DoorDash·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Two-part coding interview at DoorDash focused entirely on distributed systems fundamentals. First half was debugging a broken load balancer, second half was building a consistent hash ring from scratch. Pretty dense for a single session.

Questions Asked (2)

You're given a round-robin load balancer implementation that has bugs. Find and fix them so requests are distributed evenly across all servers.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The debugging part tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and expected behavior of the round-robin load balancer, then systematically review the code to identify bugs. Use test cases to verify even distribution and fix issues while explaining your reasoning and trade-offs.

Pro tip: Demonstrate a methodical debugging process: write a small test harness to simulate requests and observe distribution, which shows maturity and ensures no bug is missed.

1. Understand the requirements

Clarify what 'even distribution' means, the expected behavior under different conditions (e.g., server failures, concurrent requests), and any constraints.

2. Review the code

Read through the implementation to understand the logic, identify potential off-by-one errors, incorrect index updates, or concurrency issues.

3. Write test cases

Create simple tests that simulate multiple requests and check if each server receives an equal number of requests, including edge cases.

4. Identify and fix bugs

Based on test failures, pinpoint the bugs (e.g., incorrect modulo operation, race conditions) and apply fixes, explaining each change.

5. Verify and discuss trade-offs

Re-run tests to confirm even distribution, and discuss potential improvements like thread safety, scalability, and handling server failures.

Key Points to Mention

Round-robin algorithm basics: cycling through servers in order
Common bugs: off-by-one errors, incorrect modulo arithmetic, non-atomic index updates
Concurrency issues: race conditions in multi-threaded environments and need for synchronization
Testing strategies: unit tests, simulation, edge cases (e.g., zero servers, server removal)
Trade-offs: simplicity vs. thread safety, performance impact of locks, alternative load balancing algorithms
Scalability considerations: handling dynamic server additions/removals, health checks

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

Implement a consistent hash ring that supports adding and removing servers, routing a given key to the correct server, and explain why this approach minimizes key remapping compared to naive modular hashing.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This is the part I actually enjoyed.

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AI HintsAI Generated

Suggested Approach

Start by explaining the core idea of consistent hashing: mapping both servers and keys onto a circular hash space, then walking clockwise to find the responsible server. Then, describe how to implement the ring with a sorted data structure and virtual nodes for balance, and finally contrast with modular hashing to highlight the minimal key remapping property.

Pro tip: Mention that virtual nodes (replicas) are crucial for even load distribution, and that using a balanced binary search tree (e.g., TreeMap in Java) gives O(log N) lookups and updates. Also, note that consistent hashing is used in real systems like DynamoDB and Cassandra to handle scaling.

1. Explain the hash ring concept

Describe how servers and keys are hashed onto a circular space (e.g., 0 to 2^32-1). Explain that each key is assigned to the first server encountered when moving clockwise from the key's hash.

2. Detail the data structures

Use a sorted map (e.g., balanced BST) to store server hash positions for efficient lookup. For each server, create multiple virtual nodes (replicas) by hashing server ID with a replica number to ensure uniform distribution.

3. Implement add/remove operations

For adding a server, insert its virtual nodes into the ring; for removal, delete them. Explain that only keys that map to the added/removed server's range are remapped, minimizing disruption.

4. Implement key routing

To route a key, hash it, then find the first server with a hash greater than or equal to the key's hash (wrapping around if necessary). This is done via a ceiling lookup in the sorted map.

5. Compare with modular hashing

Explain that with modular hashing (hash(key) % N), changing N remaps almost all keys. In consistent hashing, only keys in the affected range are remapped, typically K/N keys on average, where K is total keys and N is number of servers.

Key Points to Mention

Hash ring: circular hash space where servers and keys are placed.
Virtual nodes: multiple replicas per server to improve load balancing.
Data structure: sorted map (e.g., TreeMap) for O(log N) lookups and updates.
Key routing: clockwise lookup to find the first server with hash >= key hash.
Minimal remapping: adding/removing a server only affects keys in its range, not all keys.
Comparison with modular hashing: modular hashing remaps nearly all keys when N changes, causing cache misses and data movement.

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