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Design MapReduce for schedule aggregation

UberSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteSystem Design

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Design MapReduce for schedule aggregation evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design a MapReduce pipeline that processes large-scale scheduling data (users’ busy intervals) to compute common available time slots of at least duration d for specified groups. Define the map outputs (keys/values), partitioning, and reduce logic; explain how you discretize time, mitigate data skew, and validate results at scale.