## Problem Given a table of sales records, write a function that computes aggregated metrics grouped by one or more columns. The function must support arbitrary grouping keys and multiple aggregation functions (sum, avg, count, min, max) simultaneously. ```python def aggregate(rows: list[dict], group_by: list[str], agg: dict[str, str]) -> list[dict]: # rows: list of flat dicts with string keys # group_by: columns to group on, e.g. ["region", "product"] # agg: {output_col: "sum(revenue)" | "avg(quantity)" | ...} # returns: list of result dicts, one per group pass ``` **Example:** ``` Input rows: [{"region": "US", "product": "A", "revenue": 100}, ...] aggregate(rows, ["region"], {"total": "sum(revenue)", "avg_rev": "avg(revenue)"}) Output: [{"region": "US", "total": 350, "avg_rev": 116.67}, ...] ``` ## Follow-ups 1. How does your implementation scale when rows exceed available memory? 2. How would you add a `HAVING` filter (e.g., only groups where total > 1000)? 3. Can you support window functions like running totals within each group? 4. How would you parallelize the aggregation across CPU cores?

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Table Aggregation: Compute Multi-Level Group-By Aggregates Across a Data Table

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Problem Given a table of sales records, write a function that computes aggregated metrics grouped by one or more columns. The function must support arbitrary grouping keys and multiple aggregation functions (sum, avg, count, min, max) simultaneously. Example: Follow-ups How does your implementation scale when rows exceed available memory? How would you add a HAVING filter (e.g., only groups where total > 1000)? Can you support window functions like running totals within each group? How would you…

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Table Aggregation: Compute Multi-Level Group-By Aggregates Across a Data Table

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