Window function: running total over category
Last updated: April 11, 2026
Quick Overview
Use window functions to compute running total partitioned by user_id.
MongoDB
Data Manipulation (SQL/Python)
Data Scientist
MongoDB
April 11, 2026Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium
244
7
4,537 solved
Use window functions to compute running total partitioned by user_id.
This question from MongoDB'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
Data cleaning and transformation
NULL handling and COALESCE
Pandas vectorized operations and groupby
Aggregate functions and GROUP BY
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?
- How would you handle this if the data was spread across multiple databases?
- Can you rewrite this without using subqueries?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task is to compute a running total of a specified numerical field (e.g., 'amount') partitioned by 'user_id'. This means for each user, we will calculate a cumulative sum of their transactions in s...
Approach
- Identify Data: Ensure the transactions table has the necessary columns: 'user_id', 'transaction_date', and 'amount'.
- Sort Data: Order the data by 'transaction_date' within each 'user_id'...
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