Flipkart·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePending

Jul 2026

Summary

Went through a Flipkart coding round that had a configurable logging system design question. Still waiting to hear back about next steps, no word yet.

Questions Asked (1)

Design and implement a configurable logging system.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This one had more depth than I expected from a phone screen.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what needs to be configurable (log levels, outputs, formats), expected scale, and integration points. Then propose a modular architecture with a core logger, pluggable appenders, and a configuration mechanism, discussing trade-offs like performance vs. flexibility. Finally, outline implementation details and how to test and deploy it.

Pro tip: Emphasize that logging should be non-blocking and have minimal performance impact, especially in high-throughput systems like Flipkart's. Mention using asynchronous logging and sampling to handle scale.

1. Clarify Requirements

Ask about configurability aspects (levels, formats, destinations), scale (logs per second), and integration needs (existing systems, frameworks).

2. High-Level Design

Propose a modular architecture: core logger, configurable appenders (console, file, network), formatters, and a configuration loader. Discuss how components interact.

3. Deep Dive into Key Components

Detail the logger API, appender interface, configuration schema (e.g., YAML/JSON), and dynamic reconfiguration. Explain how to handle log levels and filtering.

4. Address Trade-offs and Scalability

Discuss performance considerations (async logging, buffering), reliability (fallback appenders), and extensibility (custom appenders). Compare with existing solutions like Log4j.

5. Implementation and Testing

Outline a basic implementation plan, including unit tests for each component, integration tests, and performance benchmarks.

Key Points to Mention

Log levels (DEBUG, INFO, WARN, ERROR) and dynamic level adjustment
Appender abstraction for multiple outputs (console, file, Kafka, etc.)
Configuration management (file-based, environment variables, dynamic reload)
Asynchronous logging and buffering to minimize performance impact
Log formatting (JSON, plain text) and structured logging
Error handling and fallback mechanisms (e.g., if a remote appender fails)

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