11.Design Considerations

Design Considerations

This page explains in detail about the below topics.

1. API Versioning
2. Rate Limiting
3. Pagination
4. Avoid N+1 problem

API Versioning

It is very hard to foresee all the resources, which will change during the life of the application. Hence, API versioning is crucial in the development and maintenance of RESTful web services, as it allows developers to manage changes and improvements in the API without breaking existing applications that rely on it. There are various strategies for versioning APIs, each with its own advantages and trade-offs. Below is a comprehensive overview of the common methods of API versioning, guidelines to consider, and real-life use cases.

Common Methods of API Versioning

1. URI Versioning:

   • Description: The version number is embedded directly in the URL.
   • Example:
     • Version 1: https://api.example.com/v1/users
     • Version 2: https://api.example.com/v2/users
   • Use Case: This method is highly visible and intuitive for developers. For example, a company may introduce breaking changes to their user API. To avoid disrupting clients, they create a new version by changing the endpoint.

2. Query Parameter Versioning:

   • Description: The version number is provided as a query parameter in API requests.
   • Example:
     • https://api.example.com/users?version=1
     • https://api.example.com/users?version=2
   • Use Case: A service might want to expose multiple versions while keeping the URL structure clean. Companies like Twilio use this approach, enabling clients to specify the required version in requests.

3. Header Versioning:

   • Description: Clients specify the API version in the request headers.
   • Example:
     • Request Header: Accept: application/vnd.example.v1+json
   • Use Case: This is more flexible and keeps URLs clean. It’s often used by APIs like GitHub’s GraphQL API, allowing users to define which version they want through HTTP headers.

4. Custom Header Versioning:

   • Description: Clients inform the server of the desired API version via the custom header Accept-version based on media types.
   • Example:
     • Custom request header: Accept-version: v2
   • Use Case: This method indicates precisely what type of response the client prefers. It allows fine-grained control over responses, enabling backward compatibility.

5. Subdomain Versioning:

   • Description: The API version is included in the subdomain.
   • Example:
     • https://v1.api.example.com/users
     • https://v2.api.example.com/users
   • Use Case: Organizations looking to segregate environments might utilize this form. For instance, a system like Stripe might use subdomains for different versions of their API.

Real-Life Use Cases

1. Twitter API Versioning: Twitter used URI versioning when they migrated from version 1.1 to 2.0. They introduced significant changes to the data structure and endpoints while maintaining legacy support for users still on the older version.

2. Stripe API: Stripe often uses header versioning, allowing clients to request a specific version of the API as part of their application flow. This is crucial as Stripe continuously evolves features and integrations, meaning old versions need to remain accessible for existing implementations.

3. GitHub API: GitHub utilizes content negotiation for versioning their API. Instead of changing URLs, developers specify versions in their requests, allowing for a clean and scalable API design. For instance, they provide endpoints that can respond differently based on the version specified.

Guidelines for Versioning APIs

1. Http Status code:

   • Use the right status codes to notify the clients about the latest versions.
   • Status code 301 indicates that a web page has been permanently moved to a new URL and browsers should automatically redirect users to the new location when accessing the old one.
   • Status code 302 indicates that the requested resource has been temporarily moved to the URL. A browser receiving this status will automatically request the resource at the URL in the Location header, redirecting the user to the new page.

2. Semantic Versioning:

   • Use semantic versioning (major.minor.patch) as a baseline for understanding breaking and non-breaking changes, helping clients understand the scope of changes between versions.

3. Deprecation Policy:

   • Clearly communicate deprecation timelines. Clients should be informed about deprecated versions well in advance, allowing them to transition seamlessly.

4. Documentation:

   • Maintain comprehensive documentation for each version, highlighting differences, new features, and migration paths.

5. Backward Compatibility:

   • Strive to maintain backward compatibility where possible. Introduce new features without removing existing ones whenever feasible.

6. Client Education:

   • Provide clients with guides and tools for upgrading between versions, focusing on the specifics of breaking changes or new capabilities.

Conclusion

Choosing the right versioning strategy depends on the specific application, user base, and anticipated future changes. While URI versioning is straightforward and commonly used, other methods like header and content negotiation provide flexibility and cleaner designs. Whichever method you choose, it’s essential to maintain clear communication and robust documentation to facilitate a smooth transition for your API clients.

Rate Limiting

Rate limiting is a crucial mechanism in RESTful web services designed to control the number of requests a user or application can make to an API within a specified time period. This helps manage load on servers, prevents abuse, and ensures fair usage across different clients.

Definition

Rate limiting restricts the number of requests a user can make to a server over a defined time interval. It is commonly implemented using HTTP status codes (e.g., 429 Too Many Requests) to inform users when they have exceeded their allowable request rate. Server can optionally include below headers in the response. Retry-After: Duration the client should wait before making the next call. X-RateLimit-Limit: Maximum number of requests that the client can make during a specific time period. X-RateLimit-Remaining: The number of requests remaining in the current rate-limit window.

