DoorDash·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

DoorDash SWE interview with a two-part debugging and implementation round. First half was fixing bugs in a given Round-Robin load balancer, second half was building consistent hashing from scratch. Felt like a solid systems-flavored coding round, not pure leetcode.

Questions Asked (2)

You're given a buggy implementation of a Round-Robin load balancer. Find and fix the bugs.

Algorithms & Data StructuresRoot Cause Analysis

Author's notes

The bugs weren't subtle, which almost made it worse because I second-guessed myself for a minute thinking I was missing something deeper.

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AI HintsAI Generated

Suggested Approach

First, understand the intended behavior of a round-robin load balancer and the expected interface. Then, systematically trace through the code with small examples to identify logical errors, edge cases, and concurrency issues. Finally, fix the bugs, test thoroughly, and explain your reasoning.

Pro tip: Verbalize your debugging process: state what you expect, what you observe, and how you'll test your fix. This demonstrates structured problem-solving and communication skills, which are highly valued.

1. Clarify Requirements and Expected Behavior

Confirm the intended functionality: round-robin distribution, handling of server list changes, thread safety, and error cases. Ask clarifying questions if needed.

2. Review Code and Identify Suspicious Areas

Read through the code to understand its structure. Look for common bug patterns: off-by-one errors, incorrect index updates, missing bounds checks, and race conditions.

3. Trace with Examples and Test Edge Cases

Manually simulate the algorithm with a small set of servers and multiple requests. Test edge cases: empty server list, single server, server removal, and concurrent access.

4. Fix Bugs and Verify Correctness

Implement fixes for identified bugs. Re-run your test cases mentally or with code to ensure the algorithm now behaves correctly under all scenarios.

5. Discuss Improvements and Trade-offs

Mention potential enhancements like thread safety, dynamic server list updates, or alternative load balancing strategies, and discuss their implications.

Key Points to Mention

Round-robin algorithm: cycling through servers in order, using a modulo operation or index reset.
Common bugs: off-by-one errors in index increment, failure to handle empty server list, incorrect modulo when server count changes.
Thread safety: need for synchronization (e.g., mutex) if the load balancer is accessed concurrently.
Edge cases: empty list, single server, server addition/removal, and concurrent requests.
Testing strategy: unit tests for distribution, edge cases, and concurrency stress tests.
Time and space complexity: O(1) per request, O(n) space for server list.

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

Implement consistent hashing from scratch using a sorted map. Support adding and removing nodes, and route a key to the correct node via clockwise lookup on the hash ring. Optionally add virtual nodes for better load distribution.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where the round got interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then explain the core idea of consistent hashing and how a sorted map (e.g., TreeMap) enables efficient clockwise lookup. Walk through the implementation details for adding/removing nodes and routing keys, and discuss how virtual nodes improve load distribution. Finally, analyze time complexity and trade-offs, and consider edge cases like empty ring or key hashing collisions.

Pro tip: Mention that using a sorted map with O(log N) lookup is efficient, but for extreme scale, a concurrent skip list or a distributed ring (e.g., in Cassandra) might be needed. Also, highlight that virtual nodes help balance load but increase memory overhead, so tuning the number of replicas is key.

1. Clarify Requirements and Constraints

Ask about expected scale, read/write patterns, and whether the system needs to be distributed or single-node. Confirm the need for virtual nodes and any performance targets.

2. Explain Core Concept and Data Structure

Describe consistent hashing and why a sorted map (e.g., TreeMap) is ideal for O(log N) clockwise lookup. Mention that the ring is a sorted collection of node hashes.

3. Detail Implementation of Add/Remove and Routing

Explain how to add a node by hashing its identifier and inserting into the map, and how to remove it. For routing, hash the key and find the first node with hash >= key hash (wrapping around if needed).

4. Discuss Virtual Nodes and Load Distribution

Explain that virtual nodes (multiple hashes per physical node) improve balance. Describe how to implement them (e.g., hash node ID + replica index) and the trade-off between balance and memory.

5. Analyze Complexity and Edge Cases

State time complexity: O(log N) for lookup, O(log N) for add/remove. Discuss edge cases: empty ring, key hashing collisions, and node failures. Mention potential improvements like replication.

Key Points to Mention

Use of a sorted map (e.g., TreeMap) for O(log N) clockwise lookup via ceilingKey or higherKey.
Hash function choice (e.g., MD5, SHA-1) and its impact on distribution and performance.
Virtual nodes: how they work, why they improve load balancing, and the trade-off with memory.
Handling wrap-around: if no node found with hash >= key hash, use the first node in the map.
Time complexity analysis: O(log N) for routing, add, and remove operations.
Edge cases: empty ring, duplicate hashes, and node failures; mention replication for fault tolerance.

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