Transcript
Page 1: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Crawling and web indexes

Page 2: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Today’s lecture

Crawling Connectivity servers

Page 3: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2

Page 4: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Crawling picture

Web

URLs crawledand parsed

URLs frontier

Unseen Web

Seedpages

20.2

Page 5: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Simple picture – complications

Web crawling isn’t 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 site’s URL hierarchy?

Site mirrors and duplicate pages Malicious pages

Spam pages Spider traps – incl dynamically generated

Politeness – don’t hit a server too often 20.1.1

Page 6: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.1.1

Page 7: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.1.1

Page 8: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.1.1

Page 9: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Updated crawling picture

URLs crawledand parsed

Unseen Web

SeedPages

URL frontier

Crawling thread 20.1.1

Page 10: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2

Page 11: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 20.2

Page 12: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2.1

Page 13: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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:

20.2.1

Page 14: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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?

20.2.1

Page 15: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Basic crawl architecture

WWW

DNS

Parse

Contentseen?

DocFP’s

DupURLelim

URLset

URL Frontier

URLfilter

robotsfilters

Fetch

20.2.1

Page 16: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 20.2.2

Page 17: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 20.2.1

Page 18: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2.1

Page 19: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 20.2.1

Page 20: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2.1

Page 21: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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?

20.2.1

Page 22: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

20.2.1

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

ParseContentseen?

URLfilter

DupURLelim

DocFP’s

URLset

URL Frontier

robotsfilters

Hostsplitter

Tootherhosts

Fromotherhosts

Page 23: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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.)

20.2.3

Page 24: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2.3

Page 25: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

URL frontier: Mercator scheme

Prioritizer

Biased front queue selectorBack queue router

Back queue selector

K front queues

B back queuesSingle host on each

URLs

Crawl thread requesting URL 20.2.3

Page 26: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Mercator URL frontier

URLs flow in from the top into the frontier

Front queues manage prioritization

Back queues enforce politeness Each queue is FIFO

20.2.3

Page 27: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Front queues

Prioritizer

1 K

Biased front queue selectorBack queue router 20.2.3

Page 28: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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”)

20.2.3

Page 29: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

20.2.3

Page 30: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

20.2.3

Back queues

Biased front queue selectorBack queue router

Back queue selector

1 B

Heap

Page 31: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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

Host name Back queue

… 3

1

B20.2.3

Page 32: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 20.2.3

Page 33: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

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 non-empty, create heap entry for it

Page 34: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Number of back queues B

Keep all threads busy while respecting politeness

Mercator recommendation: three times as many back queues as crawler threads

20.2.3

Page 35: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Connectivity servers

20.4

Page 36: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Connectivity Server[CS1: Bhar98b, CS2 & 3: Rand01]

Support for fast queries on the web graph Which URLs point to a given URL? Which URLs does a given URL point to?

Stores mappings in memory from URL to outlinks, URL to inlinks

Applications Crawl control Web graph analysis

Connectivity, crawl optimization Link analysis 20.4

Page 37: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Champion published work

Boldi and Vigna http://www2004.org/proceedings/docs/1p595.pdf

Webgraph – set of algorithms and a java implementation

Fundamental goal – maintain node adjacency lists in memory For this, compressing the adjacency lists is the critical component

20.4

Page 38: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Adjacency lists

The set of neighbors of a node Assume each URL represented by an integer

E.g., for a 4 billion page web, need 32 bits per node

Naively, this demands 64 bits to represent each hyperlink

20.4

Page 39: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Adjaceny list compression

Properties exploited in compression: Similarity (between lists) Locality (many links from a page go to “nearby” pages)

Use gap encodings in sorted lists

Distribution of gap values20.4

Page 40: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Storage

Boldi/Vigna get down to an average of ~3 bits/link (URL to URL edge) For a 118M node web graph

How?

Why is this remarkable?

20.4

Page 41: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Main ideas of Boldi/Vigna

Consider lexicographically ordered list of all URLs, e.g., www.stanford.edu/alchemy www.stanford.edu/biology www.stanford.edu/biology/plant www.stanford.edu/biology/plant/copyright

www.stanford.edu/biology/plant/people www.stanford.edu/chemistry

20.4

Page 42: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Boldi/Vigna Each of these URLs has an adjacency list Main idea: due to templates, the adjacency list of a node is similar to one of the 7 preceding URLs in the lexicographic ordering

Express adjacency list in terms of one of these

E.g., consider these adjacency lists 1, 2, 4, 8, 16, 32, 64 1, 4, 9, 16, 25, 36, 49, 64 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144 1, 4, 8, 16, 25, 36, 49, 64Encode as (-2), remove 9, add 8

Why 7?

