The Anatomy of a Large-Scale Hypertextual Web Search Engine
Sergey Brin and Lawrence Page
{sergey, page}@cs.stanford.edu
Computer Science Department, Stanford University, Stanford, CA 94305
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
database of at least 24 million pages is available at
http://google.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 importance of 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 technical
challenges involved with using the additional information present in
hypertext to produce better search results. This paper addresses
this 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.
Keywords: World Wide Web, Search Engines, Information Retrieval,
PageRank, Google
1. Introduction
(Note: 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.)
The web creates new challenges for information retrieval. The amount of
information on the web is growing rapidly, as well as the number of new
users inexperienced in the art of web research. People are likely to
surf the web using its link graph, often starting with high quality
human maintained indices such as Yahoo! 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 matching usually return too many low quality matches. To make
matters worse, some advertisers attempt to gain people's attention by
taking measures meant to mislead automated search engines. We have built
a large-scale search engine which addresses many of the problems of
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
spelling of googol, or 10100 and fits well with our goal of building
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) [McBryan 94]
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 Watch)
. 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 handled roughly 20 million queries per day.
With the increasing number of users on the web, and automated systems
which query search engines, it is likely that top search engines will
handle hundreds of millions of queries per day by the year 2000. The
goal of our system is to address many of the problems, both in quality
and scalability, introduced by scaling search engine technology to such
extraordinary numbers.
1.2. Google: Scaling with the Web
Creating a search engine which scales even to today's web presents many
challenges. Fast crawling technology is needed to gather the web
documents and keep them up to date. Storage space must be used
efficiently to store indices and, optionally, the documents 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.
These tasks are becoming increasingly difficult as the Web grows.
However, hardware performance and cost have improved dramatically to
partially offset the difficulty. There are, however, several notable
exceptions to this progress such as disk seek time and operating 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. Its data structures are optimized for fast and
efficient access (see section 4.2 <#data>). Further, we expect that the
cost to index and store text or HTML will eventually decline relative to
the amount that will be available (see Appendix B <#b>). This will
result in favorable scaling properties for centralized systems like Google.
1.3 Design Goals
1.3.1 Improved Search Quality
Our main goal is to improve the quality of web search engines. In 1994,
some people believed that a complete search index would make it possible
to find anything easily. According to Best of the Web 1994 --
Navigators, "The best
navigation service should make it easy to find almost anything on the
Web (once all the data is entered)." However, the Web 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 name in the top ten results). One of the
main causes of this problem is that the number of documents in the
indices has been increasing by many orders of magnitude, but the user's
ability to look at documents has not. People are still only willing to
look at the first few tens of results. Because of this, as the
collection size grows, we need tools that have very high precision
(number of relevant documents returned, say in the top tens of results).
Indeed, we want our notion of "relevant" to only include the very best
documents since there may be tens of thousands of slightly relevant
documents. This very high precision is important even at the expense of
recall (the total number of relevant documents the system is able to
return). There is quite a bit of recent optimism that the use of more
hypertextual information can help improve search and other applications
[Marchiori 97 <#ref>] [Spertus 97 <#ref>] [Weiss 96 <#ref>] [Kleinberg
98 <#ref>]. In particular, link structure [Page 98 <#ref>] 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 (see
Sections 2.1 <#pr> and 2.2 <#anchor>).
1.3.2 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 search engine development has gone on at companies with little
publication of technical details. This causes search engine technology
to remain largely a black art and to be advertising oriented (see
Appendix A <#a>). 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 actually use. Usage was important to us because we
think some of the most interesting research will involve leveraging the
vast amount of usage data that is available 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 architecture that can support
novel research activities on large-scale 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 where other researchers can come in quickly, process
large chunks of the web, and produce interesting results that 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
generated by Google, and many others are underway. Another goal we have
is to set up a Spacelab-like environment where researchers or even
students can propose and do interesting experiments on our large-scale
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. This ranking is
called PageRank and is described in detail in [Page 98]. Second, Google
utilizes link to improve search results.
2.1 PageRank: Bringing Order to the Web
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, a significant
sample of the total. These maps allow rapid calculation of a web page's
"PageRank", an objective 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 a page's importance or quality. PageRank extends this
idea 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 T1...Tn which point to it (i.e., are
citations). The parameter d is a damping factor which can be set
between 0 and 1. We usually set d to 0.85. There are more details
about d in the next section. Also C(A) is defined as the number of
links going out of page A. The PageRank of a page A is given as
follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
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 a simple iterative algorithm,
and corresponds to the principal eigenvector of the normalized link
matrix of 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.
