For the British musician, see Larry Page (singer).
Page speaking at the European Parliament on June 17, 2009
|Born||Lawrence Edward Page|
(1973-03-26) March 26, 1973 (age 44)
East Lansing, Michigan, U.S.
|Residence||Palo Alto, California, U.S.|
|Alma mater||University of Michigan(B.S)|
Stanford University(M.S, PhD)
|Occupation||Computer scientist, Internet entrepreneur|
|Known for||Co-founder of Google Inc., CEO of Alphabet Inc, PageRank|
|Net worth||US$51 billion (February 2018)|
|Title||CEO of Alphabet Inc|
|Spouse(s)||Lucinda Page (m. 2007)|
Lawrence Edward Page (born March 26, 1973) is an American computer scientist and Internet entrepreneur who co-founded Google with Sergey Brin.
Page is the chief executive officer (CEO) of Google's parent company, Alphabet Inc. After stepping aside as Google CEO in August 2001, in favor of Eric Schmidt, he re-assumed the role in April 2011. He announced his intention to step aside a second time in July 2015, to become CEO of Alphabet, under which Google's assets would be reorganized. Under Page, Alphabet is seeking to deliver major advancements in a variety of industries.
As of February 16, 2018, Page was the ninth-richest person in the world, with a net worth of $51 billion.
Page is the inventor of PageRank, Google's best-known search ranking algorithm. Page received the Marconi Prize in 2004 with Brin.
Early life and education
Page was born on March 26, 1973, in East Lansing, Michigan. His mother was Jewish, and his maternal grandfather later made aliyah to Israel, but Page does not follow any formal religion. His father, Carl Victor Page, Sr., earned a PhD in computer science from the University of Michigan, when the field was being established, and BBC reporter Will Smale has described him as a "pioneer in computer science and artificial intelligence". He was a computer science professor at Michigan State University and Page's mother, Gloria, was an instructor in computer programming at Lyman Briggs College and at Michigan State University.
During an interview, Page recalled his childhood, noting that his house "was usually a mess, with computers, science, and technology magazines and Popular Science magazines all over the place", an environment in which he immersed himself. Page was an avid reader during his youth, writing in his 2013, Google founders letter: "I remember spending a huge amount of time poring over books and magazines". According to writer Nicholas Carlson, the combined influence of Page's home atmosphere and his attentive parents "fostered creativity and invention". Page also played saxophone and studied music composition while growing up. Page has mentioned that his musical education inspired his impatience and obsession with speed in computing. "In some sense, I feel like music training led to the high-speed legacy of Google for me". In an interview Page said that "In music, you're very cognizant of time. Time is like the primary thing" and that "If you think about it from a music point of view, if you're a percussionist, you hit something, it's got to happen in milliseconds, fractions of a second".
Page was first attracted to computers when he was six years old, as he was able to "play with the stuff lying around"—first-generation personal computers—that had been left by his parents. He became the "first kid in his elementary school to turn in an assignment from a word processor". His older brother also taught him to take things apart and before long he was taking "everything in his house apart to see how it worked". He said that "from a very early age, I also realized I wanted to invent things. So I became really interested in technology and business. Probably from when I was 12, I knew I was going to start a company eventually."
Page attended the Okemos Montessori School (now called Montessori Radmoor) in Okemos, Michigan, from 1975 to 1979, and graduated from East Lansing High School in 1991. He attended Interlochen Center for the Arts as a saxophonist for two summers while in high school. Page holds a Bachelor of Science in computer engineering from the University of Michigan, with honors and a Master of Science in computer science from Stanford University. While at the University of Michigan, Page created an inkjet printer made of Lego bricks (literally a line plotter), after he thought it possible to print large posters cheaply with the use of inkjet cartridges—Page reverse-engineered the ink cartridge, and built all of the electronics and mechanics to drive it. Page served as the president of the Beta Epsilon chapter of the Eta Kappa Nu fraternity, and was a member of the 1993 "Maize & Blue" University of Michigan Solar Car team. As an undergrad at the University of Michigan, he proposed that the school replace its bus system with a PRT System which is essentially a driverless monorail with separate cars for every passenger. He also developed a business plan for a company that would use software to build a music synthesizer during this time.
PhD studies and research
After enrolling in a computer sciencePhD program at Stanford University, Page was in search of a dissertation theme and considered exploring the mathematical properties of the World Wide Web, understanding its link structure as a huge graph—his supervisor, Terry Winograd, encouraged him to pursue the idea, and Page recalled in 2008 that it was the best advice he had ever received. He also considered doing research on telepresence and autonomous cars during this time.
Page focused on the problem of finding out which web pages link to a given page, considering the number and nature of such backlinks as valuable information for that page—the role of citations in academic publishing would also become pertinent for the research.Sergey Brin, a fellow Stanford PhD student, would soon join Page's research project, nicknamed "BackRub." Together, the pair authored a research paper titled "The Anatomy of a Large-Scale Hypertextual Web Search Engine", which became one of the most downloaded scientific documents in the history of the Internet at the time.
John Battelle, co-founder of Wired magazine, wrote that Page had reasoned that the:
... entire Web was loosely based on the premise of citation—after all, what is a link but a citation? If he could devise a method to count and qualify each backlink on the Web, as Page puts it "the Web would become a more valuable place."
Battelle further described how Page and Brin began working together on the project:
At the time Page conceived of BackRub, the Web comprised an estimated 10 million documents, with an untold number of links between them. The computing resources required to crawl such a beast were well beyond the usual bounds of a student project. Unaware of exactly what he was getting into, Page began building out his crawler. The idea's complexity and scale lured Brin to the job. A polymath who had jumped from project to project without settling on a thesis topic, he found the premise behind BackRub fascinating. "I talked to lots of research groups" around the school, Brin recalls, "and this was the most exciting project, both because it tackled the Web, which represents human knowledge, and because I liked Larry."
Search engine development
To convert the backlink data gathered by BackRub's web crawler into a measure of importance for a given web page, Brin and Page developed the PageRank algorithm, and realized that it could be used to build a search engine far superior to existing ones. The new algorithm relied on a new kind of technology that analyzed the relevance of the backlinks that connected one Web page to another.
Combining their ideas, the pair began utilizing Page's dormitory room as a machine laboratory, and extracted spare parts from inexpensive computers to create a device that they used to connect the nascent search engine with Stanford's broadband campus network. After filling Page's room with equipment, they then converted Brin's dorm room into an office and programming center, where they tested their new search engine designs on the Web. The rapid growth of their project caused Stanford's computing infrastructure to experience problems.
Page and Brin used the former's basic HTML programming skills to set up a simple search page for users, as they did not have a web page developer to create anything visually elaborate. They also began using any computer part they could find to assemble the necessary computing power to handle searches by multiple users. As their search engine grew in popularity among Stanford users, it required additional servers to process the queries. In August 1996, the initial version of Google, still on the Stanford University website, was made available to Internet users.
