## Problem You receive a list of raw order log lines. Each line has format: ``` " ORDER_ order_id= [key=value ...]" ``` Event types: `CREATED`, `PAID`, `SHIPPED`, `DELIVERED`, `CANCELLED`. Parse the logs and produce a summary: ```python def summarize_orders( log_lines: list[str] ) -> dict[str, dict]: # returns {order_id: {"status": str, "events": list, "duration_minutes": int | None}} # duration = minutes from CREATED to DELIVERED (None if not delivered) ``` ## Example ``` logs = [ "2024-01-10T10:00:00 ORDER_CREATED order_id=X1 amount=50.0", "2024-01-10T10:05:00 ORDER_PAID order_id=X1 method=card", "2024-01-10T10:30:00 ORDER_DELIVERED order_id=X1", ] Output: {"X1": {"status": "DELIVERED", "events": [...], "duration_minutes": 30}} ``` ## Follow-ups 1. How do you handle log lines that arrive out of order by timestamp? 2. What if `order_id` appears in `CREATED` for the same ID more than once (duplicate events)? 3. Write a SQL version: given a table `order_events(order_id, event_type, event_time)`, compute the same summary. 4. Identify orders that took more than 2 hours from CREATED to DELIVERED.

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Order Logs - Parse and Aggregate Order Event Log into Summary Report

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Problem You receive a list of raw order log lines. Each line has format: Event types: CREATED, PAID, SHIPPED, DELIVERED, CANCELLED. Parse the logs and produce a summary: Example Follow-ups How do you handle log lines that arrive out of order by timestamp? What if order_id appears in CREATED for the same ID more than once (duplicate events)? Write a SQL version: given a table order_events(order_id, event_type, event_time), compute the same summary. Identify orders that took more than 2 hours from…

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Order Logs - Parse and Aggregate Order Event Log into Summary Report

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