Crawling Ida Mele

Nutch Apache Nutch is an open source Java implementation of a search engine We can use Nutch for crawling a portion of the Web Useful links: Ida MeleCrawling1

Nutch: advantages Understanding We have the source code and we can use it to see how a large search engine works Nutch has been built using ideas from academia and industry, and it is very useful for researchers who want to try out new search algorithms Ida MeleCrawling2

Nutch: advantages Transparency The details of the ranking algorithms used by commercial search engines are secret, and usually there are economical reasons behind the ranked list of results Nutch implementation is transparent. We know how the ranking algorithms work, and we can trust on the fairness of the final rankings Ida MeleCrawling3

Nutch: advantages Extensibility Nutch is a platform for adding search to heterogeneous collections of information It allows to customize the search interface We can extend the out-of-the-box functionality through the plugin mechanism Ida MeleCrawling4

Nutch vs. Lucene Nutch is built on top of Lucene Apache Lucene is a Java library for text indexing and searching It ensures high-performance and full-featured text search It provides support for any application that requires full-text search It is used just for indexing and not for crawling Ida MeleCrawling5

Architecture Nutch can be divided into two pieces: crawler which fetches pages and turns them into an inverted index searcher which answers users' search queries The index is the interface between the crawler and the searcher The crawler and searcher systems can be on separate hardware platforms Ida MeleCrawling6

Architecture Crawler and searcher systems can be scaled independently For example, if we have a highly trafficked search page that provides searching for a relatively modest set of sites, we may use a modest crawler infrastructure, and invest more substantial resources for supporting the searcher Ida MeleCrawling7

Crawler system The crawler system is driven by the Nutch tool called crawl, and by other related tools to build and maintain the data structures Data structures are: the web database a set of segments the index Ida MeleCrawling8

WebDB The web database (WebDB) is a data structure for mirroring the structure and properties of the web graph being crawled It stores two types of entities: Page: It is indexed by its URL and the MD5 hash of its contents. Other information: the # of outlinks, fetch information, the score of the page Link: It represents the connection between the source page and the target page Ida MeleCrawling9

Segment The segment is a collection of pages that are fetched and indexed by the crawler in a run The fetchlist is a list of URLs to fetch, and it is generated from the WebDB The fetcher output is the data retrieved from the pages in the fetchlist Any segment has a lifespan (30 days is the default re- fetch interval) Ida MeleCrawling10

Index Inverted index of all pages retrieved by the system The index is created by merging all of the individual segment indexes Nutch uses Lucene to build the index. Note that in Lucene there is the concept of segment, but it is different from the segment in Nutch: In Lucene, the index segment is a portion of the index In Nutch, the segment is a fetched and indexed portion of the WebDB Ida MeleCrawling11

Crawling Nutch can operate at one of these three different scales: Local filesystem Intranet Web All scales have different characteristics. For example, crawling the file system is more reliable compared to the other two scales Ida MeleCrawling12

Crawling For crawling billions of pages from the web, we must: define the seed set (i.e., the set of pages we want to start with) decide how many crawlers we use and how partition the work among them decide how often we want to do the re-crawling cope with broken links, unresponsive sites, and unintelligible or duplicate content Ida MeleCrawling13

Crawling The crawling process is basically a cycle made of three steps: 1.the crawler generates a set of fetchlists from the WebDB (generate) 2.a set of fetchers downloads the content from the Web (fetch) 3.the crawler updates the WebDB with new links that were found (update) Ida MeleCrawling14

Crawling Nutch observes: Politeness: URLs with the same host are always assigned to the same fetchlist, so that a web site is not overloaded with requests from multiple fetchers in rapid succession Robots Exclusion Protocol: It allows site owners to control which parts of their site may be crawled Ida MeleCrawling15

Crawling: low-level tools Crawling is done by the crawl tool of Nutch, that is a front-end to lower-level tools The crawl tool can be used to get started with crawling websites, but then we need to use the lower-level tools to perform re-crawls and other maintenance on the data structures built during the initial crawl Ida MeleCrawling16

Crawling: low-level tools We can use the lower-level tools in sequence: 1.Create a new WebDB (admin db-create) 2.Inject root URLs into the WebDB (inject) 3.Generate a fetchlist from the WebDB in a new segment (generate) 4.Fetch content from URLs in the fetchlist (fetch) 5.Update the WebDB with links from fetched pages (updatedb) 6.Repeat steps 3-5 until the required depth is reached Ida MeleCrawling17

