Compute total active time per Twitter Space

You are given an event log of user activity in Twitter Spaces. Each record has: - `operation`: one of `create`, `join`, `leave` - `space_id`: identifier of the space (string) - `user_id`: identifier of the user (string) - `timestamp`: Unix timestamp in seconds (integer) A user is considered **in a space** from the time they `join` until they `leave`. The `create` operation means the user created the space and is **also considered to have joined at that timestamp**. ### Task Return the **total active time (in seconds)** for each space, defined as: > sum over all users of (time that user spent in that space) A user may join/leave the same space multiple times; all sessions should be summed. ### Example Input records: - `["create", "abc", "user_1", 1234567000]` - `["join", "abc", "user_2", 1234567100]` - `["leave", "abc", "user_2", 1234567300]` - `["create", "def", "user_2", 1234568000]` - `["leave", "def", "user_2", 1234568500]` - `["leave", "abc", "user_1", 1234569000]` Output: - `{ "abc": 2200, "def": 500 }` Explanation: `user_1` spent `2000` seconds in `abc`, `user_2` spent `200` seconds in `abc`, and `user_2` spent `500` seconds in `def`. ### Follow-up (real-time) Design a data structure / approach that can continuously output the **top-k spaces by current active user count** (i.e., number of users currently in the space), with updates arriving as join/leave events stream in.