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Systematically discover and fetch billions of pages using a multi-stage pipeline with URL frontiers, content deduplication, and politeness controls.
“Web Crawling & Data Pipelines” is free with an account. Your progress and bookmarks follow you across devices.
Sign in to continueA web crawler sounds simple in theory: visit a page, grab the links, follow them, repeat. But the internet has billions of pages, and naive crawling will get you into trouble fast. You will hit the same URLs over and over, overwhelm web servers with too many requests, fall into infinite URL traps, and burn through bandwidth fetching duplicate content.
The key insight is that a crawler is really a data pipeline -- a series of processing stages connected by queues, where each stage can be scaled independently. Once you see it that way, the design becomes a matter of choosing the right data structures for each stage and connecting them cleanly.
At its core, a crawler is a loop with four stages. Seed URLs (starting points like top-level domain pages or a sitemap) enter the URL frontier, which is a specialized queue that decides what to fetch next. The fetcher downloads the page via HTTP. The parser extracts structured data (text, metadata, images) and pulls out all hyperlinks. Those new URLs feed back into the frontier, and the loop continues.
Each stage is connected by a message queue (Kafka, SQS, or similar). This decoupling is what makes the whole thing scalable. If fetching is the bottleneck, you add more fetcher workers without touching anything else. If parsing is slow because you are doing heavy NLP, you scale that stage independently.
This is the same pattern you see in any well-designed data pipeline: independent stages, connected by queues, each with its own scaling knob.
See it in practice: The CI/CD question uses a nearly identical pipeline architecture -- build, test, and deploy stages chained through message queues with independent scaling and failure handling at each stage. The LeetCode question applies the same multi-stage approach: submissions flow through compilation, execution, and result comparison, each decoupled by queues.
The frontier is more than a simple FIFO queue. It needs to balance two competing concerns: what to fetch (priority) and when to fetch it (politeness).
Not all URLs are equally valuable. A news site's homepage changes hourly and has high PageRank. A deep archive page from 2008 rarely changes. The frontier uses multiple priority levels so high-value URLs get fetched first. Redis Sorted Sets work well here -- the score is the priority, and ZPOPMIN gives you the next URL to fetch.
How do you assign priority? A few signals: PageRank (how many other pages link to it), freshness (when was it last crawled), and domain importance (major news sites rank higher than personal blogs). You do not need a perfect ranking -- a rough tiering into 3-5 priority levels is enough.