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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:

// 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:

// 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.