Computer

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The anatomy of a large-scalehypertextual Web searchengine ’
Sergey Brin *, Lawrence Page *Z
Computer

Science

Department.

Stanford

Univer.sity

Stanford.

CA 94305,

USA

Abstract
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure
present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search
results than existing systems.The prototype with a full text and hyperlink databaseof at least 24 million pages is available
at http:llgoogle.stanford.edu/
To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of Web pages
involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importanceof large-scale search engines on the Web, very little academic research has been done on them. Furthermore, due to rapid
advance in technology and Web proliferation, creating a Web search engine today is very different from three years
ago. This paper provides an in-depth description of our large-scale Web search engine - the first such detailed public
description we know of to date.
Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technicalchallenges involved with using the additional information present in hypertext to produce better search results. This paper
addressesthis question of how to build a practical large-scale system which can exploit the additional information present
in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone
can publish anything they want. 0 1998 Published by Elsevier Science B.V. All rights reserved.
Keywords:

World Wide Web; Search engines; Information retrieval; PageRank: Google

1. Introduction
The Web creates new challenges for information
retrieval. The amount of information on the Web isgrowing rapidly, as well as the number of new users
inexperienced in the art of Web research. People are’ Corresponding
author.
’ There are two versions of this paper - a longer full version
and a shorter printed version. The full version is available on the
Web and the conference
CD-ROM.
’ E-mail:(sergey, page] @cs.stanford.edu
0169-7552/9X/$19.00
PII SOl69-7552(98)001

0 1998 Published
IO-X

by Elsevier

ScienceB.V.

likely to surf the Web using its link graph, often starting with high quality human maintained indices suchas Yahoo! 3 or with search engines. Human maintained lists cover popular topics effectively but are
subjective, expensive to build and maintain, slow to
improve, and cannot cover all esoteric topics. Automated search engines that rely on keyword matchingusually return too many low quality matches. To makematters worse, some advertisers attempt to gain people’s attention by taking measures meant to mislead’ http://www.yahoo.com
All rights

reserved.

108

S. Brin,

L. Puge/Computer

Networks

automated search engines. We have built a large-scale
search engine which addresses many of the problemsof existing systems. It makes especially heavy use of
the additional structure present in hypertext to provide
much higher quality search results. We chose our system name, Google, because it is a common spellingof googol, or 10100 and fits well with our goal ofbuilding very large-scale search engines.
1.1. Web search engines - scaling up: 1994-2000

Search engine technology has had to scale dramatically to keep up with the growth of the Web. In
1994, one of the first Web search engines, the World
Wide Web Worm (WWWW) [6] had an index of
110,000 Web pages and Web accessible documents.
As of November. 1997, the top search engines claim
to index from 2 million (WebCrawler) to 100 million
Web documents (from Search Engine Watch4). It
is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a billion
documents. At the same time, the number of queries
search engines handle has grown incredibly too. In
March and April 1994, the World Wide Web Worm
received an average of about 1500 queries per day.
In November 1997, Altavista claimed it handledroughly 20 million queries per day. With the increasing number of users on the Web, and automatedsystems which query search engines, it is likely that
top search engines will handle hundreds of millionsof queries per day by the year 2000. The goal of oursystem is to address many of the problems, both inquality and scalability, introduced by scaling searchengine technology to such extraordinary numbers.

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These tasks are becoming increasingly difficult as
the Web grows. However, hardware performance andcost have improved dramatically to partially offset
the difficulty. There are, however, several notable
exceptions to this progress such as disk seek time andoperating system robustness. In designing Google,
we have considered both the rate of growth of the
Web and technological changes. Google is designed
to scale well to extremely large data sets. It makes
efficient use of storage space to store the index. Itsdata structures are optimized for fast and efficient
access (see Section 4.2). Further, we expect thatthe cost to index and store text or HTML will
eventually decline relative to the amount that will
be available (see Appendix B in the full version).
This will result in favorable scaling properties forcentralized systems like Google.
1.3. Design goals
I .3.1. Improved search quality

Creating a search engine which scales even to today’s Web presents many challenges. Fast crawlingtechnology is needed to gather the Web documents
and keep them up to date. Storage space must be
used efficiently to store indices and, optionally, thedocuments themselves. The indexing system must
process hundreds of gigabytes of data efficiently.
Queries must be handled quickly, at a rate of hundreds to thousands per second.

