Each test is 5 questions with varying difficulty.
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Designing a web crawler is a classic system design interview question that evaluates a candidate's ability to architect a high-throughput, distributed data ingestion system. In 2026, the complexity has shifted from simple page fetching to handling massive scale, respecting complex robots.txt policies, and integrating with AI-driven content extraction pipelines. Roles such as Backend Engineer, Data Engineer, and Search Infrastructure Engineer frequently encounter this problem. Interviewers expect junior candidates to understand the basic fetch-parse-store loop, while senior candidates must demonstrate deep knowledge of URL frontier prioritization, distributed coordination, and handling the 'crawling at scale' challenges like IP rotation and content deduplication.
A web crawler is the foundation of search engines, AI training datasets, and market intelligence platforms. At scale, the system must process billions of pages, requiring extreme efficiency in network I/O and storage. The business value lies in data freshness and coverage; a poorly designed crawler leads to stale data or, worse, being blocked by target sites due to aggressive request patterns. This topic is a high-signal interview question because it forces the candidate to balance throughput against politeness, manage state in a distributed environment, and handle failure modes like partial network partitions. In 2026, the rise of LLMs has made web crawling even more critical for generating high-quality RAG datasets, where the quality and structure of the crawled content directly impact model performance. A strong candidate moves beyond the 'fetcher' concept to discuss the entire lifecycle of data, including link extraction, canonicalization, and deduplication at the petabyte scale.
The architecture centers on a centralized URL frontier that feeds distributed worker nodes. The workers fetch content, extract new links, and push them back to the frontier after filtering. A storage layer persists the raw content and metadata, while a deduplication service checks hashes before storage.
[URL Frontier]
↓
[DNS Resolver]
↓
[Worker Nodes (Fetchers)]
↓ ↓
[Parser] [Politeness Manager]
↓ ↓
[Deduplication] ← [Content Store]
↓
[Link Extractor]
↓
[URL Frontier (Loop)]Decouples the URL frontier (producer) from the fetcher nodes (consumers) using a message queue.
Trade-offs: Increases system complexity but allows independent scaling of fetchers.
Uses a probabilistic data structure to quickly check if a URL has already been visited.
Trade-offs: Saves significant memory at the cost of potential false positives.
Limits the number of requests per domain to ensure politeness.
Trade-offs: Provides fine-grained control but requires synchronized state across nodes.
| Reliability | Use circuit breakers for failing domains and implement retries with exponential back-off. |
| Scalability | Scale worker nodes horizontally and partition the URL frontier by domain hash. |
| Performance | Use asynchronous I/O (e.g., Python asyncio or Go routines) to maximize throughput per node. |
| Cost | Use object storage (S3) for raw data and implement aggressive deduplication to minimize storage footprints. |
| Security | Implement user-agent identification and rate limiting to prevent abuse. |
| Monitoring | Track 'pages fetched per second', 'error rate per domain', and 'frontier queue size'. |
A crawler (or spider) is a system that systematically browses the web to index content, often following links recursively. A scraper is typically a targeted tool designed to extract specific data points from a particular site or set of pages. Crawlers are usually infrastructure-heavy and broad, while scrapers are application-specific and narrow.
The URL frontier is the 'brain' of the crawler. It determines which pages to visit, in what order, and how frequently, directly impacting the freshness and coverage of your index. If the frontier is poorly designed, the crawler will either miss important content or overwhelm target servers, leading to bans.
Common strategies include using a pool of rotating proxy IPs, mimicking human behavior (e.g., randomizing request headers and timing), and using headless browsers to render JavaScript. However, the most sustainable approach is to respect robots.txt and maintain a polite crawl rate to avoid being flagged as a malicious bot.
A hash set stores the actual URL or content hash, which is memory-intensive at scale. A Bloom filter is a probabilistic data structure that uses significantly less memory but allows for a small percentage of false positives. In a crawler, we accept these false positives because the cost of missing a rare page is usually lower than the cost of storing duplicate data.
Horizontal scaling is key. You distribute the URL frontier across multiple nodes using consistent hashing, deploy a large pool of worker nodes that consume tasks from a distributed message queue like Kafka, and use a distributed storage system like Cassandra or S3 to handle the massive volume of fetched data.
Every fetch requires a DNS lookup. If you are crawling millions of pages per minute, the standard DNS resolution process becomes a major source of latency. High-performance crawlers implement local DNS caches or use dedicated, high-throughput DNS resolver services to minimize this overhead.
URL canonicalization converts different variations of a URL (e.g., with or without 'www', different query parameter orders) into a single, standard form. This prevents the crawler from visiting the same page multiple times through different links, saving bandwidth and storage.
Politeness is implemented by tracking the last access time for each domain and ensuring a minimum delay between requests. This is typically managed using a distributed token bucket or a similar rate-limiting algorithm stored in a shared cache like Redis, ensuring that even if multiple worker nodes are crawling the same domain, the aggregate rate remains within limits.
Breadth-first crawling visits all pages at a certain depth before moving deeper, which is better for discovering high-level content and keeping the crawl balanced. Depth-first crawling follows a single path to its end, which is useful for specific, deep-linked content but can easily lead to crawler traps and infinite loops.
Crawler traps are infinite loops of dynamically generated links. They are mitigated by setting a maximum crawl depth, using URL pattern matching to identify and ignore suspicious URL structures, and implementing a robust deduplication mechanism that detects when the crawler is revisiting the same content under different URLs.
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