I started with the obvious hashmap approach and felt pretty good about it.
Start by clarifying the problem constraints (input size, memory limits, whether the list is static or streaming) and then propose a hash map solution for the basic case, analyzing time and space complexity. Then systematically extend the solution to handle top-k, time windows, streaming, tie-breaking, and caller-callee analysis, discussing trade-offs for each extension.
Pro tip: For streaming top-k, mention the space-saving algorithm or count-min sketch to handle high-cardinality data efficiently, and always discuss how you would handle ties (e.g., lexicographic order or earliest occurrence) to show attention to detail.
Ask about input size, whether logs are static or streaming, memory limits, and expected query patterns (e.g., top-k, time windows). This ensures you design the right solution and avoid over-engineering.
Use a hash map to count occurrences of each function call in a single pass, then find the max. Analyze time complexity O(n) and space O(m) where m is unique calls.
For top-k, use a min-heap of size k or quickselect. For tie-breaking, define a rule (e.g., lexicographic order) and incorporate it into the comparison.
For time windows, use a sliding window with a deque or bucket counts. For streaming, use approximate algorithms like count-min sketch or space-saving, and discuss trade-offs between accuracy and memory.
Extend the hash map to count pairs (caller, callee) or use a stack to track call sequences. Discuss how to handle nested calls and aggregate statistics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.