Our main goal is to improve the quality of Web
search engines. In 1994, some people believed thata complete search index would make it possible tofind anything easily. According to Best of the Web
1994 - Navigators5, “The best navigation service
should make it easy to find almost anything on the
Web (once all the data is entered).” However, theWeb of 1997 is quite different. Anyone who has
used a search engine recently, can readily testify
that the completeness of the index is not the only
factor in the quality of search results. “Junk results”
often wash out any results that a user is interested
in. In fact, as of November 1997. only one of the
top four commercial search engines finds itself (returns its own search page in response to its namein the top ten results). One of the main causes ofthis problem is that the number of documents in
the indices has been increasing by many orders ofmagnitude, but the user’s ability to look at documents has not. People are still only willing to lookat the first few tens of results. Because of this, as
the collection size grows, we need tools that havevery high precision (number of relevant documents
returned, say in the top tens of results). Indeed, wewant our notion of “relevant” to only include the’ http://www.searchenginewatch.com/

5 http://botw.org/l99Wawardslnavigators.html

1.2. Google: scaling with the Web

S. Brftz. L. Pup/Cotttp~rrer

NetM.orks

very best documents since there may be tens of
thousands of slightly relevant documents. This very
high precision is important even at the expense ofrecall (the total number of relevant documents thesystem is able to return). There is quite a bit ofrecent optimism that the use of more hypertextual
information can help improve search and other applications [4.9,12,3]. In particular, link structure 171
and link text provide a lot of information for making
relevance judgments and quality filtering. Google
makes use of both link structure and anchor text (seeSections 2.1 and 2.2).
1.32. Academic search engine research
Aside from tremendous growth, the Web has also
become increasingly commercial over time. In 1993,
1.5% of Web servers were on .com domains. This
number grew to over 60% in 1997. At the same time,
search engines have migrated from the academic
domain to the commercial. Up until now most searchengine development has gone on at companies withlittle publication of technical details. This causes
search engine technology to remain largely a blackart and to be advertising oriented (see Appendix A
in the full version). With Google, we have a strong
goal to push more development and understanding
into the academic realm.
Another important design goal was to build systems that reasonable numbers of people can actuallyuse. Usage was important to us because we thinksome of the most interesting research will involve
leveraging the vast amount of usage data that isavailable from modern Web systems. For example,
there are many tens of millions of searches performed every day. However, it is very difficult to get
this data. mainly because it is considered commercially valuable.
Our final design goal was to build an architecturethat can support novel research activities on largescale Web data. To support novel research uses,
Google stores all of the actual documents it crawls
in compressed form. One of our main goals in designing Google was to set up an environment whereother researchers can come in quickly, process large
chunks of the Web, and produce interesting resultsthat would have been very difficult to produce otherwise. In the short time the system has been up, there
have already been several papers using databases

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generated by Google, and many others are underway.
Another goal we have is to set up a Spacelab-likeenvironment where researchers or even students canpropose and do interesting experiments on our largescale Web data.

2. System features
The Google search engine has two important features that help it produce high precision results. First,
it makes use of the link structure of the Web to calculate a quality ranking for each Web page. Thisranking is called PageRank and is described in detail in [7]. Second, Google utilizes links to improvesearch results.
2.1. PageRank: bringing order to the Weh
The citation (link) graph of the Web is an important resource that has largely gone unused in existing
Web search engines. We have created maps containing as many as 518 million of these hyperlinks, asignificant sample of the total. These maps allow
rapid calculation of a Web page’s “PageRank”, anobjective measure of its citation importance that corresponds well with people’s subjective idea of importance. Because of this correspondence, PageRank is
an excellent way to prioritize the results of Web keyword searches. For most popular subjects, a simple
text matching search that is restricted to Web page
titles performs admirably when PageRank prioritizes
the results (demo available at google.stanford.edu).
For the type of full text searches in the main Google
system, PageRank also helps a great deal.
2.1.1. Description of PageRank calculation
Academic citation literature has been applied to
the Web, largely by counting citations or backlinks
to a given page. This gives some approximation of apage’s importance or quality. PageRank extends thisidea by not counting links from all pages equally,and by normalizing by the number of links on a
page. PageRank is defined as follows:
We assume page A has pages TI...Tn bt*hich point
to ir (i.e., are citations). The parameter d is N
damping j&or which can he .set hetK*ern 0 and 1.
We usually Set d to 0.85. There are tnore details

