root/gs3-extensions/maori-lang-detection/MoreReading/CommonCrawl.txt @ 33409

Revision 33409, 13.8 KB (checked in by ak19, 12 months ago)

Forgot to commit 2 files with links and shuffling some links around into the correct files after moving between computers.

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1
2WET FILES:
3
4https://stackoverflow.com/questions/16649535/access-a-common-crawl-aws-public-dataset/25297965#25297965
5
6
7http://commoncrawl.org/2019/07/june-2019-crawl-archive-now-available/
8            File List   #Files  Total Size Compressed (TiB)
9    WET files   CC-MAIN-2019-26/wet.paths.gz    56000   7.59
10
11
12http://commoncrawl.org/2015/04/announcing-the-common-crawl-index/
13(Instructions)
14
15https://gist.github.com/svemir/4207353
16(Hadoop related) A Common Crawl Experiment
17
18https://gist.github.com/Smerity/afe7430fdb4371015466
19
20 Extract just the text from Common Crawl WARC WET files
21 
22https://stackoverflow.com/tags/common-crawl/hot?filter=all
23
24https://stackoverflow.com/questions/45920527/get-offset-and-length-of-a-subset-of-a-wat-archive-from-common-crawl-index-serve/46152773#46152773
25
26
27"The Common Crawl index does not contain offsets into WAT and WET files. So, the only way is to search the whole WAT/WET file for the desired record/URL. Eventually, it would be possible to estimate the offset because the record order in WARC and WAT/WET files is the same."
28
29https://dmorgan.info/posts/common-crawl-python/
30https://groups.google.com/forum/#!topic/common-crawl/pdI3w09AAbQ
31
32Example:
33WARC:
34tikauka:[142]/Scratch/anupama/maori-lang-detection>wget https://commoncrawl.s3.amazonaws.com/crawl-data/CC-MAIN-2019-30/segments/1563195526237.47/crawldiagnostics/CC-MAIN-20190719115720-20190719141720-00077.warc.gz
35WET:
36tikauka:[142]/Scratch/anupama/maori-lang-detection>wget https://commoncrawl.s3.amazonaws.com/crawl-data/CC-MAIN-2019-30/segments/1563195526237.47/wet/CC-MAIN-20190719115720-20190719141720-00508.warc.wet.gz
37tikauka:[142]/Scratch/anupama/maori-lang-detection>gunzip CC-MAIN-20190719115720-20190719141720-00508.warc.wet.gz
38
39
40--------------------------------------------
41http://webdatacommons.org/
42
43https://dzone.com/articles/need-billions-of-web-pages-dont-bother-crawling
44
45    Ran Geva 2017-04-09
46    Like (0)
47
48    Excellent article! CommonCrawl is an amazing resourouce. You should also check out webdatacommons.org that is using their data and extract structured data (using RDFa, Microdata..)
49
50    If I may add a shameless plug here and tell you about Webhose.io [PAYWARE/SERVICES]. We provide an API to structured web data. The idea is the same as the one you presented. Instead of crawling the web, we already crawl millions of sites, download the data, structure and organize it so anyone can easily consume it and plug into their own system.
51    Reply
52
53
54https://stackoverflow.com/questions/12097848/finding-all-domains-of-a-country
55
56    -> http://urlsearch.commoncrawl.org/
57    -> http://index.commoncrawl.org/
58    -> INSTRUCTIONS: https://groups.google.com/forum/#!msg/common-crawl/3QmQjFA_3y4/vTbhGqIBBQAJ
59
60
61Go to: http://index.commoncrawl.org/
62Grab the newest gzipped archive file.
63Then open it and find the cluster.idx file listed in it.
