Start by clarifying the requirements and constraints, then walk through the existing code to identify the three issues: index persistence, status check, and all-nodes-down behavior. Propose a thread-safe design using an atomic counter and an enum for node status, define a clear fallback (e.g., throw an exception or return null), and write a unit test that mocks one node as unavailable to verify the selector skips it.
Pro tip: Mention that you would use an AtomicInteger for the index and an enum for node status to avoid string comparisons, and that you would define a custom exception for the all-nodes-down case to make the failure explicit and testable.
Ask about thread-safety expectations, how node status is determined, and what should happen when all nodes are down (e.g., throw exception, return null, or block).
Replace any local or non-persistent index with a thread-safe atomic counter (e.g., AtomicInteger) that increments modulo the number of nodes, ensuring round-robin behavior across calls.
Introduce an enum (e.g., NodeStatus { AVAILABLE, UNAVAILABLE }) and update the selector to check the enum instead of raw strings, improving type safety and performance.
Decide on a clear contract: throw a custom exception (e.g., NoAvailableNodeException) or return a sentinel value, and document it. Ensure the selector checks all nodes before failing.
Create a test with a mock node list where one node is UNAVAILABLE, call the selector multiple times, and assert that the unavailable node is never returned and the round-robin order is correct among available nodes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely the hardest part of the whole thing.
Start by clarifying the hash map's contract and invariants, then systematically test each component (hashing/equality, collisions, resizing, iterators) with targeted cases to isolate bugs. Fix issues in order of dependency, ensuring that changes to hashing or equality don't break collision handling or resizing, and validate with unit tests after each fix.
Pro tip: Emphasize the importance of the equals/hashCode contract and demonstrate how a violation can cause subtle, hard-to-reproduce bugs; mention that you'd write tests first to catch regressions.
Review the hash map's expected behavior: keys that are equal must have the same hash, collisions must be handled, resizing must preserve mappings, and iterators must be fail-fast or consistent. Identify which invariants are likely violated based on symptoms.
Check if the key's hashCode and equals methods are consistent and correctly used. Look for bugs like using == instead of equals, not overriding hashCode when equals is overridden, or mutable keys.
Inspect the collision resolution strategy (e.g., chaining or open addressing). Verify that insertion, lookup, and deletion correctly handle collisions, and that the load factor is maintained.
Check the resize trigger (load factor threshold) and the rehashing process. Ensure all entries are rehashed into the new table without losing or duplicating entries, and that the new capacity is appropriate.
Test iterators for expected behavior: they should traverse all elements, reflect concurrent modifications appropriately (e.g., throw ConcurrentModificationException), and not skip or repeat elements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
After the hash map question this felt manageable.
Start by clarifying requirements: which eviction policy (TTL, LRU, or both), concurrency needs, and expected operations. Then design a class with a map for storage, a doubly-linked list for LRU order, and a min-heap or timing wheel for TTL. Implement get/set with O(1) average time, and write unit tests covering basic operations, eviction, and edge cases.
Pro tip: Mention that you'd use a doubly-linked list for O(1) LRU updates and a min-heap for TTL, but note that a timing wheel is more efficient for many timers. Also, discuss thread-safety with a mutex or sharded locks, and how you'd test concurrency.
Ask about eviction policies (TTL, LRU, LFU), maximum size, concurrency, and whether persistence is needed. Confirm expected operations and performance goals.
Choose a map for key-value storage. For LRU, combine with a doubly-linked list; for TTL, use a min-heap or timing wheel. Explain how to achieve O(1) get/set.
Write get and set methods, handling eviction when size exceeds limit or TTL expires. Ensure thread-safety if required, using locks or concurrent structures.
Create unit tests for basic get/set, eviction under size limit, TTL expiration, and edge cases like updating existing keys and concurrent access.
Talk about time/space complexity, alternative eviction policies, and how to scale (e.g., sharding, distributed cache). Mention monitoring and metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.