My first instinct was to use a deque per element and just pop from the front when things expire.
Clarify the requirements and constraints, then propose a design using a hash map to track per-element queues of timestamps and a global queue for total count. Explain how to lazily remove expired entries during add and query operations, and analyze the time and space complexity.
Pro tip: Mention that lazy deletion avoids scanning all elements on every operation, and discuss how to handle concurrent access if the system is multi-threaded, showing awareness of real-world production concerns.
Ask about expected operations per second, memory limits, thread safety, and whether timestamps are monotonically increasing. This ensures the design meets actual needs.
Use a hash map from element to a queue of timestamps for per-element counts, and a global queue of (element, timestamp) for total count. This allows O(1) amortized operations.
On each add or query, remove expired entries from the front of the queues based on the sliding window. This lazy approach avoids periodic full scans.
For add: append timestamp to both queues and increment counts. For query(element): clean expired entries for that element, then return its count. For total: clean global queue, then return total count.
Discuss time complexity (amortized O(1) per operation) and space complexity (O(n) where n is number of unexpired elements). Mention alternatives like using a balanced BST for range queries if timestamps are not monotonic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that 1M events/sec is a massive scale requiring a fundamental shift from a single counter to a distributed, partitioned system. Focus on partitioning the event stream by key (e.g., user ID or event type) to parallelize counting, then aggregate results asynchronously. Emphasize trade-offs between accuracy, latency, and cost, and propose a layered architecture with buffering, stream processing, and a scalable storage layer.
Pro tip: Mention that at Uber's scale, exact real-time counts are often unnecessary; approximate counting with probabilistic data structures (like HyperLogLog) or windowed aggregations can drastically reduce resource usage while meeting business needs. Also, highlight the importance of backpressure and idempotency to handle bursts and retries.
Ask about the required accuracy (exact vs approximate), latency (real-time vs batch), and query patterns (e.g., per-user, global). This determines the appropriate trade-offs and technology choices.
Shard incoming events by a key (e.g., user ID, event type) to distribute load across multiple counter instances. Use a consistent hashing scheme to ensure even distribution and scalability.
Place a high-throughput message queue (e.g., Kafka) between producers and counters to absorb bursts and decouple ingestion from processing. Use stream processing frameworks (e.g., Flink, Spark Streaming) to perform windowed aggregations.
Store per-partition counts in a distributed database (e.g., Cassandra, Redis) and periodically aggregate them into a global count. For approximate counts, use probabilistic data structures like HyperLogLog.
Implement idempotent processing, backpressure, and dead-letter queues to handle failures. Monitor throughput, latency, and accuracy, and set up alerts for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.