This was a lot to hold in your head at once.
Start by clarifying requirements and constraints, then propose a high-level architecture that processes the two streams with stateful operators for deduplication and counting. Detail the state management, windowing, and alerting logic, emphasizing scalability and fault tolerance. Conclude with trade-offs and potential optimizations.
Pro tip: Emphasize the use of approximate data structures like HyperLogLog for deduplication to achieve O(1) memory, and discuss how to handle late events with allowed lateness and state retention. Also, mention the importance of monitoring and tuning the alert thresholds to balance sensitivity and false positives.
Ask about expected scale (number of experiments, users, events per second), latency requirements, and data sources. Confirm the need for exactly-once semantics and the handling of late events up to 24 hours.
Propose a streaming pipeline using a distributed stream processor (e.g., Flink, Spark Streaming) with two sources: assignments and pageviews. Outline stages: ingestion, deduplication, bot filtering, windowed aggregation, and alerting.
Explain how to deduplicate per user per experiment using idempotent state, such as a keyed state store with TTL or a probabilistic data structure like HyperLogLog. Discuss how to maintain rolling counts per variant in O(1) memory using count-min sketch or similar.
Describe using event-time windows with allowed lateness of 24 hours, and how to update counts when late events arrive. Mention the need for state retention and periodic checkpointing for fault tolerance.
Outline the chi-square test with Yates correction, computed every minute, and the alert condition (p < 1e-4 and absolute difference ≥ 0.3 percentage points). Discuss horizontal sharding by experiment ID and load balancing across workers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Shorter sub-question but honestly the sneakiest part.
Start by clarifying what the SRM detector is (e.g., sample ratio mismatch check in A/B tests) and the goal of historical replay validation. Then outline a rigorous validation protocol that simulates detection on historical experiments while strictly preventing ground truth leakage, and discuss how to measure performance. Emphasize the importance of temporal integrity and pre-registration of detection logic.
Pro tip: Use a time-based split to mimic production: train/tune the detector only on data before a cutoff, then evaluate on later experiments. This prevents leakage and simulates real-world deployment where future data is unavailable.
Clearly specify the SRM detector's logic (e.g., chi-squared test on assignment counts) and what 'validation' means: does it correctly flag true SRM without false alarms? Establish success metrics like precision, recall, and detection latency.
Select a set of past experiments with known ground truth (whether SRM occurred). Ensure the data includes assignment logs, timestamps, and any metadata needed to replay the detector.
Simulate the detector as if running in production: for each experiment, feed only data available up to each point in time, and never use future data or ground truth labels. Use a time-based split: tune on older experiments, evaluate on newer ones.
Run the detector on the historical data in chronological order, recording its decisions (flag/no flag) at each step. Ensure the detector's parameters are frozen from the tuning phase.
Compare detector outputs to ground truth labels to compute performance metrics. Analyze false positives/negatives, and refine the detector if needed, repeating the process with a new time-based split to avoid overfitting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.