Used a hash map to count occurrences then did a single pass to find the max.
Clarify the input format and assumptions, then propose a hash map solution that counts occurrences of each username in a single pass. After counting, iterate through the map to find the username with the maximum count, handling ties arbitrarily. Finally, analyze the time and space complexity.
Pro tip: Mention that you would use a hash map for O(1) average-case lookups, and discuss potential edge cases like empty input or malformed entries to show thoroughness.
Ask clarifying questions about the input format, such as how the username and timestamp are separated, whether timestamps are unique, and if the list can be empty or contain malformed entries.
Select a hash map (dictionary) to store usernames as keys and their frequencies as values, enabling efficient counting.
Iterate through each log entry, extract the username, and update its count in the hash map. Then, traverse the map to find the username with the highest count.
State that the time complexity is O(n) for n log entries, and space complexity is O(u) where u is the number of unique usernames.
Mention handling empty input, ties, and potential memory optimizations if the number of unique usernames is very large.
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