The Anatomy of a Large-Scale Hypertextual Web Search Engine
Sergey Brin and Lawrence Page
Computer Science Department, Stanford University, Stanford, CA
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
(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).
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). 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] [Spertus 97] [Weiss 96] [Kleinberg
98]. In particular, link structure
[Page 98] 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
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). 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
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:
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.
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.
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].
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
This idea of propagating anchor text to the page it refers to was implemented
in the World Wide Web Worm [McBryan 94] 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
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]. 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], 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
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), 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.
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.
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.
Figure 2. Repository Data Structure
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.
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
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
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.
Parse the query.
Convert words into wordIDs.
Seek to the start of the doclist in the short barrel for every word.
Scan through the doclists until there is a document that matches all the
Compute the rank of that document for the query.
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.
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.
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
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 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
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.
|Total Size of Fetched Pages
|Short Inverted Index
|Full Inverted Index
|Temporary Anchor Data
(not in total)
|Document Index Incl.
Variable Width Data
|Total Without Repository
|Total With Repository
|Web Page Statistics
|Number of Web Pages Fetched
|Number of Urls Seen
|Number of Email Addresses
|Number of 404's
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.
|Same Query Repeated (IO mostly cached)
Table 2. Search Times
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]. 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] 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.
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.
[Abiteboul 97] Serge Abiteboul and Victor Vianu, Queries and Computation
on the Web. Proceedings of the International Conference on Database
Theory. Delphi, Greece 1997.
[Bagdikian 97] Ben H. Bagdikian. The Media Monopoly. 5th Edition.
Publisher: Beacon, ISBN: 0807061557
[Chakrabarti 98] S.Chakrabarti, B.Dom, D.Gibson, J.Kleinberg, P. Raghavan and S. Rajagopalan.
Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text. Seventh International Web Conference
(WWW 98). Brisbane, Australia, April 14-18, 1998.
[Cho 98] Junghoo Cho, Hector Garcia-Molina, Lawrence Page. Efficient
Crawling Through URL Ordering. Seventh International Web Conference
(WWW 98). Brisbane, Australia, April 14-18, 1998.
[Gravano 94] Luis Gravano, Hector Garcia-Molina, and A. Tomasic. The
Effectiveness of GlOSS for the Text-Database Discovery Problem. Proc.
of the 1994 ACM SIGMOD International Conference On Management Of Data,
[Kleinberg 98] Jon Kleinberg, Authoritative Sources in a Hyperlinked
Environment, Proc. ACM-SIAM Symposium on Discrete Algorithms, 1998.
[Marchiori 97] Massimo Marchiori. The Quest for Correct Information
on the Web: Hyper Search Engines. The Sixth International WWW Conference
(WWW 97). Santa Clara, USA, April 7-11, 1997.
[McBryan 94] Oliver A. McBryan. GENVL and WWWW: Tools for Taming the
Web. First International Conference on the World Wide Web. CERN, Geneva
(Switzerland), May 25-26-27 1994. http://www.cs.colorado.edu/home/mcbryan/mypapers/www94.ps
[Page 98] Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. The
PageRank Citation Ranking: Bringing Order to the Web. Manuscript in
[Pinkerton 94] Brian Pinkerton, Finding What People Want: Experiences
with the WebCrawler. The Second International WWW Conference Chicago,
USA, October 17-20, 1994. http://info.webcrawler.com/bp/WWW94.html
[Spertus 97] Ellen Spertus. ParaSite: Mining Structural Information
on the Web. The Sixth International WWW Conference (WWW 97). Santa
Clara, USA, April 7-11, 1997.
[TREC 96] Proceedings of the fifth Text REtrieval Conference (TREC-5).
Gaithersburg, Maryland, November 20-22, 1996. Publisher: Department
of Commerce, National Institute of Standards and Technology. Editors: D.
K. Harman and E. M. Voorhees. Full text at: http://trec.nist.gov/
[Witten 94] Ian H Witten, Alistair Moffat, and Timothy C. Bell. Managing
Gigabytes: Compressing and Indexing Documents and Images. New York:
Van Nostrand Reinhold, 1994.
[Weiss 96] Ron Weiss, Bienvenido Velez, Mark A. Sheldon, Chanathip Manprempre,
Peter Szilagyi, Andrzej Duda, and David K. Gifford. HyPursuit: A Hierarchical
Network Search Engine that Exploits Content-Link Hypertext Clustering.
Proceedings of the 7th ACM Conference on Hypertext. New York, 1996.
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]. 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], 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]. 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] 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.