


Imagine you are an architect standing on an empty field. You wouldn't start laying bricks without a blueprint, right? The same principles apply to API development. I used to use a code-first approach, writing code first and documentation later, until I learned a design-first approach. A design-first approach is to create a detailed API definition before writing any code.
Overview of this guide
Before we dive in, let’s map out our goals. Think of this as your API planning roadmap:
- Learn the basics of API planning
- Explore two different methods
- Make wise choices
- Create your API plan
What you will learn:
- What does API planning include
- Code First Approach
- Design first approach
- Comparison between code first and design first
- How to choose the right method
- Practical steps for API planning
What does API planning include
The foundation of excellent API
API planning isn’t just about technical specifications – it’s also about building a product that others will love using. It's like designing a house where every room has its purpose and is logically connected to the others.
Key questions to answer:
- Who are the consumers? (Front-end developers, third-party partners, etc.)
- What operations does it support? (CRUD operations, integration, etc.)
- How to ensure its safety? (Authentication, rate limiting, etc.)
The Art of Planning
Compare API planning to drawing a masterpiece:
- Code first is like painting without drafting
- Design first is like planning the composition first
Code First Approach
A code-first approach is about jumping directly into coding and creating functionality before writing API structural documentation or design. When I started building APIs, I was a code-first advocate. Here's what I learned:
<code>// 第一天:“这看起来很简单!” app.get('/users', getUsers); // 第二周:“哦,等等,我需要过滤……” app.get('/users', authenticateUser, validateQuery, getUsers); // 第三周:“也许我应该更好地规划一下……”</code>
Quick Tip ✨: Code-first works for prototypes, but document your decisions as you go!
How it works
- Start with backend development and mockups.
- Build API endpoints based on your database structure.
- Write API documentation after implementation.
Advantages
- Faster prototyping: ideal for small teams or personal projects.
- Straightforward implementation: focus on building functionality without upfront planning.
Challenge
- Inconsistent design: If multiple developers are involved, the API may lack consistency.
- Iteration is difficult: making significant changes after development can be costly.
Design first approach
The design-first approach emphasizes planning and defining the structure of your API before writing any code. It keeps everyone on the same page. After the API definition is agreed upon, stakeholders such as testers and technical writers can work in parallel with developers.
How it works
- Use tools such as Swagger/OpenAPI to design API patterns.
- Define endpoints, request/response formats and validation.
- Share designs with stakeholders to get feedback.
- Development begins after the design is finalized.
Advantages
- Collaboration: Facilitate early feedback from stakeholders.
- Consistency: Ensure endpoint consistency.
- Mock API: Allows front-end teams to start integrations earlier using mock responses.
Challenge
- Upfront effort: Initial design takes time.
- Expertise required: Developers must be familiar with design tools and best practices.
Code First vs. Design First: Comparison
Code First
- Speed: Faster for simple projects.
- Collaboration: Limited in initial stages.
- Consistency: This may vary by endpoint.
- Flexibility: Easy for solo development.
- Scalability: This may be difficult to scale.
Design first
- Speed: Slow due to early planning.
- Collaboration: Encourage early team collaboration.
- Consistency: Ensure standardized design.
- Flexibility: Great for teams or public APIs.
- Scalability: Designed with scalability in mind.
How to choose the right method
Select Code First if:
- You are building a quick proof of concept or internal API.
- API consumers are a single small team.
- You prioritize speed over design.
Please select Design Priority if the following conditions are met:
- Your API is exposed to external consumers or multiple teams.
- Collaboration and consistency are priorities.
- You are building a public API or a long-term API.
Practical steps for API planning
Step 1: Define the purpose of the API
Before we dive into endpoints and methods, answer these basic questions:
- What problem does your API solve?
- Who is your target user?
- What core functionality do you have to offer?
- What are your non-functional requirements?
Example statement of purpose:
<code>// 第一天:“这看起来很简单!” app.get('/users', getUsers); // 第二周:“哦,等等,我需要过滤……” app.get('/users', authenticateUser, validateQuery, getUsers); // 第三周:“也许我应该更好地规划一下……”</code>
Step 2: Identify core resources
Think of resources as nouns in the API. For our e-commerce example:
Main resources:
- Product
- Inventory
- Warehouse
- Inventory changes
Resource relationship:
<code>// 第一天:“这看起来很简单!” app.get('/users', getUsers); // 第二周:“哦,等等,我需要过滤……” app.get('/users', authenticateUser, validateQuery, getUsers); // 第三周:“也许我应该更好地规划一下……”</code>
Step 3: Define the operation
Now consider what actions (verbs) the user needs to perform on these resources:
<code>此API使电子商务平台能够实时管理多个仓库的库存,确保准确的库存水平并防止超卖。</code>
Step 4: Plan the data model
Define clear and consistent data structures:
<code>产品 └── 库存 └── 仓库 └── 库存变动</code>
Step 5: Plan for Authentication and Security
Think about security from the start:
- Authentication method
- Authorization Level
- Rate Limit
- Data Encryption
- Input verification
Step 6: Write API documentation
Create comprehensive documentation:
API Overview
- Purpose and Scope
- Getting Started Guide
- Authentication details
Endpoint documentation
- Resource Description
- Request/Response Format
- Example call
- Error handling
Use Cases
- Common Scenarios
- Integration example
- Best Practices
Conclusion
Both code-first and design-first approaches are valuable in API development. The key is to choose an approach that fits the project's needs, team size, and long-term goals. Ultimately, whether you choose a code-first or design-first approach, the goal is to create an API that developers love to use. Sometimes the journey is not as important as the destination, but having a good map can make the journey easier!
Looking ahead: CollabSphere case study
In our upcoming blog series, we will put these principles into practice by building CollabSphere, a real-time chat system. You'll see firsthand how I transform code-first projects into design-first masterpieces.
Preview of upcoming content:
- Design chat API from scratch
- Create comprehensive API documentation
- Real-time functionality
- Handling authentication and security
The above is the detailed content of A Guide to Planning Your API: Code-First VS Design-First Approach. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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