Design a Web Crawler
A web crawler is known as a robot or spider. It is widely used by search engines to discover new or updated content on the web. Content can be a web page, an image, a video, a PDF file, etc. A web crawler starts by collecting a few web pages and then follows links on those pages to collect new content.
A crawler is used for many purposes:
- Search engine indexing: This is the most common use case. A crawler collects web pages to create a local index for search engines. For example, Googlebot is the web crawler behind the Google search engine.
- Web archiving: This is the process of collecting information from the web to preserve data for future uses. For instance, many national libraries run crawlers to archive web sites. Notable examples are the US Library of Congress and the EU web archive.
- Web mining: The explosive growth of the web presents an unprecedented opportunity for data mining. Web mining helps to discover useful knowledge from the internet. For example, top financial firms use crawlers to download shareholder meetings and annual reports to learn key company initiatives.
- Web monitoring: The crawlers help to monitor copyright and trademark infringements over the Internet. For example, Digimarc utilizes crawlers to discover pirated works and reports.
Understand the problem and establish design scope
Back of the envelope estimation
- Assume 1 billion web pages are downloaded every month.
- QPS: 1,000,000,000 / 30 days / 24 hours / 3600 seconds = ~400 pages per second.
- Peak QPS = 2 * QPS = 800
- Assume the average web page size is 500k.
- 1-billion-page x 500k = 500 TB storage per month. If you are unclear about digital storage units, go through “Power of 2” section in Chapter 2 again.
- Assuming data are stored for five years, 500 TB * 12 months * 5 years = 30 PB. A 30 PB storage is needed to store five-year content.
Propose high-level design and get buy-in
Once the requirements are clear, we move on to the high-level design. Inspired by previous studies on web crawling[
Seed URLs
To crawl the entire web, we need to be creative in selecting seed URLs. A good seed URL serves as a good starting point that a crawler can utilize to traverse as many links as possible. The general strategy is to divide the entire URL space into smaller ones. The first proposed approach is based on locality as different countries may have different popular websites. Another way is to choose seed URLs based on topics; for example, we can divide URL space into shopping, sports, healthcare, etc. Seed URL selection is an open-ended question. You are not expected to give the perfect answer. Just think out loud.
For example, if you want to web-crawl focused on sports, you can decide links related to sports as seed URLs
URL Frontier
Most modern web crawlers split the crawl state into two: to be downloaded and already downloaded. The component that stores URLs to be downloaded is called the URL Frontier. You can refer to this as a First-in-First-out (FIFO) queue. For detailed information about the URL Frontier, refer to the URL Frontier Deep Dive.
HTML Downloader
The HTML downloader downloads web pages from the internet. Those URLs are provided by the URL Frontier.
DNS Resolver
To download a web page, a URL must be translated into an IP address. The HTML Downloader calls the DNS Resolver to get the corresponding IP address for the URL.
Content Parser
After a web page is downloaded, it must be parsed and validated because malformed web pages could provoke problems and waste storage space. Implementing a content parser in a crawl server will slow down the crawling process. Thus, the content parser is a separate component.
Content Seen?
We introduce the “Content Seen?” data structure to eliminate data redundancy and shorten processing time. It helps to detect new content previously stored in the system. To compare two HTML documents, we can compare them character by character. However, this method is slow and time-consuming, especially when billions of web pages are involved. An efficient way to accomplish this task is to compare the hash values of the two web pages
Content Storage
It is a storage system for storing HTML content. The choice of storage system depends on factors such as data type, data size, access frequency, life span, etc. Both disk and memory are used.
- Most of the content is stored on disk because the data set is too big to fit in memory.
- Popular content is kept in memory to reduce latency.
URL Extractor
URL Extractor parses and extracts links from HTML pages. Relative paths are converted to absolute URLs by adding the prefix.
URL Filter
The URL filter excludes certain content types, file extensions, error links and URLs in “blacklisted” sites.
URL Seen?
“URL Seen?” is a data structure that keeps track of URLs that are visited before or already in the Frontier. “URL Seen?” helps to avoid adding the same URL multiple times as this can increase server load and cause potential infinite loops. Bloom filter and hash table are common techniques to implement the “URL Seen?” component.
URL Storage
URL Storage stores already visited URLs.
Design deep dive
Up until now, we have discussed the high-level design. Next, we will discuss the most important building components and techniques in depth:
- DFS vs BFS
- URL Frontier Deep Dive
- HTML Downloader Deep Dive
- Robustness
- Extensibility
- Detect and avoid problematic content
DFS vs BFS
BFS is commonly used by web crawlers and is implemented by a FIFO queue. In a FIFO queue, URLs are dequeued in the order they are enqueued. However, this implementation has two problems:
- Most links from the same web page are linked back to the same host. Many links in wikipedia.com are internal links, making the crawler busy processing URLs from the same host (wikipedia.com). When the crawler tries to download web pages in parallel, Wikipedia servers will be flooded with requests. This is considered as “impolite”.
- Standard BFS does not take the priority of a URL into consideration. The web is large and not every page has the same level of quality and importance. Therefore, we may want to prioritize URLs according to their page ranks, web traffic, update frequency, etc.
URL Frontier Deep Dive
URL frontier helps to address these problems. A URL frontier is a data structure that stores URLs to be downloaded. The URL frontier is an important component to ensure politeness, URL prioritization, and freshness.
Politeness
The general idea of enforcing politeness is to download one page at a time from the same host. A delay can be added between two download tasks. The politeness constraint is implemented by maintaining a mapping from website hostnames to download (worker) threads. For example, each downloader thread has a separate FIFO queue and only downloads URLs obtained from that queue:
- Queue router: It ensures that each queue (b1, b2, … bn) only contains URLs from the same host.
