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Build A Web Crawler

Let’s talk about this popular system design interview question – Developing a web crawler? Web crawlers are one of the most common-used systems nowadays. The most popular example is that Google is using crawlers to gather information from all websites. Besides internet search engine, news websites need crawlers to aggregate data resources.

It seems that once you want to aggregate a huge amount of information, you might consider using crawlers. There are very a few factors when creating a web crawler, especially when you want to scale the system. That’s why it has become one of the most popular system design interview questions.

In this post, we are going to cover topics from basic crawler to large-scale crawler and discuss various questions you might be asked within an interview. How to build a rudimentary web crawler? One particular idea, we’ve talked about in 8 Things You Need to Know Before something Design Interview is to begin simple. Let’s focus on creating a very rudimentary web crawler that operates on a single machine with a single thread. With this simple solution, we will keep optimizing later on. To crawler an individual website, all we need is to issue an HTTP GET request to the corresponding URL and parse the response data, which is kind of the core of a crawler.

Start with a URL pool that contains all web sites we want to crawl. For every URL, concern a HTTP GET request to fetch the web page content. Parse this content HTML) and extract potential URLs that people want to crawl (usually. Add new URLs to the pool and keep crawling. It depends on the specific problem, sometimes we might have a separate system that generates URLs to crawl.

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For instance, a program can keep listening to RSS feeds and for every new article, the URL can be added by it into the crawling pool. As may all, any system will face a bunch of issues after scaling. Within a web crawler, there are tons of things that can make it wrong when scaling the machine to multiple machines.

Before jumping to the next program, please spend a couple of minutes considering what can be bottlenecks of a distributed web crawler and how would you solve them. In the rest of the post, we are going to discuss several major problems with solutions. How often will you crawl a website? This may not sound like a big deal unless the machine comes to certain scales and you will need very fresh content. For instance, if you would like to get the latest information from the last hour, your crawler might need to keep crawling the news headlines website every hour. But what’s wrong with this?

For some small websites, it’s more than likely that their servers cannot handle such frequent demand. One strategy is to follow the robot.txt of each site. For people who don’t know very well what robot.txt is, essentially it’s a standard utilized by websites to communicate with web crawlers. It could specify things like what data files shouldn’t be crawled & most web crawlers will follow the settings.

In addition, you could have different crawl frequency for different websites. Usually, there are only a few sites that require to be crawled multiple times each day. In a single machine, you will keep the URL pool in memory and remove duplicate entries. However, things become more complicated in a distributed system.

Basically multiple crawlers may remove the same URL from different web pages plus they all want to include this URL to the URL pool. Of course, it doesn’t seem sensible to crawl the same page multiple times. Just how can we deduct these URLs? One common approach is by using Bloom Filter. In a nutshell, a bloom filtration system is a space-efficient system that allows one to test if an element is in a collection.

However, it could have fake positive. Quite simply, if a bloom filter can tell you the URL is certainly not in the pool or it probably in the pool. To clarify how the bloom filter works briefly, a clear bloom filtration system is a bit array of m bits (all 0). There are also k hash functions that map each element to 1 of the m bits.