The inception of Hadoop

The birth and evolution of the Internet led to World Wide Web (WWW), a huge set of documents written in the markup language, HTML, and linked with one another via hyperlinks. Clients, known as browsers, became the user's window to WWW. Ease of creation, editing, and publishing of these web documents meant an explosion of document volume on the Web.

In the latter half of the 90s, the huge volume of web documents led to discoverability problems. Users found it hard to discover and locate the right document for their information needs, leading to a gold rush among web companies in the space of web discovery and search. Search engines and directory services for the Web, such as Lycos, Altavista, Yahoo!, and Ask Jeeves, became commonplace.

These search engines started ingesting and summarizing the Web. The process of traversing the Web and ingesting the documents is known as crawling. Smart crawlers, those that can download documents quickly, avoid link cycles, and detect document updates, have been developed.

In the early part of this century, Google emerged as the torchbearer of the search technology. Its success was attributed not only to the introduction of robust, spam-defiant relevance technology, but also its minimalistic approach, speed, and quick data processing. It achieved the former goals by developing novel concepts such as PageRank, and the latter goals by innovative tweaking and applying existing techniques, such as MapReduce, for large-scale parallel and distributed data processing.

Note

PageRank is an algorithm named after Google's founder Larry Page. It is one of the algorithms used to rank web search results for a user. Search engines use keyword matching on websites to determine relevance corresponding to a search query. This prompts spammers to include many keywords, relevant or irrelevant, on websites to trick these search engines and appear in almost all queries. For example, a car dealer can include keywords related to shopping or movies and appear in a wider range of search queries. The user experience suffers because of irrelevant results.

PageRank thwarted this kind of fraud by analyzing the quality and quantity of links to a particular web page. The intention was that important pages have more inbound links.

In Circa 2004, Google published and disclosed its MapReduce technique and implementation to the world. It introduced Google File System (GFS) that accompanies the MapReduce engine. Since then, the MapReduce paradigm has become the most popular technique to process massive datasets in parallel and distributed settings across many other companies. Hadoop is an open source implementation of the MapReduce framework, and Hadoop and its associated filesystem, HDFS, are inspired by Google's MapReduce and GFS, respectively.

Since its inception, Hadoop and other MapReduce-based systems run a diverse set of workloads from different verticals, web search being one of them. As an example, Hadoop is extensively used in http://www.last.fm/ to generate charts and track usage statistics. It is used for log processing in the cloud provider, Rackspace. Yahoo!, one of the biggest proponents of Hadoop, uses Hadoop clusters not only to build web indexes for search, but also to run sophisticated advertisement placement and content optimization algorithms.