Optimize a slow query on orders
Last updated: June 9, 2026
Quick Overview
A query on orders is running slowly. Identify the bottleneck and optimize it.
Datadog
Data Manipulation (SQL/Python)
Data Scientist
Datadog
June 9, 2026Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Medium
33
10
4,183 solved
A query on orders is running slowly. Identify the bottleneck and optimize it.
This question from Datadog's Technical Screen 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
Common Table Expressions (CTEs)
JOIN types and when to use each
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Date/time manipulation
Data cleaning and transformation
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
- How would you handle slowly changing dimensions in this scenario?
- What indexes would you create to support this query?
- How would you optimize this query for a table with 100 million rows?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task involves optimizing a slow SQL query that retrieves data from an 'orders' table. The relevant data includes order IDs, customer IDs, order dates, product details, and order amounts. The goal ...
Approach
- Analyze the Existing Query: Review the current query for inefficiencies such as unnecessary joins or complex subqueries.
- Identify Key Metrics: Determine the metrics needed, such as tot...
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