First, restate the expected behavior of a round-robin distributor and clarify assumptions (e.g., thread safety, list size). Then systematically trace the code to identify the three mentioned bugs, explaining how each causes incorrect distribution. Finally, propose and implement fixes, and discuss how to test the corrected logic.
Pro tip: Mention that you'd add unit tests covering edge cases like empty list, single element, and multiple full cycles to prevent regressions. Also, note that in a real system you'd consider thread safety and fairness under concurrency.
Confirm the expected round-robin behavior: each request goes to the next server in order, wrapping around. Ask about list size, concurrency, and whether the index should persist across calls.
Walk through the code line by line, checking index initialization, update logic, and variable scope. Identify the off-by-one, shadowing, and missing wrap-around issues.
For each bug, describe how it causes incorrect distribution (e.g., skipping a server, always returning the same server, or throwing an index out of bounds).
Correct the index calculation, remove variable shadowing, and add modulo wrap-around. Ensure the fix maintains the intended round-robin order.
Suggest test cases: empty list, single server, multiple cycles, and concurrent access if relevant. Verify that each server gets requests in sequence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I'd reviewed consistent hashing before but never actually written a hash ring from scratch under pressure.
Start by explaining why consistent hashing is needed (e.g., minimizing key remapping when servers change) and contrast it with round-robin. Then walk through the implementation: building the hash ring with virtual nodes, hashing keys to find the next server clockwise, and handling server additions/removals. Finally, discuss trade-offs like load balancing and complexity.
Pro tip: Mention that virtual nodes help distribute load evenly and that using a balanced binary search tree (e.g., TreeMap in Java) for the ring gives O(log n) lookup, which is efficient for large-scale systems like DoorDash.
Highlight that round-robin doesn't consider server capacity or key distribution and causes massive key remapping when servers change. Consistent hashing solves this by mapping both servers and keys to a ring.
Describe the ring as a sorted circular structure (e.g., array or balanced BST) of hash values. Each server is hashed to multiple points (virtual nodes) to improve balance.
For each server, generate multiple hash values (e.g., by hashing server ID + replica number) and place them on the ring. This ensures even distribution and reduces hotspots.
Hash the key, then find the first server point on the ring clockwise (or wrap around). Use binary search for efficiency. Return the server associated with that point.
When a server is added or removed, only keys mapped to that server's virtual nodes are remapped, minimizing disruption. Discuss how to update the ring and rebalance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining both algorithms and their core mechanics, then contrast their behavior under dynamic node changes. Use a concrete example like a caching layer or load balancer to illustrate when consistent hashing minimizes disruption, and discuss tradeoffs like complexity and load distribution.
Pro tip: Tie your answer to a real-world system like DoorDash's order routing or caching, showing you understand how these choices impact scalability and user experience. Mention that consistent hashing is not a silver bullet—sometimes round-robin's simplicity wins.
Briefly explain round-robin (sequential distribution) and consistent hashing (hash ring with virtual nodes). Highlight that round-robin assumes homogeneous, static nodes, while consistent hashing handles dynamic membership.
Discuss scenarios like distributed caches (e.g., Redis) or sharded databases where node additions/removals should minimize key remapping. Emphasize that consistent hashing reduces cache misses and data movement.
Compare complexity, load balancing, and failure handling. Consistent hashing adds overhead (virtual nodes, rebalancing) but provides better scalability; round-robin is simpler but can cause hotspots and massive reshuffling on changes.
Use a DoorDash-like scenario: e.g., caching restaurant menus across servers. With round-robin, adding a server remaps most keys; with consistent hashing, only a fraction moves, preserving cache efficiency.
Summarize that consistent hashing is ideal for dynamic, large-scale systems where stability matters, while round-robin suits static, homogeneous environments or simple stateless services.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.