2.1.2 Intuitive Justification
PageRank can be thought of as a model of user behavior. We assume there
is a "random surfer" who is given a web page at random and keeps
clicking on links, never hitting "back" but eventually gets bored and
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 each page 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 a group of pages. This allows for
personalization and can make it nearly impossible to deliberately
mislead the system in order to get a higher ranking. We have several
other extensions to PageRank, again see [Page 98 <#ref>].
Another intuitive justification is that a page can have a high PageRank
if there are many pages that point to it, or if there are some pages
that point to it and have a high PageRank. Intuitively, pages that are
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!
homepage are also generally worth looking at. If
a page was not high quality, or was a broken link, it is quite likely
that Yahoo's homepage would not link to it. PageRank handles both these
cases and everything in between by recursively propagating weights
through the link structure of the web.
2.2 Anchor Text
The text of links is treated in a special way in our search engine. Most
search engines associate the text 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 a text-based search
engine, such as images, programs, and databases. This makes it possible
to return web pages which have not actually been crawled. Note that
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, the search engine can even return a page that never actually
existed, but had hyperlinks pointing to it. However, it is possible to
sort the results, so that this particular problem rarely happens.
This idea of propagating anchor text to the page it refers to was
implemented in the World Wide Web Worm [McBryan 94 <#ref>] especially
because it helps search non-text information, and expands the search
coverage with fewer downloaded documents. We use anchor propagation
mostly because anchor text can help provide better quality results.
Using anchor text efficiently is technically difficult because of the
large amounts of data which must be processed. In our current crawl of
24 million pages, we had over 259 million anchors which we indexed.
2.3 Other Features
Aside from PageRank and the use of anchor text, Google has several other
features. First, it has location information for all hits and so it
makes extensive use of proximity in search. Second, Google keeps track
of some visual presentation details such as font size of words. Words in
a larger or bolder font are weighted higher than other words. Third,
full raw HTML of pages is available in a repository.
3 Related Work
Search research on the web has a short and concise history. The World
Wide Web Worm (WWWW) [McBryan 94]
was one of
the first web search engines. It was subsequently followed by several
other academic search engines, 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 [Pinkerton
94 ]. According to Michael
Mauldin (chief scientist, Lycos Inc) [Mauldin]
,
"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 controlled collections. In the
next two sections, we discuss some areas where this research needs to be
extended to work better on the web.
3.1 Information Retrieval
Work in information retrieval systems goes back many years and is well
developed [Witten 94 <#ref>]. However, most of the research on
information retrieval systems is on small well controlled homogeneous
collections such as collections of scientific papers or news stories on
a related topic. Indeed, the primary benchmark for information
retrieval, the Text Retrieval Conference [TREC 96 <#ref>], uses a fairly
small, well controlled collection for their benchmarks. The "Very Large
Corpus" benchmark is only 20GB compared to the 147GB from our crawl of
24 million web pages. 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 that most closely approximates the query,
given that both query and document are vectors defined by their word
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. Some argue that on the web, users
should specify more accurately what they want and add more words to
their query. We disagree vehemently with this position. If a user issues
a query like "Bill Clinton" they should get reasonable results since
there is a enormous amount of high quality information available on this
topic. Given examples like these, we believe that the standard
information retrieval work needs to be extended to deal effectively with
the web.
3.2 Differences Between the Web and Well Controlled Collections
The web is a vast collection of completely uncontrolled heterogeneous
documents. Documents on the web have extreme variation internal to the
documents, and also in the external meta information that might be
available. For example, documents differ internally in their language
(both human and programming), vocabulary (email addresses, links, zip
codes, phone numbers, product numbers), type or format (text, HTML, PDF,
images, sounds), and may even be machine generated (log files or output
from a database). On the other hand, we define external meta information
as information that can be inferred about a document, but is not
contained within it. Examples of external meta information include
things like reputation of the source, update frequency, quality,
popularity or usage, and citations. Not only are the possible sources of
external meta information varied, but the things that are being measured
vary many orders of magnitude as well. For example, compare the usage
information from a major homepage, like Yahoo's which currently receives
millions of page views every day with an obscure historical article
which might receive one view every ten years. Clearly, these two items
must be treated very differently by a search engine.
Another big difference between the web and traditional well controlled
collections is that there is virtually no control over what people can
put on the web. Couple this flexibility to publish anything with the
enormous influence of search engines to route traffic and companies
which deliberately manipulating search engines for profit become a
serious problem. This problem that has not been addressed in traditional
closed information retrieval systems. Also, it is interesting to note
that metadata efforts have largely failed with web search engines,
because any text on the page which is not directly represented to the
user is abused to manipulate search engines. There are even numerous
companies which specialize in manipulating search engines for profit.
