Optimize a slow query on sessions

Last updated: March 21, 2026

Quick Overview

A query on sessions is running slowly. Identify the bottleneck and optimize it.

Uber
Data Manipulation (SQL/Python)
Data Scientist
Uber
March 21, 2026
Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Medium

50

13

971 solved

A query on sessions is running slowly. Identify the bottleneck and optimize it.

Data manipulation questions at Uber test your ability to work with real-world datasets. This Phone Screen 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
JOIN types and when to use each
Date/time manipulation
Aggregate functions and GROUP BY
Common Table Expressions (CTEs)
Subqueries and correlated subqueries
How to Approach This
  1. Clarify the schema and expected output format before writing queries.
  2. Use CTEs (WITH clauses) to break complex queries into readable steps.
  3. Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
  4. Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
  5. For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
  • How would you validate the correctness of your query results?
  • What indexes would you create to support this query?
  • Can you rewrite this without using subqueries?
  • How would you handle slowly changing dimensions in this scenario?
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Sample Answer
Problem Understanding

In this scenario, we are dealing with a database containing sessions data related to Uber's application usage. The query needs to produce insights from these sessions, potentially aggregating data...

Approach
  1. Identify the Bottleneck: Start by analyzing the existing query's execution plan to pinpoint the slowest parts, such as large table scans, inefficient joins, or missing indexes.
  2. **Break Down ...
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