about d in the ne-vt section. Also C(A) is definedas the number of links going out of page A. The
PageRank of u page A is given as,follows:
PR(A) = (I -d)
PR( Tn)C( Tn) >C(Tl)Note that the PageRanks form a probability distribution over Web pages, so the sum of all Web
pages’ PageRanks will be one.
PageRank or PR(A) can be calculated using asimple iterative algorithm, and corresponds to theprincipal eigenvector of the normalized link matrixof the Web. Also. a PageRank for 26 million Web
pages can be computed in a few hours on a medium
size workstation. There are many other details which
are beyond the scope of this paper.
+d(pPR(TI)+...+-

2.12. ltituitit!e jitst~fificatioi~
PageRank can be thought of as a model of user
behavior. We assume there is a “random surfer” whois given a Web page at random and keeps clicking onlinks. never hitting “back” but eventually gets boredand starts on another random page. The probability
that the random surfer visits a page is its PageRank.
And, the d damping factor is the probability at eachpage the “random surfer” will get bored and request
another random page. One important variation is to
only add the damping factor d to a single page, or agroup of pages. This allows for personalization andcan make it nearly impossible to deliberately mislead
the system in order to get a higher ranking. We haveseveral other extensions to PageRank, again see [7].
Another intuitive justification is that a page canhave a high PageRank if there are many pages thatpoint to it. or if there are some pages that point toit and have a high PageRank. Intuitively, pages thatare well cited from many places around the Web
are worth looking at. Also, pages that have perhaps
only one citation from something like the Yahoo! hhomepage are also generally worth looking at. If apage was not high quality, or was a broken link,
it is quite likely that Yahoo’s homepage would notlink to it. PageRank handles both these cases andeverything in between by recursively propagating
weights through the link structure of the Web.
h http:llwww.yahoo.comi

2.2. Anchor- test
The text of links is treated in a special way inour search engine. Most search engines associate thetext of a link with the page that the link is on. In
addition, we associate it with the page the link points
to. This has several advantages. First, anchors often
provide more accurate descriptions of Web pages
than the pages themselves. Second, anchors may
exist for documents which cannot be indexed by atext-based search engine, such as images, programs.
and databases. This makes it possible to return Web
pages which have not actually been crawled. Notethat pages that have not been crawled can cause
problems. since they are never checked for validity
before being returned to the user. In this case, thesearch engine can even return a page that neveractually existed, but had hyperlinks pointing to it.
However, it is possible to sort the results, so that thisparticular problem rarely happens.
This idea of propagating anchor text to the page
it refers to was implemented in the World Wide Web
Worm [ 61 especially because it helps search non-text
information, and expands the search coverage withfewer downloaded documents. We use anchor propagation mostly because anchor text can help provide
better quality results. Using anchor text efficiently istechnically difficult because of the large amounts ofdata which must be processed. In our current crawlof 24 million pages. we had over 259 million anchors
which we indexed.

3. Related work
Search research on the Web has a short and concise history. The World Wide Web Worm (WWWW)[6] was one of the first Web search engines. It was
subsequently followed by several academic searchengines, many of which are now public companies.
Compared to the growth of the Web and the importance of search engines there are precious few
documents about recent search engines [S]. According to Michael Mauldin (chief scientist, Lycos Inc.)
1.51, “the various services (including Lycos) closely
guard the details of these databases”. However, there
has been a fair amount of work on specific features of search engines. Especially well represented

is work which can get results by post-processing
the results of existing commercial search engines, or
produce small scale “individualized’ search engines.
Finally, there has been a lot of research on information retrieval systems. especially on well controlledcollections [ 111.
However. work on information
retrieval has
mostly been on fairly small. well controlled collections such as the Text Retrieval Conference [lo].
Things that work well on TREC often do not produce
good results on the Web. For example, the standard
vector space model tries to return the document thatmost closely approximates the query, given that bothquery and document are vectors defined by theirword occurrence. On the Web, this strategy often
returns very short documents that are the query plus
a few words. For example. we have seen a major
search engine return a page containing only “Bill
Clinton Sucks” and picture from a “Bill Clinton”
query. Given examples like these, we believe thatthe standard information retrieval work needs to beextended to deal effectively with the Web.
The Web is a vast collection of completely uncontrolled heterogeneous documents. Documents varysignificantly in language, format, and style. Therecan be many orders of magnitude of difference intwo documents’ size, quality, popularity, and trustworthiness. All of these are significant challenges toeffective searching on the Web. They are somewhat
mediated by the availability of auxiliary data such as
hyperlinks and formatting and Google tries to takeadvantage of both of these.

