Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Amazon SQL interview, one meaty question about aggregating click data across a product hierarchy. The whole thing revolved around a single problem but they kept pulling the thread until I was talking about indexing and multi-department rollups.

Questions Asked (1)

Given a Products table with a department/category/subcategory hierarchy and a ClickLog table tracking user clicks on products, write SQL to count distinct users who clicked any product in a specified department within a given time range. Then explain how you'd handle indexing and partitioning at scale, and extend the query to return results across all departments.

Data ModelingSystem DesignTechnical Trade-offs

Author's notes

The core join was fine, just joining ClickLog to Products on product_id and filtering by department and a timestamp range, then COUNT(DISTINCT user_id).

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and requirements, then write a straightforward SQL query joining Products and ClickLog with filters on department and time range, counting distinct users. For scale, discuss indexing strategies (e.g., composite indexes on ClickLog) and partitioning (e.g., by date), and extend the query to group by department using a window function or subquery.

Pro tip: Mention that you'd verify the query plan and consider denormalization or materialized views for frequent queries, showing awareness of real-world performance trade-offs.

1. Clarify Requirements and Schema

Ask about table structures, relationships, and exact definitions (e.g., time range boundaries, department hierarchy). Confirm whether 'department' includes subcategories.

2. Write Basic SQL Query

Construct a query joining Products and ClickLog, filtering by department and time range, and counting distinct user IDs. Use appropriate JOIN conditions and WHERE clauses.

3. Discuss Indexing and Partitioning

Explain indexing on ClickLog (e.g., composite index on (click_time, product_id, user_id)) and partitioning by date to improve query performance at scale.

4. Extend Query for All Departments

Modify the query to group by department, using a JOIN and GROUP BY, or a window function to count distinct users per department.

5. Address Trade-offs and Scalability

Discuss trade-offs like index maintenance overhead, partition pruning, and potential use of columnar storage or pre-aggregation for large-scale analytics.

Key Points to Mention

Use of COUNT(DISTINCT user_id) for accurate user counts.
Composite indexes on ClickLog (e.g., (click_time, product_id, user_id)) to speed up filtering and joins.
Partitioning ClickLog by date (e.g., daily or monthly) to enable partition pruning.
Consider denormalizing department into ClickLog or using a materialized view for frequent queries.
For all departments, use GROUP BY department with a JOIN, or a window function if needed.
Mention query optimization techniques like EXPLAIN ANALYZE to verify performance.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.