← Pinterest Interview Insights
The aggregation part was fine, hash map keyed by user ID storing a list of timestamped entries.
Start by clarifying requirements: expected scale, query patterns, and whether updates are needed. Then propose a hash map from user ID to violation count for O(1) lookups, and for the threshold query, either iterate over the map or maintain a sorted structure or bucket counts for efficiency. Discuss trade-offs and potential optimizations like caching or indexing.
Pro tip: Mention that in a real ML system at Pinterest, violation logs might be streamed and aggregated in real-time using a system like Kafka and Flink, and the data structure could be backed by a distributed store like Redis for low-latency queries. This shows you think beyond the basic algorithm.
Ask about the expected number of users, logs, frequency of queries, and whether the data is static or dynamic. Also clarify if the threshold query needs to be real-time or can be batch.
Propose a hash map (dictionary) mapping user ID to violation count. This allows O(1) count retrieval and O(1) updates when new logs arrive.
For getViolationCount(userId), simply return the count from the hash map, or 0 if not present. Discuss handling missing users.
For getUsersAboveThreshold(k), consider options: iterate over all entries (O(n)), maintain a sorted list of counts (O(log n) update, O(k) query), or use bucket counts for counts up to max. Discuss trade-offs based on query frequency.
Mention distributed solutions (e.g., sharding by user ID), caching frequent queries, and handling streaming updates. Also consider memory usage and concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Binary search on a sorted list, which I knew immediately, but I fumbled the boundary condition.
Clarify the data structure and constraints, then propose a solution using a hash map from user to sorted timestamps (e.g., a balanced BST or sorted list) to enable efficient predecessor queries. For each query, perform a binary search to find the latest timestamp strictly less than T, and discuss time/space trade-offs and potential optimizations like caching or bucketing.
Pro tip: Mention that if timestamps are appended in order, you can use a simple list with binary search; otherwise, consider a balanced BST or skip list to maintain sorted order dynamically. Also, highlight that handling edge cases (no violation, T before first violation) gracefully shows attention to detail.
Ask about data volume, update frequency, query patterns, and whether timestamps are inserted in order. Confirm that 'strictly before' means < T, not <= T.
Propose a hash map mapping user IDs to a sorted collection of timestamps. Discuss options: sorted list (if static or append-only), balanced BST (e.g., TreeSet in Java), or skip list for dynamic inserts.
For a given user and T, retrieve the sorted collection and perform a binary search (or use floorEntry in a TreeMap) to find the largest timestamp < T. Return null or -1 if none exists.
State time complexity: O(log n) per query for binary search, O(log n) for insertion in balanced BST. Space: O(total violations). Compare with alternatives like linear scan (O(n)) or bucketing by time.
Mention caching frequent queries, bucketing timestamps by hour/day to reduce search space, and handling users with no violations or T before first violation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.