4. System anatomy

In this section, we will give a high level overview
of how the whole system works as pictured in Fig. 1.
Further sections will discuss the applications anddata structures not mentioned in this section. Mostof Google is implemented in C or C++ for efficiency
and can run in either Solaris or Linux.
In Google, the Web crawling (downloading of
Web pages) is done by several distributed crawlers.
There is a URLserver that sends lists of URLs to
be fetched to the crawlers. The Web pages that areFig

I Highlevel Goo$learchitecture

fetched are then sent to the storeserver. The storeserver then compresses and stores the Web pages into
a repository. Every Web page has an associated 1D
number called a docID which is assigned whenevera new URL is parsed out of a Web page. The indexing function is performed by the indexer and thesorter. The indexer performs a number of functions.
It reads the repository, uncompresses the documents.
and parses them. Each document is converted into a
set of word occurrences called hits. The hits record
the word, position in document, an approximation of
font size, and capitalization. The indexer distributes
these hits into a set of “barrels”, creating a partiallysorted forward index. The indexer performs anotherimportant function. It parses out all the links in every
Web page and stores important information about
them in an anchors tile. This file contains enough information to determine where each link points fromand to. and the text of the link.
The URLresolver reads the anchors tile and converts relative URLs into absolute URLs and in turninto doclDs. It puts the anchor text into the forward
index, associated with the docfD that the anchor
points to. It also generates a database of links which
are pairs of docIDs. The links database is used tocompute PageRanks for all the documents.

The sorter takes the barrels, which are sorted by
docID (this is a simplification, see Section 4.2.5 in
the full version), and resorts them by wordID togenerate the inverted index. This is done in placeso that little temporary space is needed for this operation. The sorter also produces a list of wordIDs
and offsets into the inverted index. A program called
DumpLexicon takes this list together with the lexicon produced by the indexer and generates a new
lexicon to be used by the searcher. The searcher isrun by a Web server and uses the lexicon built by
DumpLexicon together with the inverted index and
the PageRanks to answer queries.

Google’s data structures are optimized so that alarge document collection can be crawled, indexed.
and searched with little cost. Although, CPUs andbulk input output rates have improved dramaticallyover the years, a disk seek still requires about 10 ms
to complete. Google is designed to avoid disk seeks
whenever possible, and this has had a considerable
influence on the design of the data structures. The
full version of this paper contains a detailed discussion of all the major data structures. We only give a
brief overview here.
Almost all of the data for Google is stored in
Bigfiles which are virtual tiles we developed that can
span multiple tile systems and support compression.
The raw HTML repository uses roughly half of thenecessary storage. It consists of the concatenation of
the compressed HTML of every page, preceded bya small header. The document index keeps information about each document. It is a fixed width ISAM
(Index sequential access mode) index, ordered by
doclD. The information stored in each entry includes
the current document status, a pointer into the repository, a document checksum, and various statistics.
Variable width information such as URL and title
is kept in a separate file. There is also an auxiliaryindex to convert URLs into docIDs. The lexicon
has several different forms for different operations.
They all are memory-based hash tables with varying
values attached to each word.
A hit list corresponds to a list of occurrences ofa particular word in a particular document including position, font, and capitalization information. Hit

