Why a Simple 20‑Second Wait Made My SAP App Crawl – Lessons in Code Optimization

The first code‑optimization task began with a cry: “Make this code run faster!” The project was a massive SAP cloud platform application containing over 30,000 lines of code, and its data‑loading process was painfully slow.

After two days of testing and reviewing the logic, the author discovered a 20‑second wait statement hidden in a page‑reading routine. The statement had been left in the production code by a developer during debugging and was executed on every request, dramatically slowing the system. Removing the line instantly restored normal performance.

Code optimization is a double‑edged sword

Optimizing software is beneficial, but it does not guarantee positive outcomes. Optimizing for the wrong reasons or using the wrong methods can increase cost, slow development, or degrade quality. Most of the time, optimization has trade‑offs: improving speed may consume more resources, and aggressive changes can introduce bugs.

1. Don’t optimize

The first principle of code optimization is “Don’t optimize” without a clear reason.

Ask yourself:

Is the software critical?

Is it actually slow?

Is there room for improvement while keeping the code robust, correct, and clear?

If the answer to all three is yes, set a reasonable optimization goal:

Understand the target and how each optimization technique relates to it.

State the goal plainly so even a non‑technical manager can repeat it.

Stick to the goal throughout the process.

Prioritize tasks based on their impact and measure every change.

2. Use an analyzer

Never adjust code without profiling first. An analyzer measures where the program spends time, how often functions are called, and which statements dominate execution time. It helps you locate “hot spots” – the parts that consume the most time – so you can focus your effort where it matters.

3. Enable compiler optimizations

Turn on the built‑in optimization options provided by modern compilers. These can yield modest to double‑digit speed gains, but sometimes they may regress performance, so always benchmark after enabling them. Most compilers already perform aggressive global optimizations, so manual tweaking is rarely needed.

Note: The more aggressive the compiler optimizations, the higher the risk of introducing subtle bugs. Re‑run regression tests after enabling them.

4. Adjust code

Only modify code after you have identified hot spots and tried compiler optimizations.

Gather common expression calculations

If a costly calculation appears in many places, compute it once, store the result, and reuse it. Avoid repeating heavy calculations inside loops unless necessary.

Replace heavy algorithms with simple calculations

String handling and other common operations can become performance bottlenecks if implemented poorly. In some cases, bit‑shifts can replace multiplication, or simpler algorithms can replace complex ones.

Be careful: such changes may improve speed but can make the code harder to read and maintain.

Eliminate loops

Loops are often the biggest source of overhead. When possible (e.g., small iteration counts), replace loops with unrolled code or alternative constructs.

Cache frequently used values

Caching exploits locality: reuse recently accessed data instead of recomputing or fetching it repeatedly.

Rewrite in a lower‑level language

Low‑level languages can yield significant performance gains (e.g., using C for Python’s built‑ins), but they increase development time and reduce portability. Use this only when the performance benefit outweighs the costs.

5. Add code review to your management model

Make code review a mandatory part of the development process for both managers and developers. Regular reviews help catch inefficient code before it accumulates, preventing future performance degradation.

Conclusion

All code improvements start with the developer. No code is perfect on the first try; iterative changes, testing, and disciplined review are essential to achieve clean, efficient software.