Design a distributed web crawler
Design a Distributed Web Crawler
You are tasked with designing a production-grade distributed web crawler capable of operating with 1,000 heterogeneous workers. These workers can vary significantly in their CPU, memory, network quality, and reliability. The system must be safe, polite, and resilient. Assume managed devices under your control, cross-Internet crawling, long-running campaigns that can be paused/resumed, and a requirement for near-real-time progress visibility.
Your design should cover the following aspects:
- URL Frontier Management: Partitioning, deduplication, canonicalization, per-host/domain sharding, preventing duplicate enqueues, and revisit policies.
- Politeness Policies:
robots.txtparsing and caching, honoring crawl-delay and disallow rules, and per-host/domain rate limiting and concurrency. - Prioritization: Ranking of URLs based on factors like depth, freshness, host/domain fairness, and heuristics.
- Retry Strategy and Content Deduplication: Handling transient vs. permanent errors, backoff mechanisms, redirect handling, and detecting exact and near-duplicate content.
- Failure Handling and Idempotency: Strategies for worker crashes, leases, replay, idempotent writes, and dead-letter queues.
- Coordination and Fetch Semantics: Work assignment, backpressure signaling, worker capability awareness, using consistent hashing and/or queues, and managing effects like exactly-once vs. at-least-once.
- Storage Schemas: Designing schemas for fetched pages, blobs, metadata, the link graph, the
robotscache, and frontier persistence. - Monitoring, Alerting, and Debugging: Implementing metrics, logs, traces, per-host dashboards, and strategies for sampling and replay.
- Capacity Planning: Estimating throughput, bandwidth, and storage requirements, including formulas and a concrete example.
- Safety Controls: Implementing global and per-host limits, allow/block lists, legal/regulatory controls, and kill-switches.
- External APIs and Data Models: Defining APIs for enqueuing new URLs, querying status, and fetching results.
Deliverables include a high-level architecture with core components and data flows, algorithms/policies for the specified items, API shapes and data models, back-of-the-envelope capacity estimates, and explicit assumptions and guardrails.
AI interview prep
Core dilemma
Balancing the need for massive, parallel crawling across unreliable, heterogeneous workers with strict politeness, safety, and data integrity requirements.
4 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a distributed web crawler
Design a Distributed Web Crawler
You are tasked with designing a production-grade distributed web crawler capable of operating with 1,000 heterogeneous workers. These workers can vary significantly in their CPU, memory, network quality, and reliability. The system must be safe, polite, and resilient. Assume managed devices under your control, cross-Internet crawling, long-running campaigns that can be paused/resumed, and a requirement for near-real-time progress visibility.
Your design should cover the following aspects:
- URL Frontier Management: Partitioning, deduplication, canonicalization, per-host/domain sharding, preventing duplicate enqueues, and revisit policies.
- Politeness Policies:
robots.txtparsing and caching, honoring crawl-delay and disallow rules, and per-host/domain rate limiting and concurrency. - Prioritization: Ranking of URLs based on factors like depth, freshness, host/domain fairness, and heuristics.
- Retry Strategy and Content Deduplication: Handling transient vs. permanent errors, backoff mechanisms, redirect handling, and detecting exact and near-duplicate content.
- Failure Handling and Idempotency: Strategies for worker crashes, leases, replay, idempotent writes, and dead-letter queues.
- Coordination and Fetch Semantics: Work assignment, backpressure signaling, worker capability awareness, using consistent hashing and/or queues, and managing effects like exactly-once vs. at-least-once.
- Storage Schemas: Designing schemas for fetched pages, blobs, metadata, the link graph, the
robotscache, and frontier persistence. - Monitoring, Alerting, and Debugging: Implementing metrics, logs, traces, per-host dashboards, and strategies for sampling and replay.
- Capacity Planning: Estimating throughput, bandwidth, and storage requirements, including formulas and a concrete example.
- Safety Controls: Implementing global and per-host limits, allow/block lists, legal/regulatory controls, and kill-switches.
- External APIs and Data Models: Defining APIs for enqueuing new URLs, querying status, and fetching results.
Deliverables include a high-level architecture with core components and data flows, algorithms/policies for the specified items, API shapes and data models, back-of-the-envelope capacity estimates, and explicit assumptions and guardrails.