Microsoft·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Coding round at Microsoft was rough. The question itself was manageable but the CSV parsing requirement ate up most of my time and I never really recovered from it.

Questions Asked (1)

Implement an in-memory SQL engine that accepts table data as a CSV string (including a hand-written CSV parser), then solve a series of follow-up extensions on top of it.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The CSV parsing part wrecked me.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a modular architecture with separate components for CSV parsing, table storage, and query execution. Implement a minimal viable engine first, then iteratively add features like indexing and joins, discussing trade-offs at each step.

Pro tip: Demonstrate incremental development by building a working prototype early, then enhance it while explaining design decisions and performance implications. This shows you can deliver value quickly and think critically about scalability.

1. Clarify Requirements and Scope

Ask questions to understand expected SQL features, data size, performance needs, and extension priorities. This ensures you focus on what matters most.

2. Design Core Components

Outline a modular architecture: CSV parser, table storage (e.g., rows as dictionaries), and query executor. Discuss data structures for efficient lookups and updates.

3. Implement CSV Parser and Basic Queries

Write a robust CSV parser handling quotes, escapes, and delimiters. Implement basic SELECT with WHERE and simple aggregations to validate the design.

4. Extend with Advanced Features

Add support for JOINs, GROUP BY, ORDER BY, and indexing. Explain how each extension impacts performance and complexity.

5. Discuss Trade-offs and Optimizations

Compare in-memory vs. disk-based storage, indexing strategies, and query optimization techniques. Highlight scalability and maintainability considerations.

Key Points to Mention

CSV parsing edge cases: quoted fields, escaped quotes, newlines within fields, and delimiter handling.
Data structures for in-memory tables: row-oriented vs. column-oriented storage, and their impact on query performance.
Query execution models: volcano iterator model vs. materialization, and when to use each.
Indexing strategies: hash indexes for equality, B-trees for range queries, and their trade-offs.
Join algorithms: nested loop, hash join, and sort-merge join, with complexity analysis.
Concurrency and transaction handling: ACID properties, locking, and MVCC for in-memory engines.

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