語一語が音声に合わせてリアルタイムでハイライトされ、自然に読み進められます。

第3章

Embracing Change as the Only Constant

"Why not just aim for nothing changes?" This question misunderstands the nature of software. Change is inevitable because underlying technologies evolve, security vulnerabilities emerge, and hardware requirements shift. Remember the Heartbleed vulnerability? It forced countless systems to update their OpenSSL dependencies, regardless of whether they wanted to change. Google's approach isn't to resist change but to embrace it by building systems that make change manageable. This philosophy extends to how they structure their organization and processes. Policies that don't scale well create disproportionate work as an organization grows. For example, abruptly deprecating a widely-used component without a transition strategy creates enormous overhead. In contrast, scalable policies offer exceptional value. The "Beyonce Rule" at Google exemplifies this approach: if continuous integration tests don't catch an issue, it's not the fault of infrastructure changes. This rule streamlines decision-making and reduces the need for bespoke testing of every change. Consider Google's 2006 compiler upgrade experience. What should have been a simple update became complex due to subtle dependencies on compiler behaviors-a classic manifestation of Hyrum's Law. This led to a focus on automation, consistency, and expertise to ease future upgrades. The lesson? The first upgrade is costly, but establishing processes for continuous evolution pays dividends.

第4章

Shifting Left: Detecting Problems Earlier

One of the most powerful concepts in Google's engineering philosophy is "shifting left"-detecting problems earlier in the development timeline. Finding issues during design or coding is vastly cheaper than discovering them in production. This principle applies to everything from security vulnerabilities to performance bottlenecks, architectural flaws, and user experience issues. The concept of "shifting left" derives from viewing the software development lifecycle as a timeline moving from left (early stages) to right (production). Studies have shown that fixing a bug in production can cost up to 100 times more than addressing it during the design phase. This exponential cost increase occurs due to factors like system complexity, interdependencies, and the potential impact on active users. Consider these real-world scenarios: • A security vulnerability discovered during code review might take hours to fix • The same issue found in production could require emergency patches, service downtime, and potential data breach notifications • Performance bottlenecks identified during design reviews can be addressed through architectural changes • Similar issues discovered post-deployment might require extensive refactoring or system redesign Google implements this philosophy through multiple layers: 1. Automated static analysis tools that catch common issues during development 2. Comprehensive unit and integration testing requirements 3. Mandatory design reviews for significant changes 4. Regular security audits during development 5. Performance testing integrated into the development pipeline This shift-left mentality permeates Google's approach to software development and has become a cornerstone of their quality assurance strategy. Teams are encouraged to think about potential issues early, with dedicated time allocated for design reviews and architectural discussions. The company has developed sophisticated tools like Error Prone and ClangTidy that integrate directly into developers' workflows, catching issues before they even make it to code review. The return on investment for shift-left practices is compelling: • Reduced debugging time • Lower maintenance costs • Improved system reliability • Enhanced security posture • Better team morale due to fewer production emergencies By investing in tools and processes that catch problems early, they reduce the overall cost of quality while improving reliability and security. The question isn't whether you can afford to implement these practices, but whether you can afford not to. Organizations that adopt this approach typically see significant improvements in both product quality and team productivity.

