1. Claudio Scordino Ph.D. Student Crawling the Web: problems and techniques May 2004

2. Outline Introduction Crawler architectures - Increasing the throughput What pages we do not want to fetch - Spider traps - Duplicates - Mirrors

3. Introduction Job of a crawler (or spider): fetching the Web pages to a computer where they will be analyzed The algorithm is conceptually simple, but… …its a complex and underestimate activity

4. Famous Crawlers Mercator (Compaq, Altavista) Java Modular (components loaded dynamically) Priority-based scheduling for URLs downloads - The algorithm is a pluggable component Different processing modules for different contents Checkpointing - Allows the crawler to recover its state after a failure - In a distributed crawler is performed by the Queen

5. Famous Crawlers GoogleBot (Stanford, Google) C/C++ WebBase (Stanford) HiWE: Hidden Web Exposer (Stanford) Heritrix (Internet Archive) http://www.crawler.archive.org/

6. Famous Crawlers Sphinx Java Visual and interactive environment Relocatable: capable of executing on a remote host Site-specific - Customizable crawling - Classifiers: site-specific content analyzers 1.Links to follow 2.Parts to process -Not scalable

7. Crawler Architecture Load Monitor SCHEDULER Crawl Metadata Duplicate URL Eliminator URL Filter Hosts HREFs extractor and normalizer PARSER Interne t seed URLs URL FRONTIER Citations RETRIEVERS DNS HTTP

8. Web masters annoyed Web Server administrators could be annoyed by: 1. Server overload -Solution: per-server queues 2. Fetching of private pages -Solution: Robot Exclusion Protocol -File: /robots.txt

9. Crawler Architecture Per-server queues Robots

10. Mercators scheduler BACK-END: ensures politeness (no server overload) FRONT-END: prioritizes URLs with a value between 1 and k Queues containing URLs of only a single host Specifies when a server may be contacted again

11. Increasing the throughput Possible levels of parallelization: Parallelize the process to fetch many pages at the same time (~thousands per second). DNS HTTP Parsing

12. Domain Name resolution Problem: DNS requires time to resolve the server hostname

13. Domain Name resolution 1.Asynchronous DNS resolver: Concurrent handling of multiple outstanding requests Not provided by most UNIX implementations of gethostbyname GNU ADNS library http://www.chiark.greenend.org.uk/~ian/adns/ Mercator reduced the threads elapsed time from 87% to 25%

14. Domain Name resolution 2.Customized DNS component: Caching server with persistent cache largely residing in memory Prefetching Hostnames extracted by HREFs and requests made to the caching server Does not wait for resolution to be completed

15. Crawler Architecture Per-server queues Robots Async DNS prefetch DNS Cache DNS resolver client

16. Page retrieval 1.Multithreading Blocking system calls (synchronous I/O) pthreads multithreading library Used in Mercator, Sphinx, WebRace Sphinx uses a monitor to determine the optimal number of threads at runtime Mutual exclusion overhead Problem: HTTP requires time to fetch a page

17. Page retrieval 2.Asynchronous sockets not blocking the process/thread select monitors several sockets at the same time Does not need mutual exclusion since it performs a serialized completion of threads (i.e. the code that completes processing the page is not interrupted by other completions). Used in IXE (1024 connection at once)

18. Page retrieval 3.Persistent connection Multiple documents requested on a single connection Feature of HTTP 1.1 Reduce the number of HTTP connection setups Used in IXE

19. IXE Crawler

20. IXE Parser Problem: parsing requires 30% of execution time Possible solution: distributed parsing

21. IXE Parser URL1 URL2 Cache Parser URL Table Manager (Crawler) Table Citations URL1 URL2 URL1 URL2 DocID1 DocID2 DocID1 DocID2 URL1 URL2

22. A distributed parser Cache Scheduler Citations Parser 1 Table 1 Table 1 Manager Parser N Table 2 Table 2 Manager Hash (URL1) Manager2 URL1 URL2 Sched () Parser1 URL1 URL2 URL1 URL2 URL1 URL2 DocID2 DocID1 Hash(URL2) Manager1 ? New DocID HIT MISS

23. A distributed parser Does this solution scale? -High traffic on the main link Suppose that: -Average page size = 10KB -Average out-links per page = 10 -URL size = 40 characters (40 bytes) -DocID size = 5 byte X = throughput (pages per second) N = number of parsers

24. A distributed parser Bandwidth for web pages: -X*10*1024*8 = 81920*X bps Bandwidth for messages (hit): -X/N * 10 * (40+5) * 8 * N = 3600*X bps Pages per parser Outlinks per page DocID Reply Byte bit Number of parsers DocID Request Using 100Mbps : X = 1226 pages per second

25. What we dont want to fetch 1.Spider traps 2.Duplicates 2.1 Different URLs for the same page 2.2 Already visited URLs 2.3 Same document on different sites 2.4 Mirrors At least 10% of the hosts are mirrored

26. Spider traps Spider trap: hyperlink graph constructed unintentionally or malevolently to keep a crawler trapped 1.Infinitely deep Web sites Problem: using CGI is possible to generate an infinite number of pages Solution: check of the URL length

