← Lead Bank Interview Insights
The transform function part was pretty approachable.
Start by clarifying the requirements: what 'like count between two users' means (e.g., number of likes user A gave to user B, or mutual likes), and whether the data is static or streaming. Then propose a data structure like a hash map of user pairs to counts, or a graph adjacency list with edge weights, and discuss trade-offs for ingestion and query performance.
Pro tip: Mention that in a real system like a bank's social feature, you'd need to consider data consistency, concurrency, and persistence; suggesting a simple in-memory solution first, then scaling to a distributed store like Redis or Cassandra, shows maturity.
Ask questions to understand what 'like count between two users' means, whether likes are directional, if counts are cumulative or unique, and the expected scale and read/write patterns.
Propose a data model such as a hash map with composite keys (user1, user2) or a graph with adjacency lists, and explain how it supports the operations.
Define the API for ingesting bidirectional like data (e.g., increment counts for both directions) and retrieving the like count (e.g., lookup by key).
Discuss time and space complexity for each operation, and compare alternatives like using a database vs. in-memory store, considering factors like scalability and persistence.
Mention handling of concurrent updates, data consistency, and potential extensions like time-based likes or aggregation queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the data structure and the definition of 'best friend' (highest like count). Then, design an algorithm that finds the given user's best friend, and from that friend, finds their best friend, ensuring no self-recommendation and handling edge cases. Discuss time and space complexity, and consider scalability for large datasets.
Pro tip: Demonstrate awareness of real-world constraints: mention that like counts may change frequently, so caching or incremental updates might be needed. Also, consider tie-breaking rules and whether the recommendation should exclude existing friends.
Ask about the data structure (e.g., adjacency list with like counts), definition of 'best friend' (max likes), and edge cases like no friends or ties. Confirm if the recommendation should exclude the user themselves and existing friends.
Outline steps: retrieve the user's friends and their like counts, find the friend with max likes (best friend), then retrieve that friend's friends and find their best friend. Handle cases where the best friend has no other friends or the result is the original user.
Discuss time complexity (e.g., O(F + G) where F and G are friend counts) and space complexity. Suggest optimizations like precomputing best friends or using a priority queue if like counts are dynamic.
Define behavior for ties (e.g., pick the one with lowest ID or most recent interaction). Ensure the function returns null or a default if no recommendation is possible.
Walk through a simple example to verify correctness. Mention potential unit tests for edge cases like empty friend lists, ties, and cycles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.