By early 1997, the BackRub page described the state as follows:
- Some Rough Statistics (from August 29, 1996)
- Total indexable HTML URLs: 75.2306 Million
- Total content downloaded: 207.022 gigabytes
- BackRub is written in Java and Python and runs on several Sun Ultras and Intel Pentiums running Linux. The primary database is kept on a Sun Ultra series II with 28GB of disk. Scott Hassan and Alan Steremberg have provided a great deal of very talented implementation help. Sergey Brin has also been very involved and deserves many thanks.
- - Larry Page pagecs.stanford.edu
- BackRub is written in Java and Python and runs on several Sun Ultras and Intel Pentiums running Linux. The primary database is kept on a Sun Ultra series II with 28GB of disk. Scott Hassan and Alan Steremberg have provided a great deal of very talented implementation help. Sergey Brin has also been very involved and deserves many thanks.
BackRub already exhibited the rudimentary functions and characteristics of a search engine: a query input was entered and it provided a list of backlinks ranked by importance. Page recalled: "We realized that we had a querying tool. It gave you a good overall ranking of pages and ordering of follow-up pages." Page said that in mid-1998 they finally realized the further potential of their project: "Pretty soon, we had 10,000 searches a day. And we figured, maybe this is really real."
Some compared Page and Brin's vision to the impact of Johannes Gutenberg, the inventor of modern printing:
In 1440, Johannes Gutenberg introduced Europe to the mechanical printing press, printing Bibles for mass consumption. The technology allowed for books and manuscripts – originally replicated by hand – to be printed at a much faster rate, thus spreading knowledge and helping to usher in the European Renaissance ... Google has done a similar job.
The comparison was also noted by the authors of The Google Story: "Not since Gutenberg ... has any new invention empowered individuals, and transformed access to information, as profoundly as Google." Also, not long after the two "cooked up their new engine for web searches, they began thinking about information that was at the time beyond the web," such as digitizing books and expanding health information.
Mark Malseed wrote in a 2007 feature story:
Soliciting funds from faculty members, family and friends, Brin and Page scraped together enough to buy some servers and rent that famous garage in Menlo Park. ... [soon after], Sun Microsystems co-founder Andy Bechtolsheim wrote a $100,000 check to "Google, Inc." The only problem was, "Google, Inc." did not yet exist—the company hadn't yet been incorporated. For two weeks, as they handled the paperwork, the young men had nowhere to deposit the money."
In 1998, Brin and Page incorporated Google, Inc. with the initial domain name of "Googol," derived from a number that consists of one followed by one hundred zeros—this represented the vast amount of data that the search engine was intended to explore. Following inception, Page appointed himself as CEO, while Brin, named Google's co-founder, served as Google's president. Writer Nicholas Carlson wrote in 2014:
While Google is often thought of as the invention of two young computer whizzes—Sergey and Larry, Larry and Sergey—the truth is that Google is a creation of Larry Page, helped along by Sergey Brin.
The pair's mission was: "to organize the world’s information and make it universally accessible and useful." With a US$1-million loan from friends and family, the inaugural team eventually moved into a Mountain View office by the start of 2000. In 1999, Page experimented with smaller sized server units so that Google could fit more into each square meter of the third-party warehouses that the company rented to store their servers, which eventually led to a search engine that ran much faster than Google's competitors at the time.
By June 2000, Google had indexed one billion Internet URLs, or Uniform Resource Locators, making it the most comprehensive search engine on the Web at the time. The company cited NEC Research Institute data in its June 26 press release, stating that "there are more than 1 billion web pages online today," with Google "providing access to 560 million full-text indexed web pages and 500 million partially indexed URLs."
Early management style
During his first tenure as CEO, Page embarked on a passed attempt to fire all of Google's project managers in 2001. Page's plan involved all of Google’s engineers reporting to a VP of engineering, who would then report directly to him—Page explained that he didn’t like non-engineers supervising engineers due to their limited technical knowledge. Page even documented his management tenets for his team to use as a reference:
- Don't delegate: Do everything you can yourself to make things go faster.
- Don't get in the way if you're not adding value. Let the people actually doing the work talk to each other while you go do something else.
- Don't be a bureaucrat.
- Ideas are more important than age. Just because someone is junior doesn't mean they don't deserve respect and cooperation.
- The worst thing you can do is stop someone from doing something by saying, "No. Period." If you say no, you have to help them find a better way to get it done.
Even though Page's new model was unsustainable and led to disgruntlement among the affected employees, his issue with engineers being managed by non-engineering staff gained traction more broadly. Eventually, the practice of only instating engineers into the management roles of engineering teams was established as a standard across Silicon Valley.
Page also believed that the faster Google’s search engine returned answers, the more it would be used. He fretted over milliseconds and pushed his engineers—from those who developed algorithms to those who built data centers—to think about lag times. He also pushed for keeping Google’s home page famously sparse in its design because it would help the search results load faster.
Changes in management and expansion
Before Silicon Valley's two most prominent investors, Kleiner Perkins Caufield & Byers and Sequoia Capital, agreed to invest a combined total of $50 million into Google, they applied pressure on Page to step down as CEO so that a more experienced leader could build a "world-class management team." Page eventually became amenable to the idea after meeting with other technology CEOs, including Steve Jobs and Intel’s Andrew Grove. Eric Schmidt, who had been hired as Chairman of Google in March 2001, left his full-time position as the CEO of Novell to take on the same role at Google in August of the same year, and Page moved aside to assume the President of Products role.
Under Schmidt's leadership, Google underwent a period of major growth and expansion, which included its initial public offering (IPO) on August 20, 2004. However, he always acted in consultation with Page and Brin when he embarked on initiatives such as the hiring of an executive team and the creation of a sales force management system. Furthermore, Page remained the boss at Google in the eyes of the employees, as he gave final approval on all new hires and it was Page who provided the signature for the IPO, the latter making him a billionaire at the age of thirty.
Page led the acquisition of Android for $50 million in 2005 to fulfill his ambition to place handheld computers in the possession of consumers so that they could access Google from anywhere. The purchase was made without Schmidt's knowledge, but the CEO was not perturbed by the relatively small acquisition. Page became passionate about Android, and spent large amounts of time with Android CEO and cofounder Andy Rubin. By September 2008, T-Mobile launched the G1, the first phone using Android software and, by 2010, 17.2 percent of the handset market consisted of Android sales, overtaking Apple for the first time. Android became the world’s most popular mobile operating system shortly afterward.
Assumption of CEO position at Google
Following a January 2011 announcement, Page officially became the chief executive of Google on April 4, 2011, while Schmidt stepped down to become executive chairman. By this time, Google had over $180 billion market capitalization and more than 24,000 employees.
After Schmidt announced the end of his tenure as CEO on January 20, 2011, he jokingly tweeted on Twitter: "Adult-supervision no longer needed." Quartz organizational management reporter, Max Nisen, described the decade prior to Page's second appointment as Google's CEO as his "lost decade." While Page continued to exert a significant influence at Google during this time, overseeing product development and other operations, he became increasingly disconnected and less responsive over time.