lists account for most of the space used in both theforward and the inverted indices. Because of this, it
is important to represent them as efficiently as possible. We considered several alternatives for encoding
position, font, and capitalization - simple encoding(a triple of integers), a compact encoding (a hand
optimized allocation of bits), and Huffman coding.
In the end we chose a hand optimized compact encoding since it required far less space than the simple
encoding and far less bit manipulation than Huffman
coding.ding. Our compact coding uses two bytes forevery hit. The details of this coding are in the fullversion of this paper. The length of a hit list is stored
before the hits themselves. To save space, the lengthof the hit list is combined with the wordID in theforward index and the docID in the inverted index.
The forward index is actually already partiallysorted. It is stored in a number of barrels (we used64). Each barrel holds a range of wordIDs. If a document contains words that fall into a particular barrel,
the docID is recorded into the barrel, followed by alist of wordIDs with hitlists which correspond to thosewords. This scheme requires slightly more storage because of duplicated docIDs but the difference is verysmall for a reasonable number of buckets and saves
considerable time and coding complexity in the tinalindexing phase done by the sorter. The inverted index
consists of the same barrels as the forward index. except that they have been processed by the sorter. For
every valid wordID, the lexicon contains a pointerinto the barrel that wordID falls into. It points to alist of docIDs together with their corresponding hit
lists. This list is called a doclist and represents all theoccurrences of that word in all documents.
An important issue is in what order the doclDs
should appear in the doclist. One simple solution
is to store them sorted by docID. This allows forquick merging of different doclists for multiple word
queries. Another option is to store them sorted bya ranking of the occurrence of the word in eachdocument. This makes answering one word queries
trivial and makes it likely that the answers to multiple
word queries are near the start. However, merging ismuch more difticult. Also. this makes development
much more difficult in that a change to the ranking
function requires a rebuild of the index. We chosea compromise between these options, keeping two
sets of inverted barrels - one set for hit lists which

S. Brin.

L. Pugr/Computrr

Netwurks

include title or anchor hits and another set for all
hit lists. This way. we check the first set of barrels
tirst and if there are not enough matches within thosebarrels we check the larger ones.
4.3. Crmvling the Web
Running a Web crawler is a challenging task.
There are tricky performance and reliability issues
and even more importantly, there are social issues.
Crawling is the most fragile application since it
involves interacting with hundreds of thousands of
Web servers and various name servers which are all
beyond the control of the system.
In order to scale to hundreds of millions of Web
pages, Google has a fast distributed crawling system. A single URLserver serves lists of URLs to anumber of crawlers (we typically ran about 3). Boththe URLserver and the crawlers are implemented in
Python. Each crawler keeps roughly 300 connections
open at once. This is necessary to retrieve Web pages
at a fast enough pace. At peak speeds, the system
can crawl over 100 Web pages per second using four
crawlers. A major performance stress is DNS lookup
so each crawler maintains a DNS cache. Each of thehundreds of connections can be in a number of different states: looking up DNS, connecting to host, sending request. and receiving response. These factors
make the crawler a complex component of the system.
It uses asynchronous IO to manage events, and a number of queues to move page fetches from state to state.
The more than half million servers that we crawl
are run by tens of thousands of Webmasters. As
a result crawling the Web involves interacting witha fair number of people. Almost daily we receive
emails like “Wow. you looked at a lot of pages
from my Web site. How did you like it?’ Other
interactions involve copyright issues and obscurebugs which may only arise on one page out often million. Since large complex systems such as
crawlers will invariably cause problems, there needs
to be significant resources devoted to reading theemail and solving these problems as they come up.
4.4. Searching
The goal of searching is to provide quality search
results efficiently. Many of the large commercial

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search engines seemed to have made great progressin terms of efficiency. Therefore, we have focusedmore on quality of search in our research, although
we believe our solutions are scalable to commercial
volumes with a bit more effort.
Google maintains much more information about
Web documents than typical search engines. Every hitlist includes position, font, and capitalization
information. Additionally, we factor in hits fromanchor text and the PageRank of the document.
Combining all of this information into a rank is difficult. We designed our ranking function so that noone factor can have too much influence. For every
matching document we compute counts of hits 01
different types at different proximity levels. These
counts are then run through a series of lookup tables
and eventually are transformed into a rank. This process involves many tunable parameters. We have notspent much time tuning the system; instead we havedeveloped a feedback system which will help us tune
these parameters in the future.