Crawling: low-level tools 7.Update segments with scores and links from the WebDB (updatesegs) 8.Index the fetched pages (index) 9.Eliminate duplicate content, and duplicate URLs, from the indexes (dedup) 10.Merge the indexes into a single index for searching (merge) Ida MeleCrawling18

Crawling: low-level tools We create a new WebDB (step 1), and we populate it with some seed URLs (step 2) Then we use the generate/fetch/update cycle (steps 3-6) After the cycle, the crawler creates an index (steps 7-10). In particular, each segment is indexed independently (step 8) the duplicate pages are removed (step 9) the individual indexes are combined into a single index (step 10) Ida MeleCrawling19

Running a crawl with Nutch Download and unpack a Nutch distribution (for example, apache-nutch-1.1-bin.zip) Make sure that the environment variable NUTCH_JAVA_HOME or JAVA_HOME is set with the Java home path: Run the following command or add it to the.bashrc file: export NUTCH_JAVA_HOME= %pathJava Ida MeleCrawling20

Nutch configuration All of Nutch's configuration files are in the conf subdirectory The main configuration file is conf/nutch- default.xml. It contains the default settings, and should not be modified To change a setting we can create or update the conf/nutch-site.xml file Ida MeleCrawling21

Nutch configuration Add your agent name in the value field of the http.agent.name property of the file conf/nutch- site.xml, for example, we can use the name: Sapienza University Ida MeleCrawling22 http.agent.name Sapienza University HTTP 'User-Agent' request header. MUST NOT be empty - please set this to a single word uniquely related to your organization.

Url filter The crawl tool uses a filter to decide which URLs can go into the WebDB (steps 2 and 5) This can be used to restrict the crawling to the URLs that match any given pattern, specified by regular expressions For example, if we want to restrict the domain to the DIS domain, we have to update the configuration file conf/crawl-urlfilter.txt Ida MeleCrawling23

Url filter Open the file conf/crawl-urlfilter.txt and replace the line: +^ with: +^ The file conf/crawl-urlfilter.txt will contain: # accept hosts in MY.DOMAIN.NAME #+^ +^ Ida MeleCrawling24

Example Create a file called urls, that contains the root URLs. These URLs will be used to populate the initial fetchlist. For example, if we want to start from the home page of the department, we will use: echo ‘ > urls Ida MeleCrawling25

Example We run the crawler with: bin/nutch crawl urls -dir mycrawl -depth 5 > mycrawl.log where: urls is the name of the file with the seed URLs mycrawl is the name of the directory 5 is the depth of the crawling mycrawl.log is the name of the log file Ida MeleCrawling26

Results of the crawl The directory mycrawl contains the following subdirectories: crawldb linkdb segments index indexes Ida MeleCrawling27

Results of the crawl: readdb The readdb tool parses the WebDB and displays portions of it in human-readable form The stats option displays the number of pages and links: bin/nutch readdb mycrawl/crawldb -stats >stats.txt Then, we can use: more stats.txt Ida MeleCrawling28

Results of the crawl: readdb The dump option gives the dump of the pages. Each page appears in a separate block, with one field per line. The ID field is the MD5 hash of the page contents. There is also information about when the pages should be next fetched (which defaults to 30 days), and the page scores We issue the command: bin/nutch readdb mycrawl/crawldb -dump mydump then we use: more mydump/part Ida MeleCrawling29

Results of the crawl: readdb The readdb tool also supports extraction of an individual page or link by URL or MD5 hash For example, to examine the info of the page we use the option url by issuing the command: bin/nutch readdb mycrawl/crawldb -url Ida MeleCrawling30

Results of the crawl: readlinkdb The readlinkdb tool can be used to create the dump of the link structure (the graph) by using the option dump: bin/nutch readlinkdb mycrawl/linkdb/ -dump mylinks We can read the in-links by using: more mylinks/part Note that it gives us just the list of the in-links. For the out-links we have to merge the segments and read the result Ida MeleCrawling31