20.4

Page 43: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Duplicate/Near-Duplicate Detection

Duplication: Exact match with fingerprints Near-Duplication: Approximate match

Overview Compute syntactic similarity with an edit-distance measure

Use similarity threshold to detect near-duplicates

E.g., Similarity > 80% => Documents are “near duplicates”

Not transitive though sometimes used transitively

Page 44: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Computing Near Similarity

Features: Segments of a document (natural or artificial breakpoints) [Brin95]

Shingles (Word N-Grams) [Brin95, Brod98] “a rose is a rose is a rose” => a_rose_is_a rose_is_a_rose is_a_rose_is

Similarity Measure TFIDF [Shiv95] Set intersection [Brod98] (Specifically, Size_of_Intersection / Size_of_Union )

Page 45: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Shingles + Set Intersection Computing exact set intersection of shingles between all pairs of documents is expensive and infeasible Approximate using a cleverly chosen subset of shingles from each (a sketch)

Page 46: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Shingles + Set Intersection

Estimate size_of_intersection / size_of_union based on a short sketch ( [Brod97, Brod98] ) Create a “sketch vector” (e.g., of size 200) for each document

Documents which share more than t (say 80%) corresponding vector elements are similar

For doc D, sketch[ i ] is computed as follows:

Let f map all shingles in the universe to 0..2m (e.g., f = fingerprinting)

Let i be a specific random permutation on 0..2m

Pick sketch[i] := MIN i ( f(s) ) over all shingles s in D

Page 47: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Computing Sketch[i] for Doc1

Document 1

264

264

264

264

Start with 64 bit shingles

Permute on the number line

with i

Pick the min value

Page 48: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Test if Doc1.Sketch[i] = Doc2.Sketch[i]

Document 1 Document 2

264

264

264

264

264

264

264

264

Are these equal?

Test for 200 random permutations: , ,… 200

A B

Page 49: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

However…

Document 1 Document 2

264

264

264

264

264

264

264

264

A = B iff the shingle with the MIN value in the union of Doc1 and Doc2 is common to both (I.e., lies in the intersection)

This happens with probability: Size_of_intersection / Size_of_union

BA

Page 50: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Question Document D1=D2 iff size_of_intersection=size_of_union ?

Page 51: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Mirror Detection Mirroring is systematic replication of web pages across hosts. Single largest cause of duplication on the web

Host1/ and Host2/ are mirrors iffFor all (or most) paths p such that when

http://Host1/ / p exists http://Host2/ / p exists as wellwith identical (or near identical) content, and vice versa.

Page 52: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Mirror Detection example http://www.elsevier.com/ and

http://www.elsevier.nl/ Structural Classification of Proteins

http://scop.mrc-lmb.cam.ac.uk/scop http://scop.berkeley.edu/ http://scop.wehi.edu.au/scop http://pdb.weizmann.ac.il/scop http://scop.protres.ru/

Page 53: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Repackaged MirrorsAuctions.msn.com Auctions.lycos.com

Aug 2001

Page 54: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Motivation

Why detect mirrors? Smart crawling

Fetch from the fastest or freshest server Avoid duplication

Better connectivity analysis Combine inlinks Avoid double counting outlinks

Redundancy in result listings “If that fails you can try: <mirror>/samepath”

Proxy caching

Page 55: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Maintain clusters of subgraphs Initialize clusters of trivial subgraphs

Group near-duplicate single documents into a cluster Subsequent passes

Merge clusters of the same cardinality and corresponding linkage

Avoid decreasing cluster cardinality To detect mirrors we need:

Adequate path overlap Contents of corresponding pages within a small time range

Bottom Up Mirror Detection[Cho00]

Page 56: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Can we use URLs to find mirrors?

