Optimize a slow query on transactions
Last updated: November 7, 2025
Quick Overview
A query on transactions is running slowly. Identify the bottleneck and optimize it.
xAI
Data Manipulation (SQL/Python)
Data Scientist
xAI
November 7, 2025Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard
6
10
1,745 solved
A query on transactions is running slowly. Identify the bottleneck and optimize it.
xAI asks this during the Onsite because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.
What the Interviewer Expects
- Solve complex analytical problems with elegant, readable SQL
- Optimize queries for large-scale datasets with partitioning and indexing
- Use recursive CTEs, lateral joins, and advanced window functions
- Design the data model alongside the query solution
- Discuss trade-offs between SQL and programmatic approaches (Python/pandas)
- Consider the operational aspects: query scheduling, incremental processing
Key Topics to Cover
Subqueries and correlated subqueries
JOIN types and when to use each
Aggregate functions and GROUP BY
NULL handling and COALESCE
Index optimization and query performance
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
- Can you rewrite this without using subqueries?
- What would you do if this query needs to run every 5 minutes?
- How would you handle this if the data was spread across multiple databases?
Sharpen Your Skills on Codemia
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Practice SQL ProblemsSample Answer
Problem Understanding
The data involved in this query consists of a transactions table that contains details about individual transactions such as transaction_id, user_id, transaction_date, amount, and status. The goal is ...
Approach
- Identify Columns Needed: Determine which columns are necessary for the analysis, e.g., amounts and statuses.
- Aggregate Requirements: Establish the required aggregation functions like S...
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