I went with the hashmap approach first, scanning up to max key length at each position.
Start by clarifying the problem constraints and edge cases, then propose a trie-based solution for efficient longest-match lookup. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss potential trade-offs such as memory usage versus speed.
Pro tip: Mention that you can optimize the trie by storing IDs at terminal nodes and using a failure function (like Aho-Corasick) if multiple patterns need to be matched simultaneously, but for greedy longest-match a simple trie suffices. Also, highlight the importance of handling overlapping matches correctly by always choosing the longest match at each position.
Ask about input size, character set, dictionary size, and whether keys can be prefixes of each other. Discuss handling of empty strings, no matches, and overlapping matches.
Propose a trie (prefix tree) to store the dictionary keys, enabling efficient longest-match lookup by traversing the trie from the current position.
Iterate through the text from left to right. At each position, traverse the trie as far as possible to find the longest matching key. If a match is found, emit its ID and advance by the match length; otherwise, emit the current character as a literal and advance by one.
Time complexity is O(n * L) where n is text length and L is the maximum key length, but with a trie it's O(n * L) worst-case, often faster. Space complexity is O(total characters in dictionary) for the trie.
Compare trie with other approaches like sorting keys by length or using a hash set. Mention potential optimizations such as caching or using a finite automaton for repeated queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where having a trie already in place pays off.
First, clarify that the tie-breaking rule is a policy decision separate from the core matching logic. Then, propose refactoring the solution to use a comparator or strategy pattern, so the rule can be swapped without modifying the underlying algorithm. Finally, discuss how this separation improves testability and maintainability.
Pro tip: Mention that you would encapsulate the tie-breaking rule as a pluggable component and use dependency injection, which aligns with Google's emphasis on modular, testable code. Also, note that you'd add unit tests for each rule to ensure correctness when swapping.
Locate where the current rule (longest match) is applied in the code. Recognize that this is a separate concern from the matching algorithm itself.
Define an interface or function type that takes multiple matching keys and returns the winning one based on some priority. This decouples the rule from the matching process.
Create separate implementations for different rules, such as longest match, priority order, or any other custom rule. Each implementation adheres to the interface.
Modify the main algorithm to accept the tie-breaking strategy as a parameter, allowing it to be swapped at runtime or configuration time without code changes.
Write unit tests for each strategy and integration tests to ensure the overall solution works correctly with any rule. This ensures swapping is safe and cheap.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about compressed/radix tries to avoid storing every single-character edge explicitly, and briefly mentioned sharding the key space if the dictionary doesn't fit in one process.
Start by clarifying the matcher's requirements: dictionary size, query patterns, latency and memory constraints. Then propose a memory-efficient data structure like a trie or finite state transducer, and discuss trade-offs between memory and speed, including compression and caching strategies.
Pro tip: Mention that you'd measure actual memory usage and lookup latency with profiling tools before optimizing, and consider hybrid approaches like combining a Bloom filter for fast negative checks with a compressed trie for positives.
Ask about dictionary size, expected query volume, latency SLA, memory budget, and whether updates are needed. This ensures the solution fits the actual use case.
Propose a trie, DAWG, or finite state transducer (FST) that shares prefixes/suffixes. Discuss compression techniques like minimal perfect hashing or double-array tries.
Use techniques like caching frequent prefixes, memory-mapping the structure, or using SIMD for parallel character comparisons. Consider partitioning the dictionary for parallelism.
Compare memory vs. speed: e.g., a hash set is fast but memory-heavy; a trie saves memory but may be slower. Discuss hybrid approaches like Bloom filters for quick rejection.
Propose measuring memory footprint and lookup latency, then iterating. Mention profiling tools and A/B testing to ensure the solution meets SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.