Started okay, got the core tables down pretty fast.
Start by clarifying the core requirements and access patterns (e.g., how workouts are logged, how sessions relate to workouts, how devices sync data, and how subscriptions gate features). Then propose a normalized relational schema with clear entities, relationships, and indexes, and discuss trade-offs for scalability and read-heavy queries.
Pro tip: Anchor your design around the most frequent queries (e.g., fetching a user's recent workout sessions or active subscription status) and explicitly call out indexing and denormalization choices to show you optimize for real-world performance, not just theoretical purity.
Ask about expected scale, read/write ratios, and key user flows (e.g., logging a workout, viewing history, syncing devices, checking subscription). This ensures the model supports actual use cases.
Define Users, Workouts (templates/plans), Workout Sessions (instances), Devices, and Subscriptions. Map cardinalities: one user to many sessions, one workout to many sessions, one user to many devices, one user to many subscriptions (or one active).
For each entity, specify primary keys, foreign keys, and essential fields (e.g., session start/end time, device type, subscription status). Use junction tables for many-to-many if needed (e.g., workout-session exercises).
Propose indexes on foreign keys and frequent query columns (e.g., user_id + session_date). Discuss when to denormalize (e.g., storing workout name in session for faster reads) and how to partition large tables (e.g., by user_id or date).
Compare SQL vs NoSQL, mention soft deletes, audit fields, and how to handle evolving schemas. Also touch on data consistency for device sync and subscription billing events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by clarifying the business goal of DAU for a fitness app, then propose a primary active event (e.g., completing a workout) and justify it. Explain deduplication by user ID per day and address time zones by defining a canonical time zone (e.g., UTC) or user-local day, and describe how to handle edge cases.
Pro tip: Mention that DAU should align with the product's north star metric and that you'd validate the definition with stakeholders to ensure it drives the right behavior. Also, note that using user-local time zones can be more accurate for engagement but may complicate aggregation; choose based on business needs.
Ask what decisions DAU will inform (e.g., engagement tracking, feature adoption) to tailor the definition. This shows you understand the metric's role in product analytics.
Propose a core action that indicates meaningful engagement, such as logging a workout, tracking a meal, or opening the app and viewing a workout plan. Justify why this event best represents active use.
Explain that you count unique users per day, so multiple events by the same user count once. Use a unique user identifier (e.g., user_id) and deduplicate within the day's window.
Decide on a time zone convention: either a fixed time zone (e.g., UTC) for consistency or user-local time for accuracy. Describe how to handle users who travel or have events spanning midnight.
Discuss handling of bots, inactive accounts, or multiple devices. Suggest validating the metric against business goals and iterating if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty mechanical once the schema was settled.
Start by clarifying the schema and definitions: what table(s) store user activity, how to identify a daily active user, and whether 'last 30 days' includes today. Then write a query that filters events to the last 30 days, groups by date, and counts distinct users per day, handling time zones and date boundaries explicitly.
Pro tip: Mention that DAU should be computed from a deduplicated event stream and that using a date_trunc or calendar table avoids off-by-one errors; also note that for large-scale systems, pre-aggregating daily active users is often necessary for performance.
Confirm which table contains user activity, the timestamp column, and how a user is identified. Define 'active' (e.g., any event, login, order) and whether the last 30 days includes today.
Use a WHERE clause on the event timestamp to restrict to the last 30 days, being explicit about time zone and inclusive/exclusive boundaries.
Truncate the timestamp to the day (e.g., DATE(ts) or date_trunc('day', ts)) and count distinct user IDs per day.
If the business requires a row for every day even with zero DAU, left join against a calendar table or generate a date series.
Present the SQL clearly, annotate key parts, and mention assumptions (e.g., time zone, definition of active).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the event schema and defining active users, then use window functions to compute rolling DAU, WAU, and MAU over time. For retention, build a cohort-based retention curve using first activity date and subsequent activity periods, and calculate stickiness as DAU/MAU ratio.
Pro tip: Mention that for large-scale data, approximate distinct counts (e.g., HyperLogLog) or pre-aggregated tables are often used to keep queries efficient, and that stickiness should be interpreted alongside retention to avoid misleading conclusions.
Clarify what constitutes an active user (e.g., login, order, app open) and identify the relevant event table with user_id and timestamp. Define DAU, WAU, and MAU as distinct active users in a day, 7-day, and 30-day window respectively.
Use window functions like COUNT(DISTINCT user_id) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) for WAU, and 29 PRECEDING for MAU. Ensure the window is based on calendar days, not rows, to handle missing dates.
Assign each user a cohort based on their first activity date. For each subsequent period (e.g., day, week), compute the percentage of cohort users who return. Use a self-join or window functions to track retention over time.
Compute the ratio of daily active users to monthly active users for each day. This metric indicates how frequently users engage; a higher ratio means more habitual usage.
Check for data quality issues (e.g., timezone, bot traffic) and ensure metrics align with business definitions. Interpret trends and compare against benchmarks or A/B test results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.