Window function: running total over category
Last updated: March 6, 2026
Quick Overview
Use window functions to compute rank partitioned by date.
Elastic
March 6, 20266
5
4,900 solved
Use window functions to compute rank partitioned by date.
Data manipulation questions at Elastic test your ability to work with real-world datasets. This Technical Screen question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.
What the Interviewer Expects
- Write syntactically correct SQL with proper JOIN and WHERE clauses
- Use GROUP BY and aggregate functions appropriately
- Handle NULL values correctly in your queries
- Explain the query execution plan at a high level
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 indexes would you create to support this query?
- How would you handle slowly changing dimensions in this scenario?
- Can you rewrite this without using subqueries?
- How would you validate the correctness of your query results?
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Practice SQL ProblemsSample Answer
Problem Understanding
In this problem, we need to compute a running total that is partitioned by date for each category. The data involved will typically include a transactions table with at least the following fields: `da...
Approach
To construct the query, we will follow these steps:
- Select Required Columns: Identify the columns we need:
date,category, and the cumulativeamount. - Window Function: Use the `SUM(...
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