Nooks·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

System design round at Nooks for a software engineer role, focused entirely on scaling a web crawler into a production-grade distributed service. Pretty intense scope for a single question.

Questions Asked (1)

Design a distributed web crawler that can traverse a large number of pages efficiently, respects rate limits per host, handles retries and partial failures, scales across multiple workers, and includes deduplication, backoff, fairness across domains, and observability.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is a beast of a question because it's basically five questions in one.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., pages per day, number of hosts), then outline a high-level architecture with a URL frontier, fetcher workers, and storage. Dive into key components like per-host rate limiting, deduplication, retries with exponential backoff, and fairness, and discuss trade-offs and observability.

Pro tip: Emphasize that the crawler must be polite and robust: per-host rate limiting and backoff are non-negotiable, and you should design for partial failures from the start. Also, mention that deduplication should happen at multiple levels (URL and content) to avoid redundant work.

1. Clarify Requirements and Scale

Ask questions to understand the scale (e.g., pages per second, number of hosts), freshness requirements, and constraints like politeness and legal considerations. Define functional and non-functional requirements.

2. High-Level Architecture

Sketch the main components: URL frontier (priority queue with per-host queues), fetcher workers, deduplication service, storage for content and metadata, and a scheduler. Explain how they interact.

3. Deep Dive into Key Components

Detail the design of the URL frontier (fairness, prioritization), rate limiting per host (token bucket), retry mechanism (exponential backoff with jitter), deduplication (Bloom filters, hashing), and handling partial failures (idempotency, checkpoints).

4. Scalability and Fault Tolerance

Discuss how to scale horizontally (adding workers, partitioning the frontier), handle worker failures (heartbeats, reassignment), and ensure the system is resilient (retries, dead-letter queues).

5. Observability and Trade-offs

Cover monitoring (metrics, logging, tracing), alerting, and dashboards. Discuss trade-offs such as consistency vs. availability, latency vs. throughput, and cost vs. performance.

Key Points to Mention

Per-host rate limiting using token buckets or leaky buckets to respect robots.txt and avoid overloading servers.
Deduplication at URL level (canonicalization, Bloom filters) and content level (hashing) to avoid processing duplicates.
Retry mechanism with exponential backoff and jitter, and circuit breakers to handle transient failures.
Fairness across domains via a frontier that interleaves requests from different hosts, preventing starvation.
Scalability through partitioning the URL frontier (e.g., by host) and distributing workers, with coordination via a distributed queue or coordination service.
Observability with metrics (crawl rate, error rates, queue sizes), logging, and tracing to debug and monitor.

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