4 System Anatomy
First, we will provide a high level discussion of the architecture.
Then, there is some in-depth descriptions of important data structures.
Finally, the major applications: crawling, indexing, and searching will
be examined in depth.
Figure 1. High Level Google Architecture
4.1 Google Architecture Overview
In this section, we will give a high level overview of how the whole
system works as pictured in Figure 1. Further sections will discuss the
applications and data structures not mentioned in this section. Most of
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 are 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 ID
number called a docID which is assigned whenever a new URL is parsed out
of a web page. The indexing function is performed by the indexer and the
sorter. 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 partially sorted forward index. The indexer
performs another important function. It parses out all the links in
every web page and stores important information about them in an anchors
file. This file contains enough information to determine where each link
points from and to, and the text of the link.
The URLresolver reads the anchors file and converts relative URLs into
absolute URLs and in turn into docIDs. It puts the anchor text into the
forward index, associated with the docID that the anchor points to. It
also generates a database of links which are pairs of docIDs. The links
database is used to compute PageRanks for all the documents.
The sorter takes the barrels, which are sorted by docID (this is a
simplification, see Section 4.2.5 <#hits>), and resorts them by wordID
to generate the inverted index. This is done in place so 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 is run by a web server and uses the lexicon built by
DumpLexicon together with the inverted index and the PageRanks to answer
queries.
4.2 Major Data Structures
Google's data structures are optimized so that a large document
collection can be crawled, indexed, and searched with little cost.
Although, CPUs and bulk input output rates have improved dramatically
over 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.
4.2.1 BigFiles
BigFiles are virtual files spanning multiple file systems and are
addressable by 64 bit integers. The allocation among multiple file
systems is handled automatically. The BigFiles package also handles
allocation and deallocation of file descriptors, since the operating
systems do not provide enough for our needs. BigFiles also support
rudimentary compression options.
4.2.2 Repository
Figure 2. Repository Data Structure
The repository contains the full HTML of every web page. Each page is
compressed using zlib (see RFC1950
). The
choice of compression technique is a tradeoff between speed and
compression ratio. We chose zlib's speed over a significant improvement
in compression offered by bzip . The
compression rate of bzip was approximately 4 to 1 on the repository as
compared to zlib's 3 to 1 compression. In the repository, the documents
are stored one after the other and are prefixed by docID, length, and
URL as can be seen in Figure 2. The repository requires no other data
structures to be used in order to access it. This helps with data
consistency and makes development much easier; we can rebuild all the
other data structures from only the repository and a file which lists
crawler errors.
4.2.3 Document Index
The document index keeps information about each document. It is a fixed
width ISAM (Index sequential access mode) index, ordered by docID. The
information stored in each entry includes the current document status, a
pointer into the repository, a document checksum, and various
statistics. If the document has been crawled, it also contains a pointer
into a variable width file called docinfo which contains its URL and
title. Otherwise the pointer points into the URLlist which contains just
the URL. This design decision was driven by the desire to have a
reasonably compact data structure, and the ability to fetch a record in
one disk seek during a search
Additionally, there is a file which is used to convert URLs into docIDs.
It is a list of URL checksums with their corresponding docIDs and is
sorted by checksum. In order to find the docID of a particular URL, the
URL's checksum is computed and a binary search is performed on the
checksums file to find its docID. URLs may be converted into docIDs in
batch by doing a merge with this file. This is the technique the
URLresolver uses to turn URLs into docIDs. This batch mode of update is
crucial because otherwise we must perform one seek for every link which
assuming one disk would take more than a month for our 322 million link
dataset.
4.2.4 Lexicon
The lexicon has several different forms. One important change from
earlier systems is that the lexicon can fit in memory for a reasonable
price. In the current implementation we can keep the lexicon in memory
on a machine with 256 MB of main memory. The current lexicon contains 14
million words (though some rare words were not added to the lexicon). It
is implemented in two parts -- a list of the words (concatenated
together but separated by nulls) and a hash table of pointers. For
various functions, the list of words has some auxiliary information
which is beyond the scope of this paper to explain fully.
4.2.5 Hit Lists
A hit list corresponds to a list of occurrences of a particular word in
a particular document including position, font, and capitalization
information. Hit lists account for most of the space used in both the
forward 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. The
details of the hits are shown in Figure 3.
