Let’s talk about something that affects programmers at all levels, on all codebases and projects: readability. Specifically, the balance between code that is fast and resource efficient and code that is easily understood and maintained.
First, readability. It’s a given that any code produced and delivered in any professional capacity is almost certainly not being written by a single developer. Therefore, good developers will write code that others will be able to understand and pick up what the intended behavior of the code is. Commenting, appropriate use of whitespace and indentation, and effective developer documentation can make code easy to work with, for anyone.
Here’s an example of two Python code snippets that both return the summation of a given array, but are structured quite differently:
Code 1:
# Returns summation of given array of numbers
def sum_numbers_readable(nums):
total = 0 # Initalize starting total
for num in nums: # Sum all numbers in array nums
total += num
return total # Return sum
print(sum_numbers_readable([1, 2, 3, 4]))Code 2:
def sum_numbers_fast(nums):
return sum(nums)
print(sum_numbers_fast([1, 2, 3, 4]))See the difference? Both pieces of code do the same thing and achieve the same output. Code 1, however, contains comments describing the intended behavior of the function and the expected parameter type, as well as step-by-step what each line of code does in the function. Blank lines and proper use of whitespace also divide the code into relevant sections. Code 2, on the other hand, has no comments added, poor use of whitespace (or as poor as Python would allow), and uses the sum() function from Python’s native library. While the intended behavior of this function is pretty clear, this may not always be the case.
Take a more extreme example, just for the fun of it: go write or refactor some piece of Java code to be all on the same line. It’ll compile and run fine. But then go ask someone else to take the time to review your code, and see what their reaction is.
On the other hand, we have code efficiency and speed, generally measured in runtime or operations per second. Comparing our two code snippets using a tool like perfpy.com to see performance, and we find that the second, less readable code executes faster with a higher operations per second. (931 nanoseconds vs. 981 nanoseconds and 1.07 op/sec vs. 1.02 op/sec. Not a huge difference at all, but this scales with program complexity).
This gives us some perspective on the balance between performance and readability/maintainability. It also helps to keep in mind that performance and readability are both relative.
Looking back at our two Python snippets, most developers with experience would opt for the second design style. It’s faster, but also easily readable provided that you understand how Python methods work. However, people just learning programming would probably opt for the more cumbersome but readable first design. With regards to performance, the difference in runtime could be a nonissue or it could be a catastrophic slowdown. It depends entirely on the scope of the product and the needs of the development team.
As a final rule of thumb, the best developers will balance readability and efficiency as best they can, while above all else remaining consistent with their style. When looking at possible code optimizations, consider how the balance between readability and performance could shift. The phase “good enough” tends to have a negative connotation, but if your code is readable enough that other team members can work on it, and fast enough that it satisfies the product requirements, “good enough” is perfect.
Refrences:
Lazarev-Zubov, Nikita. “Code Readability vs. Performance: Shipbook Blog.” Shipbook Blog RSS, 16 June 2022, blog.shipbook.io/code-readability-vs-performance.
From the blog Griffin Butler Computer Science Blog by Griffin Butler and used with permission of the author. All other rights reserved by the author.
