The Anatomy of a Large-Scale Hypertextual Web Search Engine
Sergey Brin and Lawrence Page
Computer Science Department, Stanford University, Stanford, CA 94305
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
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,
(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
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
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 commercially valuable.
Our final design goal was to build an architecture that can support novel
research activities on large-scale web data. To support novel research uses,
Google stores all of the actual documents it crawls in compressed form. One of
our main goals in designing Google was to set up an environment where other
researchers can come in quickly, process large chunks of the web, and produce
interesting results that would have been very difficult to produce otherwise. In
the short time the system has been up, there have already been several papers
using databases generated by Google, and many others are underway. Another goal
we have is to set up a Spacelab-like environment where researchers or even
students can propose and do interesting experiments on our large-scale web data.
2. System Features
The Google search engine has two important features that help it produce high
precision results. First, it makes use of the link structure of the Web to
calculate a quality ranking for each web page. This ranking is called PageRank
and is described in detail in [Page 98]. Second, Google utilizes link to improve
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
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
Another intuitive justification is that a page can have a high PageRank if
there are many pages that point to it, or if there are some pages that point to
it and have a high PageRank. Intuitively, pages that are well cited from many
places around the web are worth looking at. Also, pages that have perhaps only
one citation from something like the Yahoo!
homepage are also generally worth looking at. If a page was not high quality, or
was a broken link, it is quite likely that Yahoo's homepage would not link to
it. PageRank handles both these cases and everything in between by recursively
propagating weights through the link structure of the web.
2.2 Anchor Text
The text of links is treated in a special way in our search engine. Most search
engines associate the text of a link with the page that the link is on. In
addition, we associate it with the page the link points to. This has several
advantages. First, anchors often provide more accurate descriptions of web pages
than the pages themselves. Second, anchors may exist for documents which cannot
be indexed by a text-based search engine, such as images, programs, and
databases. This makes it possible to return web pages which have not actually
been crawled. Note that pages that have not been crawled can cause problems,
since they are never checked for validity before being returned to the user. In
this case, the search engine can even return a page that never actually existed,
but had hyperlinks pointing to it. However, it is possible to sort the results,
so that this particular problem rarely happens.
This idea of propagating anchor text to the page it refers to was implemented
in the World Wide Web Worm [McBryan 94] 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
[McBryan 94] was one of the first web search engines. It was subsequently
followed by several other academic search engines, many of which are now public
companies. Compared to the growth of the Web and the importance of search
engines there are precious few documents about recent search engines [Pinkerton
94]. According to Michael Mauldin (chief scientist, Lycos Inc)
[Mauldin], "the various services (including Lycos) closely guard the details
of these databases". However, there has been a fair amount of work on specific
features of search engines. Especially well represented is work which can get
results by post-processing the results of existing commercial search engines, or
produce small scale "individualized" search engines. Finally, there has been a
lot of research on information retrieval systems, especially on well controlled
collections. In the next two sections, we discuss some areas where this research
needs to be extended to work better on the web.
3.1 Information Retrieval
Work in information retrieval systems goes back many years and is well developed
[Witten 94]. 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 in depth.
Figure 1. High Level Google Architecture
4.1 Google Architecture Overview
In this section, we will give a high level overview of how the whole system
works as pictured in Figure 1. Further sections will discuss the applications
and data structures not mentioned in this section. Most of Google is implemented
in C or C++ for efficiency and can run in either Solaris or Linux.
In Google, the web crawling (downloading of web pages) is done by several
distributed crawlers. There is a URLserver that sends lists of URLs to be
fetched to the crawlers. The web pages that are fetched are then sent to the
storeserver. The storeserver then compresses and stores the web pages into a
repository. Every web page has an associated ID number called a docID which is
assigned whenever a new URL is parsed out of a web page. The indexing function
is performed by the indexer and the sorter. The indexer performs a number of
functions. It reads the repository, uncompresses the documents, and parses them.
Each document is converted into a set of word occurrences called hits. The hits
record the word, position in document, an approximation of font size, and
capitalization. The indexer distributes these hits into a set of "barrels",
creating a partially sorted forward index. The indexer performs another
important function. It parses out all the links in every web page and stores
important information about them in an anchors file. This file contains enough
information to determine where each link points from and to, and the text of the
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 actual length.
