APIs play a critical role in enabling communication and data exchange between different software systems. However, relying on external APIs may introduce vulnerabilities, such as service outages or unexpected changes in functionality. To mitigate these risks and ensure the smooth functioning of your applications, building fault-tolerant custom APIs is essential. In this article, we will explore the key principles and strategies for creating resilient and reliable custom APIs.

To build a fault-tolerant custom API, it is crucial to design with resilience in mind from the outset. This involves considering potential points of failure and implementing strategies to handle them. Here are some design principles to follow:

• Design for resilience
• Implement Error Handling
• Use Caching & Rate Limiting
• Plan for Failure & Disaster Recovery

New to API’s? Here’s a started guide. Suggested Read: What are APIs and how do APIs work?

#1 – Design for Resilience

Distributed Systems

Circuit Breaker Pattern

Distributed systems refer to a network of interconnected computers or nodes that work together to achieve a common goal. In such systems, tasks and data are distributed across multiple nodes, enabling parallel processing, fault tolerance, and scalability. Let’s delve into the concept of distributed systems in more detail, along with some examples:

Client-Server Architecture: One of the most common examples of a distributed system is the client-server architecture. In this model, the system consists of client nodes that make requests and server nodes that respond to those requests. The clients and servers can be located on different machines or even geographically dispersed.

For instance, consider a web application where clients (web browsers) communicate with servers (web servers) to fetch and display web pages. The workload is distributed across multiple servers, enabling concurrent handling of client requests and improving performance.

Distributed Databases: Distributed databases store data across multiple nodes, providing benefits such as fault tolerance, scalability, and improved performance. In this setup, data is partitioned and replicated across multiple database servers.

For example, Google’s Bigtable is a distributed database that is designed to handle massive amounts of data. It distributes data across multiple servers and provides high availability and scalability for applications like Google Search, Gmail, and Google Maps.

By distributing tasks and data, these systems enhance performance, enable fault tolerance, and accommodate the increasing demands of modern applications and services.

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#2 – Implement Monitoring & Error Handling

• Comprehensive Logging: Implement detailed logging mechanisms to capture information about API requests, responses, and any errors or exceptions encountered. This data will be valuable for troubleshooting and identifying potential issues.

• Health Checks: Regularly monitor the health and status of your API components, including servers, databases, and third-party services. Implement health check endpoints that can be queried to determine the operational status of each component.

• Error Handling: Develop a systematic approach to handle errors and exceptions. This may involve providing informative error messages, implementing retry mechanisms for transient errors, and using appropriate HTTP status codes to communicate the status of API requests.

Want to read more about API issues to look out for? Check out our blog post: What are the top problems with using API’s to integrate systems together? 

#3 – Use Caching & Rate Limiting 

#4 – Plan for Failover & Disaster Recovery

In Conclusion…