Our compact encoding uses two bytes for every hit. There are two types
of hits: fancy hits and plain hits. Fancy hits include hits occurring in
a URL, title, anchor text, or meta tag. Plain hits include everything
else. A plain hit consists of a capitalization bit, font size, and 12
bits of word position in a document (all positions higher than 4095 are
labeled 4096). Font size is represented relative to the rest of the
document using three bits (only 7 values are actually used because 111
is the flag that signals a fancy hit). A fancy hit consists of a
capitalization bit, the font size set to 7 to indicate it is a fancy
hit, 4 bits to encode the type of fancy hit, and 8 bits of position. For
anchor hits, the 8 bits of position are split into 4 bits for position
in anchor and 4 bits for a hash of the docID the anchor occurs in. This
gives us some limited phrase searching as long as there are not that
many anchors for a particular word. We expect to update the way that
anchor hits are stored to allow for greater resolution in the position
and docIDhash fields. We use font size relative to the rest of the
document because when searching, you do not want to rank otherwise
identical documents differently just because one of the documents is in
a larger font.
Figure 3. Forward and Reverse Indexes and the Lexicon
The length of a hit list is stored before the hits themselves. To save
space, the length of the hit list is combined with the wordID in the
forward index and the docID in the inverted index. This limits it to 8
and 5 bits respectively (there are some tricks which allow 8 bits to be
borrowed from the wordID). If the length is longer than would fit in
that many bits, an escape code is used in those bits, and the next two
bytes contain the actual length.
4.2.6 Forward Index
The forward index is actually already partially sorted. It is stored in
a number of barrels (we used 64). Each barrel holds a range of wordID's.
If a document contains words that fall into a particular barrel, the
docID is recorded into the barrel, followed by a list of wordID's with
hitlists which correspond to those words. This scheme requires slightly
more storage because of duplicated docIDs but the difference is very
small for a reasonable number of buckets and saves considerable time and
coding complexity in the final indexing phase done by the sorter.
Furthermore, instead of storing actual wordID's, we store each wordID as
a relative difference from the minimum wordID that falls into the barrel
the wordID is in. This way, we can use just 24 bits for the wordID's in
the unsorted barrels, leaving 8 bits for the hit list length.
4.2.7 Inverted Index
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 pointer into the barrel that wordID falls
into. It points to a doclist of docID's together with their
corresponding hit lists. This doclist represents all the occurrences of
that word in all documents.
An important issue is in what order the docID's should appear in the
doclist. One simple solution is to store them sorted by docID. This
allows for quick merging of different doclists for multiple word
queries. Another option is to store them sorted by a ranking of the
occurrence of the word in each document. This makes answering one word
queries trivial and makes it likely that the answers to multiple word
queries are near the start. However, merging is much more difficult.
Also, this makes development much more difficult in that a change to the
ranking function requires a rebuild of the index. We chose a compromise
between these options, keeping two sets of inverted barrels -- one set
for hit lists which include title or anchor hits and another set for all
hit lists. This way, we check the first set of barrels first and if
there are not enough matches within those barrels we check the larger ones.
4.3 Crawling 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 a number of crawlers (we typically ran about 3). Both the
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. This amounts to roughly
600K per second of data. A major performance stress is DNS lookup. Each
crawler maintains a its own DNS cache so it does not need to do a DNS
lookup before crawling each document. Each of the hundreds 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.
It turns out that running a crawler which connects to more than half a
million servers, and generates tens of millions of log entries generates
a fair amount of email and phone calls. Because of the vast number of
people coming on line, there are always those who do not know what a
crawler is, because this is the first one they have seen. Almost daily,
we receive an email something like, "Wow, you looked at a lot of pages
from my web site. How did you like it?" There are also some people who
do not know about the robots exclusion protocol
, and
think their page should be protected from indexing by a statement like,
"This page is copyrighted and should not be indexed", which needless to
say is difficult for web crawlers to understand. Also, because of the
huge amount of data involved, unexpected things will happen. For
example, our system tried to crawl an online game. This resulted in lots
of garbage messages in the middle of their game! It turns out this was
an easy problem to fix. But this problem had not come up until we had
downloaded tens of millions of pages. Because of the immense variation
in web pages and servers, it is virtually impossible to test a crawler
without running it on large part of the Internet. Invariably, there are
hundreds of obscure problems which may only occur on one page out of the
whole web and cause the crawler to crash, or worse, cause unpredictable
or incorrect behavior. Systems which access large parts of the Internet
need to be designed to be very robust and carefully tested. Since large
complex systems such as crawlers will invariably cause problems, there
needs to be significant resources devoted to reading the email and
solving these problems as they come up.