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

毎日の新しい気づきを10万件以上のレッスンから
言葉をハイライトして音声と同期
ライブラリを保存して、どの端末からでも利用

第5章

Making Informed Trade-offs in Engineering Decisions

Software engineering involves constant trade-offs between competing values. Should you optimize for development speed or operational stability? Flexibility or simplicity? Google emphasizes transparent, data-driven decision-making over authoritarian approaches. Even seemingly trivial decisions reveal this philosophy. Consider Google's approach to whiteboard markers-keeping supply closets unlocked prioritizes unimpeded brainstorming over preventing occasional marker shortages. This reflects a culture that optimizes for practical benefits rather than strict control. More complex examples include Google's evolution of build systems. Initially, they moved from local builds to distributed builds, reclaiming substantial engineer time. However, this inadvertently masked inefficient build dependencies, creating new challenges. The lesson? Every solution creates new problems, and continuous reevaluation is necessary. When deciding whether to add a dependency or fork/reimplement, engineers must weigh time against scale. Custom solutions offer targeted optimizations and independence from external changes but may undermine scalability. Strategic forking can be pragmatic, but excessive forking complicates security updates and maintenance. These decisions require calibrated judgment based on project scope and lifespan. A data-driven culture inherently fosters learning from mistakes. Decisions must be revisited as circumstances and assumptions evolve. Admitting errors promotes respect and adaptability-essential qualities for long-term project success. Leaders need to combine data with judgment, recognizing the intangible elements that drive impactful decisions.

第6章

The Evolution of Build Systems: From Chaos to Structure

What happens without a build system? Initially, a simple compiler seems sufficient for straightforward projects. But as multiple languages and external dependencies enter the picture, manual compilation becomes impractical. Shell scripts offer temporary relief but quickly become unwieldy, with reliability issues across diverse environments. Modern build systems solve these challenges by effectively managing dependencies-both within the codebase and external to it. Task-based systems like Ant and Maven break builds into sequences of tasks but suffer from challenges in maintainability and performance optimization. They struggle with parallelization, incremental builds, and debugging complex scripts. Artifact-based systems like Bazel (Google's open-source version of Blaze) take a different approach. They focus on producing artifacts rather than executing arbitrary tasks, allowing the system to manage the "how" of building. This functional programming-inspired approach results in more efficient, deterministic, and easily parallelized builds. Bazel treats tools as dependencies, extends through custom rules, isolates actions through sandboxing, and ensures deterministic builds via workspace-wide dependency hashes. This structure enables distributed builds-essential for Google's massive codebase-by breaking work into manageable units that can be distributed across many machines. Remote caching further optimizes build times by providing immediate access to pre-built artifacts. By sharing a cache, different systems capitalize on existing build artifacts, drastically reducing rebuild necessity. Remote execution transforms build capability by distributing tasks across a scalable worker pool, coordinated by a build master.

聴きながら、すべての言葉を目で追えます

『Googleのソフトウェアエンジニアリング』の一語一語が音声に合わせてリアルタイムでハイライトされ、自然に読み進められます。

第7章

Managing Dependencies in a Complex World

The "one-version rule" at Google represents a fundamental principle in their dependency management strategy, enforcing the use of a single version of every third-party dependency across their massive codebase. This approach directly addresses the notorious "diamond dependency" problem, where different parts of an application depend on different versions of the same library. For example, if Project A requires Library X version 1.0, and Project B requires Library X version 2.0, this creates an irreconcilable conflict that can lead to runtime errors, unexpected behavior, and build failures. Google's strict adherence to this rule, while sometimes challenging, ensures system-wide consistency and predictability. Managing transitive dependencies presents an even more complex challenge. These are the hidden dependencies that come along with direct dependencies - essentially, the dependencies of your dependencies. For instance, if your application uses a logging framework that itself depends on multiple other libraries, each with their own version requirements, the dependency tree can quickly become unwieldy. While popular build tools like Maven and Gradle attempt to automatically resolve these dependencies, their solutions can sometimes violate the one-version rule. Google's build system, Bazel, takes a different approach by requiring explicit declaration and manual management of all dependencies. Though this creates more upfront work for developers, it results in a clearer, more maintainable system where dependency relationships are transparent and deliberately chosen rather than automatically determined. Third-party dependencies introduce multiple vectors of risk that need careful management: • Security vulnerabilities: External libraries may contain security flaws that could compromise the entire system • Availability risks: External repository downtime can halt development and deployment • Version consistency: Changes in external packages can introduce breaking changes • License compliance: Dependencies may have licensing requirements that need tracking Google addresses these challenges through several sophisticated strategies: 1. Dependency mirroring: Maintaining internal copies of all external dependencies on controlled servers, ensuring availability and version stability 2. Hash verification: Implementing strict verification processes to ensure dependency integrity and prevent supply chain attacks 3. Vendoring: Incorporating critical dependencies directly into their codebase, giving complete control over the code and its evolution 4. Regular security scanning: Continuously monitoring dependencies for known vulnerabilities 5. Automated update testing: Running comprehensive test suites when updating dependencies to catch breaking changes early This comprehensive approach to dependency management, while requiring significant infrastructure and process investment, has proven essential for maintaining the reliability and security of Google's enormous codebase. It allows them to balance the benefits of using third-party code with the need for stability and security in their systems.

