I started by nailing down what 'too many' actually means, which felt like the right move.
Start by clarifying requirements: what defines 'too many' (threshold), what actions to take (alert, block), and latency/accuracy needs. Then propose a streaming architecture using a distributed counting mechanism (e.g., Flink with keyed state) to track mappings per exchange, with a sliding window or decay to handle bursts. Discuss trade-offs between exact counts, approximate sketches, and scalability.
Pro tip: Emphasize that in high-throughput systems, exact counts per exchange may be infeasible; propose approximate algorithms like Count-Min Sketch with error bounds, and explain how to tune them to meet false positive/negative requirements.
Ask about event rate, definition of 'too many' (threshold, time window), required latency, and desired action (alert, throttle, block).
Outline a stream processing pipeline: ingest events via Kafka, process with a distributed stream processor (e.g., Flink), maintain per-exchange counts, and trigger alerts when thresholds are exceeded.
Choose between exact counting (e.g., keyed state with RocksDB) and approximate counting (e.g., Count-Min Sketch) based on scale and accuracy needs; discuss windowing (sliding/tumbling) and decay.
Explain partitioning by exchange ID, state management, checkpointing, and how to handle hot keys or skewed distributions.
Define alerting logic (e.g., threshold crossing, rate of change), deduplication, and discuss trade-offs: latency vs accuracy, resource usage vs precision, and false positives vs negatives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a hash map keyed by exchange ID with a deque or circular buffer to track timestamps inside the window.
Start by clarifying the requirements: what 'registration counts per exchange' means (e.g., number of registrations per exchange in a time window) and the sliding window rate threshold (e.g., max registrations per exchange per minute). Then propose a data structure that supports efficient updates and queries, such as a hash map from exchange to a deque of timestamps, and discuss how to enforce the threshold (e.g., by checking the deque size or sum of counts in the window). Finally, analyze time and space complexity and consider optimizations for high throughput.
Pro tip: Mention that you would use a lock-free or concurrent data structure if multiple threads update counts, and discuss trade-offs between exact and approximate counting (e.g., using a ring buffer or time-bucketed counters) for scalability.
Ask questions to understand the exact semantics: what is a registration, how is the sliding window defined (e.g., last N seconds), and what actions are triggered when the threshold is exceeded.
Propose a hash map (exchange ID -> deque of timestamps) for exact sliding window counts, or a time-bucketed counter (e.g., circular buffer of counts per second) for approximate but efficient counting.
Detail how to add a registration (append timestamp, remove expired timestamps) and how to check the threshold (compare current count to limit), ensuring O(1) amortized time per operation.
Discuss thread-safety (e.g., per-exchange locks or concurrent data structures) and scalability (e.g., sharding by exchange, using approximate algorithms like sliding window counters with atomic operations).
Compare exact vs. approximate methods, memory usage, and latency, and suggest which fits Citadel's high-frequency trading environment (e.g., low-latency, high-throughput).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what 'correct' means for alerts (e.g., no false positives/negatives, exactly-once semantics) and the acceptable latency. Then propose a design that handles out-of-order and duplicate events using idempotent processing, event-time windows with watermarks, and deduplication, while discussing trade-offs between consistency and availability.
Pro tip: Emphasize that you would first try to avoid the problem by using a system like Kafka with exactly-once semantics or Flink's checkpointing, but also be prepared to discuss how to handle it at the application level if needed. Showing awareness of both infrastructure and application-level solutions demonstrates depth.
Ask about the definition of 'correct' (e.g., exactly-once, at-least-once with dedup), latency requirements, and whether alerts can be delayed. This ensures you design for the right guarantees.
Use event timestamps and watermarks to handle out-of-order events, defining a windowing strategy (e.g., tumbling windows) that aligns with alert logic.
Ensure each event has a unique ID and that processing is idempotent, so duplicates don't affect the outcome. Use a deduplication store (e.g., Redis or a database) with TTL.
Define a allowed lateness policy: either drop late events, update results, or emit corrections. Use watermarks to trigger computations when confident all events for a window have arrived.
Explain the trade-offs between latency, accuracy, and complexity. Describe how to handle worker failures (e.g., checkpointing, replay) and ensure exactly-once semantics if required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.