How can you design RESTful API resources to support error handling and logging?

Using standard status codes on request outcomes is definitively something that should be done to increase how intuitive an API is. Although the machines interfacing with each other using APIs don't really care of it (they just follow what they are programmed for), the developers do. Ultimately if you are developing an API, it is other developers who will need to create an integration using them and it's always better to make that part as simple as possible.

Using standard HTTP status codes help developers writing consistent behavior for an application. For e.g. To create a single webpage, developer might need communicating with multiple endpoints. The behavior of web pages can easily be controlled using HTTP status codes. A single web utility can take decisions for multiple API requests based on the status codes. Adding descriptive message as part of response can help displaying user friendly message on the web application. These messages can also be used as part of error logging for debugging purposes as well.

HTTP status codes are numeric codes in HTTP responses that indicate the outcome of a request. They classify responses, inform about success or errors, and guide clients on how to react. They play a crucial role in error handling, redirection, and maintaining consistency and interoperability in web communication. Grouping of of status code is well know, but documentation make use of API smoother: - Helps API consumers understand how to interpret responses. - Guides developers on how to handle different outcomes. - Ensures consistency in handling errors and responses. - Simplifies troubleshooting and debugging. - Promotes interoperability and consistent use of status codes across the web.

To enhance error handling in your RESTful API, consider providing meaningful error messages in addition to the status codes. This helps clients understand why an error occurred and how to rectify it. For instance, if a user submits an incomplete form, you could return a 422 Unprocessable Entity status code along with a JSON object containing specific error details, such as which fields are missing or incorrect. This way, clients can address the issue more effectively.

All the answers I've seen on this thread are good. I would just add that in .NET Core 8 there's a new global error handler interface that can be injected at start up.

API error messages should be categorized differently for internal APIs and external APIs. While it makes sense to have fields like custom error code and type for an internal API, it would be beneficial to keep it simple and functional for the external APIs. For ex- having a debug_id as part of your error message object will help developers to reference it while making a Technical Support call to the API provider. Also, a link to developer docs explaining the error is definitely helpful to developers { "name": "AUTHENTICATION_FAILURE", "message": "detailed error message", "link": "link_to_developer_docs", "debug_id": UUID }

One additional attribute needed in error responses is a unique identifier, which can then be traced throughout the logs; this is useful for observability and it’s especially needed in a microservices arquitecture.

You are using FastAPI and Pydantic? Chances that you are raising exceptions like "HTTPException(400, detail="Invalid email address")" is quite high. Consider using pydantic models to carry more information such as mention in the corresponding section of this article: HTTPException(422, detail=Error( ----error=ErrorBody( --------code="ERR001", --------message="Invalid email address", --------type="Validation", --------source="email" ----) ).model_dump() Or even better: def endpoint(email: pydantic.EmailStr) Doing so, Pydantic will take care of your validation and return data rich error for your front-end.

Some practices that I have found useful are: - Keep your pantheon of HTTP response codes concise. You don't have to embrace the full semantics offered by HTTP. This can help adoption become straightforward. - Consider using middleware to allow you handle common exceptions in a uniform way. Allow exceptions to bubble up to error handlers that enforce a common pattern on responses. You can also use ErrorControllers for this. - Keep stacktraces out of your response. Seems obvious but worth reinforcing.

Word of caution here regarding AuthN and AuthZ error codes you might return to your users. Make sure you do not return error messages or codes that can used as proxy for your internal API security systems. As these can be used to reverse engineer your security algorithms and thus design a request or package to avoid them or crash your service. Avoid returning as part of the errors internal library names (specially open source as these can be easily found and learnt), DB names, cloud regions, IP addresses, etc. Think as if these are PII for your components :)

We must implement logging mechanisms to capture relevant information about the API request, including timestamps, endpoint, request payload, user details (if applicable), and error details. Log errors and exceptions with appropriate severity levels to help forward effective monitoring and troubleshooting.

For implementing logging in Spring boot. Use SLF4J facade along with Json logging for higher environments. For local environments use the normal Spring Text logging. How to log? Instead of writing multiple log statements in each method, I recommend utilizing a `LoggingAspect` from Spring's AOP (Aspect-Oriented Programming). Creating a single Aspect class with pointcuts and join points makes it straightforward to log entries and exits of Spring methods during execution.

Adding logging level and having this practice implemented across the team is crucial here. For example, some developer use to post it as Error loglevel, but it may be `critical` loglevel. the consistency across the team is crucial

Instead of writing comment write log statements! Logs should tell the story of what your application has been doing while it is running. Log levels should be used to highlight the various details of this story. - INFO level logs should be used to highlight key events within your application. - DEBUG should give insight into what is happening in your application. - TRACE logs should give all the specfic details like object compositions etc. - WARN to highlight handled or expected problems - ERROR should highlight severe or critical problems - FATAL should highlight errors that result in complete failure of our application.

Error levels have proven to be extremely helpful while troubleshooting, understanding the behavior of the services and tracing the client's interaction with the APIs. This makes it easier to filter out what's not relevant in each specific case that requires reading through logs, as well as it helps with implementing metrics and making monitoring more efficient.

