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We knew the web was big...
We've known it for a long time: the web is big. The first Google index in 1998 already had 26 million pages, and by 2000 the Google index reached the one billion mark. Over the last eight years, we've seen a lot of big numbers about how much content is really out there. Recently, even our search engineers stopped in awe about just how big the web is these days -- when our systems that process links on the web to find new content hit a milestone: 1 trillion (as in 1,000,000,000,000) unique URLs on the web at once!
How do we find all those pages? We start at a set of well-connected initial pages and follow each of their links to new pages. Then we follow the links on those new pages to even more pages and so on, until we have a huge list of links. In fact, we found even more than 1 trillion individual links, but not all of them lead to unique web pages. Many pages have multiple URLs with exactly the same content or URLs that are auto-generated copies of each other. Even after removing those exact duplicates, we saw a trillion unique URLs, and the number of individual web pages out there is growing by several billion pages per day.
So how many unique pages does the web really contain? We don't know; we don't have time to look at them all! :-) Strictly speaking, the number of pages out there is infinite -- for example, web calendars may have a "next day" link, and we could follow that link forever, each time finding a "new" page. We're not doing that, obviously, since there would be little benefit to you. But this example shows that the size of the web really depends on your definition of what's a useful page, and there is no exact answer.
We don't index every one of those trillion pages -- many of them are similar to each other, or represent auto-generated content similar to the calendar example that isn't very useful to searchers. But we're proud to have the most comprehensive index of any search engine, and our goal always has been to index all the world's data.
To keep up with this volume of information, our systems have come a long way since the first set of web data Google processed to answer queries. Back then, we did everything in batches: one workstation could compute the PageRank graph on 26 million pages in a couple of hours, and that set of pages would be used as Google's index for a fixed period of time. Today, Google downloads the web continuously, collecting updated page information and re-processing the entire web-link graph several times per day. This graph of one trillion URLs is similar to a map made up of one trillion intersections. So multiple times every day, we do the computational equivalent of fully exploring every intersection of every road in the United States. Except it'd be a map about 50,000 times as big as the U.S., with 50,000 times as many roads and intersections.
As you can see, our distributed infrastructure allows applications to efficiently traverse a link graph with many trillions of connections, or quickly sort petabytes of data, just to prepare to answer the most important question: your next Google search.
How do we find all those pages? We start at a set of well-connected initial pages and follow each of their links to new pages. Then we follow the links on those new pages to even more pages and so on, until we have a huge list of links. In fact, we found even more than 1 trillion individual links, but not all of them lead to unique web pages. Many pages have multiple URLs with exactly the same content or URLs that are auto-generated copies of each other. Even after removing those exact duplicates, we saw a trillion unique URLs, and the number of individual web pages out there is growing by several billion pages per day.
So how many unique pages does the web really contain? We don't know; we don't have time to look at them all! :-) Strictly speaking, the number of pages out there is infinite -- for example, web calendars may have a "next day" link, and we could follow that link forever, each time finding a "new" page. We're not doing that, obviously, since there would be little benefit to you. But this example shows that the size of the web really depends on your definition of what's a useful page, and there is no exact answer.
We don't index every one of those trillion pages -- many of them are similar to each other, or represent auto-generated content similar to the calendar example that isn't very useful to searchers. But we're proud to have the most comprehensive index of any search engine, and our goal always has been to index all the world's data.
To keep up with this volume of information, our systems have come a long way since the first set of web data Google processed to answer queries. Back then, we did everything in batches: one workstation could compute the PageRank graph on 26 million pages in a couple of hours, and that set of pages would be used as Google's index for a fixed period of time. Today, Google downloads the web continuously, collecting updated page information and re-processing the entire web-link graph several times per day. This graph of one trillion URLs is similar to a map made up of one trillion intersections. So multiple times every day, we do the computational equivalent of fully exploring every intersection of every road in the United States. Except it'd be a map about 50,000 times as big as the U.S., with 50,000 times as many roads and intersections.