4.4 Indexing the Web
* Parsing -- Any parser which is designed to run on the entire Web
must handle a huge array of possible errors. These range from
typos in HTML tags to kilobytes of zeros in the middle of a tag,
non-ASCII characters, HTML tags nested hundreds deep, and a great
variety of other errors that challenge anyone's imagination to
come up with equally creative ones. For maximum speed, instead of
using YACC to generate a CFG parser, we use flex to generate a
lexical analyzer which we outfit with its own stack. Developing
this parser which runs at a reasonable speed and is very robust
involved a fair amount of work.
* Indexing Documents into Barrels -- After each document is parsed,
it is encoded into a number of barrels. Every word is converted
into a wordID by using an in-memory hash table -- the lexicon. New
additions to the lexicon hash table are logged to a file. Once the
words are converted into wordID's, their occurrences in the
current document are translated into hit lists and are written
into the forward barrels. The main difficulty with parallelization
of the indexing phase is that the lexicon needs to be shared.
Instead of sharing the lexicon, we took the approach of writing a
log of all the extra words that were not in a base lexicon, which
we fixed at 14 million words. That way multiple indexers can run
in parallel and then the small log file of extra words can be
processed by one final indexer.
* Sorting -- In order to generate the inverted index, the sorter
takes each of the forward barrels and sorts it by wordID to
produce an inverted barrel for title and anchor hits and a full
text inverted barrel. This process happens one barrel at a time,
thus requiring little temporary storage. Also, we parallelize the
sorting phase to use as many machines as we have simply by running
multiple sorters, which can process different buckets at the same
time. Since the barrels don't fit into main memory, the sorter
further subdivides them into baskets which do fit into memory
based on wordID and docID. Then the sorter, loads each basket into
memory, sorts it and writes its contents into the short inverted
barrel and the full inverted barrel.
4.5 Searching
The goal of searching is to provide quality search results efficiently.
Many of the large commercial search engines seemed to have made great
progress in terms of efficiency. Therefore, we have focused more on
quality of search in our research, although we believe our solutions are
scalable to commercial volumes with a bit more effort. The google query
evaluation process is show in Figure 4.
1. Parse the query.
2. Convert words into wordIDs.
3. Seek to the start of the doclist in the short barrel for every word.
4. Scan through the doclists until there is a document that matches
all the search terms.
5. Compute the rank of that document for the query.
6. If we are in the short barrels and at the end of any doclist, seek
to the start of the doclist in the full barrel for every word and
go to step 4.
7. If we are not at the end of any doclist go to step 4.
Sort the documents that have matched by rank and return the top k.
Figure 4. Google Query Evaluation
To put a limit on response time, once a certain number (currently
40,000) of matching documents are found, the searcher automatically goes
to step 8 in Figure 4. This means that it is possible that sub-optimal
results would be returned. We are currently investigating other ways to
solve this problem. In the past, we sorted the hits according to
PageRank, which seemed to improve the situation.
4.5.1 The Ranking System
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 from anchor
text and the PageRank of the document. Combining all of this information
into a rank is difficult. We designed our ranking function so that no
particular factor can have too much influence. First, consider the
simplest case -- a single word query. In order to rank a document with a
single word query, Google looks at that document's hit list for that
word. Google considers each hit to be one of several different types
(title, anchor, URL, plain text large font, plain text small font, ...),
each of which has its own type-weight. The type-weights make up a vector
indexed by type. Google counts the number of hits of each type in the
hit list. Then every count is converted into a count-weight.
Count-weights increase linearly with counts at first but quickly taper
off so that more than a certain count will not help. We take the dot
product of the vector of count-weights with the vector of type-weights
to compute an IR score for the document. Finally, the IR score is
combined with PageRank to give a final rank to the document.
For a multi-word search, the situation is more complicated. Now multiple
hit lists must be scanned through at once so that hits occurring close
together in a document are weighted higher than hits occurring far
apart. The hits from the multiple hit lists are matched up so that
nearby hits are matched together. For every matched set of hits, a
proximity is computed. The proximity is based on how far apart the hits
are in the document (or anchor) but is classified into 10 different
value "bins" ranging from a phrase match to "not even close". Counts are
computed not only for every type of hit but for every type and
proximity. Every type and proximity pair has a type-prox-weight. The
counts are converted into count-weights and we take the dot product of
the count-weights and the type-prox-weights to compute an IR score. All
of these numbers and matrices can all be displayed with the search
results using a special debug mode. These displays have been very
helpful in developing the ranking system.
