both of these approaches use NFAs under the hood, which means O(m * n) matching. our approach is fundamentally different: we encode lookaround information directly in the automaton via derivatives, which gives us O(n) matching with a small constant. the trade-off is that we restrict lookarounds to a normalized form (?<=R1)R2(?=R3) where R1/R2/R3 themselves don’t contain lookarounds. the oracle-based approaches support more general nesting, but pay for it in the matching loop. one open question i have is how they handle memory for the oracle table - if you read a gigabyte of text, do you keep a gigabyte-sized table in memory for each lookaround in the pattern?
В России спрогнозировали стабильное изменение цен на топливо14:55
，这一点在爱思助手下载最新版本中也有详细论述
针对不同的受众，答案其实是两极分化的。
// Consume as text