Start by clarifying requirements and scale, then propose a data model that supports efficient recursive revenue aggregation (e.g., tree with parent pointers and cached subtree sums). Discuss trade-offs between update and query performance, and outline algorithms for insertRevenue and topK with threshold, including complexity analysis.
Pro tip: Mention that real-world referral trees can be deep and skewed, so consider balancing techniques or incremental updates to avoid O(depth) per insert; also highlight the importance of handling cycles and invalid referrer IDs gracefully.
Ask about expected number of customers, insert/query frequency, depth of referral trees, and whether revenue can be negative or updated. Clarify threshold semantics (strictly greater vs. greater or equal) and tie-breaking for topK.
Propose a tree structure where each node stores customer_id, own revenue, parent (referrer), and cached effective revenue (subtree sum). Consider additional structures like a max-heap or sorted list for topK queries.
On insert, create node, link to referrer, and propagate the revenue delta up the ancestor chain, updating cached effective revenues. Discuss complexity: O(depth) per insert, and optimizations like path compression or lazy propagation.
Maintain a global structure (e.g., balanced BST, heap, or sorted array) of customers by effective revenue. For topK, filter by threshold and return top k. Discuss trade-offs: heap gives O(n log k) per query, while maintaining sorted order gives O(log n) updates but O(k) query.
Compare approaches: naive recomputation vs. cached sums vs. segment trees. Discuss handling of cycles, invalid referrers, deep trees, and concurrent updates. Mention scalability and potential distributed solutions.
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They brought this up after I'd already committed to a tree structure, which was a bit awkward.
Start by clarifying the referral graph's structure and the business context, then propose a cycle detection and resolution strategy (e.g., DFS with visited set, union-find, or time-based tie-breaking). Address late-arriving referrer data by discussing idempotent updates, backfilling, and reconciliation of revenue attribution, highlighting trade-offs between accuracy and latency.
Pro tip: Emphasize that in production systems like Uber's, you'd likely use a combination of real-time streaming (e.g., Flink) for immediate attribution and batch processing (e.g., Spark) for corrections, and always log raw events for auditability and replay.
Ask about the scale, whether cycles are allowed, and how referrals and revenue events are represented (e.g., directed edges, timestamps). Confirm if real-time or batch processing is expected.
Propose algorithms like DFS with recursion stack, union-find, or topological sort to detect cycles. Discuss resolution strategies: break cycles by earliest referrer, ignore later edges, or use a decay/weighting scheme.
Explain how to update revenue attribution when referrer info arrives after the event. Use idempotent writes, event sourcing, and backfilling with reconciliation to avoid double-counting.
Cover cases like self-referrals, multiple referrers, out-of-order events, and data consistency. Discuss trade-offs between accuracy, latency, and system complexity.
Outline a lambda architecture: stream processing for real-time attribution and batch processing for corrections. Mention storage (e.g., graph DB, columnar store) and monitoring.
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Basically asking whether a heap, sorted set, or something else makes sense here.
Start by clarifying the problem constraints: what is K, what is the data type, and what are the latency and memory requirements? Then propose a min-heap of size K as the baseline solution, and discuss optimizations like a hash map for deduplication or a balanced BST for ordered traversal. Finally, compare trade-offs and mention distributed approaches if the scale is large.
Pro tip: Mention that for very large K or high-throughput streams, a probabilistic data structure like Count-Min Sketch combined with a heap can provide approximate top-K with bounded error, which is often acceptable in practice. Also, highlight that Uber's real-time ML pipelines often use Flink or Kafka Streams, so integrating with such systems is key.
Ask about the size of K, the data type (e.g., integers, strings), whether duplicates matter, and the required latency and memory constraints.
Suggest a min-heap of size K to maintain the top K elements. For each insert, compare with the heap root and replace if larger, giving O(log K) per insert.
If duplicates or updates are frequent, use a hash map to track counts and a heap for ordering. For ordered traversal, consider a balanced BST or skip list.
For high-throughput streams, discuss distributed approaches like sharding the stream and merging local top-K results, or using approximate algorithms like Count-Min Sketch.
Conclude by comparing time/space complexity, accuracy, and implementation complexity of each approach, and recommend one based on the constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.