4.5.2 Feedback
The ranking function has many parameters like the type-weights and the
type-prox-weights. Figuring out the right values for these parameters is
something of a black art. In order to do this, we have a user feedback
mechanism in the search engine. A trusted user may optionally evaluate
all of the results that are returned. This feedback is saved. Then when
we modify the ranking function, we can see the impact of this change on
all previous searches which were ranked. Although far from perfect, this
gives us some idea of how a change in the ranking function affects the
search results.
5 Results and Performance
Query: bill clinton
http://www.whitehouse.gov/
100.00% (no date) (0K)
http://www.whitehouse.gov/
Office of the President
99.67% (Dec 23 1996) (2K)
http://www.whitehouse.gov/WH/EOP/OP/html/OP_Home.html
Welcome To The White House
99.98% (Nov 09 1997) (5K)
http://www.whitehouse.gov/WH/Welcome.html
Send Electronic Mail to the President
99.86% (Jul 14 1997) (5K)
http://www.whitehouse.gov/WH/Mail/html/Mail_President.html
mailto:president@whitehouse.gov
99.98%
mailto:President@whitehouse.gov
99.27%
The "Unofficial" Bill Clinton
94.06% (Nov 11 1997) (14K)
http://zpub.com/un/un-bc.html
Bill Clinton Meets The Shrinks
86.27% (Jun 29 1997) (63K)
http://zpub.com/un/un-bc9.html
President Bill Clinton - The Dark Side
97.27% (Nov 10 1997) (15K)
http://www.realchange.org/clinton.htm
$3 Bill Clinton
94.73% (no date) (4K) http://www.gatewy.net/~tjohnson/clinton1.html
Figure 4. Sample Results from Google
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 to produce
better results than the major commercial search engines for most
searches. As an example which illustrates the use of PageRank, anchor
text, and proximity, Figure 4 shows Google's results for a search 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 from the
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 right ones. Notice that
there is no title for the first result. This is because it was not
crawled. Instead, Google relied on anchor text to determine this was a
good answer to the query. Similarly, the fifth 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. This is largely because they all have high
PageRank. The PageRanks are the percentages in red along with bar
graphs. Finally, there are no results about a Bill other than Clinton or
about a Clinton other than Bill. This is because we place heavy
importance on the proximity of word occurrences. Of course a true 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.
5.1 Storage Requirements
Aside from search quality, Google is designed to scale 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 and storage
requirements of Google. Due to compression the total size of the
repository is about 53 GB, just over one third of the total data it
stores. At current disk prices this makes the repository a relatively
cheap source of useful data. More importantly, the total of all the data
used by the search engine requires a comparable amount of storage, about
55 GB. Furthermore, most queries can be answered using just the short
inverted index. With better encoding and compression of the Document
Index, a high quality web search engine may fit onto a 7GB drive of a
new PC.
Storage Statistics
Total Size of Fetched Pages 147.8 GB
Compressed Repository 53.5 GB
Short Inverted Index 4.1 GB
Full Inverted Index 37.2 GB
Lexicon 293 MB
Temporary Anchor Data
(not in total) 6.6 GB
Document Index Incl.
Variable Width Data 9.7 GB
Links Database 3.9 GB
Total Without Repository 55.2 GB
Total With Repository 108.7 GB
Web Page Statistics
Number of Web Pages Fetched 24 million
Number of Urls Seen 76.5 million
Number of Email Addresses 1.7 million
Number of 404's 1.6 million
Table 1. Statistics
5.2 System Performance
It is important for a search engine to crawl and index efficiently. This
way information can be kept up to date and major changes to the system
can be tested relatively quickly. For Google, the major operations are
Crawling, Indexing, and Sorting. It is difficult to measure how long
crawling took overall because disks filled up, name servers crashed, or
any number of other problems which stopped the system. In total it took
roughly 9 days to download the 26 million pages (including errors).
However, once the system was running smoothly, 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. We ran the
indexer and the crawler simultaneously. The indexer ran just faster than
the crawlers. This is largely because we spent just enough time
optimizing the indexer so that it would not be a bottleneck. These
optimizations included bulk updates to the document index and placement
of critical data structures on the local disk. The indexer runs at
roughly 54 pages per second. The sorters can be run completely in
parallel; using four machines, the whole process of sorting takes about
24 hours.
5.3 Search Performance
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 disks are spread over a number of machines).
Furthermore, Google does not have any optimizations such as query
caching, subindices on common terms, and other common optimizations. We
intend to speed up Google considerably through distribution and
hardware, software, and algorithmic improvements. Our target is to be
able to handle several hundred queries per second. Table 2 has some
sample query times from the current version of Google. They are repeated
to show the speedups resulting from cached IO.
