Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open source web search engine, itself a part of the Lucene project.
The Origin of the Name “Hadoop”
The name Hadoop is not an acronym; it’s a made-up name.The project’s creator, Doug Cutting,explains how the name came about:
The name my kid gave a stuffed yellow elephant. Short,relatively easy to spell and pronounce, meaningless, and not used else where:those are my naming criteria. Kids are good at generating such.Googol is a kid’s term.
Sub projects and “contrib” modules in Hadoop also tend to have names that are unrelated to their function, often with an elephant or other animal theme (“Pig,” for example). Smaller components are given more descriptive (and therefore more mundane) names. This is a good principle, as it means you can generally work out what something does from its name. For example, the job racker keeps track of MapReduce jobs.
Building a web search engine from scratch was an ambitious goal, for not only is the software required to crawl and index websites complex to write, but it is also a challenge to run without a dedicated operations team, since there are so many moving parts. It’s expensive, too: Mike Cafarella and Doug Cutting estimated a system supporting a 1-billion-page index would cost around half a million dollars in hardware, with a monthly running cost of $30,000.§ Nevertheless, they believed it was a worthy goal, as it would open up and ultimately democratize search engine algorithms.
Nutch was started in 2002, and a working crawler and search system quickly emerged. However, they realized that their architecture wouldn’t scale to the billions of pages on the Web. Help was at hand with the publication of a paper in 2003 that described the architecture of Google’s distributed filesystem, called GFS, which was being used in production at Google.GFS, or something like it, would solve their storage needs for the very large files generated as a part of the web crawl and indexing process. In particular, GFS would free up time being spent on administrative tasks such as managing storage nodes. In 2004, they set about writing an open source implementation, the Nutch Distributed Filesystem (NDFS).
In 2004, Google published the paper that introduced MapReduce to the world. Early in 2005, the Nutch developers had a working MapReduce implementation in Nutch, and by the middle of that year all the major Nutch algorithms had been ported to run using MapReduce and NDFS.
NDFS and the MapReduce implementation in Nutch were applicable beyond the realm of search, and in February 2006 they moved out of Nutch to form an independent subproject of Lucene called Hadoop. At around the same time, Doug Cutting joined Yahoo!, which provided a dedicated team and the resources to turn Hadoop into a system that ran at web scale (see sidebar). This was demonstrated in February 2008 when Yahoo! announced that its production search index was being generated by a 10,000-core Hadoop cluster.
In January 2008, Hadoop was made its own top-level project at Apache, confirming its success and its diverse, active community.By this time, Hadoop was being used by many other companies besides Yahoo!,such as Last.fm, Facebook, and the New York Times. Some applications are covered in the case studies in Chapter 16 and on the Hadoop wiki.
In one well-publicized feat, the New York Times used Amazon’s EC2 compute cloud to crunch through four terabytes of scanned archives from the paper converting them to PDFs for the Web. The processing took less than 24 hours to run using 100 machines, and the project probably wouldn’t have been embarked on without the combination of Amazon’s pay-by-the-hour model (which allowed the NYT to access a large number of machines for a short period) and Hadoop’s easy-to-use parallel programming model.
In April 2008, Hadoop broke a world record to become the fastest system to sort a terabyte of data. Running on a 910-node cluster, Hadoop sorted one terabyte in 209 seconds (just under 3½ minutes), beating the previous year’s winner of 297 seconds (described in detail in “TeraByte Sort on Apache Hadoop” ). In November of the same year, Google reported that its MapReduce implementation sorted one terabyte in 68 seconds. As the first edition of this book was going to press (May 2009), it was announced that a team at Yahoo! used Hadoop to sort one terabyte in 62 seconds.
Hadoop Related Interview Questions
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The Hadoop Distributed Filesystem
Developing A Mapreduce Application
How Mapreduce Works
Mapreduce Types And Formats
Setting Up A Hadoop Cluster
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