64Copy its relative URL, prefix with https://commoncrawl.s3.amazonaws.com/
65
66THEN:
67
68wharariki:[101]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>wget https://commoncrawl.s3.amazonaws.com/cc-index/collections/CC-MAIN-2019-26/indexes/cluster.idx
69    --2019-07-29 17:40:45--  https://commoncrawl.s3.amazonaws.com/cc-index/collections/CC-MAIN-2019-26/indexes/cluster.idx
70    Resolving commoncrawl.s3.amazonaws.com (commoncrawl.s3.amazonaws.com)... 52.216.8.171
71    Connecting to commoncrawl.s3.amazonaws.com (commoncrawl.s3.amazonaws.com)|52.216.8.171|:443... connected.
72    HTTP request sent, awaiting response... 200 OK
73    Length: 125059234 (119M) [binary/octet-stream]
74    Saving to: ‘cluster.idx’
75
76    cluster.idx                       100%[============================================================>] 119.27M  8.51MB/s    in 15s     
77
78    2019-07-29 17:41:01 (7.83 MB/s) - ‘cluster.idx’ saved [125059234/125059234]
79
80wharariki:[102]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>grep '^nz,' cluster.idx | cut -f2 | uniq
81cdx-00237.gz
82cdx-00238.gz
83
84Prefix "https://commoncrawl.s3.amazonaws.com/cc-index/collections/CC-MAIN-2019-26/indexes/" to the listed gz files and wget them:
85
86    https://commoncrawl.s3.amazonaws.com/cc-index/collections/CC-MAIN-2019-26/indexes/cdx-00237.gz
87    https://commoncrawl.s3.amazonaws.com/cc-index/collections/CC-MAIN-2019-26/indexes/cc-index/collections/CC-MAIN-2019-26/indexes/cdx-00238.gz
88
89
90
91Unzip those, and we have all URLs with TLD .nz:
92    wharariki:[131]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>gunzip cdx-00237.gz
93    wharariki:[132]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>gunzip cdx-00238.gz
94
95
96The first of these files includes Norwegian TLDs (start with "no,") and the second gz file includes TLDs that start with "org,".
97So extract just those that start with "^nz," [https://www.unix.com/shell-programming-and-scripting/176608-how-copy-lines-starts-either-3-4-into-new-file.html].
98
99    wharariki:[107]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>egrep "^nz," cdx-00238 > nz-only-TLDs-from-237-238.txt
100    wharariki:[108]/Scratch/ak19/heritrix/heritrix-3.4.0-SNAPSHOT>egrep "^nz," cdx-00238 >> nz-only-TLDs-from-237-238.txt
101
102
103Checking the abacusinstitute.ac.nz is also in the current June 2019 list:
104    egrep "ac,abacusinstitute" nz-only-TLDs-from-237-238.txt
105
106
107OTHER:
108https://www.tutorialspoint.com/hadoop/hadoop_mapreduce
109http://stormcrawler.net/
110http://storm.apache.org/getting-help.html
111
112
113https://dzone.com/articles/need-billions-of-web-pages-dont-bother-crawling
114Basically, 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:
115
116    WARC files store the raw crawl data.
117    WAT files store computed metadata for the data stored in the WARC.
118    WET files store extracted plaintext from the data stored in the WARC.
119
120Note 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.
121
122
123Resources
124
125The 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.
126
127It 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.:
128
129    { "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" }
130
131
132This 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.
133
134
135https://www.forbes.com/sites/kalevleetaru/2017/09/28/common-crawl-and-unlocking-web-archives-for-research/#7067d4313b83
136One large web archive has bucked this trend and stood alone among its peers: Common Crawl. Similar to other large web archiving initiatives like the Internet Archive, Common Crawl conducts regular web wide crawls of the open web and preserves all of the content it downloads in the standard WARC file format. Unlike many other archives, it focuses primarily on preserving HTML web pages and does not archive images, videos, JavaScript files, CSS stylesheets, etc. Its goal is not to preserve the exact look and feel of a website on a given snapshot in time, but rather to collect a vast cross section of HTML web pages from across the web in a single place to enable large-scale data mining at web scale.
137...
138The project excludes sites which have robots.txt exclusion policies, following the historical policy of many other web archives, though it is worth noting that the Internet Archive earlier this year began slowly phasing out its reliance on such files due to their detrimental effect on preservation completeness. Common Crawl also allows sites to request removal from their index. Other than these cases, Common Crawl attempts to crawl as much of the remaining web as possible, aiming for a representative sample of the open web.
139...