第8章

Enabling Large-Scale Changes Across Massive Codebases

Large-scale changes (LSCs) involve modifications too extensive to apply as a single commit, often requiring sophisticated automated tools and careful planning. These changes typically include systematic refactorings, infrastructure improvements, API migrations, or transitions between major systems. At Google, specialized infrastructure teams primarily handle LSCs, leveraging their deep domain expertise and dedicated tooling to efficiently manage these complex transitions. Common examples of LSCs include: • Upgrading dependencies across thousands of files • Migrating from deprecated APIs to their modern replacements • Implementing organization-wide security improvements • Standardizing coding patterns across multiple repositories • Modernizing legacy systems to support new architectures Several significant barriers make atomic changes challenging in large repositories: 1. Technical Limitations: - Version control systems often struggle with extremely large commits - Branch management becomes unwieldy with multiple concurrent changes - Tool timeouts and memory constraints can impede large operations 2. Merge Conflicts: - Higher probability of conflicts as change size increases - Multiple teams working on overlapping code areas - Extended change windows increase collision risks 3. Legacy System Resistance ("Haunted Graveyards"): - Poorly understood legacy code with minimal documentation - Complex dependencies that resist modification - Historical workarounds that can't be easily refactored - Risk of breaking critical but fragile systems 4. Environmental Heterogeneity: - Different build systems and toolchains - Varying language versions and compiler settings - Platform-specific code requirements - Diverse deployment environments Testing becomes particularly crucial in LSCs, with Google's system implementing several key strategies: • Automated test selection to identify relevant test coverage • Staged rollouts to catch issues early • Incremental testing approaches that balance thoroughness with execution time • Specialized tools for validating changes across different environments • Monitoring systems to detect potential issues in production To manage these challenges effectively, Google has developed a systematic approach that includes careful planning, automated tools for change application, and robust testing frameworks that ensure broad coverage while balancing the trade-off between change size and test focus. This infrastructure enables teams to confidently make sweeping changes across millions of lines of code while maintaining system stability and reliability.

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

毎日の新しい気づきを10万件以上のレッスンから
言葉をハイライトして音声と同期
ライブラリを保存して、どの端末からでも利用

第9章

The Infrastructure Behind Google's Change Management

Google's LSC (Large Scale Change) infrastructure represents a sophisticated blend of technological tools and cultural practices designed to manage code modifications across their massive codebase. This framework carefully balances the need for thorough oversight with operational efficiency, preventing reviewer burnout while ensuring that changes receive appropriate expert scrutiny. Code owners and team leads must fundamentally shift their mindset from traditional change management to embrace LSCs as a critical tool for scaling engineering practices effectively. This cultural shift involves trusting domain experts to execute wide-reaching changes that align with both immediate team needs and broader organizational goals. The technical foundation relies on several sophisticated tools working in concert. Kythe, Google's primary semantic indexing framework, provides comprehensive analysis of code relationships and dependencies across multiple programming languages. This tool enables engineers to understand complex code linkages and potential impact points before implementing changes. Compiler-based indices complement this by offering detailed syntactic tree-based analysis, allowing for precise identification of code patterns and structures that need modification. These tools together provide a robust understanding of the codebase's architecture and interconnections. The Rosie platform serves as the orchestration layer for LSCs, handling the complex task of breaking down master changes into manageable components. It employs sophisticated algorithms to: • Automatically segment large changes into logical shards • Manage dependencies between different parts of the change • Coordinate testing across affected components • Handle autonomous commit processes with built-in safety checks • Monitor and report on the progress of changes across the entire codebase This infrastructure transforms previously "immutable" technical decisions into flexible, evolving aspects of the codebase. Teams can confidently implement fundamental changes that would have been practically impossible in traditional change management systems. The LSC process follows four carefully designed phases: 1. Authorization Phase: - Detailed documentation of proposed changes - Impact analysis and risk assessment - Review by relevant stakeholders and domain experts - Approval workflow management 2. Change Creation Phase: - Automated code editing using specialized tools - Syntax and semantic validation - Generation of consistent changes across multiple code bases - Integration with existing code review systems 3. Shard Management Phase: - Systematic testing of each code fragment - Parallel processing of multiple shards - Automated conflict resolution - Coordinated submission scheduling - Performance impact assessment 4. Cleanup Phase: - Regression detection and prevention - Verification of successful changes - Documentation updates - Monitoring for unexpected side effects This comprehensive process enables Google to maintain consistency and quality across its vast codebase while allowing infrastructure teams to implement critical updates efficiently. The system's success relies on both its technical sophistication and the cultural acceptance of large-scale automated changes as a standard part of software engineering practice.

