I went with a pretty standard star schema: a date dimension, a user dimension, a server dimension, and then separate fact tables for each event type.
Start by clarifying the analytics requirements and the grain of each event (server, view, join, message). Then design a star schema with a central fact table at the most granular level (e.g., message events) and conformed dimensions (server, user, channel, time) that can be rolled up to support views and joins. Finally, discuss how to handle slowly changing dimensions and optimize for query performance.
Pro tip: Mention that you would first check if a snowflake schema or a wide fact table is more appropriate based on query patterns, and emphasize the importance of defining clear grain and conformed dimensions to avoid data silos.
Ask about the key metrics (e.g., number of views, joins, messages per server) and the granularity needed (e.g., per message, per user session). Define the grain of each fact table to avoid ambiguity.
List the dimensions (server, user, channel, time, etc.) and the measures (counts, durations). Determine which raw events map to fact tables and which attributes become dimensions.
Create a central fact table (e.g., message_fact) with foreign keys to dimensions. Consider separate fact tables for views and joins if they have different grains, or a unified fact table with a type indicator.
Decide how to track changes in dimensions like server name or user status (Type 1, 2, or 3). Explain the trade-offs and choose based on analytical needs.
Discuss partitioning (e.g., by date), indexing, and aggregation strategies. Mention pre-aggregated tables or materialized views for common queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First clarify the definition of a 'week' (e.g., ISO weeks starting Monday) and confirm the event table schema. Then write a SQL query that filters events to server_view in 2020, groups by week, counts events per week, and finally averages those weekly counts.
Pro tip: Mention that you would check for weeks with zero events and decide whether to include them as zeros in the average, as this can significantly affect the result and shows you think about data completeness.
Ask about the week definition (ISO vs. Sunday-start), the event table name and columns (event_type, timestamp), and whether to include weeks with no events.
Filter rows to event_type = 'server_view' and timestamp within 2020, then extract the week number or week start date using a date function like DATE_TRUNC or EXTRACT.
Group by the extracted week and count the number of events to get a weekly count for each week that had at least one event.
Take the average of the weekly counts. If including zero-event weeks, generate a complete list of weeks in 2020 and left join the counts, filling missing weeks with 0 before averaging.
Write the final SQL, explain the logic, and mention edge cases like partial weeks at year boundaries or timezone considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Used a window function with LAG to pull the previous week's count and subtracted.
Start by clarifying the schema and definitions (e.g., event table, timestamp column, event type). Then write a SQL query that aggregates server_view events by week, calculates the previous week's count using a window function, and computes the week-over-week change, filtering to the last 12 months.
Pro tip: Mention that you'd handle edge cases like missing weeks by using a date spine or generating a series, and ensure the week definition (e.g., ISO week) is consistent with business expectations.
Ask about the table structure, timestamp column, event type column, and how weeks are defined (e.g., starting on Monday). Confirm the time range: last 12 months from today.
Write a subquery or CTE that counts server_view events per week, using DATE_TRUNC or equivalent to group by week.
Use the LAG window function over the weekly counts, ordered by week, to get the previous week's event count.
Calculate the difference or percentage change between the current and previous week's counts.
Apply a WHERE clause to include only weeks within the last 12 months. Optionally, handle missing weeks by generating a date series and left joining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business context and expected output format, then discuss the trade-offs between generating a complete date spine and using a reporting layer to fill gaps. Emphasize that the choice depends on query performance, data volume, and whether the missing weeks should be treated as zero or null.
Pro tip: Mention that generating a date spine can be expensive on large datasets, so consider pre-aggregating or using a calendar table with appropriate indexes. Also, highlight that the definition of 'missing' depends on whether the metric is additive (e.g., counts) or non-additive (e.g., averages).
Ask whether missing weeks should be filled with zeros, nulls, or omitted, and understand how the results will be consumed (e.g., visualization, further aggregation).
Discuss the source schema, volume, and query patterns to determine if a static calendar table or dynamic date generation is more appropriate.
Compare approaches: left join with a date spine, recursive CTE, or application-side filling. Consider performance, maintainability, and correctness.
Choose a solution based on trade-offs, and explain how it handles edge cases like time zones, partial weeks, and non-additive metrics.
Outline how to implement the chosen approach, including indexing, materialization, and testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Three separate subqueries or CTEs aggregated by server_id, then joined together on server_id.
First clarify the schema and definitions of 'server views', 'server joins', and 'messages' (likely separate event tables). Then aggregate each metric per server independently, and combine them with a FULL OUTER JOIN on server_id, using COALESCE to handle servers missing from any metric.
Pro tip: Mention that you'd validate the grain of each event table and check for duplicate events before aggregating; also consider whether to use COUNT(DISTINCT user_id) or COUNT(*) based on the metric definition.
Ask which tables contain server views, joins, and messages, and confirm whether counts should be raw events or distinct users. Identify the server identifier column in each table.
Write separate subqueries or CTEs that group by server_id and count the relevant events for views, joins, and messages. This keeps the logic modular and avoids fan-out from joining before aggregation.
Join the three aggregated results on server_id using FULL OUTER JOIN so servers with zero activity in one metric are still included. Use COALESCE to replace NULL counts with 0.
If there is a servers dimension table, LEFT JOIN from it to the combined metrics to include servers with no events at all. Otherwise, rely on the FULL OUTER JOIN.
Select server_id, views_count, joins_count, messages_count, and order by server_id for readability. Optionally add a total_events column if useful.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.