140Ms. Crouse [Director of Common Crawl] noted the risk adverse nature of the web archiving community as a whole (historically many adhered and still adhere to a strict “opt in” policy requiring prior approval before crawling a site) and the unwillingness of many archives to modernize their thinking on copyright and to engage more closely with the legal community in ways that could help them expand fair use horizons. In particular, she noted “since we [in the US] are beholden to the Copyright Act, while living in a digital age, many well-intentioned organizations devoted to web science, archiving, and information provision may benefit from a stronger understanding of how copyright is interpreted in present day, and its hard boundaries” and that “many talented legal advisers and groups are interested in the precedent-setting nature of this topic; some are willing to work Pro Bono.”
141...
142Returning to the difference between Common Crawl’s datasets and traditional preservation-focused web archiving, Ms. Crouse emphasized that they capture only HTML pages and exclude multimedia content like images, video and other dynamic content.
143
144She noted that a key aspect of their approach to fair use is that web pages are intended for consumption by human beings one at a time using a web browser, while Common Crawl concatenates billions of pages together in the specialized WARC file format designed for machine data mining. Specifically, “Common Crawl does not offer separate/individual web pages for easy consumption. The three data formats that are provided include text, metadata, and raw data, and the data is concatenated” and “the format of the output is not a downloaded web page. The output is in WARC file format which contains the components of a page that are beneficial to machine-level analysis and make for space- efficient archiving (essentially: header, text, and some metadata).”
145
146As Ms. Crouse put it, “this is big data intended for machine learning/readability. Further, our intention for its use is for public benefit i.e. to encourage research and innovation, not direct consumption.” She noted that “from the layperson’s perspective, it is not at all trivial at present to extract a specific website’s content (that is, text) from a Common Crawl dataset. This task generally requires one to know how to install and run a Hadoop cluster, among other things. This is not structured data. Further it is likely that not all pages of that website will be included (depending on the parameters for depth set for the specific crawl).” This means that “the bulk of [Common Crawl’s] users are from the noncommercial, educational, and research sectors. At a higher level, it’s important to note that we provide a broad and representative sample of the web, in the form of web crawl data, each month. No one really knows how big the web is, and at present, we limit our monthly data publication to approximately 3 billion pages.”
147
148
149Common Crawl believes it addresses this through the fact that its archive represents only a sample of each website crawled, rather than striving for 100% coverage. Specifically, Ms. Crouse noted that “at present, [crawls are] in monthly increments that are discontinuous month-to-month. We do only what is reasonable, necessary, and economical to achieve a representative sample. For instance, we limit the number of pages crawled from any given domain so, for large content owners, it is highly probable that their content, if included in a certain month’s crawl data, is not wholly represented and thus not ideal for mining for comprehensive results 
 if the content owner is not a large site, or in a niche market, their URL is less likely to be included in the seeds in the frontier, and, since we limit depth (# of links followed) for the sake of both economy and broader representative web coverage, 'niche' content may not even appear in a given month’s dataset.”
150
151To put it another way, Common Crawl’s mission is to create a “representative sample” of the web at large by crawling a sampling of pages and limiting the number of pages from each site they capture. Thus, their capture of any given site will represent a discontinuous sampling of pages that can change from month to month. A researcher wishing to analyze a single web site in its entirety would therefore not be able to turn to Common Crawl and would instead have to conduct their own crawl of the site or turn to a commercial aggregator that partners with the content holder to license the complete contents of the site.
152
153In Common Crawl’s view this is a critical distinction that sets it apart from both traditional web archiving and the commercial content aggregators that generate data mining revenue for content owners. By focusing on creating a “representative sample” of the web at large, rather than attempting to capture a single site in its entirety (and in fact ensuring that it does not include more than a certain number of pages per site), the crawl self-limits itself to being applicable only to macro-level research examining web scale questions. Such “web scale” questions cannot be answered through any existing open dataset and by incorporating specific design features Common Crawl ensures that more traditional research questions, like data mining the entirety of a single site, which might be viewed as redistribution of that site or competing with its owner’s ability to license its content for data mining, is simply not possible.
154------------
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