Start by clarifying requirements: expected throughput, latency, consistency needs, and whether aggregations are real-time or batch. Then propose a scalable architecture using a message queue for ingestion, a stream processor for real-time aggregation, and a storage layer for durability and querying. Discuss trade-offs between different technologies and approaches.
Pro tip: At Stripe, reliability and exactly-once processing are critical; mention how you would handle failures and ensure data integrity, such as using idempotent writes and checkpointing in stream processing.
Ask about scale (events per second), latency requirements (real-time vs. batch), consistency guarantees, and query patterns. This shapes the entire design.
Propose a pipeline: ingestion via a message queue (e.g., Kafka), stream processing (e.g., Flink, Spark Streaming) for aggregation, and storage (e.g., OLAP database, Redis) for serving queries.
Define the schema for transactions and aggregated results. Discuss how to compute total amount and count per user, handling out-of-order events and late data.
Explain partitioning (e.g., by user ID), replication, and how to recover from failures. Mention exactly-once semantics and idempotency.
Compare real-time vs. batch processing, different storage options, and consistency models. Justify your choices based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started sweating a little.
Start by clarifying requirements: query patterns, latency, consistency, and scale. Then propose a design that combines a time-series store (e.g., Redis sorted sets or a time-partitioned table) for fast windowed aggregations with an index on status for filtering. Discuss trade-offs between pre-aggregation and on-the-fly computation, and how to handle late-arriving data.
Pro tip: Mention that you'd use a sliding window with bucketed pre-aggregation (e.g., per-minute buckets) to balance accuracy and performance, and that you'd handle out-of-order events by allowing a grace period or using event-time processing.
Ask about query patterns (e.g., per-user, per-status), expected QPS, data volume, latency SLA, and consistency needs (e.g., real-time vs. eventual).
Propose a schema that supports time-windowed aggregation and status filtering, such as a time-series table partitioned by time and indexed by user_id and status, or an in-memory store like Redis sorted sets.
Decide between on-the-fly aggregation (e.g., range query + sum) and pre-aggregation (e.g., materialized views or incremental counters). Discuss sliding window techniques and bucketing.
Explain how to scale (sharding by user_id, read replicas, caching) and optimize (indexes, covering indexes, approximate algorithms for high cardinality).
Discuss late data, exactly-once semantics, consistency vs. availability, and cost implications. Summarize trade-offs and justify your choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale, latency, and consistency requirements for each reporting feature, then propose a lambda architecture with a batch layer for accurate top-K and anomaly detection and a speed layer for near-real-time updates. For each feature, discuss algorithmic choices (e.g., heap for top-K, streaming stats for anomaly detection) and trade-offs between precomputation and on-demand queries, emphasizing how to handle cross-user joins efficiently.
Pro tip: At Stripe, reporting often requires exact results for financial data, so avoid approximate algorithms unless you explicitly discuss the trade-off and get buy-in. Also, highlight how you would ensure data freshness and handle late-arriving events, as these are common pitfalls in payment systems.
Ask about data volume, required latency (real-time vs. batch), accuracy (exact vs. approximate), and consistency needs for each reporting feature. Confirm whether cross-user joins must be real-time or can be precomputed.
Propose a pipeline that ingests events, stores raw data in a data lake, and processes it into aggregated views. Use a stream processor (e.g., Kafka Streams, Flink) for real-time aggregates and a batch system (e.g., Spark) for historical accuracy.
For batch, use a distributed sort or heap-based selection per partition, then merge. For streaming, maintain a bounded min-heap per shard and periodically merge. Discuss handling updates and ensuring exactness.
Compute per-user historical mean and standard deviation using windowed aggregations (e.g., tumbling windows). For streaming, use online algorithms (Welford's) to update stats incrementally. Flag anomalies when a new spend exceeds mean + k*stddev.
Precompute user-level aggregates and store them in a low-latency store (e.g., Redis, Cassandra) keyed by user ID. For joins, either denormalize data or use a distributed join engine (e.g., Presto) with proper partitioning. Discuss trade-offs between precomputation and ad-hoc queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.