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The base implementation was fine, doubly linked list plus a hash map, pretty standard.
Start by explaining the classic O(1) LRU cache design using a hash map and a doubly linked list, then discuss thread-safety by adding synchronization primitives like a mutex or read-write lock, and finally explore advanced concurrent designs such as sharding or lock-free structures. Emphasize trade-offs between simplicity, performance, and correctness.
Pro tip: Mention that a single global lock is simple but can become a bottleneck; propose sharding the cache into multiple independent segments to reduce contention, and note that this changes eviction semantics from global LRU to per-segment LRU.
Confirm the expected operations (get, put), capacity, and whether thread-safety is required. Ask about concurrency level and performance goals to tailor the solution.
Describe using a hash map for O(1) access and a doubly linked list to track usage order. Explain how get moves a node to the front and put inserts or updates, evicting the least recently used when at capacity.
Propose wrapping all operations in a mutex or read-write lock. Discuss that this ensures correctness but may limit scalability due to lock contention.
Discuss sharding the cache into multiple independent segments, each with its own lock, to reduce contention. Mention lock-free approaches using atomic operations and concurrent data structures, but note their complexity and trade-offs.
Compare simplicity, performance, and correctness. Recommend a solution based on the expected workload, e.g., coarse-grained locking for low contention, sharding for high concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with open addressing first and then they asked me to switch to chaining, which I hadn't expected.
Start by clarifying requirements (e.g., expected load, consistency guarantees) and then design a single-threaded hash map with separate chaining, discussing time complexity and resizing. For concurrency, propose a lock striping approach (e.g., per-bucket locks) to allow concurrent reads and writes, and compare it with alternatives like read-write locks or lock-free designs, highlighting trade-offs.
Pro tip: Emphasize that concurrency correctness requires careful handling of resizing and that lock striping balances performance and simplicity; mention that you'd test with stress tests and race detectors.
Ask about expected operations per second, read/write ratio, consistency needs, and memory constraints to guide design choices.
Outline a hash map with an array of buckets, separate chaining for collisions, and resizing when load factor exceeds a threshold. Discuss hash function, put/get/remove operations, and time complexity.
Propose a concurrency strategy: lock striping (e.g., one lock per bucket or group of buckets) to allow concurrent access. Explain how put/get/remove acquire appropriate locks and how resizing is handled safely.
Compare lock striping with global lock, read-write locks, and lock-free approaches. Discuss performance, scalability, complexity, and correctness under contention.
Mention testing strategies: unit tests, stress tests with multiple threads, race detection tools, and handling edge cases like concurrent resizing and null keys/values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify requirements (points vs. rectangles, dynamic insertions, query types) and then design a quadtree with adaptive node splitting and merging. Explain the core operations (insert, query) with complexity analysis, and discuss practical optimizations like bulk loading and bounding boxes for rectangles.
Pro tip: Mention that for rectangles, storing each rectangle in all overlapping nodes (or using a loose quadtree) avoids deep recursion and improves query performance, and always discuss how you would handle duplicate points and rebalancing.
Ask whether the quadtree stores points or rectangles, if it needs to support dynamic insertions/deletions, and what types of range queries (e.g., orthogonal, circular) are expected. Also discuss expected data distribution and performance goals.
Describe the node structure: each node has a bounding box, a capacity (max points/rectangles before splitting), and four children (NW, NE, SW, SE). For rectangles, decide whether to store them in multiple nodes or use a different variant.
Detail how to insert a point/rectangle: traverse to the appropriate child based on its position, and if a node exceeds capacity, split it into four quadrants and redistribute the elements. For rectangles, handle elements that span multiple quadrants.
Explain how to perform a range query: recursively check if the query region intersects the node's bounding box; if so, test each element in the node and recurse into children that intersect. Prune branches that do not intersect.
Discuss time complexity for insert and query (average O(log n), worst-case O(n)), and mention optimizations like bulk loading, merging nodes on deletion, and using a loose quadtree for rectangles to reduce duplication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Debugging someone else's concurrent C++ code under time pressure is a different kind of stress.
Start by clarifying the program's requirements and constraints, then systematically review the code for correctness, synchronization, and performance issues. Prioritize critical bugs like data races and deadlocks, and suggest concrete fixes with trade-offs.
Pro tip: Demonstrate deep C++20 knowledge by mentioning modern features like std::atomic::wait/notify or std::jthread, and discuss how they can simplify or improve the producer-consumer pattern.
Identify the queue type (bounded/unbounded), synchronization primitives used, and the overall threading model. Clarify assumptions about message ordering, blocking behavior, and shutdown.
Look for data races on shared variables, missing locks, and improper use of condition variables (e.g., spurious wakeups, lost wakeups). Verify that all shared state is properly synchronized.
Examine lock ordering, lock granularity, and potential deadlocks. Check for busy-waiting and ensure condition variables are used correctly with predicates.
Assess the queue's interface for usability and efficiency. Consider contention, false sharing, and scalability. Suggest improvements like lock-free structures or finer-grained locking.
For each issue, propose a solution and discuss its trade-offs (e.g., complexity vs. performance). Prioritize fixes based on severity and impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.