Write a query to find churn rate from messages

Last updated: July 18, 2025

Quick Overview

Write a SQL query to calculate churn rate from the messages table, considering nulls and duplicates.

Tesla
Data Manipulation (SQL/Python)
Data Scientist
Tesla
July 18, 2025
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium

7

5

2,500 solved


Write a SQL query to calculate churn rate from the messages table, considering nulls and duplicates.

Data manipulation questions at Tesla 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
Data cleaning and transformation
Pandas vectorized operations and groupby
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Subqueries and correlated subqueries
NULL handling and COALESCE
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
  • What would you do if this query needs to run every 5 minutes?
  • Can you rewrite this without using subqueries?
  • How would you handle slowly changing dimensions in this scenario?
  • How would you validate the correctness of your query results?
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Practice SQL Problems
Sample Answer
Problem Understanding

To calculate the churn rate from the messages table, we need to understand the data involved. The messages table likely contains columns such as user_id, message_id, message_date, and `statu...

Approach
  1. Data Cleaning: Start by filtering out NULL values in the user_id column to ensure we only consider valid users. 2. Identifying Active Users: Determine the set of unique users who have sen...

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