第10章

The Human Side of Software Engineering

Throughout the book, the authors emphasize that software engineering is fundamentally a human discipline. While technical excellence is crucial, the success of software projects heavily depends on cultural practices, communication patterns, and organizational structures. Google's approach carefully balances individual creativity with systematic processes, creating an environment that fosters both innovation and stability. This balance is evident in everything from code review practices to team structures and decision-making processes. The distinction between treating commits as "cattle" versus "pets" illustrates this balance particularly well. The "cattle" approach suggests automated, systematic handling over personalized attention, emphasizing robust systems over emotional attachment to specific code. This mindset helps teams scale their operations by: • Reducing cognitive load through automation • Establishing consistent quality standards across the codebase • Enabling faster recovery from failures through standardized processes • Minimizing the "bus factor" by avoiding knowledge silos Google's review process for large-scale changes demonstrates a sophisticated understanding of human trust and expertise. Rather than requiring exhaustive reviews for every change, the process acknowledges different levels of expertise and trust: • Local code owners can often skip detailed reviews for well-understood changes • Infrastructure teams are trusted to handle complex, cross-cutting changes • Global approvers with repository-wide permission employ pattern-based reviews • Manual inspection is reserved for anomalies and high-risk changes The human element is further reflected in Google's approach to team dynamics and collaboration: • Cross-functional teams are structured to maximize knowledge sharing • Regular rotation of responsibilities helps prevent expertise isolation • Clear escalation paths exist for technical disagreements • Documentation and knowledge sharing are treated as first-class concerns This balanced approach recognizes that while processes and tools are important, the human factors - including team dynamics, individual growth, and organizational culture - are often the determining factors in a project's success. The system accommodates both the need for standardization at scale and the human aspects of software development, such as learning, collaboration, and creative problem-solving.

