## Problem You are given a list of pipeline events. Each event is a tuple `(file_id, stage, status, timestamp)` where `status` is either `"start"` or `"end"`. Compute the total processing time for each file and the per-stage breakdown. ```python def file_processing_time( events: list[tuple[str, str, str, int]] ) -> dict[str, dict[str, int]]: # Returns {file_id: {stage: duration, "total": total_duration}} pass ``` **Example:** ``` events = [ ("f1", "parse", "start", 0), ("f1", "parse", "end", 5), ("f1", "compile", "start", 5), ("f1", "compile", "end", 12), ] output -> {"f1": {"parse": 5, "compile": 7, "total": 12}} ``` ## Follow-ups 1. What if events for different files are interleaved? Does your solution still work? 2. Handle missing `"end"` events -- mark those stages as `"incomplete"`. 3. Stages can overlap (parallel execution). How do you compute wall-clock total vs. CPU-sum total? 4. The events arrive out of timestamp order from distributed workers. How do you handle sorting efficiently?

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File Processing Time: Calculate Total and Per-Stage Duration from a Pipeline Event Log

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Problem You are given a list of pipeline events. Each event is a tuple (file_id, stage, status, timestamp) where status is either "start" or "end". Compute the total processing time for each file and the per-stage breakdown. Example: Follow-ups What if events for different files are interleaved? Does your solution still work? Handle missing "end" events -- mark those stages as "incomplete". Stages can overlap (parallel execution). How do you compute wall-clock total vs. CPU-sum total? The events…

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File Processing Time: Calculate Total and Per-Stage Duration from a Pipeline Event Log

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