Importance

1. Prevent Abuse: Protects APIs from being overwhelmed by malicious actors who might attempt to exhaust resources through DoS (Denial of Service) attacks.
2. Ensure Fairness: Maintains a level playing field for all users, preventing any single user from monopolizing the service.
3. Infrastructure Protection: Helps protect the backend infrastructure, ensuring it remains responsive and available.
4. Cost Management: In cloud-based environments, excessive usage can lead to high costs. Rate limiting helps manage these costs.

Implementation Details

Rate limiting can be implemented using various techniques. Here are the most common strategies:

1. Token Bucket: Users are given a bucket that can hold a certain number of tokens. They consume tokens with each request, and tokens are replenished at a defined rate. This method allows short bursts of traffic while maintaining an average rate.

2. Leaky Bucket: Similar to the token bucket but enforces a constant output rate. Requests are added to a queue, and they “leak” out at a steady rate. If the queue overflows, requests are dropped, which prevents surges.

3. Fixed Window: Count the number of requests from a user in a defined time window (e.g., 1 minute). If the number exceeds the limit, subsequent requests are rejected.

4. Sliding Log: Keeps a log of timestamps for the requests made. Each time a new request is received, it removes timestamps that fall outside the defined window, allowing more granular control over rate limits.

5. Geolocation-Based Limits: Implement different rate limits based on geographical location, which may be useful in apps that experience varied traffic patterns across regions.

Real-Life Use Cases of Rate Limiting

1. API Gateways: API gateways often employ rate limiting to manage traffic from various clients. For example, a company that exposes its API to multiple partners can implement rate limits per partner to ensure that no single partner can overwhelm the service.

2. E-commerce Platforms: During high traffic events (such as Black Friday sales or flash sales), e-commerce sites may use rate limiting to ensure that their service remains responsive and available to all customers. This prevents certain users from sending numerous requests to check stock or pricing information.

3. Social Media Platforms: Networks like Twitter or Instagram enforce rate limits on actions like posting tweets or comments to prevent spam and maintain a healthy user experience. For instance, a user may be limited to a specific number of posts per hour.

4. Payment Processing Services: Payment gateways can implement rate limiting to protect against fraudulent activities. This ensures that only a certain number of transactions can be processed from a single account within a specified timeframe.

5. IoT Devices: For Internet of Things devices sending data to a central server, rate limiting can be crucial to prevent network congestion. For example, a temperature sensor might be limited to sending readings every minute rather than flooding the server with data every second.

Conclusion

Rate limiting is an essential pattern in RESTful web services that helps maintain the stability, security, and reliability of web applications. By thoughtfully implementing rate limiting strategies based on the application's needs and anticipated user behavior, developers can significantly enhance the overall user experience and protect their backend services from abuse and overuse.

Pagination

Response pagination is a design pattern in RESTful web services that helps manage and organize large sets of data returned from APIs. Instead of returning all results in a single response (which can be heavy and inefficient), pagination divides the data into smaller, more manageable chunks or "pages." This reduces load times, decreases memory consumption, and improves the user experience.

Why Use Pagination?

1. Performance: Large data sets can slow down response times and increase server load. Pagination allows clients to retrieve only what they need.
2. Usability: Presenting a large number of items at once can be overwhelming. Pagination creates a more manageable and user-friendly interface.
3. Bandwidth Efficiency: Reduces the data transmitted over the network, which is especially important for mobile devices or users with limited bandwidth.
4. Scalability: As data grows, pagination helps in maintaining performance and preventing timeouts.

Types of Pagination

There are several common approaches to pagination:

1. Offset-Based Pagination: Uses an offset and limit to specify which subset of results to return. The client requests a specific "offset" to start from and indicates how many records to return.

   Example:

   • URI: GET /api/items?offset=20&limit=10
   • This request retrieves 10 items starting from the 21st item (0-based index).

2. Cursor-Based Pagination: Instead of using offsets, the last item of the current page is marked as a "cursor." The next page is requested using this cursor to fetch subsequent items. This is often preferred for large datasets as it avoids the performance issues associated with offsets.

   Example:

   • URI: GET /api/items?cursor=eyJidWlkIjoxMDEyfQ&limit=10
   • This request retrieves the next 10 items starting from the specified cursor.

3. Keyset Pagination: Similar to cursor-based but uses indexed values to determine the starting point of the next page. This method provides higher performance for sorted data.

   Example:

   • URI: GET /api/items?startAfter=100&limit=10
   • This retrieves the next 10 items where the identifier is greater than 100.

4. Page Number Pagination: This method allows clients to request a specific page by specifying the page number and the number of items per page.

   Example:

   • URI: GET /api/items?page=3&perPage=10
   • This retrieves the third page of items, where each page contains 10 items.