As you can see, our distributed infrastructure allows applications to efficiently traverse a link graph with many trillions of connections, or quickly sort petabytes of data, just to prepare to answer the most important question: your next Google search.
Social Glass " The Google Enterprise Search XML API and Ruby on Rails
How to display google search results in your Rails application
CustomizeGoogle: Improve Your Google Experience -- Firefox Extension
CustomizeGoogle is a Firefox extension that enhances Google search results by adding extra information (like links to Yahoo, Ask.com, MSN etc) and removing unwanted information (like ads and spam).
Using Google is AJAX Search API with Java
the limitations to the ajax api - sigh!
Search Protocol Reference
returning search results as xml
Google Code Search
Search public source code.
opensource: del.icio.us tag/opensource
Development
Programming
search
Google
code
opensource
system:has:for
sitemaps.org - Home
The Sitemaps protocol enables webmasters to information earch engine about pages on their site that are available for crawling.
Google Global Firefox Extension V2
Google Global now allows you to view organic and paid Google search results as they appear in almost any location on Earth.
Hitting 40 languages
One of our goals is to give everyone using Google the information they want, wherever they are, in whatever language they speak, and through whatever device they're using. A huge part of that goal is making our services available in as many languages as possible. And as I’m sure you can imagine, that isn't as easy as simply as translating a few lines of text.
Take Hebrew or Arabic, which are written from right to left. An Arabic speaker may search for [world cup football 2008] [كأس العالم 2008 لكرة القدم]. Part of the query will be written from right to left in Arabic, while the numbers will be written left to right. Sometimes the right-to-left difference can mean having to change the entire layout of a page, as with Gmail.
Or take Russian, where words change depending on their placement and role in a sentence. In Russian, for example [pizza in Moscow] is [пицца в Москве] but [pizza near Moscow] is [пицца рядом с Москвой].
Then there's the whole challenge of ensuring that results are locally relevant. While many Australians searching for [freedom] are looking for the Australian furniture chain, UK and US users are often looking for the definition of the word itself. Our search results, then, have to take into account these local differences.
Our efforts to make Google products available in as many languages as possible dates to 2001, when we started Google in Your Language, which lets volunteers translate and edit translations of Google products in their native languages.
As more and more users, advertisers, and partners interact with Google across the world, the need for local products has become even more obvious. In 2007, we undertook a company-wide initiative to increase the availability of our products in multiple languages. We picked the 40 languages read by over 98% of Internet users and got going, relying heavily on open source libraries such as ICU and other internationalization technologies to design products. Do you need web search in Chinese or AdWords online support in Spanish? Perhaps Google News in Hindi or Google Scholar in Korean? Not a problem.
Here's a taste of how far we've come.
Growth in local language versions.
Take Hebrew or Arabic, which are written from right to left. An Arabic speaker may search for [world cup football 2008] [كأس العالم 2008 لكرة القدم]. Part of the query will be written from right to left in Arabic, while the numbers will be written left to right. Sometimes the right-to-left difference can mean having to change the entire layout of a page, as with Gmail.
Or take Russian, where words change depending on their placement and role in a sentence. In Russian, for example [pizza in Moscow] is [пицца в Москве] but [pizza near Moscow] is [пицца рядом с Москвой].
Then there's the whole challenge of ensuring that results are locally relevant. While many Australians searching for [freedom] are looking for the Australian furniture chain, UK and US users are often looking for the definition of the word itself. Our search results, then, have to take into account these local differences.
Our efforts to make Google products available in as many languages as possible dates to 2001, when we started Google in Your Language, which lets volunteers translate and edit translations of Google products in their native languages.
As more and more users, advertisers, and partners interact with Google across the world, the need for local products has become even more obvious. In 2007, we undertook a company-wide initiative to increase the availability of our products in multiple languages. We picked the 40 languages read by over 98% of Internet users and got going, relying heavily on open source libraries such as ICU and other internationalization technologies to design products. Do you need web search in Chinese or AdWords online support in Spanish? Perhaps Google News in Hindi or Google Scholar in Korean? Not a problem.