4.2.6 Forward Index
The forward index is actually already partially sorted. It is stored in a number
of barrels (we used 64). Each barrel holds a range of wordID's. If a document
contains words that fall into a particular barrel, the docID is recorded into
the barrel, followed by a list of wordID's with hitlists which correspond to
those words. This scheme requires slightly more storage because of duplicated
docIDs but the difference is very small for a reasonable number of buckets and
saves considerable time and coding complexity in the final indexing phase done
by the sorter. Furthermore, instead of storing actual wordID's, we store each
wordID as a relative difference from the minimum wordID that falls into the
barrel the wordID is in. This way, we can use just 24 bits for the wordID's in
the unsorted barrels, leaving 8 bits for the hit list length.
4.2.7 Inverted Index
The inverted index consists of the same barrels as the forward index, except
that they have been processed by the sorter. For every valid wordID, the lexicon
contains a pointer into the barrel that wordID falls into. It points to a
doclist of docID's together with their corresponding hit lists. This doclist
represents all the occurrences of that word in all documents.
An important issue is in what order the docID's should appear in the doclist.
One simple solution is to store them sorted by docID. This allows for quick
merging of different doclists for multiple word queries. Another option is to
store them sorted by a ranking of the occurrence of the word in each document.
This makes answering one word queries trivial and makes it likely that the
answers to multiple word queries are near the start. However, merging is much
more difficult. Also, this makes development much more difficult in that a
change to the ranking function requires a rebuild of the index. We chose a
compromise between these options, keeping two sets of inverted barrels -- one
set for hit lists which include title or anchor hits and another set for all hit
lists. This way, we check the first set of barrels first and if there are not
enough matches within those barrels we check the larger ones.
4.3 Crawling the Web
Running a web crawler is a challenging task. There are tricky performance and
reliability issues and even more importantly, there are social issues. Crawling
is the most fragile application since it involves interacting with hundreds of
thousands of web servers and various name servers which are all beyond the
control of the system.
In order to scale to hundreds of millions of web pages, Google has a fast
distributed crawling system. A single URLserver serves lists of URLs to a number
of crawlers (we typically ran about 3). Both the URLserver and the crawlers are
implemented in Python. Each crawler keeps roughly 300 connections open at once.
This is necessary to retrieve web pages at a fast enough pace. At peak speeds,
the system can crawl over 100 web pages per second using four crawlers. This
amounts to roughly 600K per second of data. A major performance stress is DNS
lookup. Each crawler maintains a its own DNS cache so it does not need to do a
DNS lookup before crawling each document. Each of the hundreds of connections
can be in a number of different states: looking up DNS, connecting to host,
sending request, and receiving response. These factors make the crawler a
complex component of the system. It uses asynchronous IO to manage events, and a
number of queues to move page fetches from state to state.
It turns out that running a crawler which connects to more than half a
million servers, and generates tens of millions of log entries generates a fair
amount of email and phone calls. Because of the vast number of people coming on
line, there are always those who do not know what a crawler is, because this is
the first one they have seen. Almost daily, we receive an email something like,
"Wow, you looked at a lot of pages from my web site. How did you like it?" There
are also some people who do not know about the
robots exclusion protocol, and think their page should be protected from
indexing by a statement like, "This page is copyrighted and should not be
indexed", which needless to say is difficult for web crawlers to understand.
Also, because of the huge amount of data involved, unexpected things will
happen. For example, our system tried to crawl an online game. This resulted in
lots of garbage messages in the middle of their game! It turns out this was an
easy problem to fix. But this problem had not come up until we had downloaded
tens of millions of pages. Because of the immense variation in web pages and
servers, it is virtually impossible to test a crawler without running it on
large part of the Internet. Invariably, there are hundreds of obscure problems
which may only occur on one page out of the whole web and cause the crawler to
crash, or worse, cause unpredictable or incorrect behavior. Systems which access
large parts of the Internet need to be designed to be very robust and carefully
tested. Since large complex systems such as crawlers will invariably cause
problems, there needs to be significant resources devoted to reading the email
and solving these problems as they come up.
4.4 Indexing the Web
Parsing -- Any parser which is designed to run on the entire Web must
handle a huge array of possible errors. These range from typos in HTML tags to
kilobytes of zeros in the middle of a tag, non-ASCII characters, HTML tags
nested hundreds deep, and a great variety of other errors that challenge
anyone's imagination to come up with equally creative ones. For maximum speed,
instead of using YACC to generate a CFG parser, we use flex to generate a
lexical analyzer which we outfit with its own stack. Developing this parser
which runs at a reasonable speed and is very robust involved a fair amount of
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.
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 search
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
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 search results.
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 at
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
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
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.
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, 1994.
[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.
[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.
[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:
[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
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
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.
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