← Walmart Labs Interview Insights
Seemed straightforward but I spent too long just writing the query instead of asking what GMV actually means in their context.
Start by clarifying the business definition of revenue or GMV and the time period granularity, then outline the SQL query structure using aggregation and filtering. Walk through a concrete example, highlighting how to handle common pitfalls like returns, discounts, and time zone differences.
Pro tip: Always confirm whether the metric should include returns, cancellations, or discounts, and whether it's based on order date or payment date—these nuances show you understand business context, not just SQL syntax.
Ask whether 'revenue' means gross merchandise value (GMV) or net revenue, and confirm the exact time period and granularity (daily, weekly, monthly).
Determine which tables contain order or transaction data, and which columns represent the amount and the date. For Walmart Labs, consider tables like orders, order_items, or transactions.
Use SUM() on the amount column, filter by the date range with WHERE, and optionally group by time period if granularity is needed. Example: SELECT SUM(order_amount) FROM orders WHERE order_date BETWEEN '2023-01-01' AND '2023-01-31'.
Account for returns, cancellations, discounts, and time zones. For example, subtract refunds or filter out cancelled orders, and ensure dates are in the correct time zone.
Check results against known totals or a sample, and consider indexing or partitioning for performance on large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of a 'new customer' by establishing the time window and the first purchase date. Then write a query that identifies customers whose first order falls within that window, using aggregation or window functions to find the earliest transaction per customer.
Pro tip: Always state your assumptions about the time frame and what constitutes a customer (e.g., unique email vs. customer ID) before writing the query. This shows you understand business context and prevents misinterpretation.
Ask or state what 'new customer' means: first purchase ever, first purchase in a given period, or first purchase after a gap. Also confirm the time window (e.g., last month, quarter).
Determine which tables contain customer and transaction data, such as customers, orders, or transactions. Note the key columns: customer_id, order_date, order_id.
Use a subquery with MIN(order_date) grouped by customer_id, or a window function like ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) to get the earliest transaction.
Apply a WHERE clause to select only customers whose first purchase date falls within the defined time window.
Combine the steps into a single SQL query, ensuring correct joins and filters. Optionally, test with sample data or explain how you would validate the results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric and time windows (e.g., 7-day vs. previous 7-day), then explain how window functions like LAG, SUM OVER, and AVG OVER with ROWS/RANGE frames can compute rolling aggregates. Finally, show how to compare periods using a self-join or window functions to calculate deltas and percent changes.
Pro tip: Mention that for large datasets, you should filter data to the necessary time range before applying window functions to avoid performance issues, and use RANGE instead of ROWS when dealing with date gaps to ensure correct rolling windows.
Define the metric (e.g., sales, active users) and the rolling periods (e.g., last 7 days vs. previous 7 days). Confirm whether the comparison is period-over-period or rolling average.
Use aggregate window functions like SUM, AVG, COUNT with OVER and specify the frame (ROWS BETWEEN 6 PRECEDING AND CURRENT ROW for 7-day rolling). For period comparison, use LAG to access previous period values.
Ensure data is ordered by date and consider using RANGE instead of ROWS if there are missing dates. Alternatively, generate a date spine to fill gaps before applying window functions.
Calculate the difference or percent change between current and previous period using arithmetic on the window function results. For example, (current_sum - previous_sum) / previous_sum.
Check results for correctness, especially at boundaries. Mention performance considerations like indexing, partitioning, and limiting data scanned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the SQL dialect (e.g., PostgreSQL, BigQuery, Snowflake) since date functions vary. Then explain common patterns for date filtering and difference calculations, using concrete examples like `WHERE date_col >= CURRENT_DATE - INTERVAL '30 days'` or `DATEDIFF(day, start_date, end_date)`. Emphasize best practices like avoiding functions on indexed columns and handling time zones.
Pro tip: Mention that for large datasets, using `BETWEEN` with explicit dates or `>=` with a computed cutoff is more index-friendly than applying functions to the column. Also, note that Walmart Labs often deals with high-volume data, so performance matters.
Ask or state which SQL dialect is being used (e.g., PostgreSQL, BigQuery, Snowflake) because date functions differ. Also, confirm if the calculation is for filtering, aggregation, or reporting.
Describe how to find records within the last 30 days using functions like `CURRENT_DATE`, `DATE_SUB`, or `INTERVAL`. Give an example: `WHERE order_date >= CURRENT_DATE - INTERVAL '30 days'`.
Show how to compute the difference between two dates using `DATEDIFF`, subtraction, or `EXTRACT`. For example, `DATEDIFF(day, start_date, end_date)` or `end_date - start_date` in PostgreSQL.
Discuss avoiding functions on indexed columns, using sargable predicates, and considering time zones. Mention that pre-computing date ranges can improve query performance.
Walk through a sample query that combines filtering and difference calculation, such as finding orders in the last 30 days and calculating delivery time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I felt least prepared for.
Start by understanding the query's purpose and the data volume, then use EXPLAIN to identify bottlenecks like full table scans or missing indexes. Prioritize optimizations based on impact, such as adding indexes, rewriting joins, or partitioning, and validate improvements with metrics.
Pro tip: Always measure performance before and after changes using actual execution plans and runtime statistics; this demonstrates a data-driven approach and avoids premature optimization.
Clarify what the query is supposed to do, the size of the tables involved, and how often it runs. This helps prioritize optimization efforts.
Use EXPLAIN or EXPLAIN ANALYZE to see the query plan, identifying operations like full table scans, nested loops, or sorts that cause slowness.
Based on the plan, look for missing indexes, outdated statistics, or inefficient joins. Consider rewriting the query, adding indexes, or updating statistics.
Evaluate if denormalization, partitioning, or materialized views could improve performance for this and similar queries.
Implement changes in a safe environment, measure performance improvements, and ensure the query still returns correct results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.