All programming languages assign "types" to the variables that exist within an application's code, which then act as instructions regarding how to treat that variable and its associated operations. General-purpose languages like Java and Python typically use familiar data types such as string for text values, int for numbers, and bool for binary true/false variables. For a program to do its job correctly, these declared value types need to match up with the values they get assigned, as well as the arguments and functions that call for them.
Currently, the two most common approaches to variable typing are static typing and dynamic typing. When it comes to handling types, both of these approaches offer their own sets of benefits and pitfalls. However, at its core, the choice comes down to what you need more: Operational stability and clean code, or agility and development flexibility.
Let's look closely at how these two methods handle typing, and the factors that influence their most practical use cases.
What is static typing?
Static typing, as is found in languages like Java, C, C++ and Go, is considered a relatively risk-averse approach to coding, mainly since the type checking process occurs at compile time. As such, these checks will catch things like missing functions, invalid type arguments or a mismatch between a data value and type of variable it's assigned to before the program even has a chance to run the erroneous code and risk crashing.
For example, imagine a developer types the following in Java:
String varX = 10;
The variable varX was declared using the type string, but subsequently assigned a number value of 10. Had the developer introduced varX using a number-based type like int, or assigned it a text-based value, this would be fine. However, in this case, the Java compiler will detect the mismatch, stop compiling the code and announce the error.
Since a programmer can't even run code unless it passes these type checks, it forces them to fix these errors up front and, ultimately, maintain cleaner code. However, this can be particularly problematic (and frustrating for developers) if small, inconsequential variable mismatches prevent an otherwise intact program from compiling. This is where dynamic typing comes into the conversation.
What is dynamic typing?
For example, imagine a Python developer declares a number-based variable, but ties it to a method designed to handle text variables (note that Python doesn't require you to declare variable types before assigning them a value):
varX = 10
Since the method print can't accept a numeric variable as an argument, this will produce an error. However, despite the typo, the compiler doesn't concern itself with the variable type and will still allow the program to run. Once the mismatch is detected during runtime, the type-checker will pause execution, throw an error and present a chance to adjust either the variable or calling function as needed.
This approach will allow developers a lot more freedom when it comes to building programs, since they can focus on functionality rather than get bogged down in the particulars of variables. It may also lead to a more streamlined end-user experience, since a small variable mismatch won't completely stop them from using the application in other ways. However, this practice can quickly lead to lazy coding practices that will cause bugs and breakages to spread through code. Over time, these bugs will only become more buried and harder to find.
Strongly-typed vs. weakly-typed
There is a common misunderstanding surrounding static and dynamic typing regarding its relationship to the concept of strongly-typed vs. weakly-typed languages. While these typing characteristics are closely related, there is a big difference between the two. Weakly-typed languages offer a lot of freedom when it comes to variable-type agreement: When they encounters a mismatch, they will often make certain assumptions regarding what the intended variable type was instead of throwing an error. Strongly-typed languages, on the other hand, are particularly sensitive to variable-type compatibility, and leave little room for misinterpretation when it comes to intended values and arguments.
How to choose between static and dynamic
Some assume that the specific kinds of errors identified by static type-checking processes are different from those identified by dynamic type-checking. While there is a difference when it comes to the errors, it doesn't revolve around the kinds of errors -- it has to do with the time that those errors occur.
The decision of using a dynamically-typed language versus a statically-typed one comes down to the core purpose of a specific project -- namely, whether the goal is reliability or agility. Static typing is typically well suited for building and maintaining programs that don't change that often, perform relatively straightforward processes, and depend heavily on operational consistency. This method often works well in large-scale application environments, where maintenance, reliability and security are priorities.
On the other hand, dynamically-typed languages are a better choice when the goal is to enable fast feature additions, operational agility and overall application availability. Some also say dynamically-typed languages are easier for novices to learn and write programs with, thanks to their non-prohibitive nature. However, dynamic typing can quickly facilitate compounding coding mistakes and cause error-inducing bugs to infiltrate application systems and perpetuate in production environments. Developers can effectively offset the disadvantages of either by using things like a just-in-time compiler, but it's still a risky choice when resiliency is a primary concern.