Initial Query Same Query Repeated (IO mostly cached)
Query CPU Time(s) Total Time(s) CPU Time(s) Total Time(s)
al gore 0.09 2.13 0.06 0.06
vice president 1.77 3.84 1.66 1.80
hard disks 0.25 4.86 0.20 0.24
search engines 1.31 9.63 1.16 1.16
Table 2. Search Times
6 Conclusions
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 to improve search quality
including page rank, anchor text, and proximity information.
Furthermore, Google is a complete architecture for gathering web pages,
indexing them, and performing search queries over them.
6.1 Future Work
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 to approximately 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
must have smart algorithms to decide what old web pages should be
recrawled and what new ones should be crawled. Work toward this goal has
been done in [Cho 98 <#ref>]. One promising area of research is using
proxy caches to build search databases, since they are demand driven. We
are planning to add simple features supported by commercial search
engines like boolean operators, negation, and stemming. However, other
features are just starting to be explored such as relevance feedback and
clustering (Google currently supports a simple hostname based
clustering). We also plan to support user context (like the user's
location), and result summarization. We are also working to extend the
use of link structure and link text. Simple experiments indicate
PageRank can be personalized by increasing the weight of a user's home
page or bookmarks. As for link text, we are experimenting with using
text surrounding links in addition 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.
6.2 High Quality Search
The biggest problem facing users of web search engines today is the
quality of the results they get back. While the results are often
amusing and expand users' horizons, they are often frustrating and
consume precious time. For example, the top result for a search for
"Bill Clinton" on one of the most popular commercial search engines was
the Bill Clinton Joke of the Day: April 14, 1997
. Google is designed to
provide higher quality search so as the Web continues to grow rapidly,
information can be found easily. In order to accomplish this Google
makes heavy use of hypertextual information consisting of link structure
and link (anchor) text. Google also uses proximity and font information.
While evaluation of a search engine is difficult, we have subjectively
found that Google returns higher quality search results than current
commercial search engines. 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 link points to helps the search engine
return relevant (and to some degree high quality) results. Finally, the
use of proximity information helps increase relevance a great deal for
many queries.
6.3 Scalable Architecture
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
important when dealing with the entire Web. In implementing Google, we
have seen bottlenecks in CPU, 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, and
sorting operations are efficient enough to be able to build 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 in
less than a month.
6.4 A Research Tool
In addition to being a high quality search engine, Google is a research
tool. The data Google has collected has already resulted in many other
papers submitted to conferences and many more on the way. Recent
research such as [Abiteboul 97 <#ref>] has shown a number of limitations
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 all around the world and will spark the next generation of
search engine technology.
7 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, and Sun 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-9411306. 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.
References
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http://botw.org/1994/awards/navigators.html
* Bill Clinton Joke of the Day: April 14, 1997.
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* Bzip2 Homepage http://www.muraroa.demon.co.uk/
* Google Search Engine http://google.stanford.edu/
* Harvest http://harvest.transarc.com/
* Mauldin, Michael L. Lycos Design Choices in an Internet Search
Service, IEEE Expert Interview
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http://www.webfirst.com/aaa/text/cell/cell0toc.htm
* Search Engine Watch http://www.searchenginewatch.com/
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ftp://ftp.uu.net/graphics/png/documents/zlib/zdoc-index.html
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http://info.webcrawler.com/mak/projects/robots/exclusion.htm
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Vitae
Sergey Brin received his B.S. degree in mathematics and computer science
from the University of Maryland at College Park in 1993. Currently, he
is a Ph.D. candidate in computer science at Stanford University where he
received his M.S. in 1995. He is a recipient of a National Science
Foundation Graduate Fellowship. His research interests include search
engines, information extraction from unstructured sources, and data
mining of large text collections and scientific data.
Lawrence Page was born in East Lansing, Michigan, and received a B.S.E.
in Computer Engineering at the University of Michigan Ann Arbor in 1995.
He is currently a Ph.D. candidate in Computer Science at Stanford
University. Some of his research interests include the link structure of
the web, human computer interaction, search engines, scalability of
information access interfaces, and personal data mining.
8 Appendix A: Advertising and Mixed Motives
Currently, the predominant business model for commercial search engines
is advertising. The goals of the advertising business model do not
always correspond to providing quality search to users. For example, in
our prototype search engine one of the top results for cellular phone is
"The Effect of Cellular Phone Use Upon Driver Attention
", a study which
explains in great detail the distractions and risk associated with
conversing on a cell phone while driving. This search result came up
first because of its high importance as judged by the PageRank
algorithm, an approximation of citation importance on the web [Page, 98
<#ref>]. It is clear that a search engine which was taking money for
showing cellular phone ads would have difficulty justifying the page
that our system returned to its paying advertisers. For this type of
reason and historical experience with other media [Bagdikian 83 <#ref>],
we expect that advertising funded search engines will be inherently
biased towards the advertisers and away from the needs of the consumers.
