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INF 2914

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    Slide 1:INF 2914 Web Search

    Lecture 2: Crawlers

    Slide 2:Todays lecture

    Crawling

    Slide 3:Basic crawler operation

    Begin with known seed pages Fetch and parse them Extract URLs they point to Place the extracted URLs on a queue Fetch each URL on the queue and repeat

    Slide 4:Crawling picture

    Unseen Web Seed pages

    Slide 5:Simple picture complications

    Web crawling isnt feasible with one machine All of the above steps distributed Even non-malicious pages pose challenges Latency/bandwidth to remote servers vary Webmasters stipulations How deep should you crawl a sites URL hierarchy? Site mirrors and duplicate pages Malicious pages Spam pages Spider traps incl dynamically generated Politeness dont hit a server too often

    Slide 6:What any crawler must do

    Be Polite: Respect implicit and explicit politeness considerations for a website Only crawl pages youre allowed to Respect robots.txt (more on this shortly) Be Robust: Be immune to spider traps and other malicious behavior from web servers

    Slide 7: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

    Slide 8: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

    Slide 9:Updated crawling picture

    Unseen Web Seed Pages URL frontier Crawling thread

    Slide 10: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

    Slide 11: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

    Slide 12: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 URL, create a file URL/robots.txt This file specifies access restrictions

    Slide 13:Robots.txt example

    No robot should visit any URL starting with "/yoursite/temp/", except the robot called searchengine": User-agent: * Disallow: /yoursite/temp/ User-agent: searchengine Disallow:

    Slide 14:Processing steps in crawling

    Pick a URL from the frontier 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 For each extracted URL Ensure it passes certain URL filter tests Check if it is already in the frontier (duplicate URL elimination) E.g., only crawl .edu, obey robots.txt, etc. Which one?

    Slide 15:Basic crawl architecture

    WWW Fetch DNS Parse Content seen? URL filter Dup URL elim Doc FPs URL set URL Frontier robots filters

    Slide 16: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

    Slide 17:Parsing: URL normalization

    When a fetched document is parsed, some of the extracted links are relative URLs E.g., at http://en.wikipedia.org/wiki/Main_Page we have a relative link to /wiki/Wikipedia:General_disclaimer which is the same as the absolute URL http://en.wikipedia.org/wiki/Wikipedia:General_disclaimer During parsing, must normalize (expand) such relative URLs

    Slide 18: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

    Slide 19:Filters and robots.txt

    Filters regular expressions for URLs 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

    Slide 20:Duplicate URL elimination

    For a non-continuous (one-shot) 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

    Slide 21: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?

    Slide 22:Communication between nodes

    The output of the URL filter at each node is sent to the Duplicate URL Eliminator at all nodes WWW Fetch DNS Parse Content seen? URL filter Dup URL elim Doc FPs URL set URL Frontier robots filters Host splitter To other hosts From other hosts

    Slide 23: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.

    Slide 24: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

    Slide 25:URL frontier: Mercator scheme

    Prioritizer Biased front queue selector Back queue router Back queue selector K front queues B back queues Single host on each URLs Crawl thread requesting URL

    Slide 26:Mercator URL frontier

    Front queues manage prioritization Back queues enforce politeness Each queue is FIFO

    Slide 27:Front queues

    Prioritizer 1 K Biased front queue selector Back queue router

    Slide 28: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 Application-specific (e.g., crawl news sites more often)

    Slide 29: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

    Slide 30:Back queues

    Biased front queue selector Back queue router Back queue selector 1 B Heap

    Slide 31:Back queue invariants

    Each back queue is kept non-empty while the crawl is in progress Each back queue only contains URLs from a single host Maintain a table from hosts to back queues

    Slide 32:Back queue heap

    One entry for each back queue The entry is the earliest time te at which the host 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

    Slide 33: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 theres already a back queue for vs host, append v to q and pull another URL from front queues, repeat Else add v to q When q is non-empty, create heap entry for it

    Slide 34:Number of back queues B

    Keep all threads busy while respecting politeness Mercator recommendation: three times as many back queues as crawler threads

    Slide 35:Determinando a frequncia de atualizao de pginas

    Estudar polticas de atualizao de uma base de dados local com N pginas As pginas na base de dados so cpias das pginas encontradas na Web Dificuldade Quando uma pgina da Web atualizada o crawler no informado

    Slide 36:Framework

    Medindo o quanto o banco de dados esta atualizado Freshness F(ei,t)=1 se a pgina ei esta atualizada no instante 1 e 0 caso contrrio F(S,t): Freshness mdio da base de dados S no instante t Age A(ei,t) = 0 se ei esta atualizada no instante t e t-tm(ei) onde tm o instante da ltima modificao de ei A(S,t) : Age mdio da base de dados S no instante t

    Slide 37:Framework

    Utiliza-se o freshness (age) mdio ao longo do tempo para comparar diferentes polticas de atualizao de pginas

    Slide 38:Framework

    Processo de Poisson (N(t)) Processo estocstico que determina o nmero de eventos ocorridos no intervalo [0,t] Propriedades Processo sem memria: O nmero de eventos que ocorrem em um intervalo limitado de tempo aps o instante t independe do nmero de eventos que ocorreram antes de t

    Slide 39:Framework

    Hiptese: Um processo de Poisson uma boa aproximao para o modelo de modificao de pginas Probabilidade de uma pgina ser modificada pelo menos uma vez no intervalo (0,t] Quanto maior a taxa ?, maior a probabilidade de haver mudana

