Optimize a slow query on orders
Last updated: January 29, 2026
Quick Overview
A query on orders is running slowly. Identify the bottleneck and optimize it.
DoorDash
January 29, 202630
9
3,144 solved
A query on orders is running slowly. Identify the bottleneck and optimize it.
This question from DoorDash's Onsite tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.
What the Interviewer Expects
- Use advanced SQL features: window functions, CTEs, subqueries
- Write efficient queries that avoid common performance pitfalls
- Handle complex data transformations with multiple joins and aggregations
- Discuss indexing strategy and query optimization
- Address data quality issues: duplicates, missing values, outliers
Key Topics to Cover
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- What would you do if this query needs to run every 5 minutes?
- Can you rewrite this without using subqueries?
- What indexes would you create to support this query?
- How would you validate the correctness of your query results?
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