Claudio Scordino Ph.D. Student May 2004 Crawling the Web: problems and techniques Computer Science Department - University of Pisa Outline • Introduction • Crawler architectures - Increasing the throughput • What pages we do not want to fetch - Spider traps - Duplicates - Mirrors 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… …it‟s a complex and underestimate activity 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 Famous Crawlers • GoogleBot (Stanford, Google) C/C++ • WebBase (Stanford) • HiWE: Hidden Web Exposer (Stanford) • Heritrix (Internet Archive) http://www.crawler.archive.org/ 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 Crawler Architecture PARSER HREFs Citations extractor and normalizer URL Filter Duplicate URL Eliminator SCHEDULER Interne Crawl Load t Monitor DNS HTTP Metadata RETRIEVERS seed URLs Hosts URL FRONTIER 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 Crawler Architecture Per-server queues Robots Mercator’s scheduler FRONT-END: prioritizes URLs with a value Queues between 1 and k containing URLs of only a single host BACK-END: ensures politeness Specifies (no server overload) when a server may be contacted again Increasing the throughput Parallelize the process to fetch many pages at the same time (~thousands per second). Possible levels of parallelization: DNS HTTP Parsing Domain Name resolution Problem: DNS requires time to resolve the server hostname 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 thread‟s elapsed time from 87% to 25% 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 Crawler Architecture Async DNS DNS Cache prefetch DNS resolver client Per-server queues Robots Page retrieval Problem: HTTP requires time to fetch a page 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 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) 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 IXE Crawler thread Table <UrlInfo> Citations synch. obj memory disk CrawlInfo Crawler Parser Host Cache queues select() Retriever Feeder Retriever Scheduler Retriever select() UrlEnumerator Hosts Robots IXE Parser • Problem: parsing requires 30% of execution time • Possible solution: distributed parsing IXE Parser Table <UrlInfo> Citations DocID1 URL1 DocID2 URL2 URL1 URL2 URL1 URL2 URL Table Parser Manager Cache (“Crawler”) DocID1 DocID2 URL1 URL2 A distributed parser MISS URL2 Hash(URL2) Table 1 → Table 1 Hash (URL1) Manager Manager1 URL1 <UrlInfo> → ? URL2 Manager2 Parser 1 URL1 DocID2 HIT Table 2 Table 2 Manager <UrlInfo> DocID1 URL1 URL2 New Parser N DocID Sched () → Parser1 URL1 Cache Citations Scheduler URL2 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 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 DocID Number parser Reply of parsers Outlinks DocID per page Request Byte → bit • Using 100Mbps : X = 1226 pages per second What we don’t 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 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 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 Avoid duplicates • Problem almost nonexistent in classic IR • Duplicate content • wastes resources (index space) • annoys users 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 http://www.cocacola.com 188.8.131.52 http://www.coke.com • Rely on canonical hostnames (CNAMEs) provided by DNS 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 Already visited URLs 1. Bloom Filter • Probabilistic data structure for set membership testing BIT VECTOR 0/1 hash function 1 0/1 hash function 2 URL hash function n 0/1 • Problem: false positivs • new URLs marked as already seen Already visited URLs 2. URL hashing • MD5 128 bits URL MD5 Digest • 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 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 24 bits 40 bits Hostname+Port Path 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! Content based techniques 2. Hashing • A digest associated with each crawled page • Used in Mercator • Cost: one seek in the index for each new crawled page Problem: pages could have minor syntatic differences ! • site mantainer‟s name, latest update • anchors modified • different formatting 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 Content based techniques Sentence: a rose is a rose is a rose Tokens: a rose is a rose is a rose a,rose,is,a rose,is,a,rose 4-shingles: is,a,rose,is a,rose,is,a rose,is,a,rose S(d,4): a,rose,is,a rose,is,a,rose is,a,rose,is Content based techniques • Each token = 32 bit w-shingle=320 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 Content based techniques • Resemblance of documents d1 and d2: S (d1, w) S (d 2, w) r (d1, d 2) S (d1, w) S (d 2, w) Jaccard coefficient • Eliminate pages too similar (pages whose resem- blance value is close to 1) Mirrors URL http://www.research.digital.com/SRC/ access hostname path method • Precision = relevant retrieved docs / retrieved docs 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 Mirrors a) Hostname matching 27% • Terms: substrings of the hostname • Term weighting: log(len(t )) 1 log( df (t )) len(t)= number of segments obtained by breaking the term at „.‟ characters • This weighting favours substrings composed by many segments very specific Mirrors b) Full path matching 59% • Terms: entire paths • Term weighting: mdf 1 log( ) mdf = max df(t) df (t ) t∈collection +19% 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 Mirrors c) Positional word bigram matching 72% • 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 Mirrors conferences/d299/advanceprogram.html conferences d* advanceprogram html conferences_d*_0 Positional d*_advanceprogram_1 Word advanceprogram_html_2 Bigrams Mirrors 2. Host connectivity based 45% • 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 host‟s node points to 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. 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.
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