As Google's new CEO, Page's two key goals were the development of greater autonomy for the executives overseeing the most important divisions, and higher levels of collaboration, communication and unity among the teams. Page also formed what the media called the "L-Team," a group of senior vice-presidents who reported directly to him and worked in close proximity to his office for a portion of the working week. Additionally, he reorganized the company’s senior management, placing a CEO-like manager at the top of Google's most important product divisions, including YouTube, AdWords, and Google Search.
In accordance with a more cohesive team environment, Page declared a new "zero tolerance for fighting" policy that contrasted with his approach during the early days of Google, when he would use his harsh and intense arguments with Brin as an exemplar for senior management. Page had changed his thinking during his time away from the CEO role, as he eventually arrived at the conclusion that his greatly ambitious goals required a harmonious team dynamic. As part of Page's collaborative rejuvenation process, Google's products and applications were consolidated and underwent an aesthetic overhaul.
Changes and consolidation process
At least 70 of Google's products, features and services were eventually shut down by March 2013, while the appearance and nature of the remaining ones were unified. Jon Wiley, lead designer of Google Search at the time, codenamed Page's redesign overhaul, which officially commenced on April 4, 2011, "Project Kennedy," based on Page's use of the term "moonshots" to describe ambitious projects in a January 2013 Wired interview. An initiative named "Kanna" previously attempted to create a uniform design aesthetic for Google's range of products, but it was too difficult at that point in the company's history for one team to drive such change. Matias Duarte, senior director of the Android user experience at the time that "Kennedy" started, explained in 2013 that "Google passionately cares about design." Page proceeded to consult with the Google Creative Lab design team, based in New York City, to find an answer to his question of what a "cohesive vision" of Google might look like.
The eventual results of "Kennedy," which were progressively rolled out from June 2011 until January 2013, were described by The Verge technology publication as focused upon "refinement, white space, cleanliness, elasticity, usefulness, and most of all simplicity." The final products were aligned with Page's aim for a consistent suite of products that can "move fast," and "Kennedy" was called a "design revolution" by Duarte. Page's "UXA" (user/graphics interface) design team then emerged from the "Kennedy" project, tasked with "designing and developing a true UI framework that transforms Google's application software into a beautiful, mature, accessible and consistent platform for its users." Unspoken of in public, the small UXA unit was designed to ensure that "Kennedy" became an "institution."
Acquisition strategy and new products
When acquiring products and companies for Google, Page asked whether the business acquisition passed the toothbrush test as an initial qualifier, asking the question "Is it something you will use once or twice a day, and does it make your life better?". This approach looked for usefulness above profitability, and long-term potential over near-term financial gain, which has been noted as rare in business acquiring processes.
With Facebook's influence rapidly expanding during the start of Page's second tenure, he finally responded to the intensive competition with Google's own social network, Google+, in mid-2011. After several delays, the social network was released through a very limited field test and was led by Vic Gundotra, Google's then senior vice president of social.
In August 2011, Page announced that Google would spend $12.5 billion to acquire Motorola Mobility. The purchase was primarily motivated by Google's need to secure patents to protect Android from lawsuits by companies including Apple Inc. Page wrote on Google's official blog on August 15, 2011 that "companies including Microsoft and Apple are banding together in anti-competitive patent attacks on Android. The United States Department of Justice had to intervene in the results of one recent patent auction to "protect competition and innovation in the open source software community"... Our acquisition of Motorola will increase competition by strengthening Google’s patent portfolio, which will enable us to better protect Android from anti-competitive threats from Microsoft, Apple and other companies".
Page also ventured into hardware and Google unveiled the Chromebook in May 2012. The hardware product was a laptop that ran on a Google operating system, Chrome OS.
In January 2013, Page participated in a rare interview with Wired, in which writer Steven Levy discussed Page's "10X" mentality—Google employees are expected to create products and services that are at least 10 times better than those of its competitors—in the introductory blurb. Astro Teller, the head of Google X, explained to Levy that 10X is "just core to who he [Page] is," while Page's "focus is on where the next 10X will come from." In his interview with Levy, Page referred to the success of YouTube and Android as examples of "crazy" ideas that investors were not initially interested in, saying: "If you’re not doing some things that are crazy, then you’re doing the wrong things." Page also stated that he was "very happy" with the status of Google+, and discussed concerns over the Internet in relation to the SOPA bill and an International Telecommunication Union proposal that had been recently introduced:
... I do think the Internet’s under much greater attack than it has been in the past. Governments are now afraid of the Internet because of the Middle East stuff, and so they’re a little more willing to listen to what I see as a lot of commercial interests that just want to make money by restricting people’s freedoms. But they’ve also seen a tremendous user reaction, like the backlash against SOPA. I think that governments fight users’ freedoms at their own peril.
At the May 2013 I/O developers conference in San Francisco, Page delivered a keynote address and said that "We're at maybe 1% of what is possible. Despite the faster change, we're still moving slow relative to the opportunities we have. I think a lot of that is because of the negativity... Every story I read is Google vs someone else. That's boring. We should be focusing on building the things that don't exist" and that he was "sad the Web isn't advancing as fast as it should be" citing a perceived focus on negativity and zero sum games among some in the technology sector as a cause for that. In response to an audience question, Page noted an issue that Google had been experiencing with Microsoft, whereby the latter made its Outlook program interoperable with Google, but did not allow for backward compatibility—he referred to Microsoft's practice as "milking off." During the question-and-answer section of his keynote, Page expressed interest in Burning Man, which Brin had previously praised—it was a motivating factor for the latter during Schmidt's hiring process, as Brin liked that Schmidt had attended the week-long annual event.
In September 2013, Page launched the independent Calico initiative, a R&D project in the field of biotechnology. Google announced that Calico seeks to innovate and make improvements in the field of human health, and appointed Art Levinson, chairman of Apple's board and former CEO of Genentech, to be the new division's CEO. Page's official statement read: "Illness and aging affect all our families. With some longer term, moonshot thinking around healthcare and biotechnology, I believe we can improve millions of lives."
Page participated in a March 2014 TedX conference that was held in Vancouver, British Columbia, Canada. The presentation was scripted by Page's chief PR executive Rachel Whetstone, and Google’s CMO Lorraine Twohill, and a demonstration of an artificially intelligent computer program was displayed on a large screen. Page responded to a question about corporations, noting that corporations largely get a "bad rap", which he stated was because they were probably doing the same incremental things they were doing "50 or 20 years ago". He went on to juxtapose that kind of incremental approach to his vision of Google counteracting calcification through driving technology innovation at a high rate. Page mentioned Elon Musk and SpaceX:
He [Musk] wants to go to Mars to back up humanity. That’s a worthy goal. We have a lot of employees at Google who’ve become pretty wealthy. You’re working because you want to change the world and make it better ... I’d like for us to help out more than we are.