5. Results and performance
The most important measure of a search engine
is the quality of its search results. While a complete
user evaluation is beyond the scope of this paper,
our own experience with Google has shown it toproduce better results than the major commercial
search engines for most searches. As an example
which illustrates the use of PageRank, anchor text,
and proximity, Fig. 2 shows Google’s results for asearch on “bill Clinton”. These results demonstrates
some of Google’s features. The results are clustered by server. This helps considerably when sifting
through result sets. A number of results are fromthe whitehouse.gov domain which is what one may
reasonably expect from such a search. Currently,
most major commercial search engines do not return
any results from whitehouse.gov, much less the rightones. Notice that there is no title for the first result.
Instead, Google relied on anchor text to determine
this was a good answer to the query. Similarly, thefifth result is an email address which, of course. is
not crawlable. It is also a result of anchor text.
All of the results are reasonably high quality
pages and. at last check, none were broken links.

Query: bill clinton
httn:/iwww.whitehouse.gov:’
100.00% (no date) (OK)
http:llwww.whitehouse.govl
Office of the President
99.67%~(Dee 23 1996) (2K)
http:/iwww.whitehouse.govWH/EOP/OPlhtmllOP_Home.htm~
Welcome To The White House
99.98% (Nov 09 1997) (5K)
http:llwww.whitehouse.govAVHAVelcome.html
Send Electronic Mail to the President
99.86% s(Jul 14 1997) (5K)

mailto:nresident(~whitehouse.aov
99.98% mailto:President&whitehouse.aov
99.27% The “Unofficial” Bill Clinton
94.06’/(Nov 11 1997) (14K)
http:ilzpub.comlunlun-bc.html
Bill Clinton Meets The Shrinks
86.27% si(Jun 29 1997) (63K)
http://zpub.com/unlun-bc9.html
PresidentBill Clinton - The Dark Side
97.27% (Nov 10 1997) (15K)
http:lAvww.realchange.orgiclinton.htm$3 Bill Clinton
94.73% P(no date) (4K)
http:/iwww.gatewy.net/-tjohnsonIc/clintonl.html
Fig. 2. Samplr

This is largely because they all have high PageRank.
The PageRanks are the percentages in red alongwith bar graphs. Finally. there are no results abouta Bill other than Clinton or about a Clinton otherthan Bill. This is because we place heavy importanceon the proximity of word occurrences. Of course atrue test of the quality of a search engine would
involve an extensive user study or results analysis
which we do not have room for here. Instead. we
invite the reader to try Google for themselves at
http://google.stanford.edu.

rrultsfrom

Googlr.

Aside from search quality, Google is designed toscale cost effectively to the size of the Web as it
grows. One aspect of this is to use storage efficiently.
Table 1 has a breakdown of some statistics andstorage requirements of Google.
It is important for a search engine to crawi and index efficiently. This way information can be kept up
to date and major changes to the system can be tested
relatively quickly. In total it took roughly 9 days todownload the 26 million pages (including errors).
However. once the system was running smoothly.

intend to speed up Google considerably in the future. Table 2 has some sample query times from thecurrent version of Google.

Table I
Statistics
Storagehtiltistics

Total

srze of fetched

pages

117.8 GB5.33 GB

Compreabedrepoaitoq
Short inverted index
Full inverted
index
LexiconTemporary

6. Conclusions

4.1 cl337.’ GB
203 MBanchor

Google is designed to be a scalable search engine. The primary goal is to provide high quality
search results over a rapidly growing World Wide
Web. Google employs a number of techniques toimprove search quality including page rank. allchar text. and proximity information. Furthermore,
Google is a complete architecture for gathering Web
pages, indexing them, and performing search queries
over them.

data(not in total 1

6.6 GB

Document
index incl.
variahlc
width data
Links database

Y.7 GB3.‘) GB

Total
Tmal

without
repositoryw.nh rcpoaitorp

55.2 GB108.7 GBWeb

page statihticx

NumberNumberof Web pagesof tirls seen

NumberNumberof E-mailof 404‘sfetched

3-1 million
76.5

addresses

millionI .7 million
I .6 million

it ran much faster, downloading the last 11 million
pages in just 63 hours, averaging just over 4 million
pages per day or 48.5 pages per second. The indexer
runs at roughly 54 pages per second. The sorters canbe run completely in parallel; using four machines,
the whole process of sorting takes about 24 hours.
Improving the performance of search was not the
major focus of our research up to this point. The
current version of Google answers most queries in
between 1 and 10 seconds. This time is mostly dominated by disk IO over NFS (since our disks are
spread over a number of machines). Furthermore,
Google does not have many of the common optimizations used to speed up information retrieval
systems. such as query caching, subindices on common terms. and other common optimizations. We

Quer!

