This one had a lot of layers and I kept peeling them back in the wrong order.
Start by clarifying requirements and constraints, then propose a solution that preprocesses the dictionary into a trie or regex pattern for efficient matching. Discuss how to handle case-insensitivity, word boundaries, multi-word aliases, and deduplication, and analyze time/space complexity and trade-offs.
Pro tip: Mention that you would normalize the headline and aliases to lowercase, and use word boundaries that account for punctuation and numbers, as headlines often contain symbols like $ or #. Also, consider using a trie for multi-word aliases to avoid false positives from partial matches.
Ask about the size of the dictionary and stream, latency requirements, and whether aliases can overlap or be ambiguous. This shows you think about scalability and edge cases.
Normalize all tickers and aliases to lowercase, and build a data structure like a trie or a regex pattern that supports multi-word matching and case-insensitive search.
For each headline, normalize to lowercase, then scan for matches using the preprocessed structure. Ensure whole-word boundaries by checking surrounding characters (e.g., non-alphanumeric).
When a match is found, map it back to the canonical ticker and add to a set to avoid duplicates. If both ticker and alias appear, the set ensures only one instance.
Discuss time complexity (e.g., O(N * L) for scanning, or O(N * M) with regex) and space complexity. Compare trie vs. regex vs. Aho-Corasick, and mention potential optimizations like caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The update question is where I got a little stuck.
Start by clarifying requirements: scale of dictionary, update frequency, latency and throughput needs. Then propose a trie-based automaton (e.g., Aho-Corasick) for multi-pattern matching, and discuss how to handle live updates via versioning, incremental rebuilds, or concurrent data structures. Conclude with trade-offs between read and write performance.
Pro tip: Mention that you would use a read-optimized immutable snapshot for matching and a separate write path that builds new snapshots, swapping them atomically to avoid locking and ensure consistency.
Ask about dictionary size, pattern length distribution, update rate, acceptable latency, and consistency requirements. This shapes the choice of data structure and update strategy.
Propose a trie or Aho-Corasick automaton for efficient multi-pattern matching. Discuss memory and time complexity, and alternatives like suffix arrays or hash-based approaches.
Explain how to handle insertions/deletions without blocking reads. Options include immutable snapshots with atomic swap, incremental updates with read-write locks, or partitioning the dictionary.
Detail how to ensure readers see a consistent view during updates. Discuss versioning, copy-on-write, or epoch-based reclamation.
Compare approaches in terms of latency, throughput, memory, and update cost. Suggest optimizations like double-array tries, compression, or batching updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.