23-02-2013, 01:11 PM
The Anatomy of a Large-Scale Hypertextual Web Search Engine
The Anatomy of a Large-Scale.pdf (Size: 120.71 KB / Downloads: 13)
Abstract
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy
use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently
and produce much more satisfying search results than existing systems. The prototype with a full
text and hyperlink database of at least 24 million pages is available at
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.
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,
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 (WW) [McBryan 94] had an index
of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines
claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine
Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a
billion documents. At the same time, the number of queries search engines handle has grown incredibly
too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries
per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the
increasing number of users on the web, and automated systems which query search engines, it is likely
that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of
our system is to address many of the problems, both in quality and scalability, introduced by scaling
search engine technology to such extraordinary numbers.
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.