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I went straight to adjacency list because it felt obvious, and the interviewer just nodded and waited.
Start by clarifying requirements (read/write ratio, depth, performance needs), then propose a schema (e.g., adjacency list with recursive CTE or closure table) and REST endpoints (POST /nodes/{parentId}/children, GET /nodes/{id}/descendants). Discuss trade-offs between models and justify your choices based on the requirements.
Pro tip: Atlassian values scalability and collaboration; mention how your design handles concurrent updates and large trees, and consider using materialized paths or closure tables for efficient descendant queries.
Ask about expected tree size, read/write patterns, and performance constraints to guide your design choices.
Evaluate adjacency list, closure table, materialized path, or nested sets; pick one and explain trade-offs (e.g., adjacency list is simple but recursive queries can be slow).
Define endpoints: POST /nodes/{parentId}/children to add a node, GET /nodes/{id}/descendants to retrieve all descendants; include pagination and filtering options.
Show tables and columns (e.g., nodes table with id, parent_id; or closure table with ancestor, descendant, depth) and explain indexing strategies.
Compare models for read vs write performance, storage overhead, and ease of moving subtrees; mention caching or denormalization for large-scale systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer by comparing each strategy's read and write performance for common tree operations like fetching descendants, ancestors, and moving subtrees. Highlight the trade-off between query simplicity (reads) and update complexity (writes), and tie it back to real-world use cases like Jira's issue hierarchy.
Pro tip: Mention that closure tables can be optimized with a depth column to limit ancestor/descendant queries, and that hybrid approaches (e.g., adjacency list + materialized path) are common in production systems like Jira.
List the key tree operations: fetch children, fetch all descendants, fetch ancestors, insert node, move subtree, delete node. This sets the evaluation criteria.
For each storage strategy, describe its structure and evaluate read/write performance for the operations. Be specific about time complexity and query patterns.
Summarize the trade-offs: adjacency list is simple but recursive queries are slow; materialized path is fast for descendants but expensive for moves; nested set is fast for reads but slow for writes; closure table is flexible but storage-heavy.
Suggest which strategy fits scenarios like read-heavy vs write-heavy, depth of tree, and need for referential integrity. Relate to Atlassian products if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data structure (e.g., tree, graph, or hierarchical data) and the operations involved. Then systematically address each edge case: cycles, deletion, moving subtrees, and depth limits, discussing detection, handling strategies, and trade-offs. Conclude with how you would test and validate these scenarios.
Pro tip: Mention that you would use a parent pointer or maintain a visited set to detect cycles, and that moving a subtree requires updating parent references and possibly rebalancing. Also, consider concurrency and transactional integrity if the structure is shared.
Ask questions to understand the exact structure (e.g., tree, DAG, graph) and the operations (insert, delete, move, depth limit). This ensures you address the right edge cases.
Explain how to detect cycles (e.g., DFS with visited set, union-find, or parent pointers) and prevent them during operations like moving a subtree.
Describe how to safely delete a subtree (e.g., recursive deletion, updating parent references) and move a subtree (e.g., detach, update parent pointers, reattach, check for cycles).
Discuss strategies to enforce depth limits, such as tracking depth during insertion/move, rejecting operations that exceed the limit, or rebalancing the tree.
Outline how you would test these edge cases (unit tests, property-based testing) and discuss trade-offs (e.g., performance vs. safety, memory overhead).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.