Part IV - Advanced Techniques

- High-performance crawling

- Recrawling and focused crawling - Link-based ranking (Pagerank, HITS)

- Structural analysis of the web graph

- Optimizing query execution

- Parallel search engines and scaling

- Meta search engines

- Document clustering and duplicate detection

• experience from research project at Poly

• need high-performance crawler: >100 pages/sec

• robust operation over several weeks

- crawler will crash - system will have to be modified

• controlled operation - other users on campus - remember the 30-second rule - things will happen

1- High-performance crawling: 100 million pages or more

• server/DNS/network latency (0.2-1 seconds minimum)

• must open hundreds of connections simultaneously

• 150 pages = 2 MB/s = 40% of max. T-3 capacity

• DNS can become bottleneck

• 10-20% additional accesses for robots.txt

• data must be streamed to disk

• OS limits and overheads: networking, files

Networking performance

• Google crawler “backRub” (see WWW’98 paper)

- python downloaders on several machines

- up to 100 pages per second

• Internet Archive Crawler• Mercator/Atrax (DEC/Altavista) (see Mercator paper)

- 2 GB machine with RAID

- implemented in Java (many performance issues)

- up to 200 pages/sec

- detailed discussion of data structure size

- optimized DNS caching

• PolyBot (Shkapenyuk/Suel ICDE 2002)

Crawler Architectures

Polybot crawler: (ICDE 2002, with V. Shkapenyuk)

• distributed implementation in C++• manager handles several request streams with priorities• manager handles DNS, exclusion, and frontier• 300 pages/sec (and more)

Crawling Strategy and Download Rate:

• crawling strategy: “What page to download next?”

• download rate: “How many pages per second?”

• different scenarios require different strategies

• lots of recent work on crawling strategy

• little published work on optimizing download rate (main exception: Mercator from DEC/Compaq/HP?)

• somewhat separate issues

• building a slow crawler is (fairly) easy ...

System Requirements:

• flexibility (different crawling strategies)

• scalabilty (high performance at low cost)

• robustness

(odd server content/behavior, crashes)

• crawling etiquette and speed control

(robot exclusion, 30 second intervals, domain level

throttling, speed control for other users)

• manageable and reconfigurable

(interface for statistics and control, system setup)

Structure:

PolyBot System Architecture

• separation of crawling strategy and basic system

• collection of scalable distributed services

(DNS, downloading, scheduling, strategy)

• for clusters and wide-area distributed

• optimized per-node performance

• no random disk accesses (no per-page access)

Basic Architecture, revisited:

• application issues requests to manager

• manager does DNS and robot exclusion

• manager schedules URL on downloader

• downloader gets file and puts it on disk

• application is notified of new files

• application parses new files for hyperlinks

• application sends data to storage component (indexing done later)

System components:

• downloader: optimized HTTP client written in Python (everything else in C++)

• DNS resolver: uses asynchronous DNS library

• manager uses Berkeley DB and STL for external and internal data structures

• manager does robot exclusion by generating requests to downloaders and parsing files

• application does parsing and handling of URLs (has this page already been downloaded?)

Scaling the system:

• small system on previous pages:

3-5 workstations and 250-400 pages/sec peak

• can scale up by adding downloaders and DNS resolvers

• at 400-600 pages/sec, application becomes bottleneck

• at 8 downloaders manager becomes bottleneck

need to replicate application and manager

• hash-based technique (Internet Archive crawler)

partitions URLs and hosts among application parts

• data transfer via file system (NFS)

Scaling up:• 20 machines

• 1500 pages/s?

• depends on crawl strategy

• hash to nodes based on site (b/c robot ex)

Data Structures and Techniques

• parsing using pcre library• NFS eventually bottleneck• URL-seen problem: - need to check if file has been parsed or downloaded before

- after 20 million pages, we have “seen” over 100 million URLs

- each URL is 50 to 75 bytes on average

• Options: compress URLs in main memory, or use disk - prefix+huffman coding (DEC, JY01) or Bloom Filter (Archive)

- disk access with caching (Mercator)

- we use lazy/bulk operations on disk

Crawling Application

• Implementation of URL-seen check: - while less than a few million URLs seen, keep in main memory

- then write URLs to file in alphabetic, prefix-compressed order

- collect new URLs in memory and periodically reform bulk

check by merging new URLs into the file on disk

• When is a newly a parsed URL downloaded?

