Chunked aggregation over a large CSV

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Aggregate revenue per region from a file too large to hold in memory. You are given an iterator of rows, not a list, and the harness measures peak memory.

Input schema

solve(rows: Iterator[dict]) -> dict[str, float] each row: {"region": str, "amount": str} # amount arrives as text

Constraints

Rows with an unparseable amount are skipped, not fatal
Peak memory limit 256 MB against a 40,000,000-row input
Materializing the iterator into a list fails the memory case
Amounts round to two decimals in the result

Example 1totals per region

Input · rows

amount	region
10.5	APAC
2	EU
1.5	APAC

Output

{"EU": 2, "APAC": 12}

Example 2an unparseable amount is skipped