The core implementation is pretty clean: build an adjacency map once, then intersect the two friend sets.
Start by clarifying the input format and constraints, then propose an adjacency list representation to efficiently compute mutual friends. Discuss the time and space complexity, edge cases, and how to adapt the solution for large-scale data using external sorting or distributed processing.
Pro tip: Mention that for large data, you can sort the friendship pairs and use a merge-join approach to find mutual friends without loading everything into memory, demonstrating practical system design thinking.
Ask about input size, data format, memory constraints, and whether the function needs to handle dynamic updates. Confirm that friendships are undirected and that user IDs are integers.
Build an adjacency list (hash map from user ID to set of friends) by iterating through the friendship pairs. Then compute the intersection of the two users' friend sets to get mutual friends.
State that building the graph takes O(E) time and O(E) space, where E is the number of friendships. Computing mutual friends takes O(min(deg(u), deg(v))) time using set intersection, or O(deg(u) + deg(v)) with hash sets.
Consider cases where users have no friends, are not in the graph, are the same user, or have many friends. Also discuss duplicate friendships and self-loops.
If data doesn't fit in memory, propose external sorting of friendship pairs by user ID, then use a merge-join to find common friends. Alternatively, use a distributed framework like MapReduce: map each friendship to (user, friend) pairs, group by user, then intersect.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.