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DoorDash·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

DoorDash coding round centered on a deliberately broken round-robin load balancer. You had to find four specific bugs, then pivot to implementing consistent hashing with virtual nodes. Time pressure was real and they wanted actual code, not hand-waving.

Questions Asked (2)

Q1

You're given a buggy round-robin request distributor. Find and fix all the bugs in it.

Algorithms & Data StructuresRoot Cause Analysis
Author's notes

There were four bugs total and I only spotted three on my own.

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AI HintsAI Generated

Suggested Approach

First, clarify the expected behavior of the round-robin distributor and identify edge cases. Then, systematically trace through the code with sample inputs to locate bugs, fix them one by one, and verify with tests including concurrency and failure scenarios.

Pro tip: Before fixing, write down the invariants (e.g., each server gets equal requests, no server is skipped) and use them as a checklist to catch subtle bugs like off-by-one errors or race conditions.

1. Understand requirements and constraints

Ask clarifying questions about the distributor's expected behavior, such as handling server failures, concurrency, and request distribution guarantees.

2. Review code and identify potential bugs

Read the code carefully, looking for common issues like incorrect index updates, missing bounds checks, thread-safety problems, and improper error handling.

3. Trace with examples and edge cases

Simulate the distributor with simple inputs (e.g., 3 servers, 10 requests) and edge cases (e.g., 0 servers, server failure) to confirm bugs and understand their impact.

4. Fix bugs and refactor if needed

Apply fixes, ensuring the solution is correct, efficient, and maintainable. Consider using atomic operations or locks for thread safety.

5. Test and validate

Write unit tests covering normal, edge, and failure cases. If possible, test concurrency to ensure the fix works under load.

Key Points to Mention

  • Off-by-one errors in index calculation (e.g., modulo operation or increment logic)
  • Thread safety and race conditions when multiple requests arrive concurrently
  • Handling server failures or removal without disrupting distribution
  • Edge cases: zero servers, single server, many servers, uneven request counts
  • Efficiency: O(1) time per request and minimal overhead
  • Code clarity and maintainability, such as using clear variable names and avoiding magic numbers

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

Q2

Now extend your fixed round-robin to use consistent hashing. Implement a hash ring with virtual nodes and explain what happens to key assignments when a server is added or removed.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is where things got tight.

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AI HintsAI Generated

Suggested Approach

Start by explaining the limitations of fixed round-robin and why consistent hashing is needed. Then describe the hash ring with virtual nodes, including how keys and servers are mapped. Finally, walk through the addition/removal of a server, quantifying the key reassignments and discussing trade-offs.

Pro tip: Mention that virtual nodes help balance load and that when a server is added or removed, only keys mapped to that server (and its virtual nodes) are reassigned, minimizing disruption. Quantify the expected fraction of keys moved (e.g., 1/N for N servers) to show depth.

1. Explain the problem with fixed round-robin

Highlight that fixed round-robin requires rehashing all keys when the number of servers changes, causing massive key reassignments and cache misses.

2. Introduce consistent hashing and the hash ring

Describe how both servers and keys are hashed onto a circular ring (e.g., 0 to 2^32-1). Each key is assigned to the first server encountered clockwise from its hash.

3. Incorporate virtual nodes

Explain that each physical server is represented by multiple virtual nodes (replicas) on the ring to improve load balancing and reduce hotspots.

4. Analyze server addition/removal

When a server is added, it takes over a portion of keys from its clockwise neighbors; when removed, its keys are redistributed to the next servers. Only keys mapped to the affected server(s) are reassigned.

5. Discuss trade-offs and implementation details

Mention the impact on load distribution, the number of virtual nodes needed, and how to handle replication and failure scenarios.

Key Points to Mention

  • Consistent hashing minimizes key reassignments when the set of servers changes, unlike modulo-based hashing.
  • Virtual nodes improve load balancing by spreading each server's keys across the ring.
  • When a server is added, it takes over keys from its immediate successor on the ring; when removed, its keys go to its successor.
  • The expected fraction of keys moved when adding/removing a server is approximately 1/N, where N is the number of servers (or virtual nodes).
  • Implementation details: hash function choice (e.g., MD5, SHA-1), ring data structure (e.g., sorted array or balanced tree), and replication for fault tolerance.
  • Trade-offs: more virtual nodes lead to better balance but increased memory and lookup overhead.

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