• Reordering request stream - want to space ot requests from same subdomain

- needed due to load on small domains and due to security tools

- sort URLs with hostname reversed (e.g., com.amazon.www),

and then “unshuffle” the stream provable load balance

Challenges and Techniques: Manager

• large stream of incoming URL request files

• goal: schedule URLs roughly in the order that they come, while observing time-out rule (30 seconds) and maintaining high speed

• must do DNS and robot excl. “right before”download

• keep requests on disk as long as possible!

- otherwise, structures grow too large after few million pages (performance killer)

Manager Data Structures:

• when to insert new URLs into internal structures?

URL Loading Policy

• read new request file from disk whenever less than x hosts in ready queue

• choose x > speed * timeout (e.g., 100 pages/sec * 30 sec)

• # of current host data structures is

x + speed * timeout + n_down + n_transit

which is usually < 2x

• nice behavior for BDB caching policy

• performs reordering only when necessary!

Experimental Results

• crawl of 120 million pages over 19 days

161 million HTTP request

16 million robots.txt requests

138 million successful non-robots requests

17 million HTTP errors (401, 403, 404 etc)

121 million pages retrieved

• slow during day, fast at night

• many downtimes due to attacks, crashes, revisions

• “slow tail” of requests at the end (4 days)

Experimental Results ctd.

bytes in bytes out frames out

Poly T3 connection over 24 hours of 5/28/01(courtesy of AppliedTheory)

Experimental Results ctd.

• sustaining performance: - will find out when data structures hit disk - I/O-efficiency vital

• speed control tricky - vary number of connections based on feedback - also upper bound on connections - complicated interactions in system - not clear what we should want

• other configuration: 140 pages/sec sustained on 2 Ultra10 with 60GB EIDE and 1GB/768MB• similar for Linux on Intel

• Atrax: recent distributed extension to Mercator

- combines several Mercators

- URL hashing, and off-line URL check (as we do)

• Ubicrawler, Nutch crawler

• P2P crawlers (grub.org and others)

• Cho/Garcia-Molina (WWW 2002)

- study of overhead/quality tradeoff in paral. crawlers

- difference: we scale services separately, and focus on

single-node performance

- in our experience, parallel overhead low

Other Work on Parallel Crawlers:

• index compression: use fast and good codes to decrease index size

• list caching: keep inverted lists in main memory as much as possible

• result caching: if same query has recently been issued, use result

• query pruning: compute best k results without scanning entire lists

Approaches:

How to optimize query throughput in large search engines.

The ranking function is often a combination of term-based ranking and a global ordering such as Pagerank”

2 - Optimized Query Processing:

• inverted index - a data structure for supporting text queries - like index in a book

Query processing in search engines (single node case)

inverted index

aalborg 3452, 11437, ….......arm 4, 19, 29, 98, 143, ...armada 145, 457, 789, ...armadillo 678, 2134, 3970, ...armani 90, 256, 372, 511, ........zz 602, 1189, 3209, ...

disks with documents

indexing

• Boolean queries: (zebra AND armadillo) OR alligator

unions/intersections of lists

• scoring function: assigns score to each document with respect to a given query

• top-k queries: return k documents with highest scores• example cosine measure for query with terms t to t

Ranking in search engines:

• can be implemented by computing score for all documents that contain any of the query words (union of inverted lists)

• in case of search engines: often intersection instead of union• in large collections, lists are many MB for average queries

0 m-1

• recall the cosine measure:

Combining Term-Based Methods and Pagerank

• Pagerank assigns a fixed score to each page (independent of query) • naïve way of integrating Pagerank value:

• works for any global ordering of page scores (e.g., based on

traffic)

• but some more details remain

• how to combine/add pagerank score and cosine? (addition)• use PR or log(PR) ?• normalize using mean of top-100 in list (Richardson/Domingo)

Using Pagerank in ranking:

pages

index

pages

index

pages

index

pages

index

pages

index

broadcasts each queryand combines the results

LAN

Cluster with global index organization

Query Processing in Parallel Search Engines

queryintegrator

• local index: every node stores and indexes subset of pages• every query broadcast to all nodes by query integrator (QI)• every node supplies top-10, and QI computes global top-10• note: we don’t really need top-10 from all, maybe only top-2

• low-cost cluster architecture (usually with additional replication)

• IR: optimized evaluation of cosine measures (since 1980s)

• DB: top-k queries for multimedia databases (Fagin 1996)

• does not consider combinations of term-based and global scores• Brin/Page 1998: fancy lists in Google

Related Work on top-k Queries

• basic idea: “presort entries in each inverted list by contribution to

cosine”

• also process inverted lists from shortest to longest list• various schemes, either reliable or probabilistic• most closely related: - Persin/Zobel/Sacks-Davis 1993/96

- Anh/Moffat 1998, Anh/deKretzer/Moffat 2001

• typical assumptions: many keywords/query, OR semantics

Related Work (IR)

• motivation: searching multimedia objects by several criteria• typical assumptions: few attributes, OR semantics, random access

• FA (Fagin’s algorithm), TA (Threshold algorithm), others

• formal bounds: for k lists if lists independent

• term-based ranking: presort each list by contribution to cosine

Related Work (DB) (Fagin 1996 and others)

kN mmm 11

• “fancy lists” optimization in Google• create extra shorter inverted list for “fancy matches” (matches that occur in URL, anchor text, title, bold face, etc.)

