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Meta·Software Engineer·Onsite - System Design / Architecture·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Meta SWE interview with a system design question on web crawlers. The setup was a distributed crawling problem and I'd seen a similar version floating around online, so I felt pretty prepared going in.

Questions Asked (1)

Q1

Design a distributed web crawler that can scale across many nodes crawling pages simultaneously.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

Had seen this one before so I wasn't going in blind.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then outline a high-level architecture with key components like URL frontier, fetcher, parser, and storage. Dive into critical design decisions such as partitioning, deduplication, politeness, and fault tolerance, and discuss trade-offs between consistency, availability, and performance.

Pro tip: Emphasize the importance of a distributed URL frontier with consistent hashing and a two-level deduplication strategy (in-memory Bloom filters + persistent storage) to balance efficiency and accuracy. Also, proactively discuss how to handle dynamic content and JavaScript rendering, as Meta's crawler likely deals with complex web apps.

1. Clarify Requirements and Scale

Ask questions to understand the scope: expected number of pages, crawl rate, freshness requirements, politeness constraints, and whether dynamic content needs rendering. Establish assumptions for the design.

2. High-Level Architecture

Sketch the main components: URL frontier (scheduler), fetcher nodes, parser, content storage, and deduplication service. Explain how they interact and scale horizontally.

3. Deep Dive into Critical Components

Detail the design of the URL frontier (partitioning, prioritization, politeness), deduplication (Bloom filters, checksums), and fault tolerance (retries, checkpointing). Discuss trade-offs.

4. Address Scalability and Reliability

Explain how to handle node failures, dynamic scaling, and monitoring. Discuss consistency models for deduplication and storage, and how to avoid overloading target servers.

5. Wrap Up and Trade-offs

Summarize key decisions, mention potential bottlenecks, and suggest future improvements (e.g., using ML for prioritization). Be prepared to discuss alternatives.

Key Points to Mention

  • Distributed URL frontier with consistent hashing for partitioning and prioritization
  • Deduplication using Bloom filters and content checksums to avoid duplicate crawling
  • Politeness policies: robots.txt, crawl-delay, and per-domain rate limiting
  • Fault tolerance: retries, checkpointing, and idempotent operations
  • Storage: distributed file system (e.g., HDFS) for raw content and a database for metadata
  • Scalability: horizontal scaling of fetchers, use of message queues for decoupling

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