Window function: lead/lag over date
Last updated: September 4, 2025
Quick Overview
Use window functions to compute running total partitioned by user_id.
Snapchat
Data Manipulation (SQL/Python)
Data Scientist
Snapchat
September 4, 2025Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium
4
6
4,168 solved
Use window functions to compute running total partitioned by user_id.
Data manipulation questions at Snapchat 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
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
NULL handling and COALESCE
JOIN types and when to use each
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
- How would you handle this if the data was spread across multiple databases?
- How would you validate the correctness of your query results?
- How would you optimize this query for a table with 100 million rows?
- What indexes would you create to support this query?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task is to compute a running total of some metric (e.g., user activity, engagement, etc.) partitioned by user_id over a specified date range. The dataset likely contains columns such as `user_id...
Approach
- Identify the Data: Ensure we have the necessary columns:
user_id,activity_date, and the metric column (e.g.,metric_value). - Sort the Data: Use
activity_dateto order the data ...
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