Here's a taste of how far we've come.
Growth in local language versions. - 30 in 30: Today we have more than 30 products in more than 30 languages, up from 5 products in 30 languages just a year ago.
- In 2004, we had 150 local-language versions of various products (e.g. a product local to the UK, not just the English-speaking world); today we're at more than 1500.
- From January to March of 2008, we launched 256 local-language versions of various products, compared to 55 in the same period of 2007.
- We've upgraded to Unicode 5.1 to make sure that we can handle any characters people read or write in.
Technologies behind Google ranking
In my previous post, I introduced the philosophies behind Google ranking. As part of our effort to discuss search quality, I want to tell you more about the technologies behind our ranking. The core technology in our ranking system comes from the academic field of Information Retrieval (IR). The IR community has studied search for almost 50 years. It uses statistical signals of word salience, like word frequency, to rank pages. (See "Modern Information Retrieval: A Brief Overview" for a quick overview of IR technology.) IR gave us a solid foundation, and we have built a tremendous system on top using links, page structure, and many other such innovations.
Search in the last decade has moved from give me what I said to give me what I want. User expectations from search have rightly increased. We work hard to fulfill the expectations of each and every user, and to do that we need to better understand the pages, the queries, and our users. Over the last decade we have pushed the technologies for understanding these three components (of the search process) to completely new dimensions.
When we talk about queries at Google, we use square brackets [ ] to mark the beginning and end of queries (see "How to write queries" by Matt Cutts), a notation I will use throughout this post. (Pages and search results change frequently, so in time, some examples used here may not behave as explained.)
Search in the last decade has moved from give me what I said to give me what I want. User expectations from search have rightly increased. We work hard to fulfill the expectations of each and every user, and to do that we need to better understand the pages, the queries, and our users. Over the last decade we have pushed the technologies for understanding these three components (of the search process) to completely new dimensions.
When we talk about queries at Google, we use square brackets [ ] to mark the beginning and end of queries (see "How to write queries" by Matt Cutts), a notation I will use throughout this post. (Pages and search results change frequently, so in time, some examples used here may not behave as explained.)
- Understanding pages: Over years we have invested heavily in our crawl and indexing system. As a result we have a very large and very fresh index. In addition to size and freshness, we have improved our index in other ways. One of the key technologies we have developed to understand pages is associating important concepts to a page even when they are not obvious on the page. We find the official homepage for Sprovieri Gallery in London for the Italian query [galleria sprovieri londra], even though the official page does not have either London or Londra on it. In the U.S., a user searching for [cool tech pc vancouver, wa] finds the homepage www.cooltechpc.com even though the page does not mention anywhere that they are in Vancouver, WA. Other technologies we have developed include distinctions between important and less important words in the page and the freshness of the information on the page.
- Understanding queries: It is critical that we understand what our users are looking for (beyond just the few words in their query). We have made several notable advances in this area including a best-in-class spelling suggestion system, an advanced synonyms system, and a very strong concept analysis system.
Most users have used our spelling suggestion system at one time or another. It knows that someone searching for [kofee annan] is really searching for Mr. Kofi Annan, and is prompted: Did you mean: kofi annan; whereas someone searching for [kofee beans] is actually looking for coffee beans. Doing this internationally with very high accuracy is hard, and we do it well.
Synonyms are the foundation of our query understanding work. This is one of the hardest problems we are solving at Google. Though sometimes obvious to humans, it is an unsolved problem in automatic language processing. As a user, I don't want to think too much about what words I should use in my queries. Often I don't even know what the right words are. This is where our synonyms system comes into action. Our synonyms system can do sophisticated query modifications, e.g., it knows that the word 'Dr' in the query [Dr Zhivago] stands for Doctor whereas in [Rodeo Dr] it means Drive. A user looking for [back bumper repair] gets results about rear bumper repair. For [Ramstein ab], we automatically look for Ramstein Air Base; for the query query [b&b ab] we search for Bed and Breakfasts in Alberta, Canada. We have developed this level of query understanding for almost one hundred different languages, which is what I am truly proud of.
