Spent the first couple minutes just making sure I understood what 'top users' meant since that could be by request count, data volume, or something else entirely.
Start by clarifying the scale of the logs and the definition of 'top' (e.g., top N, threshold). Then propose an efficient algorithm using a hash map to count requests per user, followed by a heap or sorting to extract the top K. Discuss trade-offs between memory and speed, and consider distributed approaches if data is large.
Pro tip: Mention that you would first check if the logs are already aggregated or if you need to process raw logs, and discuss using a streaming approach with a min-heap of size K to avoid storing all counts in memory.
Ask about the size of the logs, the expected number of unique users, the definition of 'top' (e.g., top 10, top 1%), and whether the logs are batch or streaming. This ensures the solution fits the context.
Use a hash map to count API calls per user. If data is too large for memory, consider distributed counting (e.g., MapReduce) or approximate algorithms like Count-Min Sketch.
Use a min-heap of size K to find the top K users in O(n log K) time, or sort the counts if K is close to n. Discuss trade-offs between time and space.
Decide how to handle ties (e.g., include all tied users or break ties by user ID). Also consider missing data, malformed logs, and time windows (e.g., daily top users).
If logs are huge, propose partitioning by user ID, using a distributed framework like Spark, or employing streaming algorithms. Mention memory optimizations like using arrays for user IDs if they are integers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.