• note: fancy matches can be modeled by higher weights in the term-based vector space model

• no details given or numbers published

Related Work (Google) (Brin/Page 1998)

chair

table

fancy list

fancy list

rest of list with other matches

rest of list with other matches

• top-10 queries on 16 machines with 120 million pages• up to 10 queries/sec with reliable pruning• up to 20 queries per second with first-30 scheme

Throughput and Latency for 16 Nodes

Note: reliable pruning not implemented in purely incremental manner

• Ragerank (Brin&Page/Google)

“significance of a page depends on significance of those referencing it”

• HITS (Kleinberg/IBM)

“Hubs and Authorities”

3 - Link-based ranking techniques

• initialize the rank value of each node to 1/n (0.2 for 5 nodes)

• a node with k outgoing links transmits a 1/k fraction of its current rank value over that edge to its neighbor

• iterate this process many times until it converges

• NOTE: this is a random walk on the link graph

• Pagerank: stationary distribution of this random walk

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Pagerank

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•Attempts to computationally determine hubs and authorities on a particular topic through analysis of a relevant subgraph of the web.

• Based on mutually recursive facts:Hubs point to lots of authorities. Authorities are pointed to by lots of hubs.

Hubs Authorities

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0.286

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..

ConvergenceLeak

Sink

• dealing with leaks:- pruning, or- adding back links

• dealing with sinks- add a random jump to escape sink- with prob. b = 0.15 jump to random node

• assume: if in a leak, always take random jump

• in this case, always converges to unique solution!

• stationary distribution: vector x with xA = x• A is primitive, and x Eigenvector of A

• computed using Jacobi or Gauss Seidel iteration

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Matrix notation

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0 1/2 0 0 1/2

0 0 1/2 0 1/2

0 0 0 1 0

0 0 1/2 0 1/2

1 0 0 0 0

A

• “HITS” (Jon Kleinberg)

(4) Mathematical Techniques

Other iterative techniques for link analysis

• query-dependent: first get 100 docs using term-based techniques

• build a subgraph on these nodes and their neighbors

• run iterative process on this subgraph

• each node has hub score and authority score

• “HITS” (Jon Kleinberg)

(4) Mathematical Techniques

•Authorities are pages that are recognized as providing significant, trustworthy, and useful information on a topic.

•In-degree (number of pointers to a page) is one simple measure of authority.However in-degree treats all links as equal.•Hubs are index pages that provide lots of useful links to relevant content pages (topic authorities).

(4) Mathematical Techniques

• For a query Q, let the set of documents returned by a standard search engine be called the root set R.

• Initialize base set S to R.

• Add to S all pages pointed to by any page in R.

• Add to S all pages that point to any page in R.

R

S

(4) Mathematical Techniques

•To limit computational expense:

– Limit number of root pages to the top 100 pages retrieved for the query.

•To eliminate purely navigational links:

– Eliminate links between two pages on the same host.

•To eliminate “non-authority-conveying” links:

– Allow only m (m 48) pages from a given host as pointers to any individual page.

pqq

qp ha:

qpq

qp ah:

Pagerank vs. HITS

(4) Mathematical Techniques

• Pagerank does not attempt to capture the distinction between hubs and authorities, but ranks just by authority

• Pagerank is applied to the entire web rather than a local neighborhood of result pages

• HITS is sensitive to local topology

• HITS is noisy!1

2

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5

Authority scores of nodes 2 and 4 is lost, and node 4’s authority score vanishes to zero compared to those of nodes 2 and 5.

• many other techniques based on techniques such as markov chains, SVD, principal component analysis

• web graph result of billions of decisions by millions of independent web page creators (or not!)

• data mining of web graphs and web usage patterns

• techniques not really new - analysis of social networks (Katz 1953) - citation analysis (Garfield)

• security-related applications: call pattern analysis

(4) Mathematical Techniques

Other iterative techniques for link analysis