Write efficient code without sacrificing readability

  • 8 minutes

Balancing code efficiency and readability is a critical skill for software developers. While performance is crucial, it shouldn't come at the expense of code clarity and maintainability.

Clarity first, optimize when needed

A guiding principle in software engineering is to first make the code work correctly and clearly, then optimize if and where necessary. Don't start optimizing before you know what really needs it.

When you attempt to optimize specific code sections without evidence that the code is slow, you risk the following issues:

  • Making the code harder to understand and maintain: Complex optimizations (especially micro-optimizations) can introduce convoluted logic, obscure hacks, or special-case code. Future maintainers might struggle to understand it, or worse, introduce bugs when modifying it.

  • Wasting time: You might spend hours tweaking something that has negligible effect on overall performance, while neglecting a bigger problem elsewhere.

  • Reducing reliability or flexibility: Sometimes extreme performance tweaks remove abstraction layers or error-checks. For example, using pointer arithmetic for speed in C# (unsafe code) might gain a little performance but at a high risk to safety and a loss of portability. This type of change is hard to justify in business applications.

Start with a clear solution. Remember that developers read code more often than they write it. Emphasize readable naming, structure, and simplicity. High-level algorithmic choices aside, many micro-optimizations (like caching trivial computations or saving a few CPU cycles) aren't worth the loss in clarity. Modern compilers and hardware are good at running straightforward code efficiently.

When do optimizations make sense?

After writing the initial version of your code, identify any critical sections that need optimization (the "critical 3%"). Here are signs and scenarios where optimizing (even if it complicates the code a bit) is justified:

  • Confirmed Hotspots: Profiling shows a particular method or loop consumes a significant percentage of execution time. Example: You profile and find one function takes 60% of the program's runtime. If an optimization can cut that function's time in half, it yields a significant overall win.

  • Obvious Algorithmic Inefficiency: Sometimes you know a simpler approach is dramatically less efficient in big-O terms. For instance, using a double nested loop to compare elements of two large lists is O(n*m); if you instead use a hash set for one list, you can potentially drop to O(n+m). If n and m can be large, the difference is huge. In such cases, an experienced developer might implement the more efficient approach from the start – it's not "premature" if the need is evident. Crucially, many algorithmic improvements don't make the code less readable if done well (using descriptive method names, comments, etc.).

  • Repeated Operations: If a piece of code runs occasionally, small inefficiencies are fine. But if it runs thousands of times a second (for example, inside a tight loop or a high-frequency service call), you scrutinize it more. For example, constructing a new object might be fine, but doing that in a loop 100,000 times per second (when you could reuse an object) might warrant a change.

  • Performance-critical domain: In certain domains (like game development, real-time systems, or embedded systems), performance requirements are stringent. Here, developers often think about efficiency from the start, because a naive approach might not meet the requirements. Even so, they lean on known patterns and best practices rather than unpredictable low-level tweaks.

The goal is to optimize when data supports it or domain context demands it, and even then to do it in a maintainable way.

Readability versus optimization

There's often a trade-off between writing code that's easy to read and code that's highly optimized. However, many optimizations can be achieved without sacrificing clarity. Let's look at a few examples that illustrate the balance between readability and efficiency.

Use appropriate data structures

Suppose you have a collection and you need to repeatedly check whether the collection contains a certain value. You have several options:

  • Readable but less efficient: Iterate through a List<T> each time to find the value. This approach has O(n) complexity for each check, and the code remains clear and simple (either through a basic loop or by using List.Contains, which performs internal iteration).

  • Efficient and still readable: Use a HashSet<T> or a Dictionary<TKey, TValue> for lookups, giving O(1) average time per check. There's a bit more code (you populate the HashSet and use its Contains method), but it's still clear. In fact, using a HashSet might even be more expressive: it tells the reader "we need fast lookups". This is a case where the more efficient solution is also clean.

  • Over-optimized and less readable: A contrived alternative might involve low-level bit manipulation or a custom hashing algorithm tailored to this specific data set. This approach would probably confuse code maintainers and deliver only minimal performance gains over the standard HashSet (if any improvement at all). You should avoid this strategy unless profiling demonstrates that the built-in data structure creates a performance bottleneck and a custom implementation is truly essential (which rarely occurs).

Loop expansion or manual inlining

Sometimes developers try to "optimize" loops by unrolling them or manually inlining code to save on loop overhead. Consider a loop that processes an array:

  • Readable: Write a loop that processes an array of 100 elements. The code is concise and clear. The compiler can optimize it well, and any modern CPU can handle 100 iterations easily.