Page also mentioned Nikola Tesla with regard to invention and commercialization:
Invention is not enough. Tesla invented the electric power we use, but he struggled to get it out to people... You have to combine both things: invention and innovation focus, plus the company that can commercialize things and get them to people.
Page announced a major management restructure in October 2014 so that he would no longer need to be responsible for day-to-day product-related decision making. In a memo, Page said that Google's core businesses would be able to progress in a typical manner, while he could focus on the next generation of ambitious projects, including Google X initiatives; access and energy, including Google Fiber; smart-home automation through Nest Labs; and biotechnology innovations under Calico. Page maintained that he would continue as the unofficial "chief product officer." Subsequent to the announcement, the executives in charge of Google's core products reported to then Google Senior Vice President Sundar Pichai, who reported directly to Page.
In a November 2014 interview, Page stated that he prioritized the maintenance of his "deep knowledge" of Google's products and breadth of projects, as it had been a key motivating factor for team members. In relation to his then role as the company's CEO, Page said: "I think my job as CEO—I feel like it’s always to be pushing people ahead."
On August 10, 2015, Page announced on Google's official blog that Google had restructured into a number of subsidiaries of a new holding company known as Alphabet Inc with Page becoming CEO of Alphabet Inc and Sundar Pichai assuming the position of CEO of Google Inc. In his announcement, Page described the planned holding company as follows:
|“||Alphabet is mostly a collection of companies. The largest of which, of course, is Google. This newer Google is a bit slimmed down, with the companies that are pretty far afield of our main Internet products contained in Alphabet instead. [...] Fundamentally, we believe this allows us more management scale, as we can run things independently that aren’t very related.||”|
As well as explaining the origin of the company's name:
|“||We liked the name Alphabet because it means a collection of letters that represent language, one of humanity's most important innovations, and is the core of how we index with Google search! We also like that it means alpha‑bet (Alpha is investment return above benchmark), which we strive for!||”|
Page wrote that the motivation behind the reorganization is to make Google "cleaner and more accountable." He also wrote that there was a desire to improve "the transparency and oversight of what we’re doing," and to allow greater control of unrelated companies previously within the Google ecosystem.
Page is an investor in Tesla Motors. He has invested in renewable energy technology, and with the help of Google.org, Google's philanthropic arm, promotes the adoption of plug-in hybrid electric cars[clarification needed] and other alternative energy investments.
Page is also interested in the socio-economic effects of advanced intelligent systems and how advanced digital technologies can be used to create abundance (as described in Peter Diamandis' book), provide for people's needs, shorten the workweek, and mitigate the potential detrimental effects of technological unemployment.
Page also helped to set up Singularity University, a transhumanist think-tank. Google is one of the institution's corporate founders and still funds scholarships at Singularity University.
In 2007, Page married Lucinda Southworth on Necker Island, the Caribbean island owned by Richard Branson. Southworth is a research scientist and the sister of actress and model Carrie Southworth. Page and Southworth have two children, born in 2009 and 2011.
On February 18, 2005, Page was granted the deed on a 9000-sq ft Spanish Colonial Revival architecture house in Palo Alto, California designed by American artistic polymathPedro Joseph de Lemos, a former curator of the Stanford Art Museum and founder of the Carmel Art Institute, after the historic building had been on the market for years with an asking price of US$7.95 million. A two-story stucco archway spans the driveway and the home features intricate stucco work, as well as stone and tile in California Arts and Crafts movement style built to resemble de Lemos family's castle in Spain. The hacienda was constructed between 1931-41 by de Lemos. It is also on the National Register of Historic Places.
In 2009 Page began purchasing properties and tearing down homes adjacent to his home in Palo Alto to make room for a large ecohouse. The existing buildings were "deconstructed" and the materials donated for reuse. The ecohouse was designed to "minimize the impact on the environment." Page worked with an arborist to replace some trees that were in poor health with others that used less water to maintain. Page also applied for Green Point Certification, with points given for use of recycled and low or no-VOC (volatile organic compound) materials and for a roof garden with solar panels. The house's exterior features zinccladding and plenty of windows, including a wall of sliding-glass doors in the rear. It also includes eco-friendly elements such as permeable paving in the parking court and a pervious path through the trees on the property. The 6,000-sq ft house also observes other green home design features such as organic architecture building materials and low volatile organic compound paint.
In 2011, Page became the owner of the US$ 45 million 193-ft superyacht 'Senses', which comes equipped with a helipad, gym, multi-level sun decks, ten luxury suites, a crew of 14 and interior design by famed French designer Philippe Starck. 'Senses' also has extensive ocean exploration capabilities, the superyacht was created to explore the world’s oceans in comfort and it carries a very comprehensive inventory of equipment for that purpose. 'Senses' was built by Fr. Schweers Shipyard in Germany at their Berneshipyard. 'Senses' features a displacement steel hull and a steel/aluminium superstructure, with teak decks. 'Senses' is equipped with an ultra-modern stabilization system which reduces the free surface effect and results in a smoother cruising experience underway.
Page announced on his Google+ profile in May 2013 that his right vocal cord is paralyzed from a cold that he contracted the previous summer, while his left cord was paralyzed in 1999. Page explained that he has been suffering from a vocal cord issue for 14 years, and, as of his May 2013 post, doctors were still unable to identify the exact cause of the problem. The Google+ post also revealed that Page had donated a considerable sum of money to a vocal-cord nerve-function research program at the Voice Health Institute in Boston, U.S. The program, at Massachusetts General Hospital, is led by Steven Zeitels, the Eugene B. Casey Professor of Laryngeal Surgery. An anonymous source stated that the donation exceeded $20 million.
In October 2013, Business Insider reported that Page's paralyzed vocal cords are caused by an autoimmune disease called Hashimoto's thyroiditis, and prevented him from undertaking Google quarterly earnings conference calls for an indefinite period.
In November 2014, Page's family foundation, the Carl Victor Page Memorial Fund, reportedly holding assets in excess of a billion dollars at the end of 2013, gave $15 million to aid the effort against the Ebola virus epidemic in West Africa. Page wrote on his Google+ page that "My wife and I just donated $15 million....Our hearts go out to everyone affected."
Awards and accolades
PC Magazine has praised Google as among the Top 100 Web Sites and Search Engines (1998) and awarded Google the Technical Excellence Award for Innovation in Web Application Development in 1999. In 2000, Google earned a Webby Award, a People's Voice Award for technical achievement, and in 2001, was awarded Outstanding Search Service, Best Image Search Engine, Best Design, Most Webmaster Friendly Search Engine, and Best Search Feature at the Search Engine Watch Awards." In 2002, Page was named a World Economic Forum Global Leader for Tomorrow and along with Brin, was named by the Massachusetts Institute of Technology (MIT)'s Technology Review publication as one of the top 100 innovators in the world under the age of 35, as part of its yearly TR100 listing (changed to "TR35" after 2005).
