Approach: Use sliding window with hash map to track character positions, maintaining O(n) time complexity.

Considerations: Handle edge cases: empty strings, single characters, and entire strings with unique characters.

Key Points: Demonstrate sliding window pattern and how it scales for large text processing workloads.

Approach: Use level-order or pre-order traversal with null markers to encode tree into string, reconstruct using queue or recursion.

Considerations: Balance readability versus compactness. Handle complete, skewed trees. Ensure serialization then deserialization yields original tree.

Key Points: Show tree property knowledge, serialization formats (JSON), and relevance to distributed systems transmitting trees across services.

Approach: Build trie structure for O(m) prefix matching, rank results by frequency using min-heaps, cache hot queries for sub-millisecond latency.

Considerations: Trade off memory (trie size) versus speed (lookup latency). Handle real-time updates, typo tolerance, and personalization at scale.

Key Points: Address Google Search scale: trie indexing, frequency ranking, caching strategies (Redis), sharding distributes load across backend instances.