Always consider computational complexity and performance when implementing algorithms. Look for opportunities to optimize through early exits, efficient comparison strategies, and avoiding nested loops that create O(N*M) complexity. Key optimization techniques: - **Early loop exits**: Break out of loops as soon as the desired condition is met rather than continuing unnecessary iterations - **Efficient comparisons**: Instead of concatenating strings for comparison, compare fields individually in priority order, returning early when differences are found - **Avoid nested complexity**: Be mindful of nested loops that can create quadratic or higher time complexity, especially when processing large datasets Example of optimized comparison: ```go // Instead of string concatenation comparison: return fmt.Sprintf("%s/%s/%s", left.Type, left.Message, left.Status) < fmt.Sprintf("%s/%s/%s", right.Type, right.Message, right.Status) // Use field-by-field comparison with early returns: if left.Type != right.Type { return left.Type < right.Type } if left.Message != right.Message { return left.Message < right.Message } return left.Status < right.Status ``` Example of early loop exit: ```go // Add break to avoid unnecessary iterations for _, r := range resources { if condition_met { bAllNeedPrune = false break // Exit early once condition is found } } ``` Consider the algorithmic impact of your implementation choices, especially when dealing with collections or repeated operations that could affect system performance at scale.