In 2003, both Page and Brin received a MBA from IE Business School, in an honorary capacity, "for embodying the entrepreneurial spirit and lending momentum to the creation of new businesses." In 2004, they received the Marconi Foundation's prize and were elected Fellows of the Marconi Foundation at Columbia University. In announcing their selection, John Jay Iselin, the Foundation's president, congratulated the two men for "their invention that has fundamentally changed the way information is retrieved today.". Page and Brin were also Award Recipients and National Finalists for the EY Entrepreneur of the Year Award in 2003.
Also in 2004, X PRIZE chose Page as a trustee of their board and he was elected to the National Academy of Engineering. In 2005, Brin and Page were elected Fellows of the American Academy of Arts and Sciences.
In 2008 Page received the Communication Award from King Felipe at the Princess of Asturias Awards on behalf of Google.
In 2009, Page received an honorary doctorate from the University of Michigan during a graduation commencement ceremony. In 2011, he was ranked 24th on the Forbes list of billionaires, and as the 11th richest person in the U.S.
In 2015, Page's "Powerful People" profile on the Forbes site states that Google is "the most influential company of the digital era".
As of July 2014, the Bloomberg Billionaires Index lists Page as the 17th richest man in the world, with an estimated net worth of $32.7 billion. At the completion of 2014, Fortune magazine named Page its "Businessperson of the Year," declaring him "the world’s most daring CEO".
In October 2015, Page was named number one in Forbes' "America's Most Popular Chief Executives", as voted by Google's employees.
In August 2017, Page was awarded honorary citizenship of Agrigento, Italy
- ^ abcForbes (2014). "Larry Page". Forbes. Retrieved 3 March 2014.
- ^"Larry Page's house in Palo Alto, California". Retrieved May 7, 2016.
- ^"Larry Page Profile". Forbes. Retrieved January 5, 2017.
- ^Corley, Brent (2017). "Corley Page". Cloud. Stanford Web Site. Retrieved May 18, 2010.
- ^"In The Garage Where Google Was Born". Mashable. September 27, 2013. Retrieved July 20, 2016.
- ^Jay Yarow (August 10, 2015). "Google new operating structure - Business Insider". Business Insider.
- ^"Larry Page". Forbes. Retrieved 2018-01-24.
- ^ abcdefghijklmnopNicholas Carlson (April 24, 2014). "The Untold Story Of Larry Page's Incredible Comeback". Business Insider. Business Insider, Inc. Retrieved February 2, 2015.
- ^"Gmail Now Has 425 Million Users, Google Apps Used By 5 Million Businesses And 66 Of The Top 100 Universities". TechCrunch. AOL.
- ^"60 Amazing Google Search Statistics and Facts". DMR - Digital Marketing Ramblings.
- ^"Google Search Statistics". internetlivestats.com.
- ^"Google locations". google.com.
- ^"Google Inc. Announces Fourth Quarter and Fiscal Year 2014 Results".
- ^"Management team". Google Company. Google. February 2, 2015. Retrieved February 2, 2015.
- ^"The Marconi Society Fellows". marconisociety.org. Archived from the original on October 17, 2012.
- ^Sergey Brin; Lawrence Page (1998). "The Anatomy of a Large-Scale Hypertextual Web Search Engine". Stanford University
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/
To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date.
Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
1. Introduction(Note: There are two versions of this paper -- a longer full version and a shorter printed version. The full version is available on the web and the conference CD-ROM.)
The web creates new challenges for information retrieval. The amount of information on the web is growing rapidly, as well as the number of new users inexperienced in the art of web research. People are likely to surf the web using its link graph, often starting with high quality human maintained indices such as Yahoo! or with search engines. Human maintained lists cover popular topics effectively but are subjective, expensive to build and maintain, slow to improve, and cannot cover all esoteric topics. Automated search engines that rely on keyword matching usually return too many low quality matches. To make matters worse, some advertisers attempt to gain people's attention by taking measures meant to mislead automated search engines. We have built a large-scale search engine which addresses many of the problems of existing systems. It makes especially heavy use of the additional structure present in hypertext to provide much higher quality search results. We chose our system name, Google, because it is a common spelling of googol, or 10100 and fits well with our goal of building very large-scale search engines.
1.1 Web Search Engines -- Scaling Up: 1994 - 2000Search engine technology has had to scale dramatically to keep up with the growth of the web. In 1994, one of the first web search engines, the World Wide Web Worm (WWWW) [McBryan 94] had an index of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a billion documents. At the same time, the number of queries search engines handle has grown incredibly too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the increasing number of users on the web, and automated systems which query search engines, it is likely that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of our system is to address many of the problems, both in quality and scalability, introduced by scaling search engine technology to such extraordinary numbers.
1.2. Google: Scaling with the WebCreating 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 QualityOur 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 and 2.2).
1.3.2 Academic Search Engine ResearchAside 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 FeaturesThe Google search engine has two important features that help it produce high precision results. First, it makes use of the link structure of the Web to calculate a quality ranking for each web page. This ranking is called PageRank and is described in detail in [Page 98]. Second, Google utilizes link to improve search results.
2.1 PageRank: Bringing Order to the WebThe citation (link) graph of the web is an important resource that has largely gone unused in existing web search engines. We have created maps containing as many as 518 million of these hyperlinks, a significant sample of the total. These maps allow rapid calculation of a web page's "PageRank", an objective measure of its citation importance that corresponds well with people's subjective idea of importance. Because of this correspondence, PageRank is an excellent way to prioritize the results of web keyword searches. For most popular subjects, a simple text matching search that is restricted to web page titles performs admirably when PageRank prioritizes the results (demo available at google.stanford.edu). For the type of full text searches in the main Google system, PageRank also helps a great deal.
2.1.1 Description of PageRank CalculationAcademic 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 JustificationPageRank can be thought of as a model of user behavior. We assume there is a "random surfer" who is given a web page at random and keeps clicking on links, never hitting "back" but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank. And, the d damping factor is the probability at each page the "random surfer" will get bored and request another random page. One important variation is to only add the damping factor d to a single page, or a group of pages. This allows for personalization and can make it nearly impossible to deliberately mislead the system in order to get a higher ranking. We have several other extensions to PageRank, again see [Page 98].
Another intuitive justification is that a page can have a high PageRank if there are many pages that point to it, or if there are some pages that point to it and have a high PageRank. Intuitively, pages that are well cited from many places around the web are worth looking at. Also, pages that have perhaps only one citation from something like the Yahoo! homepage are also generally worth looking at. If a page was not high quality, or was a broken link, it is quite likely that Yahoo's homepage would not link to it. PageRank handles both these cases and everything in between by recursively propagating weights through the link structure of the web.
2.2 Anchor TextThe 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 FeaturesAside 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 WorkSearch research on the web has a short and concise history. The World Wide Web Worm (WWWW) [McBryan 94] was one of the first web search engines. It was subsequently followed by several other academic search engines, many of which are now public companies. Compared to the growth of the Web and the importance of search engines there are precious few documents about recent search engines [Pinkerton 94]. According to Michael Mauldin (chief scientist, Lycos Inc) [Mauldin], "the various services (including Lycos) closely guard the details of these databases". However, there has been a fair amount of work on specific features of search engines. Especially well represented is work which can get results by post-processing the results of existing commercial search engines, or produce small scale "individualized" search engines. Finally, there has been a lot of research on information retrieval systems, especially on well controlled collections. In the next two sections, we discuss some areas where this research needs to be extended to work better on the web.
