My first instinct was to just throw a hashmap at it, which was the right direction, but I fumbled around with how to define a 'sequence' before settling on something.
Clarify the problem by defining what constitutes a 'sequence' (e.g., contiguous events, ordered subsequences) and how frequency is measured. Then propose an efficient algorithm, such as using a hash map to count occurrences of sequences of a given length or a trie to mine frequent sequential patterns, and analyze its time and space complexity.
Pro tip: Discuss how to handle large-scale logs by using streaming or distributed approaches (e.g., MapReduce) and mention pruning strategies to avoid counting all possible sequences, which shows practical scalability awareness.
Ask questions to define 'sequence' (contiguous vs. non-contiguous, fixed vs. variable length), 'recurring' (minimum frequency threshold), and 'most frequently' (top-k or all above threshold). Also confirm input format and constraints.
Select appropriate data structures: a hash map to count sequence occurrences, a trie or suffix tree for efficient sequence mining, or a priority queue to track top-k frequent sequences.
Outline the algorithm: iterate through logs, extract sequences (e.g., using sliding window for fixed length), update counts, and then filter/sort by frequency. Consider optimizations like pruning or Apriori-based methods for variable-length sequences.
Analyze time and space complexity. For fixed-length sequences, O(N*L) time where N is number of logs and L is sequence length; for variable-length, discuss exponential worst-case and how pruning helps.
Address edge cases (empty logs, ties, very long sequences) and scalability (streaming, distributed processing, memory limits). Propose testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.