Clarify the table schemas and the relationship between orders and driver requests, then use a semi-join (EXISTS or IN) to return orders that have at least one matching driver request. Discuss performance implications and edge cases like duplicate requests.
Pro tip: Mention that EXISTS is often more efficient than IN for large datasets because it can short-circuit, and explicitly state your assumption about the join key (e.g., order_id) to show you think about data modeling.
Identify the primary keys and foreign keys. Confirm that driver_requests has a foreign key referencing orders (e.g., order_id) and that an order can have multiple driver requests.
Select a semi-join approach: either EXISTS, IN, or an INNER JOIN with DISTINCT. Consider performance and readability for the given database.
Write the SQL query using the chosen construct, ensuring correct join condition and filtering. For example: SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM driver_requests dr WHERE dr.order_id = o.order_id);
Check for duplicates, NULLs, and performance. Explain how the query handles orders with no requests and multiple requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data schema and business context, then outline a SQL-based solution using window functions to rank offers per order and flag the highest. Discuss handling ties, nulls, and performance considerations for large datasets.
Pro tip: Mention that ranking should be done with RANK() or DENSE_RANK() depending on tie-handling needs, and that flagging the highest offer can be done efficiently with a CASE expression on the rank. Also, consider partitioning by order_id and ordering by offer_amount DESC.
Ask about the table structure, columns (e.g., order_id, driver_id, offer_amount), and whether ties need special handling. Confirm the definition of 'highest' (e.g., maximum amount, earliest timestamp).
Select RANK(), DENSE_RANK(), or ROW_NUMBER() based on tie behavior. For flagging the highest, RANK() is often suitable as it assigns the same rank to ties.
Use a window function partitioned by order_id and ordered by offer_amount DESC to compute rank. Then use a CASE statement to flag the highest offer (e.g., rank = 1).
Address nulls, ties, and orders with a single offer. Discuss whether to include all offers or only the top one in the output.
Consider indexing on (order_id, offer_amount) for performance. Validate results with sample data and check for correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and defining 'most recent driver offer' and 'one before it' using window functions partitioned by order and ordered by offer timestamp. Then filter orders where the latest offer amount exceeds the previous offer, count them, and compute the average difference. Finally, validate edge cases like single-offer orders and ties.
Pro tip: Explicitly state how you handle orders with only one offer or ties in timestamps—these edge cases often trip up candidates and show you think about data quality. Also, mention that you'd check if the improvement is statistically significant or driven by outliers before drawing conclusions.
Confirm what constitutes an 'offer' (e.g., driver bid, delivery offer) and how to identify the most recent vs. previous offer per order. Ensure you understand the grain of the table and timestamp fields.
Use a window function like ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY offer_timestamp DESC) to assign ranks, so the most recent offer gets rank 1 and the previous gets rank 2.
Self-join or use LAG to bring the previous offer amount alongside the latest. Filter for orders where the latest offer amount > previous offer amount.
Count the number of such orders and calculate the average improvement (latest - previous) across them. Consider if you need to handle nulls or ties.
Check for edge cases (e.g., orders with only one offer, ties in timestamps) and assess whether the average improvement is meaningful (e.g., outliers, distribution).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Define bounded and unbounded window frames clearly, emphasizing how they control the set of rows used in window function calculations. Then provide a concrete SQL example for each, ideally using a business-relevant scenario like calculating running totals or moving averages. Finally, discuss trade-offs such as performance and use cases to demonstrate practical understanding.
Pro tip: Mention that unbounded frames can lead to performance issues on large datasets due to processing all preceding/following rows, and suggest using bounded frames when possible for efficiency. Also, relate the concept to common analytics tasks like calculating rolling metrics, which are frequent in food delivery analytics.
Explain that a window frame specifies the subset of rows within a partition that a window function operates on, defined using ROWS or RANGE between boundaries.
Describe bounded frames as having fixed start and end points (e.g., ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING), while unbounded frames extend to the partition boundaries (e.g., UNBOUNDED PRECEDING).
Write a query for each: a bounded frame for a moving average over 3 days, and an unbounded frame for a cumulative sum from the start of the partition.
Highlight performance implications (unbounded frames can be slower) and typical applications (bounded for smoothing, unbounded for running totals).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.