Went with a single-pass approach using a hashmap to accumulate totals and counts, then computed averages at the end.
Clarify the input format and constraints, then propose a single-pass hash map solution that accumulates count and sum of response times per status code. After processing, compute averages and return the aggregated results, discussing time and space complexity.
Pro tip: Mention that you would use a dictionary keyed by status code and store both count and sum to avoid storing all response times, which is memory efficient for large logs. Also, discuss handling edge cases like empty input or non-integer response times.
Ask about the input format (e.g., list of objects, arrays), data types, expected size, and whether response times are integers or floats. Confirm output format (e.g., dictionary mapping status code to {count, average}).
Use a hash map (dictionary) where each key is a status code and the value is a pair (count, sum of response times). This allows O(1) updates per entry.
Loop through each log entry, extract status code and response time, and update the corresponding count and sum in the hash map. Handle missing keys by initializing count=0 and sum=0.
After processing all entries, iterate over the hash map to compute average = sum / count for each status code. Return a new structure with count and average (rounded if necessary).
State time complexity O(n) and space complexity O(k) where k is number of unique status codes. Discuss edge cases: empty input, single entry, division by zero (none since count>0), and potential integer overflow for large sums.
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