Since it is very difficult even for experts to evaluate search engines,
search engine bias is particularly insidious. A good example was
OpenText, which was reported to be selling companies the right to be
listed at the top of the search results for particular queries
[Marchiori 97 <#ref>]. This type of bias is much more insidious than
advertising, because it is not clear who "deserves" to be there, and who
is willing to pay money to be listed. This business model resulted in an
uproar, and OpenText has ceased to be a viable search engine. But less
blatant bias are likely to be tolerated by the market. For example, a
search engine could add a small factor to search results from "friendly"
companies, and subtract a factor from results from competitors. This
type of bias is very difficult to detect but could still have a
significant effect on the market. Furthermore, advertising income often
provides an incentive to provide poor quality search results. For
example, we noticed a major search engine would not return a large
airline's homepage when the airline's name was given as a query. It so
happened that the airline had placed an expensive ad, linked to the
query that was its name. A better search engine would not have required
this ad, and possibly resulted in the loss of the revenue from the
airline to the search engine. In general, it could be argued from the
consumer point of view that the better the search engine is, the fewer
advertisements will be needed for the consumer to find what they want.
This of course erodes the advertising supported business model of the
existing search engines. However, there will always be money from
advertisers who want a customer to switch products, or have something
that is genuinely new. But we believe the issue of advertising causes
enough mixed incentives that it is crucial to have a competitive search
engine that is transparent and in the academic realm.
9 Appendix B: Scalability
9. 1 Scalability of Google
We have designed Google to be scalable in the near term to a goal of 100
million web pages. We have just received disk and machines to handle
roughly that amount. All of the time consuming parts of the system are
parallelize and roughly linear time. These include things like the
crawlers, indexers, and sorters. We also think that most of the data
structures will deal gracefully with the expansion. However, at 100
million web pages we will be very close up against all sorts of
operating system limits in the common operating systems (currently we
run on both Solaris and Linux). These include things like addressable
memory, number of open file descriptors, network sockets and bandwidth,
and many others. We believe expanding to a lot more than 100 million
pages would greatly increase the complexity of our system.
9.2 Scalability of Centralized Indexing Architectures
As the capabilities of computers increase, it becomes possible to index
a very large amount of text for a reasonable cost. Of course, other more
bandwidth intensive media such as video is likely to become more
pervasive. But, because the cost of production of text is low compared
to media like video, text is likely to remain very pervasive. Also, it
is likely that soon we will have speech recognition that does a
reasonable job converting speech into text, expanding the amount of text
available. All of this provides amazing possibilities for centralized
indexing. Here is an illustrative example. We assume we want to index
everything everyone in the US has written for a year. We assume that
there are 250 million people in the US and they write an average of 10k
per day. That works out to be about 850 terabytes. Also assume that
indexing a terabyte can be done now for a reasonable cost. We also
assume that the indexing methods used over the text are linear, or
nearly linear in their complexity. Given all these assumptions we can
compute how long it would take before we could index our 850 terabytes
for a reasonable cost assuming certain growth factors. Moore's Law was
defined in 1965 as a doubling every 18 months in processor power. It has
held remarkably true, not just for processors, but for other important
system parameters such as disk as well. If we assume that Moore's law
holds for the future, we need only 10 more doublings, or 15 years to
reach our goal of indexing everything everyone in the US has written for
a year for a price that a small company could afford. Of course,
hardware experts are somewhat concerned Moore's Law may not continue to
hold for the next 15 years, but there are certainly a lot of interesting
centralized applications even if we only get part of the way to our
hypothetical example.
Of course a distributed systems like Gloss [Gravano 94 <#ref>] or
Harvest will often be the most efficient
and elegant technical solution for indexing, but it seems difficult to
convince the world to use these systems because of the high
administration costs of setting up large numbers of installations. Of
course, it is quite likely that reducing the administration cost
drastically is possible. If that happens, and everyone starts running a
distributed indexing system, searching would certainly improve drastically.
Because humans can only type or speak a finite amount, and as computers
continue improving, text indexing will scale even better than it does
now. Of course there could be an infinite amount of machine generated
content, but just indexing huge amounts of human generated content seems
tremendously useful. So we are optimistic that our centralized web
search engine architecture will improve in its ability to cover the
pertinent text information over time and that there is a bright future
for search.