Another technology we use in our ranking system is concept identification. Identifying critical concepts in the query allows us to return much more relevant results. For example, our algorithms understand that in the query [new york times square church] the user is looking for the well-known church in Times Square and not for articles from the New York Times. We don't just stop at identifying concepts; we further enhance the query with the right concepts when, for instance, someone looking for [PC and its impact on people] is in fact looking for impact of computers on society, or someone who searches for [rainforest instructional activities for vocabulary] is really looking for rain forest lesson plans. Our query analysis algorithms have many such state-of-the-art techniques built into them, and once again, we do this internationally in almost every language we serve.
- Understanding users: Our work on interpreting user intent is aimed at returning results people really want, not just what they said in their query. This work starts with a world class localization system, and adds to it our advanced personalization technology, and several other great strides we have made in interpreting user intent, e.g. Universal Search.
Our clear focus on "best locally relevant results served globally" is reflected in our work on localization. The same query typed in multiple countries may deserve completely different results. A user looking for [bank] in the US should get American banks, whereas a user in the UK is either looking for the Bank Fashion line or for British financial institutions. The results for this query should return local financial institutions in other English speaking countries like Australia, Canada, New Zealand, South Africa. The fun really starts when this query is typed in non-English-speaking countries like Egypt, Israel, Japan, Russia, Saudi Arabia, Switzerland. Likewise the query [football] refers to entirely different sports in Australia, the UK, and the US. These examples mostly show how we get the localized version of the same concept correctly (financial institution, sport, etc.). However, the same query can mean entirely different things in different countries. For example, [Côte d'Or] is a geographic region in France - but it is a large chocolate manufacturer in neighboring French-speaking Belgium; and yes, we get that right too :-).
Personalization is another strong feature in our search system which tailors search results to individual users. Users who are logged-in while searching and have signed up for Web History get results that are more relevant for them than the general Google results. For example, someone who does a lot football-related searches might get more football related results for [giants], while other users might get results related to the baseball team. Similarly, if you tend to prefer results from a particular shopping site, you will be more likely to get results from that site when you search for products. Our evaluation shows that users who get personalized results find them to be more relevant than non-personalized results.
Another case of user intent can be observed for the query [chevrolet magnum]. Magnum is actually made by Dodge and not Chevrolet. So we present the results for Dodge Magnum with the prompt See results for: dodge magnum in our result set.
Our work on Universal Search is another example of how we interpret user intent to give them what they (sometimes) really want. Someone searching for [bangalore] not only gets the important web pages, they also get a map, a video showing street life, traffic, etc. in Bangalore -- watching this video I almost feel I am there :-) -- and at the time of writing there is relevant news and relevant blogs about Bangalore.
Finally let me briefly mention the latest advance we have made in search: Cross Language Information Retrieval (CLIR). CLIR allows users to first discover information that is not in their language, and then using Google's translation technology, we make this information accessible. I call this advance: give me what I want in any language. A user looking for Tony Blair's biography in Russia who types the query in Russian [Тони Блэр биография] is prompted at the bottom of our results to search the English web with:
Similarly a user searching for Disney movie songs in Egypt with the query [أغاني أفلام ديزني] is prompted to search the English web. We are very excited about CLIR as it truly brings us closer to our mission to organize the world's information and make it universally accessible and useful.I hope my two posts about Google ranking have made it clear that we live and breathe search, and we are more passionate than ever about it. Our fervor for serving all our users worldwide is unprecedented. We pride ourselves in running a very good ranking system, and are working incredibly hard every day to make it even better.
I could go on and on showing examples of state-of-the-art technology that we have developed to make our ranking system as good as it is, but the fact is that search is nowhere close to being a solved problem. Many queries still don't get satisfactory results from Google, and each such query is an opportunity to improve our ranking system. I am confident that with numerous techniques under development in our group, we will make large improvements to our ranking algorithms in the near future.
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