Monday, December 21, 2009

Software API Design

In this article I present my view on the design of software APIs. Several other people have written extensively about the importance of API design and best practices, mostly in the Java community, but still I find so many poorly designed software APIs.

What is a Good API?

At the high level only a few design principles: complete, easy-to-use and minimum, are enough to design good API. The API should be complete, without any missing element, to achieve all the necessary functions of the software. At the same time, the API should be minimum, without much redundancy. For example, if the same thing can be done using two method, pick the best, and use that. Most importantly the API should be easy to use.

Consider the Unix socket API. It provides the core functions such as connect, sendto, bind, using explicit methods whereas additional rarely used functions are supported using setsockopt or ioctl. The best feature of the API is that it treats a socket similar to a file descriptor, so you could indeed use the file related functions on a socket.
s = socket(...);
write(s, ...); /* use it like a file */

Asynchrounous I/O

The annoying part in Unix socket is the select and poll set of function for doing asynchronous I/O. The primary reason is that there is no standardized way for asynchronous API in Unix. On the other hand Windows invented the WSAAsyncSelect function which is more difficult to use. An event-based API for asynchronous I/O is clean and easy to use, provided the event is dispatched by the relevant object.
s = new Socket();
s.addEventListener("socketData", handlerFunction);

Return vs exception

Exception is better for indicating failure cases, if the failure is less likely to happen. On the other hand, return value of failure is suitable if the failure is part of the result. For example, a findUser() function can return the User object on success, and null on failure such as not-found. What if there is other kind of failure? Should it return null or throw an exception?

Exceptions make more cleaner code, at least in Python. This avoids several if-else constructs to check the return values, and you can accumulate all error handling at one place, either in this function or in the caller stack frame.

Value vs reference

You would have studied pass-by-value and pass-by-reference in your programming class. This topic goes beyond that. For example, if a Camera object represents a default-camera for your machine, should the Camera object automatically switch to the new camera if the user changes the default camera from the control panel independent of your software?
cam = Camera.getCamera("default")
# will cam be the current snapshot of default-camera or will it automatically
# change when default-camera changes?
In your software, if you need to represent the local logged in user as a LocalUser object, should you create a new object when the login changes or should the same object update its state to reflect the new logged in user? If you use a local structure to represent the local listening IP address of your network application, should this structure automatically change its IP value whenever your machine's IP address changes?

The value object is easy to implement and understand. However, if an application needs to detect any change in the value they need periodic polling or event dispatching on change. The reference object is more convenient and clean to the application developer, but needs additional work in the implementation of the API. One option is to have generic reference wrapper, which can be used to convert any value object to reference object, as long as the change detection is uniform.

Synchronous vs non-blocking

Synchronous APIs are easy to understand and use whereas non-blocking event-based APIs are difficult to use. Non-blocking calls have advantage that your thread is not held-up waiting for the response. This is particularly a problem for single threaded software systems. In some platforms, e.g., ActionScript on (single threaded) Flash Player, there is no choice, and you must do non-blocking calls. Python provides constructs such as yield, which allows you to write synchronous co-operative multitasking software. Thus, even though your application code looks like synchronous, it actually yields to other task behind the scene. Clearly critical section and shared resources must be protected appropriately for read and write access as needed.
task1: data = yield multitask.recv(sock, timeout=10)
task2: yield multitask.send(sock, somedata)

Generic vs specific

While most API designers tend to write methods that are specific for one task, the arguments should be generic. In C++ or Java, templates allow you to specify generic algorithm. The question on generic vs specific spans more use cases: should your supply a String as your method name, or should you define an Enum and use that? If you already have a function getUser(String name), should you define another function getUserById(int id) or should you overload using argument type?

Generic API is more extensible but care must be take to handle various edge cases and throw appropriate exceptions.

Document and testing

How should the API be specified? Should there be a set of documents that describe each and every function in various use cases, or should there be built-in test cases that show how to use the API? or both? With document alone, how do you make sure that the document is updated every time someone updates the implementation? Python's doctest module allows you to integrate the unit testing in the API document itself. This cleanly makes sure that your unit test will fail if your API implementation changed.

Analogy

The single feature which improves the usability of an API is using an analogy with some existing thing. For example, a web interface modeled after Unix file I/O is very easy to understand and use. On the other hand, a completely new paradigm for your API will make it hard to understand. Other examples of existing paradigms are the listener-provider, event dispatcher, property getter setter, read-only vs read-write property, attribute vs container access, dictionary or hash map collection. For example, if you want to implement a new P2P storage module, consider exposing it as a dictionary where user can put and get values using keys, and can replace his existing code that uses local dict or HashMap with your new distribute dictionary.

Conclusion

Software APIs tend to last longer than expected. Careful thoughts in the design process can help your API withstand the test of time. When you design an API, weigh your options with respect to these properties: synchronous vs asynchronous, return vs exception, value vs reference, generic vs specific, and incorporate analogy with existing paradigm, and automatic documentation and testing tools.

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