Let’s talk about this popular system design interview question – How to build an online crawler? Web crawlers are one of the most typical used systems nowadays. Typically the most popular example is that Google is using crawlers to collect information from all websites. Besides search engine, news websites need crawlers to aggregate data sources.
It seems that whenever you want to aggregate a large amount of information, you may consider using crawlers. There are very a few factors when building a web crawler, especially when you want to scale the system. That’s why it has become one of the very most popular system design interview questions.
In this post, we will cover topics from basic crawler to large-scale crawler and discuss various questions you might be asked in an interview. Developing a rudimentary web crawler? One particular idea we’ve discussed in 8 Things You should know Before something Design Interview is to begin simple. Let’s concentrate on building a very rudimentary web crawler that operates on a single machine with one thread. With this simple solution, we can keep optimizing down the road. To crawler an individual web page, 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 the crawler.
Start with a URL pool which has all the websites we want to crawl. For every URL, concern a HTTP GET request to fetch the web page content. Parse this content (usually HTML) and draw out potential URLs that we want to crawl. Add new URLs to the pool and keep crawling. It depends upon the specific problem, sometimes we may have another system that generates URLs to crawl.
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For instance, a scheduled program can keep listening to RSS feeds and for every new article, the Web address can be added because of it into the crawling pool. As may all, any system will face a lot of issues after scaling. In the web crawler, there are tons of things that can make it wrong when scaling the functional system to multiple machines.
Before jumping to another session, please spend a couple of minutes considering what can be bottlenecks of the distributed web crawler and exactly how can you solve them. In the rest of the post, we will talk about several major problems with solutions. How do you want to crawl a website often? This may not sound like a huge deal unless the machine comes to certain scales and you need very fresh content. For example, if you would like to get the latest information from the last hour, your crawler may 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 manage such frequent request. One approach is to check out the robot.txt of each site. For those who don’t know what robot.txt is, basically it’s a standard utilized by websites to communicate with web crawlers. It could identify things such as what documents should not be crawled and most web crawlers shall follow the settings.
In addition, you can have different crawl rate of recurrence for different websites. Usually, there are just a few sites that require to be crawled multiple times per day. In one machine, you can 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 webpages plus they all want to include this URL to the URL pool. Of course, it doesn’t make sense to crawl the same page multiple times. So how can we deduct these URLs? One common strategy is to use Bloom Filter. In a nutshell, a bloom filter is a space-efficient system which allows you to test if an element is in a set.
However, it might have false positive. In other words, if a bloom filter can tell you the URL is definitely not in the pool or it probably in the pool. To explain how bloom filter works briefly, a clear bloom filter is a little selection of my pieces (all 0). There’s also the hash functions that map each component to 1 of the m bits.