聴きながら、すべての言葉を目で追えます

『Googleのソフトウェアエンジニアリング』の一語一語が音声に合わせてリアルタイムでハイライトされ、自然に読み進められます。

第11章

Building for the Long Term: The Essence of Software Engineering

Software engineering at Google isn't just about writing code-it's about building systems that remain valuable and adaptable over time. This requires thoughtful trade-offs, robust processes, and a culture that embraces change while maintaining stability. The principles described in this book apply beyond Google's scale, offering valuable lessons for organizations of all sizes, from startups to established enterprises. The key insight? Software engineering is programming integrated with time. It encompasses not just the immediate functionality but the entire lifecycle of the system. This temporal dimension manifests in several critical ways: • Code Maintainability: Writing clear, well-documented code that future developers can understand and modify • Technical Debt Management: Making conscious decisions about when to take on technical debt and how to systematically address it • Architecture Flexibility: Designing systems that can evolve with changing requirements and technologies • Knowledge Transfer: Ensuring critical information and expertise aren't siloed within individuals or teams Successful long-term software engineering requires balancing competing priorities: 1. Speed vs. Stability: Finding the right balance between rapid development and system reliability 2. Innovation vs. Maintenance: Allocating resources between new features and system upkeep 3. Simplicity vs. Flexibility: Creating systems simple enough to maintain but flexible enough to adapt 4. Short-term vs. Long-term Goals: Making decisions that satisfy immediate needs without compromising future options Whether you're managing a small project or an enterprise system, these principles can help you build more resilient, maintainable software. The implementation might vary based on your context, but the fundamental concepts remain constant: • Regular code reviews and documentation updates • Automated testing and continuous integration • Systematic monitoring and observability • Planned refactoring and system evolution • Investment in developer tools and infrastructure The question isn't whether you can afford to implement these practices, but whether you can afford not to. In a world increasingly dependent on software, engineering for the long term isn't just good practice-it's essential for survival and success. Organizations that ignore these principles often find themselves trapped with legacy systems that become increasingly expensive and risky to maintain or modify. The return on investment for good software engineering practices compounds over time, much like financial investments. Initial costs in setting up proper processes and infrastructure are typically far outweighed by the long-term benefits of reduced maintenance costs, faster feature development, and improved system reliability.

★★★★★

『Googleのソフトウェアエンジニアリング』を最後まで読み終えました

100万人以上の学習者に愛用されています

“23日間、毎日使い続けています。今では日課の一部です。”

jayallen

『Googleのソフトウェアエンジニアリング』の主要テーマ

software sustainability
technical debt management
monorepo development
engineering culture
continuous integration testing

『Googleのソフトウェアエンジニアリング』の名言

Hygiene is the collection of practices that make it easier to produce and maintain code.
Sustainability isn't optional-it's essential.
Change is inevitable because underlying technologies evolve.

『Googleのソフトウェアエンジニアリング』の登場人物

Titus WintersAuthor and Google's Large-Scale C++ Expert
Hyrum WrightEngineer whose law describes API user behaviors
BeyoncéCultural figure referenced in Google's CI policy

著者について

『Software Engineering at Google』の著者であり、大規模ソフトウェアシステムの第一人者であるタイタス・ウィンタース（Titus Winters）氏は、Googleのシニア・スタッフ・ソフトウェア・エンジニアを務めています。2010年以来、同社の2億5,000万行に及ぶC++コードベースの保守と進化を率いてきました。

彼の著書は、Googleの比類なきエンジニアリング環境から得られた数十年にわたる知見を凝縮したものであり、長期的なコード管理、システムのスケーリング、そしてエビデンスに基づいた技術的意思決定のための持続可能なプラクティスに焦点を当てています。

C++標準ライブラリ小委員会の議長も務めたウィンタース氏は、ソフトウェア史上最大規模のリファクタリングをいくつか指揮しており、その経験が長期的なコードの健全性に対する実用的なアプローチに直接反映されています。彼は頻繁に講演を行い、『Software Engineering Daily』などのポッドキャストにも出演しており、学術的理論と業界の実践の架け橋となっています。

2020年にオライリー社から出版された『Software Engineering at Google』は、進化し続けるコードベースの複雑性に取り組むための実践的なフレームワークとして高く評価され、世界中のエンジニアリングチームにとって不可欠なリソースとなっています。

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As software platforms increasingly prioritize monetization over utility, developers must learn to identify predatory technical patterns. This guide is essential for software architects and engineers who want to build resilient stacks and avoid becoming locked into decaying ecosystems.

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学習プラン

Designing and Programming Software

This comprehensive path is designed for developers looking to transition from writing scripts to engineering robust, production-ready systems. It provides the essential bridge between basic syntax and complex architectural decision-making required in modern tech roles.

5 h 53 m•4 セクション

学習プラン

Computer science under software

This learning plan bridges the gap between basic coding and professional software engineering. It is ideal for self-taught developers or students looking to master the architectural principles and advanced systems required by top tech companies.

