Elastic·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026Remote

Summary

Elastic coding round for a software engineer role, one question the whole time: build a live log aggregation view in React. More involved than it sounds once you get into the timestamp handling and state update edge cases.

Questions Asked (1)

Build a React component that consumes a stream of log events one at a time, aggregates them by category in memory, and renders a live-updating table showing each category's event count and most recent timestamp.

Technical Trade-offsSystem DesignAPI & Integrations

Author's notes

The core part wasn't hard to sketch out but I kept second-guessing the state structure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a component design that separates stream consumption, state aggregation, and rendering. Emphasize performance optimizations like batching and memoization, and discuss trade-offs between simplicity and scalability.

Pro tip: Use a reducer to manage aggregation state and batch updates with requestAnimationFrame or a short debounce to avoid excessive re-renders, showing you care about performance in a live-updating UI.

1. Clarify Requirements

Ask about the stream source (e.g., WebSocket, SSE), event shape, expected volume, and update frequency. Confirm whether the table should be sorted, filtered, or paginated.

2. Design Component Architecture

Decide on a container component that subscribes to the stream and a presentational table component. Use hooks like useReducer for aggregation and useMemo for derived data.

3. Implement Aggregation Logic

Process each event by updating a map of category to { count, latestTimestamp }. Ensure immutability for React state updates and handle out-of-order timestamps.

4. Optimize Rendering

Batch state updates to avoid re-rendering on every event. Use React.memo for table rows and consider virtualization if the category list is large.

5. Discuss Trade-offs and Edge Cases

Talk about memory usage for unbounded categories, handling stream errors, and cleanup on unmount. Compare alternatives like using a state management library or server-side aggregation.

Key Points to Mention

Stream consumption patterns (WebSocket, SSE, or async iterators) and error handling
State management with useReducer vs. useState, and immutability
Performance optimizations: batching updates, memoization, and virtualization
Trade-offs between client-side and server-side aggregation
Handling out-of-order events and timestamp comparison
Cleanup and resource management (unsubscribe, abort controllers)

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