Node.js – Fast and Package-Rich
Similar to Python’s Twisted, Node.js employs an event model, rather than using traditional threads. The advantage to this model is that users can avoid dead-lock, since no process will ever perform I/O directly. Rather than using event-loop callbacks, Node.js enters a given event loops as soon as an input script is executed, and exits the same loop when all callbacks have been performed.
Another reason Node remains popular is that there are more than 100,000 packages available for it, which can obviously reduce costs and time investment for various projects drawing at least in part on ready-made components.
While these features lend themselves well to dynamic development for tight loops and simple benchmark applications, there are some limitations to Node.js in CPU- or memory-bound tasks, and generally with applications of higher order application logic. Latency becomes a significant issue in more complex applications because the V8 virtual machine is known to lag when attempting to infer the type being employed, thus suffering optimization issues. Additional drawbacks of Node.js include a dearth of shared code between server and client applications, as well the incorporation of CoffeeScript in many packages. Obviously, both features mean a coder will have to be proficient in, and agile at utilizing, more code types.
Go – Less Code, More Concurrency
Go, or Golang, is a compiled systems-oriented language that is simultaneously more concise and more expressive than Node.js. For instance, in Go a developer does not need to specify a variable type when defining it, nor do “if” statements require bracketing. Go is easier to pick up for new programmers or those used to other codes, as it guides the user towards good code with appropriate error handling helped along by strong built-in testing capability.
What is more, unlike its direct predecessors, namely C and C++, Go does not sacrifice performance to ease of coding, thanks to multi-core and parallel computation support, and the static compilation of Go’s statically-typed code. Specifically, the ability to inspect code before runtime, versus dynamic JS compilation in the case of Node.js, means that coders have far greater flexibility in terms of optimization.
Go also offers greater concurrency support thanks to its utilization of goroutines, lightweight threads managed by the Go runtime that communicate with one another using channels rather than an event-callback mechanism. Dead-lock is not a problem with Go, either, as send and receive commands block out the system until the other side is ready, thus enabling goroutines to synchronize without interference from locks or condition variables.
In applications requiring significant scalability, Go is the go-to choice, as it was designed from the ground up with scalability in mind, utilizing, as mentioned, native executable code of a fast and compact nature. Indeed, Go’s primitives are aimed at facilitating scalability and concurrency by sharing memory through channels, rather than requiring memory sharing in order to make communication possible, which tends to create race-conditions.
The key negative of Go is its still fledgling community of users and developers, though this is growing by the month. Packages and third-party libraries are lacking, but steadily increasing in number as more big name firms, like Dropbox and Webcreek Technology, switch over successfully to the newer language.
The Bottom Line
Ultimately, the choice of code will come down to a user’s application needs. Perhaps the key attraction of Node.js, based as it is on the well-established JS language, is the wealth of available packages, tools, and general community depth that it enjoys. In contrast, Go has been around a much shorter time (since 2007) and its resource base is still expanding. That being said, Go does boast a host of complete core libraries and the availability of an ever-increasing number of “go get”-able packages from its growing community of users. Moreover, Go’s syntactically cleaner coding boasts better concurrency, which, combined with its generally more efficient CPU- and memory-bound performance, will likely make it the right choice for projects needing significant scalability, particularly in network applications.