3.1 Information RetrievalWork 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 CollectionsThe 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 AnatomyFirst, 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.
4.1 Google Architecture OverviewIn this section, we will give a high level overview of how the whole system works as pictured in Figure 1. Further sections will discuss the applications and data structures not mentioned in this section. Most of Google is implemented in C or C++ for efficiency and can run in either Solaris or Linux.
In Google, the web crawling (downloading of web pages) is done by several distributed crawlers. There is a URLserver that sends lists of URLs to be fetched to the crawlers. The web pages that are fetched are then sent to the storeserver. The storeserver then compresses and stores the web pages into a repository. Every web page has an associated ID number called a docID which is assigned whenever a new URL is parsed out of a web page. The indexing function is performed by the indexer and the sorter. The indexer performs a number of functions. It reads the repository, uncompresses the documents, and parses them. Each document is converted into a set of word occurrences called hits. The hits record the word, position in document, an approximation of font size, and capitalization. The indexer distributes these hits into a set of "barrels", creating a partially sorted forward index. The indexer performs another important function. It parses out all the links in every web page and stores important information about them in an anchors file. This file contains enough information to determine where each link points from and to, and the text of the link.
The URLresolver reads the anchors file and converts relative URLs into absolute URLs and in turn into docIDs. It puts the anchor text into the forward index, associated with the docID that the anchor points to. It also generates a database of links which are pairs of docIDs. The links database is used to compute PageRanks for all the documents.
The sorter takes the barrels, which are sorted by docID (this is a simplification, see Section 4.2.5), and resorts them by wordID to generate the inverted index. This is done in place so that little temporary space is needed for this operation. The sorter also produces a list of wordIDs and offsets into the inverted index. A program called DumpLexicon takes this list together with the lexicon produced by the indexer and generates a new lexicon to be used by the searcher. The searcher is run by a web server and uses the lexicon built by DumpLexicon together with the inverted index and the PageRanks to answer queries.
4.2 Major Data StructuresGoogle's data structures are optimized so that a large document collection can be crawled, indexed, and searched with little cost. Although, CPUs and bulk input output rates have improved dramatically over the years, a disk seek still requires about 10 ms to complete. Google is designed to avoid disk seeks whenever possible, and this has had a considerable influence on the design of the data structures.
4.2.1 BigFilesBigFiles are virtual files spanning multiple file systems and are addressable by 64 bit integers. The allocation among multiple file systems is handled automatically. The BigFiles package also handles allocation and deallocation of file descriptors, since the operating systems do not provide enough for our needs. BigFiles also support rudimentary compression options.
4.2.3 Document IndexThe document index keeps information about each document. It is a fixed width ISAM (Index sequential access mode) index, ordered by docID. The information stored in each entry includes the current document status, a pointer into the repository, a document checksum, and various statistics. If the document has been crawled, it also contains a pointer into a variable width file called docinfo which contains its URL and title. Otherwise the pointer points into the URLlist which contains just the URL. This design decision was driven by the desire to have a reasonably compact data structure, and the ability to fetch a record in one disk seek during a search
Additionally, there is a file which is used to convert URLs into docIDs. It is a list of URL checksums with their corresponding docIDs and is sorted by checksum. In order to find the docID of a particular URL, the URL's checksum is computed and a binary search is performed on the checksums file to find its docID. URLs may be converted into docIDs in batch by doing a merge with this file. This is the technique the URLresolver uses to turn URLs into docIDs. This batch mode of update is crucial because otherwise we must perform one seek for every link which assuming one disk would take more than a month for our 322 million link dataset.
4.2.4 LexiconThe 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 ListsA 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.
4.2.6 Forward IndexThe 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 IndexThe 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 WebRunning a web crawler is a challenging task. There are tricky performance and reliability issues and even more importantly, there are social issues. Crawling is the most fragile application since it involves interacting with hundreds of thousands of web servers and various name servers which are all beyond the control of the system.
In order to scale to hundreds of millions of web pages, Google has a fast distributed crawling system. A single URLserver serves lists of URLs to a number of crawlers (we typically ran about 3). Both the URLserver and the crawlers are implemented in Python. Each crawler keeps roughly 300 connections open at once. This is necessary to retrieve web pages at a fast enough pace. At peak speeds, the system can crawl over 100 web pages per second using four crawlers. This amounts to roughly 600K per second of data. A major performance stress is DNS lookup. Each crawler maintains a its own DNS cache so it does not need to do a DNS lookup before crawling each document. Each of the hundreds of connections can be in a number of different states: looking up DNS, connecting to host, sending request, and receiving response. These factors make the crawler a complex component of the system. It uses asynchronous IO to manage events, and a number of queues to move page fetches from state to state.
It turns out that running a crawler which connects to more than half a million servers, and generates tens of millions of log entries generates a fair amount of email and phone calls. Because of the vast number of people coming on line, there are always those who do not know what a crawler is, because this is the first one they have seen. Almost daily, we receive an email something like, "Wow, you looked at a lot of pages from my web site. How did you like it?" There are also some people who do not know about the robots exclusion protocol, and think their page should be protected from indexing by a statement like, "This page is copyrighted and should not be indexed", which needless to say is difficult for web crawlers to understand. Also, because of the huge amount of data involved, unexpected things will happen. For example, our system tried to crawl an online game. This resulted in lots of garbage messages in the middle of their game! It turns out this was an easy problem to fix. But this problem had not come up until we had downloaded tens of millions of pages. Because of the immense variation in web pages and servers, it is virtually impossible to test a crawler without running it on large part of the Internet. Invariably, there are hundreds of obscure problems which may only occur on one page out of the whole web and cause the crawler to crash, or worse, cause unpredictable or incorrect behavior. Systems which access large parts of the Internet need to be designed to be very robust and carefully tested. Since large complex systems such as crawlers will invariably cause problems, there needs to be significant resources devoted to reading the email and solving these problems as they come up.
4.4 Indexing the Web
- Parsing -- Any parser which is designed to run on the entire Web must handle a huge array of possible errors. These range from typos in HTML tags to kilobytes of zeros in the middle of a tag, non-ASCII characters, HTML tags nested hundreds deep, and a great variety of other errors that challenge anyone's imagination to come up with equally creative ones. For maximum speed, instead of using YACC to generate a CFG parser, we use flex to generate a lexical analyzer which we outfit with its own stack. Developing this parser which runs at a reasonable speed and is very robust involved a fair amount of work.