27. Spider traps 2.Large number of dummy pages Example: http://www.troutbums.com/Flyfactory/flyfactory/flyfactory/hatchlin e/hatchline/flyfactory/hatchline/flyfactory/hatchline/flyfactory/flyfa ctory/flyfactory/hatchline/flyfactory/hatchline/ Solution: disable crawling a guard removes from consideration any URL from a site which dominates the collection

28. Avoid duplicates Problem almost nonexistent in classic IR Duplicate content wastes resources (index space) annoys users

29. Virtual Hosting Problem: Virtual Hosting Allows to map different sites to a single IP address Could be used to create duplicates Feature of HTTP 1.1 Rely on canonical hostnames (CNAMEs) provided by DNS http://www.cocacola.com http://www.coke.com 129.33.45.163

30. Already visited URLs Problem: how to recognize an already visited URL ? The page is reachable by many paths We need an efficient Duplicate URL Eliminator

31. Already visited URLs 1.Bloom Filter Probabilistic data structure for set membership testing Problem: false positivs new URLs marked as already seen URL hash function 1 hash function 2 hash function n BIT VECTOR 0/1

32. Already visited URLs 2.URL hashing MD5 Using a 64-bit hash function, a billion URLs requires 8GB -Does not fit in memory -Using the disk limit the crawling rate to 75 downloads per second MD5 URL Digest 128 bits

33. Already visited URLs 3.two-level hash function The crawler is luckily to explore URLs within the same site Relative URLs create a spatiotemporal locality of access Exploit this kind of locality using a cache PathHostname+Port 24 bits 40 bits

34. Content based techniques Problem: how to recognize duplicates basing on the page contents? 1.Edit distance Number of replacements required to transform one document to the other Cost: l1*l2, where l1 and l2 are the lenghts of the documents: Impractical!

35. Content based techniques Problem: pages could have minor syntatic differences ! site mantainers name, latest update anchors modified different formatting 2.Hashing A digest associated with each crawled page Used in Mercator Cost: one seek in the index for each new crawled page

36. Content based techniques 3.Shingling Shingle (or q-gram): contiguous subsequence of tokens taken from document d representable by a fixed length integer w-shingle: shingle of width w S(d,w): w-shingling of document d unordered set of distinct w-shingles contained in document d

37. Content based techniques a rose is a rose is a rose Sentence: Tokens: a rose is a rose isa rose a,rose,is,a rose,is,a,rose is,a,rose,is a,rose,is,a rose,is,a,rose 4-shingles: S(d,4): a,rose,is,a rose,is,a,rose is,a,rose,is

38. Content based techniques Each token = 32 bit w = 10 (suitable value) S(d,10) = set of 320-bits numbers We can hash the w-shingles and keep 500 bytes of digests for each document w-shingle=320 bit

39. Content based techniques Resemblance of documents d1 and d2: Jaccard coefficient Eliminate pages too similar (pages whose resem- blance value is close to 1)

40. Mirrors http://www.research.digital.com/SRC/ access method hostname path URL Precision = relevant retrieved docs / retrieved docs

41. Mirrors 1.URL String based Vector Space model: term vector matching to compute the likelyhood that a pair of hosts are mirrors terms with df(t) < 100

42. Mirrors a)Hostname matching Terms: substrings of the hostname Term weighting: len(t)= number of segments obtained by breaking the term at. characters This weighting favours substrings composed by many segments very specific 27%

43. Mirrors b)Full path matching Terms: entire paths Term weighting: Connectivity based filtering stage: Idea: mirrors share many common paths Testing for each common path if it has the same set of out-links on both hosts Remove hostnames from local URLs mdf = max df(t) t collection 59% +19%

44. Mirrors c)Positional word bigram matching Terms creation: Break the path into a list of words by treating / and. as breaks Eliminate non-alphanumeric characters Replace digits with * (effect similar to stemming) Combine successive pairs of words in the list Append the ordinal position of the first word 72%

45. Mirrors conferences/d299/advanceprogram.html conferences d* advanceprogram html conferences_d*_0 d*_advanceprogram_1 advanceprogram_html_2 Positional Word Bigrams

46. Mirrors 2.Host connectivity based Consider all documents on a host as a single large document Graph: host node document on host a pointing to a document on host B directed edge from A to B Idea: two hosts are likely to be mirrors if their nodes point to the same nodes Term vector matching -Terms: set of nodes that a hosts node points to 45%

47. References S. Chakrabarti and M. Kaufmann, Mining the Web: Analysis of Hypertext and Semi Structured Data, 2002. Pages 17-43,71-72. S.Brin and L.Page, The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th World Wide Web Conference (WWW7), 1998. A.Heydon and M.Najork, Mercator: A scalable, extensible Web crawler, World Wide Web Conference, 1999. K.Bharat, A.Broder, J.Dean, M,R.Henzinger, A comparison of Techniques to Find Mirrored Hosts on the WWW, Journal of the American Society for Information Science, 2000.

48. References A.Heydon and M.Najork, High performance Web Crawling, Technical Report, SRC Research Report, 173, Compaq Systems Research Center, 26 September 2001. R.C.Miller and K.Bharat, SPHINX: a framework for creating personal, site-specific web crawlers, Proceedings of the 7th World-Wide Web Conference, 1998. D. Zeinalipour-Yazti and M. Dikaiakos. Design and Implementation of a Distributed Crawler and Filtering Processor, Proceedings of the 5th Workshop on Next Generation Information Technologies and Systems (NGITS 2002), June 2002.