How to Design a Web Crawler from Scratch

Scaling Out: Distributed Web Crawler Design

Now, how do we go from a simple crawler running on one machine to a scalable web crawler that can handle billions of pages?

The answer is to distribute the work across multiple servers and threads – essentially a distributed web crawler.

Here are key points in designing for scale:

Parallelism and Partitioning

We can run many crawling processes in parallel.

To avoid them stepping on each other’s toes, we partition the URL space among them. A common approach is to partition by host or domain.

For example, use a hash of the hostname to assign each URL to one of N crawler machines. That way, each machine is responsible for a set of sites and there’s no overlap (and it naturally helps politeness, because one site’s traffic isn’t coming from multiple machines at once).

Another benefit: if you have crawler servers in different regions, you might partition so that each crawls sites nearer to it (to reduce latency), although domain names don’t always map cleanly to geography.

Central Coordination (URL Frontier)

In a distributed setup, managing the frontier (the queue of URLs) is tricky.

You could have a central queue service (like a distributed message queue) that all crawler workers pull from.

Alternatively, each node maintains its own queue after partitioning.

A typical architecture is to have a URL distributor or host splitter component that routes URLs to the right crawler node based on the partition policy.

For example, when one page is parsed and new links are found, those new URLs might be sent to different nodes’ queues.

This requires nodes to communicate discovered URLs to each other.

Using a message broker (Kafka, RabbitMQ, AWS SQS, etc.) can help distribute URL tasks to crawler workers.

Scaling the Components

Every piece of the crawler pipeline needs to scale.

You might have multiple fetcher threads per machine, multiple parser threads, etc., all working in parallel.

A distributed crawler might consist of dozens or hundreds of machines, each running many threads, collectively fetching thousands of pages per second.

The storage and indexing backends also must scale to handle the volume of data (hundreds of millions of pages).

This usually means using scalable storage systems (distributed file systems, NoSQL databases, or cloud storage) and scalable indexing systems (like a distributed search index).

Fault Tolerance

With many moving parts, things will fail.

Crawler nodes might crash or network issues might occur.

A robust system design ensures that if a node dies, the URLs it was working on aren’t lost – they can be retried by others. This is where using a central queue can help (e.g., messages stay in the queue until processed).

Alternatively, systems like Kafka keep a log of URLs and track offsets, so if one consumer fails, another can continue from where it left off.

Also, implementing checkpoints or writing progress to a database (e.g., which URLs have been fetched) helps not to duplicate work.

Consistency and Duplicate Handling

In a distributed crawler, two nodes might encounter the same URL from different pages at the same time.

We need a global check to avoid duplicate crawling. This could be a distributed hash set or database of seen URLs.

Some architectures use a Bloom filter on each node plus a shared coordination to reduce dupes. It’s challenging because a purely partitioned approach (checking seen-URL only on one node) might miss that another node saw it.

Often, the duplicate URL eliminator might be centralized or use a consistent hashing scheme to route “have I seen this URL?” queries to a specific node that can answer definitively.

Designing a distributed crawler is certainly complex, but an interview answer can simplify it: use multiple workers with a shared URL queue, partition by domain, and ensure each worker respects politeness.

Mention that distribution is essential for scaling to billions of pages, and that coordination is needed to avoid overlap.

Example Scenario

Imagine we have 4 crawler servers.

We decide that server #1 handles URLs where the hash of the hostname ends in 0, server #2 for hash ending in 1, and so on (this is just a simple modulo partition).

We start with some seed URLs which get routed to the appropriate server.

Each server crawls pages and as it finds new URLs, it hashes their hostnames and forwards them to whichever server is responsible.

Over time, each server is busy crawling its share of the web.

If we need more capacity, we add a 5th server and update the hashing scheme (which might involve moving some data or having a dynamic assignment).

Each server runs many threads to maximize CPU and network usage, but all follow the shared rules of politeness using a coordinated schedule or a shared rate-limit tracker.

FAQs

Q1: What is a web crawler and how does it work?
A web crawler is an automated program (often called a spider) that systematically browses web pages and collects data. It starts with a set of seed URLs, fetches the page content, extracts hyperlinks on those pages, then adds those new URLs to its queue (frontier) to visit next. The crawler repeats this process, thereby “crawling” from link to link. As it crawls, it may store the page data in an index so that a search engine can quickly retrieve information later. Essentially, it’s like a web-traversing robot librarian that discovers and catalogs web pages for search engines or other applications.

Q2: How do web crawlers avoid overloading websites (politeness)?

Crawlers implement politeness policies to respect website resources. Before fetching a site, a good crawler checks the site’s robots.txt file, which lists which parts of the site can be crawled and how fast. The crawler will skip disallowed areas and honor any crawl-delay specified. Additionally, crawlers self-impose rate limits (often ~1 request per second per domain) to avoid sending too many requests in a short time. They also spread out requests across many sites so no single server gets hammered. By following robots.txt rules, pacing their requests, and identifying themselves via User-Agent, well-behaved crawlers avoid overloading or annoying website owners.

Q3: How can a web crawler be scaled to handle billions of pages?
To handle massive scale, web crawlers are designed to be distributed across many machines. This means running many crawling processes in parallel and dividing the work among them. A common method is to partition URLs by host or domain, so each server crawls a distinct set of sites. A centralized queue or coordination service helps manage the URLs to crawl and ensures two machines don’t crawl the same URL. The crawler’s components (fetching, parsing, storage) are built to scale horizontally – for example, using distributed storage for the fetched data and a distributed index for search. By adding more servers (and threads), the crawler can increase its throughput (pages per second). Fault tolerance mechanisms (like reassigning URLs if a node fails, or using message queues that don’t lose data) make sure the crawl can continue reliably. In summary, a scalable crawler is achieved by parallelization, clever work partitioning, and robust coordination across a cluster of machines.