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Basic crawler operation

Basic crawler operation
Introduction to Information Retrieval Introduction to Information Retrieval Crawling and web indexes www.ThesisScientist.comIntroduction to Information Retrieval Previous lecture recap  Web search  Spam  Size of the web  Duplicate detection  Use Jaccard coefficient for document similarity  Compute approximation of similarity using sketches www.ThesisScientist.comIntroduction to Information Retrieval Today’s lecture  Crawling www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2 Basic crawler operation  Begin with known “seed” URLs  Fetch and parse them  Extract URLs they point to  Place the extracted URLs on a queue  Fetch each URL on the queue and repeat www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2 Crawling picture URLs crawled and parsed Unseen Web URLs frontier Seed pages Web www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.1.1 Simple picture – complications  Web crawling isn’t feasible with one machine  All of the above steps distributed  Malicious pages  Spam pages  Spider traps – incl dynamically generated  Even nonmalicious pages pose challenges  Latency/bandwidth to remote servers vary  Webmasters’ stipulations  How “deep” should you crawl a site’s URL hierarchy  Site mirrors and duplicate pages  Politeness – don’t hit a server too often www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.1.1 What any crawler must do  Be Polite: Respect implicit and explicit politeness considerations  Only crawl allowed pages  Respect robots.txt (more on this shortly)  Be Robust: Be immune to spider traps and other malicious behavior from web servers www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.1.1 What any crawler should do  Be capable of distributed operation: designed to run on multiple distributed machines  Be scalable: designed to increase the crawl rate by adding more machines  Performance/efficiency: permit full use of available processing and network resources www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.1.1 What any crawler should do  Fetch pages of “higher quality” first  Continuous operation: Continue fetching fresh copies of a previously fetched page  Extensible: Adapt to new data formats, protocols www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.1.1 Updated crawling picture URLs crawled and parsed Unseen Web Seed Pages URL frontier Crawling thread www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2 URL frontier  Can include multiple pages from the same host  Must avoid trying to fetch them all at the same time  Must try to keep all crawling threads busy www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2 Explicit and implicit politeness  Explicit politeness: specifications from webmasters on what portions of site can be crawled  robots.txt  Implicit politeness: even with no specification, avoid hitting any site too often www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Robots.txt  Protocol for giving spiders (“robots”) limited access to a website, originally from 1994  www.robotstxt.org/wc/norobots.html  Website announces its request on what can(not) be crawled  For a server, create a file /robots.txt  This file specifies access restrictions www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Robots.txt example  No robot should visit any URL starting with "/yoursite/temp/", except the robot called “searchengine": Useragent: Disallow: /yoursite/temp/ Useragent: searchengine Disallow: www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Processing steps in crawling  Pick a URL from the frontier Which one  Fetch the document at the URL  Parse the URL  Extract links from it to other docs (URLs)  Check if URL has content already seen  If not, add to indexes E.g., only crawl .edu, obey robots.txt, etc.  For each extracted URL  Ensure it passes certain URL filter tests  Check if it is already in the frontier (duplicate URL elimination) www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Basic crawl architecture DNS URL Doc robots set FP’s filters WWW Parse Dup Fetch URL Content URL seen filter elim URL Frontier www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.2 DNS (Domain Name Server)  A lookup service on the internet  Given a URL, retrieve its IP address  Service provided by a distributed set of servers – thus, lookup latencies can be high (even seconds)  Common OS implementations of DNS lookup are blocking: only one outstanding request at a time  Solutions  DNS caching  Batch DNS resolver – collects requests and sends them out together www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Parsing: URL normalization  When a fetched document is parsed, some of the extracted links are relative URLs  E.g., http://en.wikipedia.org/wiki/MainPage has a relative link to /wiki/Wikipedia:Generaldisclaimer which is the same as the absolute URL http://en.wikipedia.org/wiki/Wikipedia:Generaldisclaimer  During parsing, must normalize (expand) such relative URLs www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Content seen  Duplication is widespread on the web  If the page just fetched is already in the index, do not further process it  This is verified using document fingerprints or shingles www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Filters and robots.txt  Filters – regular expressions for URL’s to be crawled/not  Once a robots.txt file is fetched from a site, need not fetch it repeatedly  Doing so burns bandwidth, hits web server  Cache robots.txt files www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Duplicate URL elimination  For a noncontinuous (oneshot) crawl, test to see if an extracted+filtered URL has already been passed to the frontier  For a continuous crawl – see details of frontier implementation www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Distributing the crawler  Run multiple crawl threads, under different processes – potentially at different nodes  Geographically distributed nodes  Partition hosts being crawled into nodes  Hash used for partition  How do these nodes communicate and share URLs www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.1 Communication between nodes  Output of the URL filter at each node is sent to the Dup URL Eliminator of the appropriate node To DNS URL Doc robots other nodes set FP’s filters WWW Parse Host Dup Fetch splitter URL Content URL seen filter elim From other nodes www.ThesisScientist.com URL FrontierIntroduction to Information Retrieval Sec. 20.2.3 URL frontier: two main considerations  Politeness: do not hit a web server too frequently  Freshness: crawl some pages more often than others  E.g., pages (such as News sites) whose content changes often These goals may conflict each other. (E.g., simple priority queue fails – many links out of a page go to its own site, creating a burst of accesses to that site.) www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Politeness – challenges  Even if we restrict only one thread to fetch from a host, can hit it repeatedly  Common heuristic: insert time gap between successive requests to a host that is time for most recent fetch from that host www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 URL frontier: Mercator scheme URLs Prioritizer K front queues Biased front queue selector Back queue router B back queues Single host on each Back queue selector www.ThesisScientist.com Crawl thread requesting URLIntroduction to Information Retrieval Sec. 20.2.3 Mercator URL frontier  URLs flow in from the top into the frontier  Front queues manage prioritization  Back queues enforce politeness  Each queue is FIFO www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Front queues Prioritizer 1 K Biased front queue selector Back queue router www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Front queues  Prioritizer assigns to URL an integer priority between 1 and K  Appends URL to corresponding queue  Heuristics for assigning priority  Refresh rate sampled from previous crawls  Applicationspecific (e.g., “crawl news sites more often”) www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Biased front queue selector  When a back queue requests a URL (in a sequence to be described): picks a front queue from which to pull a URL  This choice can be round robin biased to queues of higher priority, or some more sophisticated variant  Can be randomized www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Back queues Biased front queue selector Back queue router 1 B Back queue selector Heap www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Back queue invariants  Each back queue is kept nonempty while the crawl is in progress  Each back queue only contains URLs from a single host  Maintain a table from hosts to back queues Host name Back queue … 3 1 B www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Back queue heap  One entry for each back queue  The entry is the earliest time t at which the host e corresponding to the back queue can be hit again  This earliest time is determined from  Last access to that host  Any time buffer heuristic we choose www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Back queue processing  A crawler thread seeking a URL to crawl:  Extracts the root of the heap  Fetches URL at head of corresponding back queue q (look up from table)  Checks if queue q is now empty – if so, pulls a URL v from front queues  If there’s already a back queue for v’s host, append v to q and pull another URL from front queues, repeat  Else add v to q  When q is nonempty, create heap entry for it www.ThesisScientist.comIntroduction to Information Retrieval Sec. 20.2.3 Number of back queues B  Keep all threads busy while respecting politeness  Mercator recommendation: three times as many back queues as crawler threads www.ThesisScientist.comIntroduction to Information Retrieval Resources  IIR Chapter 20  Mercator: A scalable, extensible web crawler (Heydon et al. 1999)  A standard for robot exclusion www.ThesisScientist.com
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