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When implementing algorithms, prioritize simplicity and maintainability over premature optimization. Complex algorithms should be broken down into smaller, reusable components or replaced with simpler approaches when possible.

Key principles:

• Choose the simplest algorithm that meets requirements first, then optimize if needed
• Break complex algorithms into smaller, testable functions with clear responsibilities
• Consider computational complexity tradeoffs - sometimes a slightly less optimal but much simpler approach is better
• Avoid nested complexity layers that make code hard to understand and debug
• Use well-established algorithms and data structures rather than custom complex solutions

For example, instead of implementing complex bucket-level histogram manipulation:

// Complex approach - manipulating individual buckets
for bucketIndex, bucketVal := range resultHistogram.PositiveBuckets {
    if bucketVal <= 0 {
        continue
    }
    bucketStartVal := firstH.PositiveBuckets[bucketIndex]
    bucketDelta := bucketVal * deltaScale
    predictedAtStart := bucketStartVal - (bucketDelta / sampledInterval * durationToStart)
    // ... more complex logic
}

Prefer a simpler count-based approach:

// Simpler approach - use overall count for decisions
durationToZero := sampledInterval * (samples.Histograms[0].H.Count / resultHistogram.Count)
if durationToZero < durationToStart {
    durationToStart = durationToZero
}

This approach reduces complexity, improves maintainability, and often performs better while being easier to reason about and test.