3 h 4 m•4 セクション

学習プラン

Learn software dev

This comprehensive learning path takes you from programming beginner to professional software engineer, covering everything from writing your first line of code to making architectural decisions for large-scale systems. It's ideal for career changers entering tech, self-taught programmers looking to formalize their skills, or junior developers aiming to reach senior-level expertise. The structured progression ensures you build both technical proficiency and the professional mindset needed to succeed in modern software development teams.

4 h 50 m•4 セクション

学習プラン

Architecting for Scale: The Interview Playbook

This playbook is essential for software engineers aiming for senior or staff-level roles at top-tier tech companies. It provides a structured methodology to tackle the high-pressure system design interview by focusing on data scalability and the art of the trade-off.

1 h 30 m•3 セクション

学習プラン

Bridge the Business-Tech Gap

In modern software development, technical excellence is only valuable if it drives business success. This plan is designed for engineers and product leaders who want to stop being 'order takers' and start becoming strategic partners by aligning their architecture with organizational goals.

1 h 12 m•3 セクション

学習プラン

Learn to code from scratch

This comprehensive path bridges the gap between writing your first line of code and architecting complex systems. It is ideal for aspiring developers who want to move beyond basic syntax to master the professional engineering standards required in the tech industry.

4 h 23 m•4 セクション

コロンビア大学卒業生がサンフランシスコで開発

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4.7

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@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

BeFreedがウェブ上でどのように話題になっているかをもっと見る

コロンビア大学卒業生がサンフランシスコで開発

LOVED BY

1,000,000+ curious minds.

4.7

avg rating

1M+ learners

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

BeFreedがウェブ上でどのように話題になっているかをもっと見る

今すぐ学習の旅を始めよう

第9章

Googleのソフトウェアエンジニアリング

概要『Googleのソフトウェアエンジニアリング』の概要

1冊の本。さまざまな聴き方。

Google's Billion-Dollar Engineering Secrets Revealed

The Critical Distinction Between Programming and Engineering

聴きながら、すべての言葉を目で追えます

Embracing Change as the Only Constant

Shifting Left: Detecting Problems Earlier

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

Making Informed Trade-offs in Engineering Decisions

The Evolution of Build Systems: From Chaos to Structure

聴きながら、すべての言葉を目で追えます

Managing Dependencies in a Complex World

Enabling Large-Scale Changes Across Massive Codebases

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

The Infrastructure Behind Google's Change Management

The Human Side of Software Engineering

聴きながら、すべての言葉を目で追えます

Building for the Long Term: The Essence of Software Engineering

『Googleのソフトウェアエンジニアリング』を最後まで読み終えました

『Googleのソフトウェアエンジニアリング』の主要テーマ

『Googleのソフトウェアエンジニアリング』の名言

『Googleのソフトウェアエンジニアリング』の登場人物

著者について

Googleのソフトウェアエンジニアリングの要約をダウンロード

他のユーザーのお気に入り

この本に関するよくある質問

What is Software Engineering at Google about?

Who should read Software Engineering at Google?

Is Software Engineering at Google worth reading?

What are Google’s key principles for software engineering?

もっと見る

1,000,000+ curious minds.

1,000,000+ curious minds.

1冊の本。さまざまな聴き方。

Google's Billion-Dollar Engineering Secrets Revealed

The Critical Distinction Between Programming and Engineering

聴きながら、すべての言葉を目で追えます

Embracing Change as the Only Constant

Shifting Left: Detecting Problems Earlier

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

Making Informed Trade-offs in Engineering Decisions

The Evolution of Build Systems: From Chaos to Structure

聴きながら、すべての言葉を目で追えます

Managing Dependencies in a Complex World

Enabling Large-Scale Changes Across Massive Codebases

『Googleのソフトウェアエンジニアリング』をもっと深く楽しむ

The Infrastructure Behind Google's Change Management

The Human Side of Software Engineering

聴きながら、すべての言葉を目で追えます

Building for the Long Term: The Essence of Software Engineering

『Googleのソフトウェアエンジニアリング』を最後まで読み終えました

他のユーザーのお気に入り

もっと見る

『Googleのソフトウェアエンジニアリング』の概要