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Design a DAU/MAU metrics system

VantaSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteSystem Design

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A Vanta software-engineer system-design interview question: design a scalable pipeline to compute and serve DAU and MAU engagement metrics for a large consumer app. It tests event ingestion, identity and de-duplication, exact vs. approximate distinct counting (HyperLogLog/bitmaps), handling of time zones and late/out-of-order events, streaming-vs-batch correctness, dashboard serving, and monitoring/privacy.

Question

Design a system that computes and serves product engagement metrics — DAU (Daily Active Users) and MAU (Monthly Active Users) — for a large consumer application.

An "active user" is a distinct user who performed at least one qualifying event (e.g., app open, page view, login, session start) within the time window.

Your design should address:

  1. Metric definitions. DAU is the number of unique active users per calendar day. For MAU, choose and justify one interpretation — calendar-month uniques or a rolling 30-day window — and explain the trade-off with the other.
  2. Event ingestion and data modeling. Accept activity events from both web and mobile clients. Specify the required event fields.
  3. De-duplication and identity. Handle duplicate events; define the canonical user key across user_id, device_id/anonymous_id, and logged-out users.
  4. Accurate DAU/MAU computation. Address time-zone/day boundaries, late-arriving events, out-of-order events, and backfills.
  5. Storage and compute choices at large scale. Justify your streaming vs. batch and exact vs. approximate counting decisions.
  6. Serving queries and dashboards. Support near-real-time dashboards for product and leadership teams plus historical queries by date range, platform, country, and app version. Discuss latency, caching, and correctness guarantees.
  7. Monitoring, data quality, failure handling, and privacy. Cover anomaly detection, reconciliation, fault tolerance, and PII/retention controls.