  • Over-optimized: "Unroll" the loop by writing 100 repeated statements to avoid loop overhead. This approach might save a few CPU cycles of loop control, but your code is now 100 lines of repetitive statements – clearly not worth it. Maintenance nightmare if you ever change it to 101 elements!

  • When it matters: For performance-sensitive inner loops (found in high-performance computing or algorithmic libraries), developers occasionally employ partial loop unrolling for optimization. However, compilers typically handle this scenario automatically or through other optimization mechanisms, rather than requiring manual implementation in application code. As an application developer, rely on the compiler's optimization capabilities and maintain simple, clear code.

String concatenation in C#

String concatenation is a common scenario where performance and readability can conflict. When string concatenation is done repeatedly, the choice of method can significantly impact performance.

  • Naive approach: Using string += string in a loop. Example: building a long SQL query or CSV by appending lines in a loop. This technique is easy to read, but in .NET each += on string creates a new string (since strings are immutable). If you append 1,000 times, you create a lot of intermediate string objects – this code is inefficient in both time and memory.

  • Better approach: Use a StringBuilder for multiple concatenations. This class is designed for that scenario; it builds the string in a buffer and produces one final string at the end. The code is slightly more verbose (you must call Append instead of +=), but it's still easy to understand. It clearly signals "we're building a string efficiently." In fact, best practice guides for .NET recommend StringBuilder for concatenating inside loops. Using StringBuilder for repeated string concatenations is both more readable (for experienced developers) and more performant.

This example shows that sometimes a small change (using a different API) yields large performance gains with minimal effect on readability. The initial approach might work for small strings, but if you ever hit large inputs, the performance difference is significant.

Cache results

Caching is a common optimization technique that can improve performance by storing the results of expensive function calls and reusing them when the same inputs occur again.

  • Without caching: Imagine a function GetExchangeRate(currency) that fetches the current exchange rate via an HTTP call. If you call it repeatedly for the same currency, and it doesn't cache, you're doing redundant work (and network I/O). It's straightforward but not efficient.

  • With caching: You add a dictionary to store results after fetching, so subsequent calls return immediately from memory. This technique adds some complexity (you have to manage the cache, possibly invalidation if rates change), but for frequently requested data, it can drastically improve performance by avoiding unnecessary calls.

The decision to cache often depends on usage patterns. The code becomes slightly more complex (you need to handle the cache logic), and you must ensure it stays correct (stale data, thread safety if accessed from multiple threads, etc.). Caching is a classic scenario of trading some complexity for performance. Caching delivers substantial performance improvements when data is accessed repeatedly.

Best practices for balancing efficiency and readability

Here are some best practices to help you balance efficiency and readability in your code:

  • Prefer algorithmic clarity: When choosing how to implement something, think about the algorithmic complexity first (is it linear, quadratic, etc.?). Choose a design that gives good complexity without contorting your code. Often the most elegant solution algorithmically is also clean code.

  • Use the right tool for the job: High-level languages and libraries provide optimized functionality that you should use. For example, Language Integrated Query (LINQ) in C# can express certain data operations clearly, and it's reasonably optimized internally. Similarly, parallel processing libraries (Parallel.ForEach, PLINQ) enable concurrent execution while maintaining relatively simple code structure. Don't reinvent the wheel unless you have to.

  • Comment on non-obvious optimizations: If you do something in an unintuitive way for performance reasons, add a comment explaining why. Example: "Using a manual object pool here to reduce garbage collection pressure, because this method is called in a tight loop and we can't afford frequent allocations." Adding a comment helps future readers (and yourself in six months) remember why the code is that way.

  • Incremental improvement: You can often start with a simple design, then incrementally improve the parts that need it. Always compare the optimized version of your code with the original to ensure code behavior is unchanged. Version control can help you rollback changes if necessary.

  • Don't compromise safety/security for speed: For instance, skipping input validation or error handling might make code run a bit faster, but it's almost never worth the trade-off. Robustness is more important. Aim for optimizations that don't undermine the correctness or security of the code.

Avoid premature optimization

Rushing to optimize code before you know where the real bottlenecks are is a common pitfall.

In practice:

  • Write your code cleanly with sound structure.
  • Identify if any part is a bottleneck.
  • Optimize that part, in a maintainable way, and verify the improvement.

This approach ensures you spend time on what matters and keep your codebase both efficient and healthy.

Summary

Writing efficient code doesn't have to come at the cost of readability. By prioritizing clarity first and optimizing based on evidence, you can achieve a balance that serves both performance and maintainability. Use appropriate data structures, implement built-in libraries, and apply optimizations judiciously. Always document nonobvious choices and avoid premature optimization. This balanced approach leads to robust, efficient, and understandable code that stands the test of time.