- IndexingDocuments into Barrels -- After each document is parsed, it is encoded into a number of barrels. Every word is converted into a wordID by using an in-memory hash table -- the lexicon. New additions to the lexicon hash table are logged to a file. Once the words are converted into wordID's, their occurrences in the current document are translated into hit lists and are written into the forward barrels. The main difficulty with parallelization of the indexing phase is that the lexicon needs to be shared. Instead of sharing the lexicon, we took the approach of writing a log of all the extra words that were not in a base lexicon, which we fixed at 14 million words. That way multiple indexers can run in parallel and then the small log file of extra words can be processed by one final indexer.
- Sorting -- In order to generate the inverted index, the sorter takes each of the forward barrels and sorts it by wordID to produce an inverted barrel for title and anchor hits and a full text inverted barrel. This process happens one barrel at a time, thus requiring little temporary storage. Also, we parallelize the sorting phase to use as many machines as we have simply by running multiple sorters, which can process different buckets at the same time. Since the barrels don't fit into main memory, the sorter further subdivides them into baskets which do fit into memory based on wordID and docID. Then the sorter, loads each basket into memory, sorts it and writes its contents into the short inverted barrel and the full inverted barrel.
4.5 SearchingThe 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.
Sort the documents that have matched by rank and return the top k.
To put a limit on response time, once a certain number (currently 40,000) of matching documents are found, the searcher automatically goes to step 8 in Figure 4. This means that it is possible that sub-optimal results would be returned. We are currently investigating other ways to solve this problem. In the past, we sorted the hits according to PageRank, which seemed to improve the situation.
4.5.1 The Ranking SystemGoogle maintains much more information about web documents than typical search engines. Every hitlist includes position, font, and capitalization information. Additionally, we factor in hits from anchor text and the PageRank of the document. Combining all of this information into a rank is difficult. We designed our ranking function so that no particular factor can have too much influence. First, consider the simplest case -- a single word query. In order to rank a document with a single word query, Google looks at that document's hit list for that word. Google considers each hit to be one of several different types (title, anchor, URL, plain text large font, plain text small font, ...), each of which has its own type-weight. The type-weights make up a vector indexed by type. Google counts the number of hits of each type in the hit list. Then every count is converted into a count-weight. Count-weights increase linearly with counts at first but quickly taper off so that more than a certain count will not help. We take the dot product of the vector of count-weights with the vector of type-weights to compute an IR score for the document. Finally, the IR score is combined with PageRank to give a final rank to the document.
For a multi-word search, the situation is more complicated. Now multiple hit lists must be scanned through at once so that hits occurring close together in a document are weighted higher than hits occurring far apart. The hits from the multiple hit lists are matched up so that nearby hits are matched together. For every matched set of hits, a proximity is computed. The proximity is based on how far apart the hits are in the document (or anchor) but is classified into 10 different value "bins" ranging from a phrase match to "not even close". Counts are computed not only for every type of hit but for every type and proximity. Every type and proximity pair has a type-prox-weight. The counts are converted into count-weights and we take the dot product of the count-weights and the type-prox-weights to compute an IR score. All of these numbers and matrices can all be displayed with the search results using a special debug mode. These displays have been very helpful in developing the ranking system.
4.5.2 FeedbackThe 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 PerformanceThe most important measure of a search engine is the quality of its search results. While a complete user evaluation is beyond the scope of this paper, our own experience with Google has shown it to produce better results than the major commercial search engines for most searches. As an example which illustrates the use of PageRank, anchor text, and proximity, Figure 4 shows Google's results for a search on "bill clinton". These results demonstrates some of Google's features. The results are clustered by server. This helps considerably when sifting through result sets. A number of results are from the whitehouse.gov domain which is what one may reasonably expect from such a search. Currently, most major commercial search engines do not return any results from whitehouse.gov, much less the right ones. Notice that there is no title for the first result. This is because it was not crawled. Instead, Google relied on anchor text to determine this was a good answer to the query. Similarly, the fifth result is an email address which, of course, is not crawlable. It is also a result of anchor text.
All of the results are reasonably high quality pages and, at last check, none were broken links. This is largely because they all have high PageRank. The PageRanks are the percentages in red along with bar graphs. Finally, there are no results about a Bill other than Clinton or about a Clinton other than Bill. This is because we place heavy importance on the proximity of word occurrences. Of course a true test of the quality of a search engine would involve an extensive user study or results analysis which we do not have room for here. Instead, we invite the reader to try Google for themselves at http://google.stanford.edu.
5.1 Storage RequirementsAside 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.
5.2 System PerformanceIt is important for a search engine to crawl and index efficiently. This way information can be kept up to date and major changes to the system can be tested relatively quickly. For Google, the major operations are Crawling, Indexing, and Sorting. It is difficult to measure how long crawling took overall because disks filled up, name servers crashed, or any number of other problems which stopped the system. In total it took roughly 9 days to download the 26 million pages (including errors). However, once the system was running smoothly, it ran much faster, downloading the last 11 million pages in just 63 hours, averaging just over 4 million pages per day or 48.5 pages per second. We ran the indexer and the crawler simultaneously. The indexer ran just faster than the crawlers. This is largely because we spent just enough time optimizing the indexer so that it would not be a bottleneck. These optimizations included bulk updates to the document index and placement of critical data structures on the local disk. The indexer runs at roughly 54 pages per second. The sorters can be run completely in parallel; using four machines, the whole process of sorting takes about 24 hours.
5.3 Search PerformanceImproving 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.
6 ConclusionsGoogle 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 WorkA 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 SearchThe 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 ArchitectureAside 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 ToolIn addition to being a high quality search engine, Google is a research tool. The data Google has collected has already resulted in many other papers submitted to conferences and many more on the way. Recent research such as [Abiteboul 97] has shown a number of limitations to queries about the Web that may be answered without having the Web available locally. This means that Google (or a similar system) is not only a valuable research tool but a necessary one for a wide range of applications. We hope Google will be a resource for searchers and researchers all around the world and will spark the next generation of search engine technology.
7 AcknowledgmentsScott Hassan and Alan Steremberg have been critical to the development of Google. Their talented contributions are irreplaceable, and the authors owe them much gratitude. We would also like to thank Hector Garcia-Molina, Rajeev Motwani, Jeff Ullman, and Terry Winograd and the whole WebBase group for their support and insightful discussions. Finally we would like to recognize the generous support of our equipment donors IBM, Intel, and Sun and our funders. The research described here was conducted as part of the Stanford Integrated Digital Library Project, supported by the National Science Foundation under Cooperative Agreement IRI-9411306. Funding for this cooperative agreement is also provided by DARPA and NASA, and by Interval Research, and the industrial partners of the Stanford Digital Libraries Project.
- [Abiteboul 97] Serge Abiteboul and Victor Vianu, Queries and Computation on the Web. Proceedings of the International Conference on Database Theory. Delphi, Greece 1997.
- [Bagdikian 97] Ben H. Bagdikian. The Media Monopoly. 5th Edition. Publisher: Beacon, ISBN: 0807061557
- [Chakrabarti 98] S.Chakrabarti, B.Dom, D.Gibson, J.Kleinberg, P. Raghavan and S. Rajagopalan. Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text. Seventh International Web Conference (WWW 98). Brisbane, Australia, April 14-18, 1998.
