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Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Instacart software engineer interview with a SQL pivot problem that also had a Java implementation angle. The constraint about no external date libraries felt a bit artificial but whatever, it is what it is.

Questions Asked (1)

Q1

You have an orders table with columns like order_id, shopper_id, order_date, and status. Write a query or Java solution to pivot the data so each row represents one shopper and each column shows how many orders they have per status (e.g., DELIVERED, CANCELED, RETURNED). The solution should also work without any external date-parsing libraries.

Algorithms & Data StructuresData ModelingTechnical Trade-offs
Author's notes

The SQL pivot part was fine, conditional aggregation with CASE WHEN is pretty standard.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then present both SQL and Java solutions. For SQL, use conditional aggregation with CASE statements to pivot the data. For Java, use a HashMap to group orders by shopper and status, then output the counts.

Pro tip: Mention that the SQL solution is more efficient and simpler for this task, but demonstrate Java proficiency by showing a clean implementation with proper data structures. Also, note that avoiding date-parsing libraries is straightforward since we only need to count orders per status, not manipulate dates.

1. Clarify Requirements

Confirm the expected output format, the list of statuses, and whether the solution should be in SQL or Java. Ask about performance considerations and data volume.

2. Design SQL Solution

Write a SQL query using conditional aggregation: SELECT shopper_id, SUM(CASE WHEN status = 'DELIVERED' THEN 1 ELSE 0 END) AS delivered_count, ... FROM orders GROUP BY shopper_id.

3. Design Java Solution

Use a HashMap<shopper_id, Map<status, count>> to aggregate counts. Iterate through orders, update counts, then output each shopper's counts per status.

4. Discuss Trade-offs

Compare SQL vs Java: SQL is declarative and efficient for this task; Java offers more flexibility for complex transformations but requires more code. Mention that avoiding date-parsing libraries is trivial here.

5. Handle Edge Cases

Consider shoppers with no orders (should they appear with zero counts?), unknown statuses, and null values. Ensure the solution is robust.

Key Points to Mention

  • Conditional aggregation in SQL using CASE statements
  • Using HashMap for grouping in Java
  • Efficiency: SQL is better for large datasets due to set-based operations
  • Avoiding external libraries: no date parsing needed for this pivot
  • Handling missing statuses: ensure all status columns appear even if count is zero
  • Scalability: discuss indexing on shopper_id and status for performance

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