I started with a pretty standard event-driven setup, message queue in the middle, separate services for inventory, payment, and fulfillment.
Start by clarifying requirements (throughput, latency SLA, consistency needs) and then propose a high-level architecture using event-driven microservices with partitioning and replication. Walk through scaling via horizontal partitioning and auto-scaling, and detail fault-tolerance with replication, retries, and circuit breakers. Emphasize trade-offs between consistency and availability, and how data science components (e.g., fraud detection, demand forecasting) integrate.
Pro tip: Anchor your design in Amazon's leadership principles: customer obsession (low latency), ownership (end-to-end), and insist on high standards (fault tolerance). Also, mention how you'd use AWS services like Kinesis, DynamoDB, and Lambda to achieve scalability and availability without reinventing the wheel.
Ask questions to understand expected order volume, latency targets, consistency requirements, and failure scenarios. Define what 'highly available' and 'low-latency' mean in this context.
Propose an event-driven architecture with decoupled services: API gateway, order service, payment service, inventory service, and notification service. Use message queues (e.g., Kafka/Kinesis) for asynchronous communication and data stores like DynamoDB for orders.
Explain horizontal scaling via partitioning (e.g., by customer ID or order ID) and auto-scaling groups. Use read replicas and caching (e.g., ElastiCache) to reduce latency. Discuss how to scale data science components (e.g., model serving) independently.
Detail mechanisms: multi-AZ deployment, replication, retries with exponential backoff, circuit breakers, dead-letter queues, and idempotent operations. Mention monitoring and alerting with CloudWatch.
Discuss trade-offs (e.g., consistency vs. availability, latency vs. durability). Explain how data science models (e.g., fraud detection, demand forecasting) are integrated without impacting latency, perhaps via async processing or precomputed results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the sorting key tripped me up for a second.
Use a BFS traversal to group nodes by their horizontal distance (column) from the root, storing nodes in a map from column to list of node values. For each column, sort the nodes by row first, then by value to handle ties. Finally, output the values column by column from leftmost to rightmost.
Pro tip: Clarify the tie-breaking rule: when two nodes share the same row and column, sort by value. Also, mention that you can avoid sorting by using a priority queue or by tracking row and value during BFS, but sorting is simpler and acceptable.
Confirm the definition of vertical order: columns from leftmost to rightmost, and within each column, nodes sorted by row, then by value. Discuss edge cases like empty tree, single node, and nodes with same row and column.
Use BFS (level order) to traverse the tree while tracking each node's column and row. Use a hash map (dictionary) to map column indices to a list of (row, value) tuples.
Perform BFS with a queue of (node, row, col). For each node, append (row, value) to the list for its column. After traversal, sort the columns and within each column sort the list by row then value.
Iterate over the sorted columns and extract the values from the sorted lists to form the final list of lists.
Time: O(N log N) due to sorting, where N is the number of nodes. Space: O(N) for the map and queue. Mention that if using a priority queue, time could be O(N log N) as well, but with different constants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.