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Design DAU and MAU Metrics
Distributed Stream ProcessingUnique Cardinality EstimationIdempotency and Deduplication StrategiesData Warehousing / OLAP DatabasesEvent Time vs. Processing Time
System Design: Daily and Monthly Active Users (DAU/MAU) Metrics
Design a robust system to compute and serve Daily Active Users (DAU) and Monthly Active Users (MAU) metrics for a large-scale consumer application.
Requirements
- DAU Definition: The number of unique active users per calendar day.
- MAU Definition: The number of unique active users over a rolling 30-day window or a calendar month. The candidate must explicitly state their chosen interpretation.
- Event Ingestion: The system must ingest user activity events originating from both web and mobile clients.
- Real-time Dashboards: Support near-real-time dashboards for product and leadership teams, displaying current metrics.
- Historical Queries: Enable querying historical metrics based on various dimensions, including date range, platform (e.g., web, iOS, Android), country, and application version.
- Event Handling: The system must gracefully handle late, duplicated, and out-of-order user activity events to ensure data integrity and accuracy.
Discussion Points
Candidates should be prepared to discuss the following architectural considerations:
- Storage Strategy: How will user activity data and aggregated metrics be stored?
- Aggregation Strategy: What methods will be used to aggregate raw events into DAU/MAU?
- Correctness Trade-offs: What are the implications of various design choices on data accuracy and consistency?
- Scalability: How will the system scale to accommodate a large and growing user base and event volume?
- Monitoring: How will the system's health, performance, and data quality be monitored?
- Failure Handling: What mechanisms will be in place to ensure resilience and data recovery in case of system failures?
AI interview prep
Core dilemma
Achieving accurate, scalable, and timely unique counting of high-volume, potentially messy event streams across various time windows and dimensions, while supporting both real-time dashboards and complex historical queries.
Mid-level signals3
Senior signals5
Red flags4
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design DAU and MAU Metrics
Distributed Stream ProcessingUnique Cardinality EstimationIdempotency and Deduplication StrategiesData Warehousing / OLAP DatabasesEvent Time vs. Processing Time
System Design: Daily and Monthly Active Users (DAU/MAU) Metrics
Design a robust system to compute and serve Daily Active Users (DAU) and Monthly Active Users (MAU) metrics for a large-scale consumer application.
Requirements
- DAU Definition: The number of unique active users per calendar day.
- MAU Definition: The number of unique active users over a rolling 30-day window or a calendar month. The candidate must explicitly state their chosen interpretation.
- Event Ingestion: The system must ingest user activity events originating from both web and mobile clients.
- Real-time Dashboards: Support near-real-time dashboards for product and leadership teams, displaying current metrics.
- Historical Queries: Enable querying historical metrics based on various dimensions, including date range, platform (e.g., web, iOS, Android), country, and application version.
- Event Handling: The system must gracefully handle late, duplicated, and out-of-order user activity events to ensure data integrity and accuracy.
Discussion Points
Candidates should be prepared to discuss the following architectural considerations:
- Storage Strategy: How will user activity data and aggregated metrics be stored?
- Aggregation Strategy: What methods will be used to aggregate raw events into DAU/MAU?
- Correctness Trade-offs: What are the implications of various design choices on data accuracy and consistency?
- Scalability: How will the system scale to accommodate a large and growing user base and event volume?
- Monitoring: How will the system's health, performance, and data quality be monitored?
- Failure Handling: What mechanisms will be in place to ensure resilience and data recovery in case of system failures?