The
Anatomy of a Large-Scale Hypertextual Web Search Engine 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.
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
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.
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 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 . 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.
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 .
Further, we expect that the cost to index and store text or HTML
will eventually decline relative to the amount that will be
available . This will result in favorable scaling properties for
centralized systems like Google.
Design Goals
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
, "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 . In particular, link structure 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
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
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.
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.
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 . For the
type of full text searches in the main Google system, PageRank
also helps a great deal.
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.
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 .
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.
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] 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.
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.
Related Work
Search research on the web has a short and concise
history. The World Wide Web Worm 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) ,
"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.
Information Retrieval
Work in information retrieval systems goes back many
years and is well developed . 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 , 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.
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.
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.
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.
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
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.
Repository
The repository contains the full HTML of every web page.
Each page is compressed using zlib . 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. 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.
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.
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.
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.
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.
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.
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.
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 , 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.
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.
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.
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.
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.
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.
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 at http://google.stanford.edu/.
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.
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.
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.
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.
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 . 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.
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 . 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.
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.
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 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.
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.