Refer this weblink for additional details: pagination-in-production

Implementing Pagination in RESTful APIs

A typical implementation of pagination in a RESTful API involves server-side logic to handle the parameters specified in the requests and return the appropriate subset of data. Here’s an example of how this might look.

Example API Definition

Imagine an e-commerce platform with an API endpoint for retrieving product listings:

1. Base URL:

   https://api.example.com/products
   

2. Pagination URI using Offset-Based Pagination:

   GET /products?offset=10&limit=5
   

   Response:

   {
       "totalCount": 100,
       "items": [
           {
               "id": 11,
               "name": "Product 11",
               "price": 29.99
           },
           {
               "id": 12,
               "name": "Product 12",
               "price": 39.99
           },
           ...
       ],
       "nextPage": "https://api.example.com/products?offset=15&limit=5"
   }

In the response,

• totalCount indicates the total number of products available.
• items array contains the paginated products.
• nextPage field provides a link to the next page of results, enhancing usability.

Real-Time Use Cases

1. E-commerce Applications: An online store may have thousands of products. Pagination helps users browse through categories and search results without overwhelming them with too many items at once.

2. Social Media Feeds: A social media platform might use pagination to load posts in a user’s feed. Users can view a limited number of posts at one time and load more as they scroll down.

3. Data Reporting Tools: Applications that generate reports, like analytics dashboards, can use pagination to show the user a summary with deeper insights available through subsequent pages.

4. Document Retrieval: In applications that manage vast libraries of documents or images, such as a content management system, pagination allows users to find specific content without browsing through extensive lists.

5. Search Results in Directory Services: For APIs that provide search capabilities (like job postings or database records), results can be paginated to make browsing easier for users looking for specific entries.

Conclusion

Response pagination is a critical feature in RESTful web services, facilitating efficient data management and enhancing user experience. By wisely implementing pagination and offering different types of pagination strategies, developers can ensure that their APIs perform well under varying load conditions and scale effectively with user demand. This approach is essential for modern web applications that deal with significant amounts of data.

N+1 problem

The N+1 problem is a common performance issue encountered in software development. It occurs when a query is executed to retrieve a set of entities, and then for each entity, an additional query is executed to retrieve related data. This can lead to poor performance, as it results in a large number of database queries, typically increasing the response time of applications. Though this is a common problem particularly when dealing with ORM (Object-Relational Mapping) systems and relational databases, this is applicable to the restful webservices also if they are poorly designed.

What is the N+1 Problem?

To illustrate, consider you have a list of users and each user has associated posts. If you want to retrieve all users and their posts using the following approach:

1. Add a API to fetch all users (1 request).
2. For each user returned (N users), run a another API call to fetch their posts (N requests).

In total, this results in 1 + N requests. If there are 100 users, it would result in 101 requests to the backend service.

Real-Time Use Case

Example Scenario: Blog Application

Imagine a blogging platform with the following data model:

• User: Represents a user of the platform.
• Post: Represents a blog post created by users.

When retrieving data for a dashboard that shows a list of all users along with their posts:

-- Step 1: Fetch all users  api/users/ 

-- Step 2: Fetch posts for each user (executed for each user)
api/posts/{userId};   for userId 1 -- request 2
api/posts/{userId};   for userId 2 -- request 3
api/posts/{userId};   for userId 3 -- request 4
...

If there are 100 users, this leads to a total of 101 requests.

Consequences of the N+1 Problem

1. Performance Degradation: The most direct consequence is slow performance due to increased load and latency in response times.
2. Increased Server Load: Each additional request requires more resources from the database server.
3. Increased Network Traffic: More requests correspond to more network requests, which can slow down application responsiveness, particularly in distributed systems.

Possible Solutions to Avoid the N+1 Problem

1. Eager Loading: Use eager loading to pre-fetch related data with a single request, often using appropriate request attributes. This allows all required data to be retrieved in one go.

   For instance, instead of the separate queries for each user’s posts, you can retrieve all relevant data at once:

2. Batching: Instead of making separate requests for related entities, batch multiple related queries into a single request. For example, if you know how to format the necessary queries, you can pull posts for multiple users simultaneously.

3. Caching: Implement caching mechanisms to store the results of common queries to reduce the need for repeated queries. For example, cache the results of user and post queries so that they can be served quickly without hitting the database.

4. GraphQL: Utilizing GraphQL can also help to mitigate the N+1 issue by allowing clients to specify exactly what data they need in a single request. GraphQL can automatically deduct the necessary requests to optimize performance.

5. Pagination and Filtering: Implementing pagination when dealing with large datasets can reduce the overhead and frequency of accessing related data. Fetching fewer records at a time helps control the load on the database.

Conclusion

The N+1 problem is a significant concern when designing RESTful web services, especially those that utilize ORM tools for database interactions. By implementing strategies such as eager loading, batching, caching, adopting GraphQL, and controlling data access with pagination and filtering, you can avoid performance pitfalls associated with the N+1 problem. These practices enhance the efficiency of web services, improving responsiveness and user experience.