The base problem is pretty straightforward, a single pass with a hash map to count frequencies, then a second pass to find the first with count one.
Start by clarifying the problem: define 'first' as the earliest in the list, and confirm that logs are strings or objects. Then propose a two-pass hash map solution: first pass counts occurrences, second pass finds the first with count 1. For the follow-up, discuss scaling to 10 million logs by considering memory constraints and distributed processing.
Pro tip: Mention that you would use a streaming approach with a hash map of counts, but if memory is a concern, you could use a Bloom filter or external sorting. Also, highlight that the 'first' log depends on the order of arrival, so you need to preserve order.
Ask questions to understand the input format, definition of 'first', and constraints like memory and time. Confirm whether logs are strings or structured objects.
Describe a hash map approach: iterate through logs, count occurrences, then iterate again to find the first with count 1. Analyze time and space complexity: O(n) time, O(n) space.
Discuss how to handle 10 million logs: consider memory limits, use of distributed systems (e.g., MapReduce), or external sorting. Mention trade-offs between memory and speed.
Explore alternatives like using a linked list to maintain order while counting, or two-pass with a database. Discuss when to use approximate methods like Bloom filters.
Recap the chosen approach, its complexity, and why it's suitable for the given constraints. Mention potential edge cases like no unique log.
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