Assessing the root cause is always crucial. While engineers are writing algorithms in a high level programming language, we tend to ignore the complexities of certain abstractions. However, trying to deeply understand the cost of each function call, database operation or network request will help the developers to know what part of their code they should optimize for a more efficient algorithm. For example: An API can enclose a remote procedural call (stub) which is internally carrying out a lot of network level operations. But developers often tend to view an RPC as just another function call. Avoiding this can help the submission of a more well-crafted piece of code.

When facing a slow algorithm, I approach it step-by-step: Analyze the Problem: Use profiling to identify resource-heavy parts of the code. Revisit the Algorithm: Explore alternatives with better time complexity, like switching from O(n²) to O(n log n). Optimize Data Structures: Use structures that align with the task, like hash maps for faster lookups. Avoid Repetition: Precompute or cache results to eliminate redundant work. Consider Scale: Handle large data efficiently with sorting, chunking, or leveraging external systems like GPUs. Iterate and Test: Make incremental improvements and measure their impact. Small, thoughtful changes can yield significant performance gains!

To improve the runtime efficiency of a slow algorithm, it's important to focus on key optimization strategies. Refactoring the code allows you to rewrite inefficient sections, improving overall performance. Optimizing data structures ensures that you're using the most appropriate and efficient types for your tasks, reducing unnecessary overhead. Implementing caching helps by storing the results of repeated computations, reducing the need to perform the same calculations multiple times and thus saving valuable processing time.

First, I will try to identify the bottleneck using tools for profiling, observability, and monitoring like JProfiler, New Relic etc. When I have an idea of what is causing the code to run slowly, a few possible solutions might include; - Use efficient algorithms and data structures for operations like searching, sorting, and iteration - Lazy Loading - load data only when it’s needed, especially for heavy or rarely-used resources - Implement caching to reduce redundant computations or database hits. - Use gzip or similar compression for large responses to speed up transfer. - Leverage multi-threading for CPU-bound tasks.

Doing simple things Find the time complexity See others way to solve it Make the solution by doing roughs in notebook Look to optimise the code.