Streaming Points: Emit K Within Distance

Process a stream of float positions. Whenever three points satisfy the distance condition, output and remove those three points; a follow-up generalizes from 3 points to K points. The important ambiguity is whether the condition means a span of length `d` or pairwise distance within `d`. Requirements Input is a continuous stream of floating-point positions. Maintain all active points not yet emitted. When a qualifying group of 3 points exists under the given distance threshold, output those 3 points and remove them from the active set. Follow-up: support K points instead of exactly 3. The critical clarification is the distance predicate: One interpretation: the 3 points all fit inside an interval of length d. Interviewer-preferred interpretation in the onsite variant: every pair among the selected points must be within the distance threshold. Notes A balanced ordered set / TreeSet-style structure is a natural fit because each new point only needs nearby neighbors. Be careful with floating-point comparison and epsilon policy. A wrong clarification can lead to checking subset(target, target + d) when the interviewer expects a wider candidate range around the target. Confirm the predicate with a tiny numeric example before coding. Preparation Practice range queries over sorted points with insertion and deletion. Write the K-generalization in words before coding: identify the local candidate window, test the predicate, then remove exactly the emitted points. Rehearse explaining float comparison, tie-breaking, and what to do when multiple groups qualify.