The round-robin part itself is not bad, just a pointer you increment mod N.
Start by clarifying requirements and constraints, then design a simple round-robin router using an atomic counter to ensure thread safety. Implement the connect method to return the next target index in cyclic order, and discuss how to extend it to a stateful, multi-tenant system with per-tenant routing state.
Pro tip: Mention that round-robin is just the starting point; in production, you'd likely need health checks, weighted routing, and sticky sessions for stateful connections. Also, highlight the importance of thread safety in a multi-tenant environment.
Ask about the number of tenants, expected connection rate, thread safety needs, and whether connections are long-lived or short-lived. Confirm that the initial implementation should be basic round-robin.
Use an atomic integer counter to track the next target index, ensuring thread-safe increments. For multi-tenancy, consider a map from tenant ID to their own router state.
In connect(connId), atomically increment the counter modulo N to get the target index. Return the index. Ensure the counter wraps around correctly.
Explain how to extend to weighted round-robin, health checks, or consistent hashing for stateful connections. Mention that stateful routing may require tracking connection-to-target mappings.
State that time complexity is O(1) per connection, and space is O(1) for basic round-robin. Discuss trade-offs like fairness vs. load distribution and the impact of thread contention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you have the hashmap.
Start by clarifying the existing data structures and invariants of the connect function, then propose a simple early-return check that leverages the existing lookup mechanism. Emphasize that the solution must be idempotent and thread-safe if applicable, and discuss how to verify the target is returned without side effects.
Pro tip: Mention that duplicate connection requests are common in distributed systems and that idempotency is key to avoiding race conditions and resource leaks. Also, suggest adding logging or metrics to track duplicate attempts for observability.
Ask about the expected behavior: should it return the existing target silently, or log a warning? Are there concurrency concerns? What is the data structure used to track connections?
Determine how to check if connId is already connected. This likely involves a map or dictionary from connId to target. Ensure the lookup is O(1) and does not mutate state.
At the beginning of connect, check if connId exists in the connection registry. If so, return the existing target immediately, bypassing any state changes.
If the system is multithreaded, use appropriate locking or atomic operations to ensure the check-and-return is atomic, preventing race conditions.
Write unit tests for duplicate connection requests, verifying that the target is returned and no side effects occur (e.g., no new connections, no counter increments).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data structures and requirements: how connections are stored, what 'target' means, and whether thread safety is needed. Then outline an algorithm that looks up the connection, updates the target's count, removes the connection, and returns true; otherwise returns false. Finally, discuss edge cases, complexity, and potential concurrency issues.
Pro tip: Mention that you would use a hash map for O(1) lookup and removal, and emphasize the importance of atomicity in concurrent environments—this shows you think about real-world reliability, which Stripe values.
Ask questions to understand the data model: how connections are stored, what 'target' refers to, and whether the method must be thread-safe. Confirm the expected return values and side effects.
Propose using a hash map (or similar) to store connections by connId for O(1) lookup, and a way to track each target's connection count (e.g., a counter per target).
Describe the steps: look up connId; if not found, return false; otherwise, retrieve the associated target, decrement its connection count, remove the connection from the map, and return true.
Discuss handling of invalid connId, ensuring no state change on failure, and if needed, using locks or atomic operations to prevent race conditions.
State time and space complexity (O(1) for hash map operations), and suggest test cases: existing connId, non-existing connId, concurrent disconnects, and count reaching zero.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the round-robin scan gets a little more involved.
Start by clarifying the data structures and constraints, then outline an algorithm that scans targets in round-robin order while skipping full ones. Emphasize that if all targets are full, return -1 without mutating any state, and discuss how to achieve this efficiently.
Pro tip: Mention that you would track the number of full targets to avoid infinite loops and enable early termination, and that you would use a two-phase approach: first check if any capacity is available, then perform the assignment only if possible.
Ask about the data structure for targets, how capacity is tracked, and whether the round-robin pointer should be updated on failure. Confirm that state must remain unchanged if no target is available.
Propose scanning targets in round-robin order, skipping those at capacity. Keep a count of full targets or limit iterations to the number of targets to detect when all are full.
If no target is found after scanning all, return -1 and ensure no state (e.g., round-robin index) is modified. Discuss how to achieve this by deferring state updates until a target is selected.
Explain time complexity (O(n) worst case) and space complexity (O(1) extra). Discuss alternatives like maintaining a separate list of available targets for O(1) selection, and their trade-offs.
Walk through scenarios: all targets full, some full, single target, and wrap-around in round-robin. Verify that state remains unchanged on failure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The ordering requirement is what makes this interesting.
Clarify the data model and requirements, then design a data structure that supports efficient shutdown and ordered eviction. Discuss trade-offs between different approaches (e.g., linked list vs. tree) and handle edge cases like duplicate shutdowns or invalid indices.
Pro tip: Mention that you would maintain a doubly linked list of connections per target to preserve creation order, and a hash map for O(1) access to connections. This shows you understand the importance of both time complexity and order preservation.
Ask about the expected scale (number of targets, connections), concurrency requirements, and whether the order is based on creation timestamp or insertion sequence. Confirm that shutdown should be idempotent and that the target remains unavailable.
Propose maintaining a mapping from targetIndex to a list of connections (e.g., a doubly linked list) that preserves creation order, and a global map from connId to connection details for quick lookup. Consider using a balanced BST or skip list if connections are frequently added/removed.
On shutdown, mark the target as unavailable (e.g., set a flag or remove from active targets), then iterate through its connections in order, collect their connIds, and remove them from all relevant structures. Return the collected list.
Discuss time complexity: O(k) where k is the number of connections for that target, and space complexity. Compare alternatives like using a min-heap ordered by creation time, which would be O(k log k) but simpler if connections are not stored per target.
Address cases: targetIndex out of bounds, target already shut down, no connections. If concurrent, suggest locking per target or using concurrent data structures to avoid race conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.