Results of the crawl: readseg The crawl creates a few segments in timestamped subdirectories, one for each generate/fetch/update cycle The readseg tool is the segment reader The option list gives a summary of all of the generated segments: bin/nutch readseg -list -dir mycrawl/segments/ Ida MeleCrawling32

Results of the crawl: readseg The option dump gives a dump of a given segment: bin/nutch readseg -dump mycrawl/segments/YYYYMMDDhhmmss/ dump_seg1 Where YYYYMMDDhhmmss is the name of the segment, and it is given by the date and time we created the segment Then we can use: more dump_seg1/dump Ida MeleCrawling33

Results of the crawl: mergeseg We have seen that the readlinkdb tool can be used to have the list of in-links To have the out-links we need to merge the segments and read the result We use the mergesegs tool: bin/nutch mergesegs whole-segments -dir mycrawl/segments/* Then we can use the dump option of the readseg tool on the result of the merge: bin/nutch readseg -dump whole- segments/YYYYMMDDhhmmss/ dump-outlinks Ida MeleCrawling34

Exercise We want to create the webgraph of a portion of the Web First of all, install and configure Nutch For the crawling: Create the file with the seed set (example urls) Update the conf/url-filter.txt file Decide the depth of the crawling and crawl a portion of the web using the crawl tool. For example, for depth 5 we issue: bin/nutch crawl urls -dir mycrawl -depth 5 > mycrawl.log Ida MeleCrawling35

Exercise Once the crawling is completed, you can create the webgraph Download the directory with libraries lib.zip available at: Download the file set-classpath.sh available at: Update the file set-classpath.sh with the path to your lib directory Put the set-classpath.sh file in the Nutch home, open the terminal, and set the classpath with source set-classpath.sh Ida MeleCrawling36

Exercise Create the file with in-links using the following commands: bin/nutch readlinkdb mycrawl/linkdb/ -dump mylinks egrep -v $'^$' mylinks/part >inlinks.txt Ida MeleCrawling37

Exercise Create the file with the out-links 1) Merge the segments: bin/nutch mergesegs whole-segments -dir mycrawl/segments/* 2) Use readseg to read the segments, and then create the file with out-links: bin/nutch readseg -dump whole- segments/YYYYMMDDhhmmss/dump-outlinks cat dump-outlinks/dump | egrep 'URL|toUrl' >outlinks.txt Ida MeleCrawling38

Exercise Print the in-links and out-links in the links.txt file by issuing the following commands: java nutchGraph.PrintInlinks inlinks.txt >links.txt java nutchGraph.PrintOutlinks outlinks.txt >>links.txt Remove the duplicates: LANG=C sort links.txt | uniq > cleaned-links.txt Ida MeleCrawling39

Exercise Create the map of urls with the following commands: cut -f1 links.txt >url-list.txt cut -f2 links.txt >>url-list.txt LANG=C sort url-list.txt | uniq > sorted-url-list.txt java -Xmx2G it.unimi.dsi.util.FrontCodedStringList -u -r 32 umap.fcl < sorted-url-list.txt java -Xmx2G it.unimi.dsi.sux4j.mph.MWHCFunction umap.mph sorted-url-list.txt Ida MeleCrawling40

Exercise Create the graph: java -Xmx2G nutchGraph.PrintEdges cleaned-links.txt umap.mph > webgraph.dat numNodes=$(wc -l < sorted-url-list.txt) java -Xmx2G nutchGraph.IncidenceList2Webgraph $numNodes webgraph java -Xmx2G it.unimi.dsi.webgraph.BVGraph –g ASCIIGraph webgraph webgraph Ida MeleCrawling41

Indexing Once the crawling operation is completed, we have the graph and the indexed pages Remember that Nutch uses Lucene for the indexing phase If we want to use MG4J for building the inverted index, we can collect the pages fetched during the crawling by using: wget -i sorted-url-list.txt Then we can use MG4J for indexing and querying the resulting collection of web pages Ida MeleCrawling42

WEB db Link structure RankPR Nutch ParserDB readdb graph.txt PageRank getfiles files MG4J QueryMG4J Query Ida MeleCrawling43 ASCIIGraph BVGraph

Homework Repeat the exercise using a different seed set and/or depth. Create the corresponding webgraph. Compute the Pagerank for the nodes of the webgraph. Plot the distribution of the Pagerank values Ida MeleCrawling44