    Slide 40:Framework

    Probabilidade de uma pgina ser modificada pelo menos uma vez no intervalo (0,t] Quanto maior a taxa ?, maior a probabilidade de haver mudana

    Slide 41:Framework

    Evoluo da base de dados Taxa uniforme de modificao (nico ?) razovel quando no se conhece o parmetro Taxa no uniforme de modificao (um ? diferente para cada pgina)

    Slide 42:Polticas de Atualizao

    Frequncia de Atualizao Deve-se decidir quantas atualizaes podem ser feitas por unidade de tempo Assumimos que os N elementos so atulizados em I unidades de tempo Diminuindo I aumentamos a taxa de atualizao A relao N/I depende do nmero de cralwlers, banda disponvel, banda nos servidores, etc.

    Slide 43:Polticas de Atualizao

    Alocao de Recursos Aps decidir quantos elementos atualizar devemos decidir a frequncia de atualizao de cada elemento Podemos atualizar todos elementos na mesma taxa ou atualizar mais frequentemente elementos que se modificam com maior frequncia

    Slide 44:Polticas de Atualizao

    Exemplo Um banco de dados contem 3 elementos {e1,e2,e3} As taxas de modificao 4/dia , 3/dia e 2/dia, respectivamente. So permitidas 9 atualizaes por dia Com que taxas as atualizaes devem ser feita ?

    Slide 45:Polticas de Atualizao

    Poltica Uniforme 3 atualizaes / dia para todas as pginas Poltica Proporcional: e1: 4 atualizaes / dia e2: 3 atualizaes /dia e3: 2 atualizaes /dia Qual da polticas melhor ?

    Slide 46:Poltica tima

    Calculando a frequncia de atualizao tima. Dada uma frequncia mdia f=N/I e a taxa mdia ? i para cada pgina, deve-se calcular a frequncia fi com que cada pgina i deve ser atualizada

    Slide 47:Poltica tima

    Considere um banco de dados com 5 elementos e com as seguintes taxas de modificao 1,2,3,4 e 5. Assumindo que 5 atualizaes por dias so possveis.

    Slide 48:Poltica tima

    Frequncia de atualizao tima. Para maximizar o freshness temos a seguinte curva

    Slide 49:Poltica tima

    Maximizando o Freshenss Pginas que se modificam pouco devem ser pouco atualizadas Pginas que se modificam demais devem ser pouco atualizadas Uma atualizao consome recurso e garante o freshness da pgina atualizada por muito pouco tempo

    Slide 50:Experimentos

    Foram escohidos 270 sites. Para cada um destes 3000 pginas so atualizadas todos os dias. Experimentos realizados de 9PM as 6AM durante 4 meses 10 segundos de intervalo entre requisies ao mesmo site

    Slide 51: Estimando frequncias de modificao

    O intervalo mdio de modificao de uma pgina estimado dividindo o perodo em que o experimento foi realizado pelo total de modificaes no perodo

    Slide 52:Experimentos

    Verificao do processo de Poisson Seleciona-se as pginas que se modificam a uma taxa mdia (e.g. a cada 10 dias) e plota-se a distribuio do intervalo de mudanas

    Slide 53: Verificando o Processo de Poisson

    Eixo horizontal: tempo entre duas modificaes Eixo vertical: frao das modificaes que ocorreram no dado intervalo As retas so as predies Para a pginas que se modificam muito e pginas que se modificam muito pouco no foi possvel obter concluses.

    Slide 54:Alocao de Recursos

    Baseado nas frequncias estimadas, calcula-se o freshness esperado para cada uma das trs polticas consideradas. Consideramos que possvel visitar 1Gb pginas/ms

    Slide 55:Resources

    IIR 20 See also Effective Page Refresh Policies For Web Crawlers Cho& Molina

    Slide 56:Trabalho 2 - Proposta

    Estudar polticas de atualizao de pginas Web Effective Page Refresh Policies For Web Crawlers Optimal Crawling Strategies for Web Search Engines User-centric Web crawling http://www.cs.cmu.edu/~olston/publications/wic.pdf

    Slide 57:Trabalho 3- Proposta

    Estudar polticas para distribuio de Crawlers Parallel Crawlers Realizar pesquisa bibliogrfica em artigos que citam este

    Slide 58:Parallel crawling

    Why? Aggregate resources of many machines Network load dispersion Issues Quality of pages crawled (as before) Communication overhead Coverage; Overlap

    Slide 59:Parallel crawling approach [Cho+ 2002]

    Partition URLs; each crawler node responsible for one partition many choices for partition function e.g., hash(host IP) What kind of coordination among crawler nodes? 3 options: firewall mode cross-over mode exchange mode

    Slide 60:Coordination of parallel crawlers

    i h g f e d a c b crawler node 1 crawler node 2 modes: ? firewall ? cross-over ? exchange

    Slide 61:Empirical findings by Cho+

    Firewall has acceptable coverage if number of crawler nodes small (<= 4) Exchange incurs small overhead (< 1% of network bandwidth) Can reduce exchanges by replicating the most popular URLs

    Slide 62:Trabalho 4 - Proposta

    Google File System Map Reduce http://labs.google.com/papers.html

    Slide 63:Trabalho 5 - Proposta

    XML Parsing, Tokenization, and Indexing