Traceability is critical in a distributed ecosystem. Integrations can get complicated over time. I would like to add that incorporating standards plays a critical in system maintainability and troubleshooting. Adopting a standardized logging format or even better embracing industry standards like Open Tracing across all services ensures logs are easily readable, consistent, and can be seamlessly integrated into monitoring and alerting tools. Having a traceable system is a key to improving the resiliency of the system. Adopting industry standards would make adopting open source or enterprise tools simpler, enabling you to pinpoint inefficiencies, understanding system bottlenecks, and ultimately providing a top-notch user experience.

It's always recommended to use a unique transaction Id for every api call. It saves a great amount of time on debugging errors.

Generate and include a unique correlation ID in each API response and log entry to link requests and responses, aiding in troubleshooting and tracking through distributed systems. You may follow these steps: 1. Generate a Correlation ID or UUID for each request 2. Associate the Correlation ID with the request 3. Include the Correlation ID in the API response 4. Retrieve and use the Correlation ID on the client side PS: A Correlation ID is a unique identifier value that is attached to requests that allow reference to a particular transaction.

Just by generating a request ID, we'd be saving considerable amounts of time when attempting to follow the traces of each request. It's common to have multiple log outputs per a single request, so a unique ID helps avoiding to lose track of each step the server takes to resolve any incoming request.

I found it helpful, as it helps to track end to end workflow of the request which gives a full picture of where the issue might be. Easy to implement, generate a hash based on anything relevant you feel and add that to every request and response which will be logged.

A wide number centralized logging services of all kinds are currently available, and it's so easy to integrate most of them with our web services, that we shouldn't hesitate to consider this one of the first steps to take when building a new API, and even if we miss it at the beginning of the project it's always worth pausing feature development to include the logging centralization as well as other monitoring tools, so that the future increase in traffic, possible introduction of bugs, and auditing needs are covered by this good practice.

Once a company scales up from a few services, central logging is critical. The natural first stop is the ELK stack hosted on-prem. This is a great choice but developers will have to do a lot more leg work to plug all their services and infrastructure in. 3rd party providers are great as scale increases or you move into a hybrid/cloud environment. They continually add more support for new tech. Usually by pushing an update for the logging agent or updating a package you get new telemetry. Beyond logs, the ability to create dashboards for app support and create alerts is critical to resolving critical incidents.

Centralized logging needs standardization to make use of its capabilities. Add the domain, module, time, transaction id, log level. These will help in differentiation and help us to analyse ,debug and smoothen all other operations. Designing a single logging system for the entire organisation has unlimited advantages.

I'm skeptical about "use a centralized logging service" as part of "API design guidelines". API resources must provide enough functionality to report health state, performance, and other parameters for monitoring, proactive risk management and governance purposes. But the API resource itself is rarely expected to have or use a dedicated "logging service". I'd reformat this proposal as "Implement flexible and reliable activity and health state protocol functionality". Use of centralized or distributed or separated subscribers and consumers of the logging data is tailored to concrete implementation, infrastructural requirements etc.

Definitely a centralized logging service is useful to identify and debug an issue. This is more useful when a task requires multiple API calls(sequentially) from different services. In order to stitch the related invocation, an invocation key can be passed to all the API invocation, which can then be used to stitch the flow to identify and debug.

We can also consider Exception Handling and Versioning. Implement robust exception handling within the API code to catch and appropriately handle exceptions, ensuring they are transformed into meaningful error responses. Versioning allows for backward-compatible changes, ensuring that updates or modifications do not break existing client implementations.

- Avoid having non-standard HTTP calls, e.g., DELETE with payload. - When having many old existing services, it is hard to identify circular dependencies. Enable trace ID/span ID and use tools like zipkin to see the graph of service dependencies.

Here are some highlights to keep an eye while designing or developing REST APIs : - Resource naming should also be consistent - Considering security aspect these days the error messages should not be giving out too much information that may expose any weakness which in term will be an opportunity for attackers. - Versioning is must - All security aspects like XSS, SQL-injection, CORS, CSRF etc should be taken care of. - Appropriate logging of information, irrespective of success or failures, plays vital role because logged data are fed into monitoring system. An intelligent monitoring system has become a thing in detecting any probable or predict any possible attack on API.

Use your platform's capabilities to enforce consistency. Every modern web application framework should support an extensible "middleware" that enables you to intercept the request process across every endpoint. This makes it incredibly easy to ensure that cross-cutting concerns like error handling, response formatting, and so on are automatically included without the need of repetitive boilerplate code in every endpoint. In effect, it makes it almost impossible to omit these centralized concerns. This also simplifies adding, removing, or changing those cross-cutting concerns everywhere without needing to change every endpoint individually.

The logging solution I've found gives the best results in Production with Monitoring Platforms: - Log complex processes of business logic - Log downstream traffic - Log inbound traffic In each of these area's you have a few goals: - Log Once only - Track total time - Capture the important details -- Success Details that might be helpful in identifying the type of success -- Failure Details that might be helpful in triaging an issue - Follow a format that can be consumed - Log with mass consumption in mind You can build Dashboards and Charts on every log you output. Consider Transactions instead of Sessions. If your Transactional Logs are done right. Tracking Sessions becomes even easier. And Performance issues become simple to find.