I recently finished the Snowflake OA. Overall, it was more approachable than expected. With the right approach, the two questions were very manageable within the time limit. Snowflake's OA style is more focused on engineering implementation rather than pure algorithm tricks. The key is understanding the operation order and avoiding unnecessary re-computation.
Question 1: Minimum Height (Tree Optimization)
Given a rooted tree, you can perform at most max_operations operations.
In each operation, you choose a non-root node and move the entire subtree rooted at that
node directly under the root. The goal is to minimize the final tree height.
Core Idea: Greedy Strategy — Move the Deepest Nodes First
The height of a tree is determined by its maximum depth. Therefore, each operation should target the node that contributes the most to the current height.
The approach:
- Run DFS/BFS to calculate the depth of every node.
- Sort nodes by depth in descending order.
- Process the deepest nodes first instead of recalculating the entire tree after every operation.
- Maintain the set of nodes that currently determine the maximum height.
The key challenge is avoiding repeated traversal after every move.
Key Concepts
- Tree traversal (DFS/BFS)
- Greedy selection
- Optimization to avoid repeated computation
Question 2: Horizontal Pod Autoscaler (Auto Scaling Simulation)
There are n services, each with an initial number of pods.
A sequence of logs describes operations:
-
[1, service_id, x]: Update the pod count of a specific service tox. -
[2, -1, x]: Increase all services whose current pod count is smaller thanxtox.
Return the final pod count of every service after executing all operations.
Core Idea: Process Logs Backwards
A direct simulation will repeatedly scan all services during global scaling operations, which can easily exceed the time limit.
Instead, process operations from the end to the beginning:
- Maintain a global minimum pod threshold caused by the latest scaling operation.
- When encountering a global scaling operation, update this threshold.
- When encountering a single-service update: if this service has not been processed before, this is its final effective update, so record it directly.
-
Services without a later individual update take:
max(initial_value, global_threshold).
Overall complexity: O(n + m).
Overall Experience
This Snowflake OA mainly tests observation skills and implementation ability rather than advanced algorithm knowledge.
The two most important patterns are:
- Greedy decisions: handle operations with the largest impact first.
- Reverse simulation: reconstruct the final state by processing operations backwards.
For preparation, it is worth practicing greedy problems with limited operations and reverse-processing problems involving logs or state changes. These patterns appear frequently in data infrastructure companies.
Prepare for North America SWE OA & VO
Need help preparing for North American software engineering interviews? Interview Aid focuses on technical interview preparation, including OA practice, VO mock interviews, and one-on-one guidance.
We provide targeted preparation strategies for companies such as Snowflake, Databricks, and other data infrastructure companies.
You can visit Interview Aid for more interview resources and preparation support.
Good luck with your OA and interviews!
Top comments (0)