The open-ended part tripped me up more than the actual implementation.
Start by clarifying requirements and constraints, then propose a scalable event-driven architecture using a sliding window counter (e.g., Redis sorted sets or a ring buffer) to track mappings per exchange. Discuss trade-offs between accuracy, latency, and memory, and define alerting logic with thresholds and window behavior.
Pro tip: Emphasize idempotency and exactly-once processing to avoid false alerts, and suggest using a probabilistic data structure like a count-min sketch for high-cardinality exchanges if memory is a concern.
Ask about scale (number of applications, exchanges, mappings per second), latency requirements, and whether alerts should be per-exchange or global. Confirm if the window is fixed or sliding, and the threshold definition.
Propose an event-driven pipeline: mappings are ingested as events, processed by a stream processor (e.g., Kafka + Flink) that maintains per-exchange sliding window counts, and triggers alerts when counts exceed thresholds.
Detail the data structure: use a time-bucketed counter (e.g., per-second buckets) with a ring buffer of buckets covering the window, or a sorted set in Redis with timestamps as scores. Discuss eviction of old entries.
Define threshold (e.g., >100 distinct applications in 5 minutes) and alert deduplication (e.g., alert once per exchange per window, or escalate). Consider hysteresis to avoid flapping.
Discuss trade-offs: exact vs approximate counting, memory vs accuracy, centralized vs distributed state. Mention partitioning by exchange ID for scalability and fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the distributed environment, then propose a scalable architecture that decouples rule evaluation from data ingestion. Focus on how to partition and parallelize rule evaluation while handling windowing and threshold checks efficiently, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize the importance of idempotency and exactly-once processing in distributed alerting to avoid duplicate or missed alerts, and mention how you would handle late-arriving data with watermarks or allowed lateness.
Ask about scale (events per second, number of rules), latency requirements, consistency needs, and failure tolerance. This shows you understand the problem space before designing.
Propose a distributed stream processing system (e.g., Flink, Spark Streaming) with partitioned data streams and parallel rule evaluation. Discuss how to shard rules and data to avoid bottlenecks.
Explain how to manage multiple window sizes (tumbling, sliding, session) and thresholds per rule. Suggest using keyed state and timers for efficient window aggregation, and discuss how to evaluate thresholds in parallel.
Describe mechanisms for exactly-once processing, checkpointing, and recovery. Discuss how to handle late data and out-of-order events with watermarks and allowed lateness.
Compare approaches (e.g., centralized vs. distributed rule evaluation) and mention optimizations like rule indexing, pre-aggregation, and dynamic scaling. Highlight trade-offs between latency, throughput, and resource usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.