This was basically the whole interview in one question.
Start by clarifying requirements: scale (events per day, DAU/MAU magnitude), latency needs (real-time vs batch), and query patterns (historical trends, ad-hoc). Then design a pipeline: ingestion (Kafka), deduplication (exactly-once semantics), storage (columnar for analytics), computation (batch + streaming), and serving layer (OLAP for queries). Emphasize trade-offs between accuracy, cost, and latency.
Pro tip: Mention the importance of idempotent processing and how you'd handle late-arriving events to avoid double-counting, as this is a common pitfall in production systems.
Ask about expected event volume, DAU/MAU numbers, required freshness (real-time vs daily), and query patterns (historical, ad-hoc). This shapes architecture choices.
Use a scalable message queue (e.g., Kafka) to ingest events. Implement deduplication via unique event IDs and idempotent processing, possibly using a streaming engine with exactly-once semantics.
Store raw events in a data lake (e.g., S3) for reprocessing, and aggregated user activity in a columnar store (e.g., Druid, ClickHouse) for fast queries. Model data to support efficient distinct counts.
Use a hybrid approach: streaming for real-time approximate counts (e.g., HyperLogLog) and batch for accurate daily/monthly aggregations. Handle late data with windowing and watermarks.
Expose an API or query layer that can retrieve DAU/MAU for any date range. Use pre-aggregated tables and possibly a time-series database for efficient historical queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got pushed on this specifically after I mentioned HyperLogLog offhand.
Start by defining the requirements for DAU/MAU deduplication: exact vs approximate counts, memory constraints, and query patterns. Then compare HyperLogLog, bitmaps, and raw storage across dimensions like memory usage, accuracy, and operational complexity. Finally, recommend a hybrid approach based on scale and use case.
Pro tip: Mention that HyperLogLog is ideal for large-scale approximate distinct counts with low memory, but bitmaps offer exact counts for moderate cardinality and raw storage provides flexibility for ad-hoc queries. Emphasize that the choice depends on the acceptable error rate and whether you need to merge sets across time windows.
Ask about scale (number of users), accuracy needs (exact vs approximate), and query patterns (e.g., daily/weekly/monthly unique users, retention).
Discuss its memory efficiency (e.g., 12KB for 0.81% error) and suitability for large-scale approximate distinct counts, but note it cannot retrieve individual users.
Explain that bitmaps provide exact counts with memory proportional to the maximum user ID (or cardinality), and support set operations like AND/OR, but can be memory-heavy for sparse or very large user bases.
Describe raw storage (e.g., storing user IDs in a database or set) as flexible and exact, allowing complex queries and user-level analysis, but with higher storage and compute costs.
Propose a solution based on trade-offs: e.g., HyperLogLog for real-time approximate DAU/MAU, bitmaps for exact counts if user base is bounded, and raw storage for detailed analytics or small scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a two-path answer: streaming for near-real-time approximate counts, batch for nightly reconciliation.
Start by clarifying the product requirements and scale, then compare real-time and batch approaches for DAU/MAU computation, highlighting trade-offs in accuracy, cost, and latency. Recommend a hybrid solution that balances these factors, and explain how you would implement and monitor it.
Pro tip: Emphasize that DAU/MAU are often used for business metrics where approximate values are acceptable, so you can optimize for cost without sacrificing decision-making quality. Also, mention the importance of defining what 'active' means and ensuring consistency across time zones and late-arriving data.
Ask about the expected scale (events per day, number of users), latency requirements (how fresh the data needs to be), and accuracy tolerance (exact vs approximate).
Discuss real-time (streaming) computation: low latency, higher cost, potential for inaccuracy due to late data; batch: high accuracy, lower cost, but higher latency.
Explain that real-time may overcount or undercount due to windowing and late events, while batch can reprocess and deduplicate; real-time requires always-on infrastructure, batch can use spot instances.
Suggest using batch for historical accuracy and real-time for recent trends, or lambda architecture with a speed layer and batch layer, merging results.
Outline how to implement (e.g., Kafka + Flink for real-time, Spark for batch) and monitor for data quality, cost, and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about pre-aggregating daily counts into a time-series table and keeping the raw bitmap or sketch data around for a rolling window in case you need recomputation.
Start by clarifying the requirements: what granularity (daily/monthly), how far back, and query patterns (point lookups vs. trends). Then propose a layered design: an ingestion pipeline that computes and stores daily active user counts per day, and a query layer that aggregates these counts for MAU and historical lookups, using pre-aggregation and appropriate indexing for efficiency.
Pro tip: Mention that DAU/MAU is often a 'distinct count' problem, so discuss trade-offs between exact (e.g., HyperLogLog) and approximate counting, and how you'd handle late-arriving data and backfills without breaking historical accuracy.
Ask about query patterns (e.g., single-day DAU, MAU over a month, trends over time), data volume, latency SLAs, and retention period. This shapes the storage and indexing strategy.
Propose a fact table of daily active users per day (e.g., date, user_id) or pre-aggregated daily counts. For MAU, you need distinct users over a rolling window, so consider storing daily distinct user sets or using sketches.
Use a columnar store (e.g., Redshift, BigQuery) for analytical queries, with partitioning by date and clustering on user_id. For fast point lookups, a key-value store or a summary table with daily counts can work.
Pre-aggregate daily counts for DAU. For MAU, either pre-aggregate monthly distinct counts or use approximate algorithms (HyperLogLog) to merge daily sketches. Cache frequent queries.
Discuss handling late data, backfills, and idempotent updates. Ensure the design scales with user growth and supports efficient historical lookups without full scans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.