I went straight to a trie-backed structure with a hashmap at each node for children.
Start by clarifying requirements and edge cases, then propose a trie-like tree structure where each node represents a path segment and stores an optional value. Discuss operations (create, get) with time and space complexity, and handle error cases such as missing parent or duplicate path.
Pro tip: Mention that you would use a sentinel value (e.g., null or a boolean flag) to distinguish between a path that exists with value -1 and a non-existent path, since -1 is a valid value. This shows attention to detail and avoids ambiguity.
Ask questions to confirm: path format (e.g., '/a/b'), whether values can be negative, what happens if parent doesn't exist, and if paths are case-sensitive. Confirm that root '/' is implicit and cannot be created.
Propose a tree where each node has a map of child name to node, and an optional value. Explain that this trie-like structure allows efficient lookup and insertion by splitting the path into segments.
Describe create(path, value): traverse from root, ensure parent exists, then add child node with value; return false if path already exists or parent missing. Describe get(path): traverse and return value if exists, else -1.
State that both operations take O(k) time where k is number of path segments, and O(total nodes) space. Mention potential optimizations like path compression or caching frequently accessed paths.
Consider follow-ups: concurrency (thread-safety), persistence, or supporting deletion. Discuss trade-offs between using a tree vs. a hash map with full path strings (simpler but less efficient for hierarchical operations).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current design and the data model (e.g., tree structure, parent-child references, indexes). Then propose update and delete operations with clear semantics, focusing on subtree deletion rules (cascade vs. orphan prevention) and consistency mechanisms (transactions, soft deletes, async cleanup). Finally, discuss trade-offs around performance, concurrency, and data integrity.
Pro tip: Demonstrate awareness of real-world constraints: propose soft deletes with a background hard-delete job to avoid long locks, and mention how you'd handle concurrent updates using optimistic locking or versioning. This shows you think beyond the happy path.
Ask about the existing data model (e.g., adjacency list, nested sets, materialized paths) and expected update/delete patterns (frequency, scale, consistency needs). Confirm whether subtree deletion should be cascading or restricted.
Specify what fields can be updated (e.g., node value, parent change) and how to handle moves (reparenting) without breaking tree integrity. Propose using transactions and locking (e.g., SELECT FOR UPDATE) or optimistic concurrency control.
For subtree deletion, choose between cascade (delete all descendants) or restrict (prevent if children exist). Implement cascade via recursive CTE or application-level traversal, and consider soft delete (mark as deleted) to avoid immediate data loss.
Ensure atomicity with transactions, handle concurrent updates/deletes via locking or versioning, and optimize for performance (e.g., batch deletes, background jobs for hard deletes, indexing on parent_id).
Compare hard vs. soft delete, synchronous vs. asynchronous cleanup, and different tree models (e.g., adjacency list vs. closure table) for update/delete efficiency. Mention monitoring and rollback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what wildcard semantics are needed (e.g., '*' matches exactly one segment or any number of segments), and whether the registry is static or dynamic. Then propose a solution that balances lookup efficiency and update cost, such as a trie with wildcard branches or a regex-based approach, and discuss trade-offs.
Pro tip: Mention that wildcard matching can explode combinatorially; suggest limiting wildcards to a single segment or using a trie with a dedicated wildcard child to keep lookups efficient. Also, consider precompiling patterns or caching results for repeated queries.
Ask whether '*' matches exactly one path segment or any number of segments, and whether the registry is read-heavy or write-heavy. This determines the appropriate data structure and algorithm.
Propose a trie (prefix tree) where each node represents a path segment, and wildcard '*' is treated as a special child. Alternatively, consider a regex-based approach if patterns are complex.
For insertion, add the path segments to the trie, creating a wildcard branch when '*' is encountered. For lookup, traverse the trie, branching into both exact and wildcard children when a wildcard is present.
Discuss time complexity: O(k) for exact match, but wildcard matching may explore multiple branches, potentially exponential in worst case. Suggest optimizations like memoization or limiting wildcards.
Address multiple wildcards, overlapping patterns, and dynamic updates. Mention that if patterns are known in advance, a compiled regex or automaton could be more efficient.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was genuinely fun to think through.
Start by clarifying the requirements: what kind of data store (e.g., hierarchical config, file system, in-memory tree) and what consistency guarantees are needed. Then propose a design that supports both exact-path watches and subtree watches, using a trie or prefix tree to efficiently match paths and notify callbacks. Finally, discuss trade-offs around performance, scalability, and consistency.
Pro tip: Mention that you would use a trie to store watchers, where each node represents a path segment, and callbacks are stored at nodes. For subtree watches, you can store a flag at the node and traverse descendants to notify. This shows you understand efficient data structures for path-based operations.
Ask about the data model (e.g., hierarchical key-value store), expected scale, and consistency requirements (e.g., immediate vs. eventual).
Propose a trie (prefix tree) to map paths to watchers. Each node stores callbacks for exact path watches and a flag/list for subtree watches.
For exact path, insert callback at the corresponding node. For subtree, mark the node as a subtree watch root and store callback there.
On a change at path P, traverse from root to P, collecting exact watchers at P and subtree watchers at ancestors. Also, if P is a subtree watch root, notify its descendants.
Address performance (e.g., O(k) where k is path depth), memory, and potential optimizations like caching or batching notifications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.