Seemed straightforward until I had to decide whether to count unique users globally or unique (page, user) pairs.
Start by clarifying the schema of the document access events table, especially how 'collaborator' and 'how they ended up on documents' are defined. Then write a SQL query that groups by the acquisition channel (e.g., invited, shared link, duplicate, template) and computes counts and percentages. Finally, validate the results and discuss potential edge cases or data quality issues.
Pro tip: Always clarify ambiguous terms like 'collaborator' and 'ended up on documents' before diving into SQL—this shows you think like a product analyst who cares about definitions and data quality. Also, mention that percentages should sum to 100% and consider using window functions for efficiency.
Ask clarifying questions about the table structure, what constitutes a 'collaborator', and the possible values for 'how they ended up on documents' (e.g., invited, shared link, duplicate, template).
Determine which columns to use for grouping (e.g., acquisition_channel) and any necessary filters (e.g., exclude deleted documents or inactive users).
Use GROUP BY on the acquisition channel to count distinct collaborators per channel, then compute percentages using window functions or subqueries.
Check that percentages sum to 100%, handle NULLs or 'other' categories, and discuss what the distribution implies for product decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Self-join on page_id after deduping, then group and count.
Start by clarifying the data model: a many-to-many relationship between users and document pages. Then, for the given user ID, find all pages they appear on, identify all other users on those pages, and count co-occurrences to find the user with the highest count. Discuss efficient algorithms and data structures to handle large-scale data.
Pro tip: Mention that you would precompute collaboration counts offline using a batch job (e.g., MapReduce or Spark) to enable fast lookups, and consider edge cases like ties or no collaborators.
Confirm the schema: users, pages, and a mapping table (e.g., page_editors) linking users to pages. Ask about scale, update frequency, and whether the result should be real-time or can be precomputed.
For a given user, retrieve all pages they are associated with, then for each page, retrieve all other users. Count occurrences of each collaborator and return the one with the maximum count.
Propose precomputing collaboration counts using a batch process (e.g., self-join on page_id, group by user pairs, count). Store results in a key-value store for fast retrieval. Discuss partitioning and indexing strategies.
Define behavior when multiple users have the same maximum count (e.g., return any, or most recent). Handle cases where the user has no collaborators or appears on no pages.
Compare real-time vs. precomputed approaches. Mention potential extensions: weighting by page importance, time decay, or considering collaboration types (e.g., editing vs. commenting).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining collaboration as a meaningful user action (e.g., sharing, commenting, or editing together) and propose a rate like daily active collaborators per daily active users, segmented by country. Then discuss how to compute it using event data, ensuring to handle data quality issues and consider alternative definitions.
Pro tip: Acknowledge that collaboration can be defined in multiple ways and propose a primary metric while mentioning alternatives; this shows you understand the ambiguity and can make pragmatic choices.
Clarify what constitutes a collaborative action in Notion's context, such as sharing a page, commenting, or co-editing. Choose a definition that aligns with the product's core value.
Select a numerator (e.g., number of users who performed at least one collaborative action) and denominator (e.g., total active users) for the rate. Justify why these capture collaboration intensity per country.
Identify the data sources (e.g., event logs, user profiles) and segment by country. Ensure you have reliable country attribution and sufficient data per country.
Calculate the collaboration rate for each country and rank them. Consider statistical significance and potential confounders like user base size or cultural differences.
Interpret the results, check for anomalies, and validate with alternative definitions or time periods. Discuss limitations and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.