Introduction:The good news about the Internet and its most visible component, the World Wide Web, is that there are hundreds of millions of pages available, waiting to present information on an amazing variety of topics. The bad news about the Internet is that there are hundreds of millions of pages available, most of them titled according to the whim of their author, almost all of them sitting on servers with cryptic names. When you need to know about a particular subject, how do you know which pages to read? If you're like most people, you visit an Internet search engine.
Internet search engines are special sites on the Web that are designed to help people find information stored on other sites. There are differences in the ways various search engines work, but they all perform three basic tasks:
- They search the Internet -- or select pieces of the Internet -- based on important words.
- They keep an index of the words they find, and where they find them.
- They allow users to look for words or combinations of words found in that index.
Looking at the web:
Before a search engine can tell you where a file or document is, it must be found. To find information on the hundreds of millions of Web pages that exist, a search engine employs special software robots, called spiders, to build lists of the words found on Web sites. When a spider is building its lists, the process is called Web crawling.
In order to build and maintain a useful list of words, a search engine's spiders have to look at a lot of pages.
How does any spider start its travels over the Web? The usual starting points are lists of heavily used servers and very popular pages. The spider will begin with a popular site, indexing the words on its pages and following every link found within the site. In this way, the spidering system quickly begins to travel, spreading out across the most widely used portions of the Web.
Each spider could keep about 300 connections to Web pages open at a time. At its peak performance, using four spiders, their system could crawl over 100 pages per second, generating around 600 kilobytes of data each second. When the Google spider looked at an HTML page, it took note of two things: 
- The words within the page
- Where the words were found
Words occurring in the title, subtitles, meta tags and other positions of relative importance were noted for special consideration during a subsequent user search. The Google spider was built to index every significant word on a page, leaving out the articles "a," "an" and "the." Other spiders take different approaches.
These different approaches usually attempt to make the spider operate faster, allow users to search more efficiently, or both. For example, some spiders will keep track of the words in the title, sub-headings and links, along with the 100 most frequently used words on the page and each word in the first 20 lines of text. Lycos is said to use this approach to spidering the Web.
Other systems, such as AltaVista, go in the other direction, indexing every single word on a page, including "a," "an," "the" and other "insignificant" words. The push to completeness in this approach is matched by other systems in the attention given to the unseen portion of the Web page, the meta tags.
Meta tags:
Meta tags allow the owner of a page to specify key words and concepts under which the page will be indexed. This can be helpful, especially in cases in which the words on the page might have double or triple meanings -- the meta tags can guide the search engine in choosing which of the several possible meanings for these words is correct. There is, however, a danger in over-reliance on meta tags, because a careless or unscrupulous page owner might add meta tags that fit very popular topics but have nothing to do with the actual contents of the page. To protect against this, spiders will correlate meta tags with page content, rejecting the meta tags that don't match the words on the page.
Building the index:
Once the spiders have completed the task of finding information on Web pages (and we should note that this is a task that is never actually completed -- the constantly changing nature of the Web means that the spiders are always crawling), the search engine must store the information in a way that makes it useful. There are two key components involved in making the gathered data accessible to users:
- The information stored with the data
- The method by which the information is indexed
In the simplest case, a search engine could just store the word and the URL where it was found. In reality, this would make for an engine of limited use, since there would be no way of telling whether the word was used in an important or a trivial way on the page, whether the word was used once or many times or whether the page contained links to other pages containing the word. In other words, there would be no way of building the ranking list that tries to present the most useful pages at the top of the list of search results.
To make for more useful results, most search engines store more than just the word and URL. An engine might store the number of times that the word appears on a page. The engine might assign a weight to each entry, with increasing values assigned to words as they appear near the top of the document, in sub-headings, in links, in the meta tags or in the title of the page. Each commercial search engine has a different formula for assigning weight to the words in its index. This is one of the reasons that a search for the same word on different search engines will produce different lists, with the pages presented in different orders.
Regardless of the precise combination of additional pieces of information stored by a search engine, the data will be encoded to save storage space. For example, the original Google paper describes using 2 bytes, of 8 bits each, to store information on weighting -- whether the word was capitalized, its font size, position, and other information to help in ranking the hit. Each factor might take up 2 or 3 bits within the 2-byte grouping (8 bits = 1 byte). As a result, a great deal of information can be stored in a very compact form. After the information is compacted, it's ready for indexing.
An index has a single purpose: It allows information to be found as quickly as possible. There are quite a few ways for an index to be built, but one of the most effective ways is to build a hash table. In hashing, a formula is applied to attach a numerical value to each word. The formula is designed to evenly distribute the entries across a predetermined number of divisions. This numerical distribution is different from the distribution of words across the alphabet, and that is the key to a hash table's effectiveness.
In English, there are some letters that begin many words, while others begin fewer. You'll find, for example, that the "M" section of the dictionary is much thicker than the "X" section. This inequity means that finding a word beginning with a very "popular" letter could take much longer than finding a word that begins with a less popular one. Hashing evens out the difference, and reduces the average time it takes to find an entry. It also separates the index from the actual entry. The hash table contains the hashed number along with a pointer to the actual data, which can be sorted in whichever way allows it to be stored most efficiently. The combination of efficient indexing and effective storage makes it possible to get results quickly, even when the user creates a complicated search.
Building a search:Searching through an index involves a user building a query and submitting it through the search engine. The query can be quite simple, a single word at minimum. Building a more complex query requires the use of Boolean operators that allow you to refine and extend the terms of the search.
The Boolean operators most often seen are:
- AND - All the terms joined by "AND" must appear in the pages or documents. Some search engines substitute the operator "+" for the word AND.
- OR - At least one of the terms joined by "OR" must appear in the pages or documents.
- NOT - The term or terms following "NOT" must not appear in the pages or documents. Some search engines substitute the operator "-" for the word NOT.
- FOLLOWED BY - One of the terms must be directly followed by the other.
- NEAR - One of the terms must be within a specified number of words of the other.
- Quotation Marks - The words between the quotation marks are treated as a phrase, and that phrase must be found within the document or file.
