APIs (Application Programming Interfaces) have become an integral part of software development and allow different applications to communicate with each other. However, errors can occur during API calls which need to be handled gracefully. These errors are denoted by specific error codes that provide insights into the issue.
In this comprehensive guide, we will dive into different types of error codes encountered in APIs and how to deal with them.
Introduction to API Error Codes
An API error code is a specific numeric or alphanumeric value that indicates a problem or unexpected behavior encountered during an API call. It allows developers to easily identify issues when integrating with an API.
Here are some key points about API error codes:
- Error codes are returned in the response of a failed API call.
- The error response usually contains an error code, error message, details about the issue and sometimes recommended actions.
- Error codes help segregate API issues into specific buckets making it easier to handle them programmatically.
- The numeric error codes generally correspond to HTTP status codes like 400 for bad request, 403 for forbidden, 404 for not found etc.
- Error codes are standardized by the API provider to maintain consistency across services.
Equipped with error codes, developers can handle issues like incorrect authorization, invalid parameters, missing resources, service outages etc. gracefully. The error flows in the application can be customized as per the error scenario without breaking the user experience.
Common Types of API Error Codes
There are no hard and fast rules and the error codes differ based on the API provider. However, some commonly encountered categories of error codes are:
1. Client Errors
These error codes in the 400 range indicate an issue from the client-side when interacting with the API. Some examples are:
- 400 Bad Request: This indicates an invalid request was sent to the API. This could be missing parameters, invalid data types etc.
- 401 Unauthorized: This error means the call is not authenticated properly to access the API.
- 403 Forbidden: The client does not have permission to access the requested resource.
- 404 Not Found: The requested resource does not exist.
- 429 Too Many Requests: The request was throttled as too many requests were sent in a given time span.
2. Server Errors
These error codes in the 500 range indicate an issue with the API server. Some examples are:
- 500 Internal Server Error: A generic server error occurred.
- 503 Service Unavailable: The server is temporarily unable to process the request due to high load or maintenance.
- 504 Gateway Timeout: The upstream server took too long to respond.
3. Platform Errors
For cloud platforms like AWS, Google Cloud, Azure etc. that provide APIs, specific error codes are defined. Example for AWS API error codes:
- AccessDeniedException: The user does not have access to the requested resource
- InternalFailure: The service experienced an internal failure
- ResourceNotFoundException: Requested resource does not exist
- ServiceUnavailable: The service is temporarily unavailable
4. Payment Errors
APIs dealing with payments like Stripe have errors specific to the domain. ExampleStripe error codes:
- expired_card: Credit card expired
- incorrect_number: Card number is incorrect
- invalid_expiry_year: Invalid expiration year
- processing_error: Payment processing failed
Best Practices for Handling API Error Codes
Here are some best practices that must be followed when dealing with API error codes:
1. Catch errors and exceptions
Use try-catch blocks and error handling to account for failed API calls. This prevents the code from breaking whenever an issue occurs.
2. Log errors
Log API errors along with error codes, messages, related context etc. to a file or error monitoring tool. This creates a permanent audit trail and helps in debugging.
3. Gracefully handle failures
Provide fallback behavior based on error scenarios – like retry, default values, notifications etc. This improves user experience.
4. Understand error code taxonomy
Review the API documentation to understand the categories of errors and how to interpret them. This knowledge helps build robust integrations.
5. Consult error guides and support
For complex issues, refer to the API provider’s troubleshooting guides based on error or reach out to developer support.
6. Exponential backoff
Use techniques like exponential backoff while retrying failed requests to give time for issues to resolve.
7. Monitor error metrics
Track API error codes, frequency, latency etc. to identify recurring problems and troubleshoot.
Example Scenarios for Handling API Error Codes
Let us explore some example scenarios to understand how to handle API errors appropriately in real-world applications.
1. 404 Resource Not Found Error
When consuming a REST API that returns a 404 error, it indicates the resource i.e. API endpoint, is not found. There are a few ways to handle it:
- Log 404 errors to monitor bad requests
- Check with API documentation if endpoint path is correct
- Notify user that page or content is not found
- Provide a fallback redirect to home or search page
2. 500 Internal Server Error
A 500 internal server error shows the API service is facing an issue. Possible ways to handle it:
- Log 500 errors with timestamps and request information
- Retry the request 2-3 times before notifying user
- For third party APIs, might need to wait before retrying
- Show an error message asking user to try again later
3. 429 Too Many Requests
When an API hits a 429 error due to rate limits, here is how it can be handled:
- Understand the rate limits imposed by the API
- Implement exponential backoff retry with increasing delays
- Collect metrics on number of 429s to optimize request rate
- Consider using caches and throttling requests
4. Invalid API Key
For APIs requiring an API key for access, an invalid key would result in a 401 unauthorized error. Ways to handle this:
- Make sure API key is correct and stored securely
- Implement key rotation and disable compromised keys
- Prompt user to reconnect API key if invalid
- Guide users to regenerate API key if necessary
Tools for API Error Monitoring
There are various tools both paid and open source to help track and monitor API errors efficiently:
1. Raygun
Raygun provides error and performance monitoring tools for web and mobile applications. It integrates with various frameworks like .NET, Java, Python etc. Raygun dashboards make it easy to triage issues by status, environment, frequency etc. It also allows setting up event hooks on errors for tasks like notifications.
2. New Relic
New Relic is a robust performance monitoring system for applications. For APIs, it provides error analytics to discover root causes. Errors can be correlated with deployment events to pinpoint breaking changes. Granular filters help zoom into specific API errors.
3. Rollbar
Rollbar focuses on error monitoring by aggregating errors and exceptions in a single place. It captures complete request context and stack traces for API errors. Error grouping, analytics and slack/email notifications streamline debugging workflows. Rollbar integrates with all major code languages and frameworks.
4. Sentry
Sentry provides open source error tracking tailored for monitoring API errors with context data. It surfaces the source code causing the issue along with environment details like browser, OS, stack trace etc. It has plugins for integration with most web frameworks.
Conclusion
API error codes provide a structured way to handle exceptions and issues when connecting with external services. By following API error handling best practices and using monitoring tools, developers can build fault-tolerant integrations. This results in robust applications with minimal disruptions for customers.
Going forward, most APIs will aim for standardized error responses to ease developer experience. With the evolution of cloud-native architecture using microservices, having proper error handling and observability will be critical. Understanding the core concepts around API errors puts developers in a position to create resilient cloud applications.