The first part was manageable, just restructuring the data and grouping by sub-forum.
Start by clarifying the log format and desired aggregations, then propose a straightforward solution using a hash map keyed by sub-forum ID to group and aggregate entries. For scale, discuss partitioning the data by sub-forum ID and using distributed processing frameworks like MapReduce or Spark, while considering memory and I/O trade-offs.
Pro tip: Mention that you would first validate the sub-forum ID extraction logic and handle edge cases like missing or malformed IDs, as data quality issues can skew aggregations at scale.
Ask about the log structure, what aggregations are needed (e.g., count, average, top-k), and the expected output format. Confirm whether sub-forum ID is always present and how to handle missing values.
Propose a single-pass algorithm using a hash map where keys are sub-forum IDs and values are aggregation states (e.g., counts, sums). Extract the sub-forum ID from each log entry, update the corresponding state, and output results.
Identify that with millions of entries, a single machine may run out of memory or become I/O bound. Discuss time and space complexity, and note that the hash map may grow large if there are many sub-forums.
Suggest partitioning the log by sub-forum ID (e.g., using consistent hashing) and processing partitions in parallel on multiple machines. Use distributed frameworks like MapReduce or Spark, or stream processing if real-time. Consider using approximate algorithms (e.g., HyperLogLog) for cardinality if exact counts aren't needed.
Compare batch vs. stream processing, exact vs. approximate aggregations, and memory vs. disk usage. Mention that partitioning may cause skew if some sub-forums are much larger, and suggest techniques like salting or dynamic load balancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.