www.synthesis.org

a b

cd

synthesis.stanford.edu

a b

cd

www.synthesis.org/Docs/ProjAbs/synsys/synalysis.htmlwww.synthesis.org/Docs/ProjAbs/synsys/visual-semi-quant.htmlwww.synthesis.org/Docs/annual.report96.final.htmlwww.synthesis.org/Docs/cicee-berlin-paper.htmlwww.synthesis.org/Docs/myr5www.synthesis.org/Docs/myr5/cicee/bridge-gap.htmlwww.synthesis.org/Docs/myr5/cs/cs-meta.htmlwww.synthesis.org/Docs/myr5/mech/mech-intro-mechatron.htmlwww.synthesis.org/Docs/myr5/mech/mech-take-home.htmlwww.synthesis.org/Docs/myr5/synsys/experiential-learning.htmlwww.synthesis.org/Docs/myr5/synsys/mm-mech-dissec.htmlwww.synthesis.org/Docs/yr5arwww.synthesis.org/Docs/yr5ar/assesswww.synthesis.org/Docs/yr5ar/ciceewww.synthesis.org/Docs/yr5ar/cicee/bridge-gap.htmlwww.synthesis.org/Docs/yr5ar/cicee/comp-integ-analysis.html

synthesis.stanford.edu/Docs/ProjAbs/deliv/high-tech-…synthesis.stanford.edu/Docs/ProjAbs/mech/mech-enhanced…synthesis.stanford.edu/Docs/ProjAbs/mech/mech-intro-…synthesis.stanford.edu/Docs/ProjAbs/mech/mech-mm-case-…synthesis.stanford.edu/Docs/ProjAbs/synsys/quant-dev-new-…synthesis.stanford.edu/Docs/annual.report96.final.htmlsynthesis.stanford.edu/Docs/annual.report96.final_fn.htmlsynthesis.stanford.edu/Docs/myr5/assessmentsynthesis.stanford.edu/Docs/myr5/assessment/assessment-…synthesis.stanford.edu/Docs/myr5/assessment/mm-forum-kiosk-…synthesis.stanford.edu/Docs/myr5/assessment/neato-ucb.htmlsynthesis.stanford.edu/Docs/myr5/assessment/not-available.htmlsynthesis.stanford.edu/Docs/myr5/ciceesynthesis.stanford.edu/Docs/myr5/cicee/bridge-gap.htmlsynthesis.stanford.edu/Docs/myr5/cicee/cicee-main.htmlsynthesis.stanford.edu/Docs/myr5/cicee/comp-integ-analysis.html

Page 57: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Top Down Mirror Detection[Bhar99, Bhar00c]

E.g., www.synthesis.org/Docs/ProjAbs/synsys/synalysis.htmlsynthesis.stanford.edu/Docs/ProjAbs/synsys/quant-dev-new-teach.html

What features could indicate mirroring? Hostname similarity:

word unigrams and bigrams: { www, www.synthesis, synthesis, …}

Directory similarity: Positional path bigrams { 0:Docs/ProjAbs, 1:ProjAbs/synsys, … }

IP address similarity: 3 or 4 octet overlap Many hosts sharing an IP address => virtual hosting by an ISP

Host outlink overlap Path overlap

Potentially, path + sketch overlap

Page 58: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Implementation

Phase I - Candidate Pair Detection Find features that pairs of hosts have in common Compute a list of host pairs which might be

mirrors Phase II - Host Pair Validation

Test each host pair and determine extent of mirroring

Check if 20 paths sampled from Host1 have near-duplicates on Host2 and vice versa

Use transitive inferences: IF Mirror(A,x) AND Mirror(x,B) THEN Mirror(A,B) IF Mirror(A,x) AND !Mirror(x,B) THEN !Mirror(A,B)

Evaluation 140 million URLs on 230,000 hosts (1999) Best approach combined 5 sets of features

Top 100,000 host pairs had precision = 0.57 and recall = 0.86

Page 59: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Resources

www.seochat.com/ www.google.com/webmasters/seo.html www.google.com/webmasters/faq.html www.smartmoney.com/bn/ON/index.cfm?story=ON-20041215-000871-1140

research.microsoft.com/research/sv/sv-pubs/p97-fetterly/p97-fetterly.pdf

news.com.com/2100-1024_3-5491704.html www.jupitermedia.com/corporate/press.html

Page 60: Crawling and web indexes. Today’s lecture Crawling Connectivity servers

Resources

IIR Chapter 20 Mercator: A scalable, extensible web crawler (Heydon et al. 1999)

A standard for robot exclusion The WebGraph framework I: Compression techniques (Boldi et al. 2004)


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