Snowflake·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Snowflake SWE interview with a tree traversal problem that looks easy until you think about the follow-up. The core question is straightforward but they pushed on optimization for repeated calls, which is where things got interesting.

Questions Asked (1)

Design and implement a class that models a royal family's line of succession. The class should support adding children to a parent, marking members as deceased, and returning the current inheritance order (depth-first preorder, skipping the dead). As a follow-up: how would you optimize repeated calls to the inheritance order query as the tree changes over time?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The base problem is just a DFS with a dead-set filter, pretty mechanical.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then design a tree structure with parent-child pointers and a deceased flag. Implement the inheritance order via depth-first preorder traversal that skips deceased members. For the follow-up, discuss caching the order and updating it incrementally on changes, analyzing trade-offs between update and query costs.

Pro tip: Mention that the follow-up is about optimizing for repeated queries, so caching the order and updating it lazily or eagerly based on the frequency of queries vs. updates shows you understand real-world trade-offs.

1. Clarify requirements and edge cases

Ask about input constraints, whether multiple children are allowed, how to handle deceased members with living descendants, and if the order should be stable. Clarify the expected frequency of queries vs. updates.

2. Design the data structure

Propose a node class with fields for name, parent, list of children, and a boolean isAlive. Use a map from name to node for O(1) access. Discuss whether to maintain a global root or multiple roots.

3. Implement core operations

For addChild(parent, child), create a node and append to parent's children. For markDeceased(name), set isAlive to false. For getInheritanceOrder(), perform a depth-first preorder traversal from the root, skipping deceased nodes, and return the list of names.

4. Analyze complexity and optimize

Naive getInheritanceOrder is O(n) per call. For the follow-up, propose caching the order and updating it on changes. Discuss incremental update strategies: e.g., on addChild, insert the new child's subtree into the cached order; on markDeceased, remove the node and its subtree if no living descendants, or just skip it. Analyze trade-offs: update cost vs. query cost, and memory overhead.

5. Discuss trade-offs and alternatives

Compare eager vs. lazy updates, and consider using a balanced tree or maintaining a linked list of living members. Mention that if queries are frequent and updates rare, caching is beneficial; if updates are frequent, a lazy approach or recomputing on demand might be better.

Key Points to Mention

Depth-first preorder traversal: visit node, then recursively visit children in order.
Skipping deceased members: if a node is deceased, do not include it in the order, but still traverse its children.
Data structures: tree with parent-child pointers, hash map for O(1) node lookup.
Caching the inheritance order and invalidating/updating on changes.
Incremental update strategies: inserting a new child's subtree into the cached order, removing a deceased node's subtree if no living descendants.
Trade-offs: time complexity of queries vs. updates, memory overhead, and concurrency considerations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.