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Need Billions of Web Pages? Don't bother Crawling.

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Need Billions of Web Pages? Don't bother Crawling.

How to get started using free, preprocessed CommonCrawl web crawl datasets that you could use for machine learning, natural language processing, and more.

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How Big Did You Say?

I am often contacted by prospective clients to help them crawl the web on a very large scale or find questions such as this one on StackOverflow. What people want to achieve with web data varies greatly from one case to the next: some need to extract specific data from as many pages as possible, some want to build search engines, while others wish to test the accuracy of a machine learning model on real data.  

Luckily, there are resources available for large scale web crawling, both on the platform side (e.g. Amazon Web Services) and the software side (StormCrawler, Apache Nutch), however large scale crawling (think billions of pages and hundreds of servers) is costly, complex and time-consuming. 

CommonCrawl to the Rescue

CommonCrawl is a non-profit organization which provides web crawl data for free. Their datasets are used by various organizations, both in academia and industry, as can be seen on the examples page. The applications range from machine learning to natural language processing or computational linguistics. For instance, at DigitalPebble, we have used the CommonCrawl dataset for some of our clients for information extraction (phone numbers and contact details that are publicly available), machine learning (to check the accuracy of a classifier on real, big, messy data) as well as lexicometry (get frequencies of anchor tags). I should also mention that CommonCrawl themselves are clients of ours: we developed Apache Nutch resources for them and also ran their February 2016 web crawl. We also contributed to the set up of their news crawl (see below).
CommonCrawl provides two types of datasets, both hosted on Amazon S3 as part of the Amazon Public Datasets program.

Web Crawl

The main dataset is released on a monthly basis and consists of billions of web pages stored in WARC format on AWS S3. The latest release had 3.08 billion web pages and about 250 TiB of uncompressed content: that’s a lot of data to play with, and it comes for free!

These pages are mainly HTML documents, but there are also a few PDFs and images. Until recently, the coverage was very US-centric and the datasets contained mostly the same URLs from one release to the next, but this is no longer the case as European domain names and the top 1 million Alexa domains are crawled (see details here). Interestingly, CommonCrawl usesApache Nutch to generate their datasets, albeit with a few home-made modifications.
Basically, each release is split into 100 segments. Each segment has three types of files: WARC, WAT, and WET. As explained on the Get Started page:

  • WARC files store the raw crawl data.
  • WAT files store computed metadata for the data stored in the WARC.
  • WET files store extracted plaintext from the data stored in the WARC.

Note that WAT and WET are in the WARC format too! In fact, the WARC format is nothing more than an envelope with metadata and content. In the case of the WARC files, that content is the HTTP requests and responses, whereas, for the WET files, it is simply the plain text extracted from the WARCs. The WAT files contain a JSON representation of metadata extracted from the WARCs, e.g. title, links etc.

So, not only has CommonCrawl given you loads of web data for free, they’ve also made your life easier by preprocessing the data for you. For many tasks, the content of the WAT or WET files will be sufficient and you won’t have to process the WARC files.

This should not only help you simplify your code but also make the whole processing faster. We recently ran an experiment on CommonCrawl where we needed to extract anchor text from HTML pages. We initially wrote some MapReduce code to extract the binary content of the pages from their WARC representation, processed the HTML with JSoup and reduced on the anchor text. Processing a single WARC segment took roughly 100 minutes on a 10-node EMR cluster. We then simplified the extraction logic, took the WAT files as input, and the processing time dropped to 17 minutes on the same cluster. This gain was partly due to not having to parse the web pages, but also to the fact that WAT files are a lot smaller than their WARC counterparts.

News Dataset

Unlike the main web crawl, the news dataset is released continuously. As its name suggests, it consists exclusively of news pages and articles as described on CommonCrawl. There are between 3 to 5 WARC files (1GB each) generated daily, corresponding to 300 to 400 thousand pages. In total, over 25 million news pages have been crawled to date. The dataset contains WARC files only so you will have to write some code to extract the text and metadata yourself.

The news dataset is generated using our very own StormCrawler and the code of the news crawl is publicly available on CommonCrawl’s GitHub account.


The Get Started page on the CommonCrawl website contains useful pointers to libraries and code in various programming languages to process the datasets. There is also a list of tutorials and presentations.

It is also worth noting that CommonCrawl provides an index per release, allowing you to search for URLs (including wildcards) and retrieve the segment and offset therein where the content of the URL is stored, e.g.:

{ "urlkey": "org,apache)/", "timestamp": "20170220105827", "status": "200", "url": "http://apache.org/", "filename": "crawl-data/CC-MAIN-2017-09/segments/1487501170521.30/warc/CC-MAIN-20170219104610-00206-ip-10-171-10-108.ec2.internal.warc.gz", "length": "13315", "mime": "text/html", "offset": "14131184", "digest": "KJREISJSKKGH6UX5FXGW46KROTC6MBEM" }

This is useful but only if you are interested in a limited number of URLs which you know in advance. In many cases, what you know in advance is what you want to extract, not where it will be extracted from. For situations such as these, you will need distributed batch-processing using MapReduce in Apache Hadoop or Apache Spark.

As hinted above, I tend to use AWS EMR (ElasticMapReduce). Running the code in AWS makes sense as the data sets are stored on S3 so access is fast and there is no transfer cost, also the EC2 instances will have the credentials pre-set so there is no additional configuration needed to access the data. There is an additional cost in using EMR but this saves me from having to configure Hadoop. In addition, I usually store the output of the reduce steps on an S3 bucket so that nothing is kept on HDFS and I can use spot instances to keep the cost down. If they get terminated, nothing is lost. Of course, other platforms (Azure, Google) or alternatives to EMR (Hortonworks HDP) can be used instead.

Finally, I implement the logic with MapReduce in Java thanks to libraries such as warc-hadoop which deals with the low-level access to WARC files. If you need to process CommonCrawl with existing frameworks and libraries such as Apache UIMA, Tika, or GATE, our good old open source project Behemoth could help as it can ingest WARCs too!


As we’ve seen, CommonCrawl is an awesome resource and should be the first thing you try before embarking on web scale crawling. It is large, it is free, it is relatively easy to process, and a lot of effort has been put into making your life easier.

Web data is big, messy, and often doesn’t give the results you expect. Processing the CommonCrawl dataset is a great way of checking your assumptions at a fraction of the cost of a web scale crawl. It also saves you time, as the fetch politeness has been done for you. But on the minus side, you will be able to process only content allowed by robots.txt directives as CommonCrawl’s crawler is polite (but then yours should be too).

I hope you will give CommonCrawl a try and if you find it useful, you can donate to the project.

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web crawling ,datasets ,big data

Published at DZone with permission of Julien Nioche, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.


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