Evolving API Designs

API evolution is the process of refining an API over time while maintaining compatibility with existing client applications. This involves introducing fresh features, phasing out obsolete ones, and making enhancements that boost API functionality without disrupting user experience. The primary goal? Keeping the API relevant and up-to-date while ensuring it remains accessible to current clients.

Adapting to Shifting Usage Patterns

Sometimes, API consumers utilize the service in unforeseen ways. Take Twitter, for instance. In the past, the Twitter app had to constantly poll the backend API for new tweets in real-time. This led to a surge in traffic to the REST APIs, causing scalability challenges for the rapidly expanding user base.

How did Twitter tackle this issue? In August 2020, they rolled out a new streaming API that pushed new data to clients, reducing the need for polling. This allowed developers to subscribe to specific keywords or users and receive new tweets through a persistent connection.

Complementary API Methods

There are times when essential APIs are resource-intensive and return large responses. Developers don’t always need the entire dataset and might only require a fraction of the response. If the only options are all-or-nothing, developers might end up requesting full responses, leading to wasted resources on unused data.

While modifying an existing API can be challenging, adding a new method is relatively straightforward. Slack encountered this issue with their popular Real Time Messaging (RTM) API method, rtm.start. As team sizes grew, the payload size became problematic. In response, Slack introduced a new method, rtm.connect, which only returned data relevant to the WebSocket session, addressing scaling concerns and reducing resource waste.

Result Filtering

When an API returns numerous objects, it’s wise to offer result filtering options. This empowers developers to limit the number of objects returned by the API based on their specific needs, enhancing scalability. Some common API filters include:

  1. Date filters: Limit results to a specified date range (e.g., Facebook feed, Twitter timeline)
  2. Order filters: Allow sorting results by specific properties
  3. Field selection: Give options to exclude certain fields, especially those that are computationally expensive

Embracing Bulk Operations

In scenarios where developers need to perform the same operation on multiple items (like adding several users to a group chat), making individual API calls for each operation can increase latency and strain large-scale systems. The solution? Bulk endpoints.

Bulk endpoints allow multiple calls of the same type to be combined into a single request, reducing HTTP round trips and improving efficiency. Slack, for example, enhanced their channels.invite method to support inviting multiple users in a single API call:

JavaScript
{

  "channel": "C1234567890",

  "users": ["U2345678901", "U3456789012", "U4567890123"]

}

This approach saves resources for both Slack and its developers.

graph TD
    subgraph "Individual API Calls"
        A[Client] -->|"POST /invite<br>user: U2345678901"| B[API Server]
        A -->|"POST /invite<br>user: U3456789012"| B
        A -->|"POST /invite<br>user: U4567890123"| B
    end

    subgraph "Bulk API Call"
        C[Client] -->|"POST /bulk_invite<br>users: [U2345678901,<br>U3456789012,<br>U4567890123]"| D[API Server]
    end

    B -->|"3 Responses"| A
    D -->|"1 Response"| C

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#bfb,stroke:#333,stroke-width:2px
    style D fill:#fbb,stroke:#333,stroke-width:2px

Evolution vs. Versioning: What’s the Difference?

While API evolution and versioning are related concepts, they serve different purposes.

Evolution focuses on gradual improvements to an existing API, while versioning creates distinct iterations of the entire API.

When to opt for evolution:

  1. Minor enhancements: If changes don’t break existing functionality, evolution is often the best choice.
  2. Backward compatibility: When you can maintain support for existing clients while adding new features.
  3. Frequent updates: For APIs that require regular small improvements to stay current.

When versioning might be necessary:

  1. Major overhauls: If significant changes to the API structure or functionality are needed.
  2. Breaking changes: When modifications will disrupt existing client integrations.
  3. Long-term support: If you need to maintain multiple API versions for an extended period.

Consider this analogy: Think of API evolution as renovating a house while people are living in it. You’re making improvements without forcing residents to move out. Versioning, on the other hand, is like building a new house next door and giving residents the option to relocate when it’s ready.

Effective Communication of API Changes

Clear communication is vital when evolving or versioning an API. Here are some strategies to keep your consumers informed and prepared:

  1. Deprecation notices: When phasing out features or endpoints, provide ample warning. For example, GitHub uses custom headers to indicate when an API version will be deprecated:
    • X-GitHub-Media-Type: github.v3; deprecation-date="2022-11-15"
  2. Changelog: Maintain a detailed, easily accessible changelog that outlines all modifications, additions, and deprecations.
  3. Documentation updates: Keep your API documentation current, highlighting new features and changes. Consider using visual cues to draw attention to recent updates.
  4. Developer newsletters: Send regular updates to your API consumers, summarizing recent changes and upcoming modifications.
  5. Beta programs: For significant changes, consider offering a beta program allowing developers to test and provide feedback before full implementation.
  6. Versioning in URLs: If you do introduce a new version, clearly indicate it in the API URL. For instance: https://api.swequiz.com.com/v2/users
  1. Grace periods: When deprecating features, provide a transition period where both old and new versions are supported, giving developers time to adapt.
graph TD
    A[Identify Need for Change] --> B[Plan and Document]
    B --> C[Announce Changes]
    C --> D[Beta Program]
    D --> E[Release New Version v2]
    E --> F[Deprecation Period]
    F --> G[Remove Old Version v1]

    B -.-> H[Update Documentation]
    C -.-> I[Developer Newsletter]
    D -.-> J[Gather Feedback]
    E -.-> K[Update Changelog]
    F -.-> L[Deprecation Headers]

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#bfb,stroke:#333,stroke-width:2px
    style D fill:#fbf,stroke:#333,stroke-width:2px
    style E fill:#bff,stroke:#333,stroke-width:2px
    style F fill:#ffb,stroke:#333,stroke-width:2px
    style G fill:#fbb,stroke:#333,stroke-width:2px