Pretty standard stuff if you've seen it before.
Clarify the requirements and edge cases first, then propose a doubly linked list with a current pointer for O(1) visit, back, and forward operations. Walk through the design, implement the methods, and analyze time and space complexity.
Pro tip: Mention that using a dynamic array with an index is simpler but can be O(n) for back/forward if you need to truncate history; a doubly linked list gives true O(1) for all operations. Also, explicitly handle edge cases like going back/forward beyond the available history.
Ask about constraints: maximum steps, whether URLs are unique, and what happens when back/forward exceeds history. Confirm that visiting a new URL clears forward history.
Propose a doubly linked list with a current node pointer. Explain why it supports O(1) visit, back, and forward, and how it handles truncation of forward history.
Outline the BrowserHistory class with a constructor, visit(url), back(steps), and forward(steps). Describe how the current pointer moves and how new nodes are inserted.
Write clean code for each method, handling edge cases like steps larger than available history. Walk through a few test cases to verify correctness.
State that all operations are O(1) time and O(n) space for n visited URLs. Discuss trade-offs with alternative approaches like arrays.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was to scan the history list linearly and I said it out loud before catching myself.
Clarify the requirements: what constitutes a visit, how URLs are normalized, and the expected scale. Then propose a data structure like a hash set for O(1) lookups, and discuss trade-offs such as memory usage, persistence, and concurrency. Finally, outline how you would implement and test the method.
Pro tip: Mention that you would normalize URLs (e.g., lowercase host, strip fragments) before storing to avoid false negatives, and consider using a Bloom filter if memory is constrained but accept false positives.
Ask about the definition of 'visited', URL normalization rules, expected scale, and whether the history is persistent or in-memory.
Select a hash set for O(1) average-case lookups, or a Bloom filter for memory efficiency with probabilistic results.
Compare memory usage, lookup speed, persistence, and concurrency considerations for each option.
Describe how to add URLs to the structure and implement haveVisited(url) with proper normalization.
Mention unit tests for normalization, concurrency, and large-scale scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where it got interesting and also where I started rambling a bit.
Start by clarifying the requirements: each tab has its own back/forward history, but haveVisited is global across all tabs. Then propose a design with a global visited set (e.g., hash set) and per-tab history stacks (back and forward), explaining operations and complexity. Discuss trade-offs like memory usage and concurrency, and consider extensions like persistence or eviction.
Pro tip: Mention that the global visited set can be a concurrent data structure or sharded to handle high concurrency, and that per-tab histories can be stored in memory or persisted per tab session. Also, note that haveVisited should be O(1) on average, and back/forward operations should be O(1) amortized.
Confirm that each tab maintains independent back/forward history, while haveVisited checks globally across all tabs. Assume operations: visit(url) adds to current tab's history and global visited set; back() and forward() navigate within the tab; haveVisited(url) checks global set.
Use a global hash set for visited URLs. For each tab, maintain two stacks: backStack and forwardStack. When visiting a new URL, push current URL to backStack, clear forwardStack, and add new URL to global set. Back() pops from backStack, pushes current to forwardStack, and sets current to popped URL. Forward() does the reverse.
All operations (visit, back, forward, haveVisited) are O(1) on average. Space complexity is O(total unique URLs visited) for the global set plus O(total history entries across tabs) for the stacks.
Consider memory growth: global set can grow unbounded; suggest eviction policies (e.g., LRU) or persistence. For concurrency, use thread-safe structures or locks. Also, consider if tabs can be closed and reopened, and whether history should persist across sessions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.