Window function: running total over date
Last updated: December 23, 2025
Quick Overview
Use window functions to compute running total partitioned by date.
Postmates
Data Manipulation (SQL/Python)
Data Scientist
Postmates
December 23, 2025Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium
50
7
1,819 solved
Use window functions to compute running total partitioned by date.
Data manipulation questions at Postmates test your ability to work with real-world datasets. This Onsite question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.
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
Date/time manipulation
Index optimization and query performance
JOIN types and when to use each
Common Table Expressions (CTEs)
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
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 indexes would you create to support this query?
- How would you handle this if the data was spread across multiple databases?
- What would you do if this query needs to run every 5 minutes?
- How would you optimize this query for a table with 100 million rows?
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Practice SQL ProblemsSample Answer
Problem Understanding
In this problem, we are tasked with calculating a running total of a specific metric (e.g., order value) for each day in our dataset. The relevant data is likely contained in an 'orders' table, which ...
Approach
- Identify the Source Table: Start with the 'orders' table, which contains order details, including the date and value.
- Use a Common Table Expression (CTE): Create a CTE to simplify the ma...
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