Just look at the numbers for processing the entire planet for a car profile:
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。关于这个话题，搜狗输入法2026提供了深入分析
Crucially, this distribution of border points is agnostic of routing speed profiles. It’s based only on whether a road is passable or not. This means the same set of clusters and border points can be used for all car routing profiles (default, shortest, fuel-efficient) and all bicycle profiles (default, prefer flat terrain, etc.). Only the travel time/cost values of the shortcuts between these points change based on the profile. This is a massive factor in keeping storage down – map data only increased by about 0.5% per profile to store this HH-Routing structure!
这一次，我在修复过程中借助 AI 辅助梳理了不同格式解析的内存路径，快速定位到问题出在全量加载和未及时释放中间对象上。调整为流式读取并优化缓存策略后，内存占用迅速下降。
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