A product tracks activity using `user_id` from login events, and computes MAU as: - **MAU (L30D)** on date `d` = number of **distinct `user_id`** with at least one login in the window `[d-29, d]` (inclusive). ### Data change event On a single day **T**, the company performs a one-time **rehash** of all user IDs: - For dates **< T**, events use the *old* `user_id_old`. - For dates **≥ T**, events use the *new* `user_id_new`. - Each real person gets exactly one new ID (a 1-to-1 remapping), but **your metric pipeline does not have the mapping** between old and new IDs. ### Questions 1) For dates whose L30D window overlaps both sides of **T**, how can this rehash bias the computed MAU if you naïvely count distinct `user_id`? 2) What is the **maximum possible MAU overestimate** (as a percentage) and the **minimum possible MAU overestimate** (as a percentage), relative to the true number of distinct real users in the window? 3) Operationally, how would you redesign tracking/warehouse modeling to make MAU robust to this type of ID change?