Initial

query

CPU

time

0.09
I .77
0.15
1.31

A large-scale Web search engine is a complex system and much remains to be done. Our immediate
goals are to improve search efficiency and to scale toapproximately 100 million Web pages. Some simple
improvements to efficiency include query caching,smart disk allocation, and subindices. Another area
which requires much research is updates. We musthave smart algorithms to decide what old Web pages
should be recrawled and what new ones should becrawled. Work toward this goal has been done in[2]. One promising area of research is using proxycaches to build search databases. since they aredemand driven. We are planning to add simple features supported by commercial search engines likeboolean operators. negation. and stemming. However. other features are just starting to be exploredsuch as relevance feedback and clustering (Google
currently supports a simple hostname based clustering). We also plan to support user context (like theSame query
(5)

Total

time

(s)

CPU

3.13
3.X-I

0.06
I.66

1.86
9.63

0.20
I.16

time

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user’s location), and result summarization. We arealso working to extend the use of link structure andlink text. Simple experiments indicate PageRank canbe personalized by increasing the weight of a user’s
home page or bookmarks. As for link text, we areexperimenting with using text surrounding links inaddition to the link text itself. A Web search engine
is a very rich environment for research ideas. We
have far too many to list here so we do not expect
this Future Work section to become much shorter in
the near future.

memory access, memory capacity, disk seeks. disk
throughput, disk capacity, and network IO. Google
has evolved to overcome a number of these bottlenecks during various operations. Google’s major
data structures make efficient use of available storage space. Furthermore, the crawling, indexing. andsorting operations are efficient enough to be able tobuild an index of a substantial portion of the Web 24 million pages, in less than one week. We expect
to be able to build an index of 100 million pages inless than a month.

6.2. High quality search

6.4. A wsearch tool

The biggest problem facing users of Web searchengines today is the quality of the results they getback. While the results are often amusing and expand users’ horizons, they are often frustrating andconsume precious time. For example, the top result for a search for “Bill Clinton” on one of themost popular commercial search engines was the

In addition to being a high quality search engine.
Google is a research tool. The data Google hascollected has already resulted in many other papers
submitted to conferences and many more on the way.
Recent research such as [ 11 has shown a number oflimitations to queries about the Web that may be
answered without having the Web available locally.
This means that Google (or a similar system) is not
only a valuable research tool but a necessary one
for a wide range of applications. We hope Google
will be a resource for searchers and researchers allaround the world and will spark the next generationof search engine technology.

Bill Clinton

Joke of the Day: April

14, 1997 ‘.

Google is designed to provide higher quality searchso as the Web continues to grow rapidly, information can be found easily. In order to accomplish this
Google makes heavy use of hypertextual informationconsisting of link structure and link (anchor) text.
Google also uses proximity and font information.
While evaluation of a search engine is difficult, wehave subjectively found that Google returns higher
quality search results than current commercial searchengines. The analysis of link structure via PageRank
allows Google to evaluate the quality of Web pages.
The use of link text as a description of what the linkpoints to helps the search engine return relevant (andto some degree high quality) results. Finally, the useof proximity information helps increase relevance agreat deal for many queries.
6.3. Scalable urchitecturr
Aside from the quality of search, Google is designed to scale. It must be efficient in both space
and time, and constant factors are very importantwhen dealing with the entire Web. In implementing Google, we have seen bottlenecks in CPU,
7 http://www.io.com/-cjburke/clinton/9704l4.html

Acknowledgments
Scott Hassan and Alan Steremberg have been critical to the development of Google. Their talented
contributions are irreplaceable, and the authors owe
them much gratitude. We would also like to thank
Hector Garcia-Molina, Rajeev Motwani, Jeff Ullman, and Terry Winograd and the whole WebBase
group for their support and insightful discussions.
Finally we would like to recognize the generous
support of our equipment donors IBM, Intel, andSun and our funders. The research described here
was conducted as part of the Stanford Integrated
Digital Library Project, supported by the National
Science Foundation under Cooperative Agreement
IRI-94 11306. Funding for this cooperative agreement is also provided by DARPA and NASA, and by
Interval Research, and the industrial partners of the
Stanford Digital Libraries Project.

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