- [Cho 98] Junghoo Cho, Hector Garcia-Molina, Lawrence Page. Efficient Crawling Through URL Ordering. Seventh International Web Conference (WWW 98). Brisbane, Australia, April 14-18, 1998.
- [Gravano 94] Luis Gravano, Hector Garcia-Molina, and A. Tomasic. The Effectiveness of GlOSS for the Text-Database Discovery Problem. Proc. of the 1994 ACM SIGMOD International Conference On Management Of Data, 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. http://www.cs.colorado.edu/home/mcbryan/mypapers/www94.ps
- [Page 98] Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. The PageRank Citation Ranking: Bringing Order to the Web. Manuscript in progress. http://google.stanford.edu/~backrub/pageranksub.ps
- [Pinkerton 94] Brian Pinkerton, Finding What People Want: Experiences with the WebCrawler. The Second International WWW Conference Chicago, USA, October 17-20, 1994. http://info.webcrawler.com/bp/WWW94.html
- [Spertus 97] Ellen Spertus. ParaSite: Mining Structural Information on the Web. The Sixth International WWW Conference (WWW 97). Santa Clara, USA, April 7-11, 1997.
- [TREC 96] Proceedings of the fifth Text REtrieval Conference (TREC-5). Gaithersburg, Maryland, November 20-22, 1996. Publisher: Department of Commerce, National Institute of Standards and Technology. Editors: D. K. Harman and E. M. Voorhees. Full text at: http://trec.nist.gov/
- [Witten 94] Ian H Witten, Alistair Moffat, and Timothy C. Bell. Managing Gigabytes: Compressing and Indexing Documents and Images. New York: Van Nostrand Reinhold, 1994.
- [Weiss 96] Ron Weiss, Bienvenido Velez, Mark A. Sheldon, Chanathip Manprempre, Peter Szilagyi, Andrzej Duda, and David K. Gifford. HyPursuit: A Hierarchical Network Search Engine that Exploits Content-Link Hypertext Clustering. Proceedings of the 7th ACM Conference on Hypertext. New York, 1996.
Sergey Brin received his B.S. degree in mathematics and computer science from the University of Maryland at College Park in 1993. Currently, he is a Ph.D. candidate in computer science at Stanford University where he received his M.S. in 1995. He is a recipient of a National Science Foundation Graduate Fellowship. His research interests include search engines, information extraction from unstructured sources, and data mining of large text collections and scientific data.
Lawrence Page was born in East Lansing, Michigan, and received a B.S.E. in Computer Engineering at the University of Michigan Ann Arbor in 1995. He is currently a Ph.D. candidate in Computer Science at Stanford University. Some of his research interests include the link structure of the web, human computer interaction, search engines, scalability of information access interfaces, and personal data mining.
8 Appendix A: Advertising and Mixed MotivesCurrently, the predominant business model for commercial search engines is advertising. The goals of the advertising business model do not always correspond to providing quality search to users. For example, in our prototype search engine one of the top results for cellular phone is "The Effect of Cellular Phone Use Upon Driver Attention", a study which explains in great detail the distractions and risk associated with conversing on a cell phone while driving. This search result came up first because of its high importance as judged by the PageRank algorithm, an approximation of citation importance on the web [Page, 98]. It is clear that a search engine which was taking money for showing cellular phone ads would have difficulty justifying the page that our system returned to its paying advertisers. For this type of reason and historical experience with other media [Bagdikian 83], we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers.
Since it is very difficult even for experts to evaluate search engines, search engine bias is particularly insidious. A good example was OpenText, which was reported to be selling companies the right to be listed at the top of the search results for particular queries [Marchiori 97]. This type of bias is much more insidious than advertising, because it is not clear who "deserves" to be there, and who is willing to pay money to be listed. This business model resulted in an uproar, and OpenText has ceased to be a viable search engine. But less blatant bias are likely to be tolerated by the market. For example, a search engine could add a small factor to search results from "friendly" companies, and subtract a factor from results from competitors. This type of bias is very difficult to detect but could still have a significant effect on the market. Furthermore, advertising income often provides an incentive to provide poor quality search results. For example, we noticed a major search engine would not return a large airline's homepage when the airline's name was given as a query. It so happened that the airline had placed an expensive ad, linked to the query that was its name. A better search engine would not have required this ad, and possibly resulted in the loss of the revenue from the airline to the search engine. In general, it could be argued from the consumer point of view that the better the search engine is, the fewer advertisements will be needed for the consumer to find what they want. This of course erodes the advertising supported business model of the existing search engines. However, there will always be money from advertisers who want a customer to switch products, or have something that is genuinely new. But we believe the issue of advertising causes enough mixed incentives that it is crucial to have a competitive search engine that is transparent and in the academic realm.
9 Appendix B: Scalability
9. 1 Scalability of GoogleWe 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 ArchitecturesAs the capabilities of computers increase, it becomes possible to index a very large amount of text for a reasonable cost. Of course, other more bandwidth intensive media such as video is likely to become more pervasive. But, because the cost of production of text is low compared to media like video, text is likely to remain very pervasive. Also, it is likely that soon we will have speech recognition that does a reasonable job converting speech into text, expanding the amount of text available. All of this provides amazing possibilities for centralized indexing. Here is an illustrative example. We assume we want to index everything everyone in the US has written for a year. We assume that there are 250 million people in the US and they write an average of 10k per day. That works out to be about 850 terabytes. Also assume that indexing a terabyte can be done now for a reasonable cost. We also assume that the indexing methods used over the text are linear, or nearly linear in their complexity. Given all these assumptions we can compute how long it would take before we could index our 850 terabytes for a reasonable cost assuming certain growth factors. Moore's Law was defined in 1965 as a doubling every 18 months in processor power. It has held remarkably true, not just for processors, but for other important system parameters such as disk as well. If we assume that Moore's law holds for the future, we need only 10 more doublings, or 15 years to reach our goal of indexing everything everyone in the US has written for a year for a price that a small company could afford. Of course, hardware experts are somewhat concerned Moore's Law may not continue to hold for the next 15 years, but there are certainly a lot of interesting centralized applications even if we only get part of the way to our hypothetical example.
Of course a distributed systems like Gloss [Gravano 94] or Harvest will often be the most efficient and elegant technical solution for indexing, but it seems difficult to convince the world to use these systems because of the high administration costs of setting up large numbers of installations. Of course, it is quite likely that reducing the administration cost drastically is possible. If that happens, and everyone starts running a distributed indexing system, searching would certainly improve drastically.
Because humans can only type or speak a finite amount, and as computers continue improving, text indexing will scale even better than it does now. Of course there could be an infinite amount of machine generated content, but just indexing huge amounts of human generated content seems tremendously useful. So we are optimistic that our centralized web search engine architecture will improve in its ability to cover the pertinent text information over time and that there is a bright future for search.