- Mapping table: It maps each host to a queue.
- FIFO queues: Each queue contains URLs from the same host.
- Queue selector: Each worker thread is mapped to a FIFO queue, and it only downloads URLs from that queue. The queue selection logic is done by the Queue selector.
- Worker thread: A worker thread downloads web pages one by one from the same host. A delay can be added between two download tasks.
Priority
We prioritize URLs based on usefulness, which can be measured by PageRank, website traffic, update frequency, etc. “Prioritizer” is the component that handles URL prioritization:
- Prioritizer: It takes URLs as input and computes the priorities.
- Queues: Each queue has an assigned priority. Queues with high priority are selected with higher probability.
- Queue selector: Randomly choose a queue with a bias towards queues with higher priority.
Freshness
Web pages are constantly being added, deleted, and edited. A web crawler must periodically recrawl downloaded pages to keep our data set fresh. Recrawl all the URLs is time-consuming and resource intensive. Few strategies to optimize freshness are listed as follows:
- Recrawl based on web pages’ update history.
- Prioritize URLs and recrawl important pages first and more frequently.
Storage for URL Frontier
Putting everything in memory is neither durable nor scalable. Keeping everything in the disk is undesirable neither because the disk is slow; and it can easily become a bottleneck for the crawl.
We adopted a hybrid approach. The majority of URLs are stored on disk, so the storage space is not a problem. To reduce the cost of reading from the disk and writing to the disk, we maintain buffers in memory for enqueue/dequeue operations. Data in the buffer is periodically written to the disk.
HTML Downloader Deep Dive
Robots.txt
Robots.txt, called Robots Exclusion Protocol, is a standard used by websites to communicate with crawlers. It specifies what pages crawlers are allowed to download. Before attempting to crawl a web site, a crawler should check its corresponding robots.txt first and follow its rules.
To avoid repeat downloads of robots.txt file, we cache the results of the file. The file is downloaded and saved to cache periodically.
Besides robots.txt, performance optimization is another important concept we will cover for the HTML downloader.
Performance Optimization
Distributed Crawl
To achieve high performance, crawl jobs are distributed into multiple servers, and each server runs multiple threads. The URL space is partitioned into smaller pieces; so, each downloader is responsible for a subset of the URLs.
Cache DNS Resolver
DNS Resolver is a bottleneck for crawlers because DNS requests might take time due to the synchronous nature of many DNS interfaces. DNS response time ranges from 10ms to 200ms. Once a request to DNS is carried out by a crawler thread, other threads are blocked until the first request is completed. Maintaining our DNS cache to avoid calling DNS frequently is an effective technique for speed optimization. Our DNS cache keeps the domain name to IP address mapping and is updated periodically by cron jobs.
Locality
Distribute crawl servers geographically. When crawl servers are closer to website hosts, crawlers experience faster download time. Design locality applies to most of the system components: crawl servers, cache, queue, storage, etc.
Short timeout
Some web servers respond slowly or may not respond at all. To avoid long wait time, a maximal wait time is specified. If a host does not respond within a predefined time, the crawler will stop the job and crawl some other pages.
Robustness
Besides performance optimization, robustness is also an important consideration. We present a few approaches to improve the system robustness:
- Consistent hashing: This helps to distribute loads among downloaders. A new downloader server can be added or removed using consistent hashing.
- Save crawl states and data: To guard against failures, crawl states and data are written to a storage system. A disrupted crawl can be restarted easily by loading saved states and data.
- Exception handling: Errors are inevitable and common in a large-scale system. The
Extensibility
As almost every system evolves, one of the design goals is to make the system flexible enough to support new content types. The crawler can be extended by plugging in new modules:
Detect and avoid problematic content
Reducdant content
As discussed previously, nearly 30% of the web pages are duplicates. Hashes or checksums help to detect duplication
Spider traps
A spider trap is a web page that causes a crawler in an infinite loop. For instance, an infinite deep directory structure is listed as follows: www.spidertrapexample.com/foo/bar/foo/bar/foo/bar/…
Websites containing spider traps are easy to identify due to an unusually large number of web pages discovered on such websites. It is hard to develop automatic algorithms to avoid spider traps; however, a user can manually verify and identify a spider trap, and either exclude those websites from the crawler or apply some customized URL filters.
Data noise
Some of the contents have little or no value, such as advertisements, code snippets, spam URLs, etc. Those contents are not useful for crawlers and should be excluded if possible.
Wrap up
- Server-side rendering: Numerous websites use scripts like JavaScript, AJAX, etc to generate links on the fly. If we download and parse web pages directly, we will not be able to retrieve dynamically generated links. To solve this problem, we perform server-side rendering (also called dynamic rendering) first before parsing a page [12].
- Filter out unwanted pages: With finite storage capacity and crawl resources, an anti-spam component is beneficial in filtering out low quality and spam pages [13] [14].
- Database replication and sharding: Techniques like replication and sharding are used to improve the data layer availability, scalability, and reliability.
- Horizontal scaling: For large scale crawl, hundreds or even thousands of servers are needed to perform download tasks. The key is to keep servers stateless.
- Availability, consistency, and reliability: These concepts are at the core of any large system’s success. We discussed these concepts in detail in Chapter 1. Refresh your memory on these topics.
- Analytics: Collecting and analyzing data are important parts of any system because data is key ingredient for fine-tuning.
References
[^1] By Christopher Olston, Marc Najork: Web Crawling.