Design a DAU/MAU Metrics System
Design a system capable of computing and serving product engagement metrics: Daily Active Users (DAU) and Monthly Active Users (MAU).
An "active user" is defined as a distinct user who has performed at least one qualifying event (e.g., app open, page view, session start) within the specified time window (daily for DAU, monthly for MAU).
Your comprehensive system design should address the following key areas:
- Event Ingestion and Data Modeling: Detail the process of ingesting raw user events and the necessary data schema (fields required) to support metric computation.
- De-duplication and Identity Resolution: Explain how the system handles de-duplication of events and resolves user identity across various identifiers (e.g.,
user_id,device_id) and scenarios, including logged-out users. - Accurate DAU/MAU Computation: Describe the mechanisms for accurately calculating DAU/MAU, considering challenges such as time zones, late-arriving events, and the need for historical data backfills.
- Scalable Storage and Compute: Propose appropriate storage and compute technologies and architectures to handle large-scale event volumes and metric calculations efficiently.
- Serving Queries and Dashboards: Outline how the computed metrics will be served to queries and dashboards, addressing concerns like query latency, caching strategies, and correctness guarantees.
- Operational Considerations: Discuss aspects related to monitoring the system, ensuring data quality, and adhering to privacy regulations.
AI interview prep
Core dilemma
Balancing the need for high-throughput, low-latency event ingestion with the challenge of accurately computing distinct user counts across various identity types, time zones, and late-arriving data, all while ensuring data quality and cost-efficiency at massive scale.
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 a DAU/MAU Metrics System
Design a system capable of computing and serving product engagement metrics: Daily Active Users (DAU) and Monthly Active Users (MAU).
An "active user" is defined as a distinct user who has performed at least one qualifying event (e.g., app open, page view, session start) within the specified time window (daily for DAU, monthly for MAU).
Your comprehensive system design should address the following key areas:
- Event Ingestion and Data Modeling: Detail the process of ingesting raw user events and the necessary data schema (fields required) to support metric computation.
- De-duplication and Identity Resolution: Explain how the system handles de-duplication of events and resolves user identity across various identifiers (e.g.,
user_id,device_id) and scenarios, including logged-out users. - Accurate DAU/MAU Computation: Describe the mechanisms for accurately calculating DAU/MAU, considering challenges such as time zones, late-arriving events, and the need for historical data backfills.
- Scalable Storage and Compute: Propose appropriate storage and compute technologies and architectures to handle large-scale event volumes and metric calculations efficiently.
- Serving Queries and Dashboards: Outline how the computed metrics will be served to queries and dashboards, addressing concerns like query latency, caching strategies, and correctness guarantees.
- Operational Considerations: Discuss aspects related to monitoring the system, ensuring data quality, and adhering to privacy regulations.