Decagon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Interviewed for a software engineering role at Decagon and got hit with a meaty design-plus-implementation question about a sliding window CSAT tracker. More involved than I expected for what I thought would be a straightforward coding round.

Questions Asked (1)

Design and implement a class that tracks customer satisfaction scores over a sliding time window, supporting record, update, get_average, and get_percentile operations with efficient expiration of old entries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The basic record and get_average parts came pretty naturally.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: window size, operations, and performance constraints. Then propose a data structure combining a hash map for O(1) record/update and a balanced BST or order-statistic tree for efficient percentile queries, with lazy expiration or a time-indexed queue for sliding window eviction. Discuss trade-offs between exact and approximate methods, and outline the implementation with complexity analysis.

Pro tip: Mention that you would use a monotonic queue or a timestamped deque to efficiently expire old entries, and consider using a Fenwick tree for percentile queries if scores are bounded. This shows you balance simplicity and performance.

1. Clarify Requirements

Ask about window size (fixed or sliding), score range, expected operations per second, and whether percentile needs to be exact. Confirm if updates can change timestamps.

2. Choose Data Structures

Propose a hash map for O(1) record/update by ID, and a balanced BST (e.g., order-statistic tree) or Fenwick tree for percentile queries. For expiration, use a time-ordered queue or heap.

3. Handle Expiration

Describe lazy expiration: on each operation, remove entries older than the window from the queue and the BST. Alternatively, use a background thread or periodic cleanup.

4. Implement Operations

Outline record: insert into map and BST, add to queue. Update: remove old score from BST, update map, insert new score. get_average: maintain running sum. get_percentile: query BST for k-th element.

5. Analyze Trade-offs

Discuss time/space complexity: O(log n) for record/update/percentile, O(1) for average. Mention alternatives like approximate percentiles (t-digest) for high throughput, and concurrency considerations.

Key Points to Mention

Use a hash map for O(1) access by customer ID.
Use a balanced BST or order-statistic tree for O(log n) percentile queries.
Maintain a running sum for O(1) average.
Use a time-ordered queue (e.g., deque) for efficient expiration of old entries.
Consider lazy expiration to avoid background threads.
Discuss trade-offs: exact vs approximate percentiles, memory vs speed.

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