Elastic N.V.·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Interviewed for a full-stack role at Elastic and got a fairly focused coding problem around log stream processing. Not a brutal round but it had enough nuance to trip you up if you weren't thinking carefully about edge cases.

Questions Asked (1)

Given a stream of log entries, design a solution that tracks the frequency count per category and the timestamp of the most recent update for each category. How would you handle out-of-order timestamps efficiently?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went straight for a hash map keyed by category, which felt obvious.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: single-threaded or distributed, memory constraints, and whether timestamps are monotonic. Then propose a hash map keyed by category, storing count and latest timestamp, and handle out-of-order timestamps by comparing incoming timestamps with the stored latest, updating only if newer. Discuss trade-offs between simplicity and scalability, and mention potential optimizations like windowing or approximate counting for high-cardinality streams.

Pro tip: Emphasize that out-of-order handling is crucial for correctness in distributed systems, and mention that using a monotonic clock or watermarking can help, but always validate against the business requirement for 'latest'.

1. Clarify Requirements

Ask about stream volume, latency requirements, memory limits, and whether timestamps are event-time or processing-time. Confirm if 'most recent' means highest timestamp or latest arrival.

2. Design Core Data Structure

Propose a hash map (or concurrent hash map) mapping category to a struct containing count and last_updated_timestamp. Explain that this gives O(1) average update and lookup.

3. Handle Out-of-Order Timestamps

For each log entry, compare its timestamp with the stored last_updated_timestamp. If newer, update both count and timestamp; if older, increment count but do not update timestamp. Discuss if count should be incremented regardless.

4. Address Scalability and Trade-offs

Discuss partitioning by category for distributed processing, using a time window to bound memory, and trade-offs between exact counts and approximate algorithms (e.g., Count-Min Sketch) for high cardinality.

5. Consider Edge Cases and Optimizations

Mention handling of duplicate timestamps, late data beyond a watermark, and potential use of a priority queue or sorted structure if frequent queries for top categories are needed.

Key Points to Mention

Hash map with category as key and (count, latest_timestamp) as value for O(1) updates.
Out-of-order handling: update timestamp only if incoming timestamp > stored timestamp; always increment count.
Trade-offs: exact vs approximate counting, memory vs accuracy, and single-node vs distributed.
Scalability: partitioning by category, using concurrent data structures, and windowing for bounded memory.
Edge cases: duplicate timestamps, late data, and clock skew in distributed systems.
Potential optimizations: caching frequent categories, using a min-heap for top-K queries, or watermarking for late events.

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