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Choose efficient data structures and algorithms to minimize computational overhead and improve performance. This involves selecting appropriate containers based on usage patterns, avoiding unnecessary operations like redundant copies or conversions, and leveraging optimized library functions.

Key practices:

• Use performance-optimized containers like absl::flat_hash_map instead of std::unordered_map, or base::MakeFixedFlatSet for compile-time known sets
• Avoid redundant type conversions or parsing by consolidating logic in a single location
• Eliminate unnecessary copies in loops by using references (const auto& item) or range-based algorithms
• Prefer standard library algorithms like base::ranges::any_of over custom implementations
• Use direct object comparisons instead of converting to intermediate representations when possible
• Avoid unnecessary std::move on temporary values that already have move semantics

Example of optimization:

// Less efficient - custom loop with copies
for (size_t i = 0; i < val.planes.size(); ++i) {
  auto plane = val.planes[i];  // Unnecessary copy
  // process plane...
}

// More efficient - range-based with references
for (const auto& plane : val.planes) {
  // process plane...
}

// Even better - use library algorithms when applicable
auto v8_planes = base::ToVector(val.planes, [isolate](const auto& plane) {
  // transform logic...
});

This approach reduces computational complexity, memory usage, and improves overall performance by making algorithmic choices that align with actual usage patterns.