Use a window function like ROW_NUMBER() partitioned by user_id and ordered by purchase timestamp ascending, then filter to the first row per user. Alternatively, use a correlated subquery or join with a subquery that finds the minimum timestamp per user. Ensure the query handles ties and returns exactly one product per user.
Pro tip: Mention that if there are ties (same timestamp), you need a tiebreaker like the smallest product_id or transaction_id to ensure deterministic results. Also, clarify that 'first' means earliest timestamp, not first inserted row.
Identify the relevant table(s) and columns: user_id, product_id, purchase_timestamp. Clarify that 'first product' means the product with the earliest purchase timestamp for each user.
Decide between using a window function (ROW_NUMBER, RANK, DENSE_RANK) or a subquery with MIN(timestamp). Window functions are often more efficient and easier to extend.
For window function: SELECT user_id, product_id, purchase_timestamp FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY purchase_timestamp ASC) AS rn FROM purchases) t WHERE rn = 1. For subquery: SELECT p.user_id, p.product_id, p.purchase_timestamp FROM purchases p JOIN (SELECT user_id, MIN(purchase_timestamp) AS min_ts FROM purchases GROUP BY user_id) m ON p.user_id = m.user_id AND p.purchase_timestamp = m.min_ts.
If multiple products share the same earliest timestamp, decide on a tiebreaker (e.g., smallest product_id) and incorporate it into the ORDER BY or join condition. Also consider users with no purchases (they won't appear).
Check that the query returns one row per user. Consider indexing on (user_id, purchase_timestamp) for performance. Test with sample data including ties and nulls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on how to structure this.
First, aggregate purchases by user and calendar day to count daily transactions per user. Then filter to users with at least two purchases on any day, and finally count the distinct users who meet this condition.
Pro tip: Clarify how to handle time zones and whether 'same calendar day' refers to UTC or local time, as this can significantly affect results in global apps like Uber.
Identify the relevant tables (e.g., purchases, users) and columns (user_id, purchase_date). Clarify the definition of 'calendar day' and any time zone considerations.
Group the purchase data by user_id and the calendar day (using DATE() or equivalent) and count the number of purchases for each group.
Apply a HAVING clause to keep only groups where the purchase count is >= 2.
Use COUNT(DISTINCT user_id) on the filtered result to get the number of unique users who made at least two purchases on the same day.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and defining 'purchase' (e.g., completed orders, distinct transactions). Then write a SQL query that joins the purchases table to the products table, groups by product, counts purchases, orders descending, and limits to 3. Also discuss handling ties and data quality issues.
Pro tip: Mention that at a company like Uber, you'd likely need to consider time windows (e.g., last 30 days) and possibly segment by city or user cohort to make the metric actionable. Also, be prepared to discuss how you'd handle ties for third place.
Ask about the table structure, what constitutes a purchase (e.g., order status, refunds), and whether 'top' means all-time or a specific period. Confirm if ties should be included or broken arbitrarily.
Use a JOIN between purchases and products, GROUP BY product, COUNT(*) or COUNT(DISTINCT order_id), ORDER BY count DESC, and LIMIT 3. Ensure you handle NULLs and duplicates appropriately.
Discuss how to handle ties for third place (e.g., using RANK() or DENSE_RANK() in a subquery) and how to exclude cancelled or refunded orders.
Mention indexing on foreign keys, partitioning by date if needed, and using approximate algorithms (e.g., HyperLogLog) for large-scale distinct counts if performance is a concern.
Explain how you'd present the top 3 products, possibly with additional context like revenue or trend over time, and how this insight could drive business decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one actually tripped me up more than I expected.
Start by clarifying the table schema and the definition of 'total daily purchases'. Then write a SQL query that aggregates daily purchases, and use a window function to compute the 7-day rolling average over the daily totals.
Pro tip: Mention that you would handle missing dates by generating a date series to ensure the rolling window is based on calendar days, not just days with purchases. Also, discuss the choice between ROWS and RANGE in the window frame.
Ask about the table structure (e.g., purchases table with user_id, purchase_date, amount) and confirm that 'total daily purchases' means the sum of purchase amounts per day. Also clarify the date range and whether to include days with zero purchases.
Write a subquery or CTE that groups by date and sums the purchase amounts to get total daily purchases.
Use a window function with AVG over an ORDER BY date and a frame of ROWS BETWEEN 6 PRECEDING AND CURRENT ROW to calculate the 7-day rolling average.
If there are gaps in dates, generate a complete date series and left join the daily totals to ensure the rolling window covers consecutive calendar days.
Select the date and rolling average, and consider ordering by date. Validate the query with sample data or edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Switching from SQL to pandas mid-interview felt a little abrupt.
Start by clarifying the dataset structure and the definition of daily active users (unique user IDs per date). Then, use pandas groupby on the date column and apply nunique on the user ID column to compute the metric, ensuring the date column is in datetime format.
Pro tip: Mention that you would validate the result by checking for missing values and considering timezone handling, as Uber operates globally and date boundaries can affect daily active user counts.
Confirm the dataset columns (e.g., user_id, date) and the definition of daily active users. Ask about timezone considerations and whether the date is already in the correct format.
Convert the date column to datetime if needed, handle missing values, and ensure user IDs are consistent (e.g., no leading/trailing spaces).
Use df.groupby('date')['user_id'].nunique() to count unique user IDs per date. Alternatively, use drop_duplicates() before grouping for efficiency.
Check for anomalies such as zero counts or spikes, and consider plotting the trend to ensure the metric makes sense. Discuss any assumptions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.